Review Paper DOE

APPLICATION OF CONJOINT ANALYSIS IN NEW PRODUCT DEVELOPMENT

bull Industri otomotif gtgt kaca spion pada bajajbull Tujuan ingin membuat product development dari rear-view mirror of an auto-rickshaw (kaca spion)bull Sebelum membuat produknya terlebih dulu dibuat prototype-nya DOE digunakan untuk mengetahui atribut-

atribut apa saja yang mempengaruhi kaca spion dan level untuk masing-masing atributbull For example for the material of the back cover of the mirror we have two levels plastic or steel for protrusion

outside the auto body 5cm or 10 cm for the mirror pivot side or bottom for back cover shape dome or flat for clamping position bottom or top and for size of the mirror small medium and large

DOE pada kasus ini digunakan untuk menentukan kombi-nasi atribut DOE yang digunakan pada kasus ini adalah Mixed Fractional Factorial Design Berikut adalah kom-binasinya

QUALITY IMPROVEMENT IN WATER DRINKING PROCESS A RESPONSE SURFACE METHOD

bull This study aims to analyze the influence of coagulant dose (ie a mixture of alum and alum sludge) stirring speed and stirring time on the water turbidity which can then be determined the optimal combination of these factors by using Re-sponse Surface Method

bull Industri pemurnian airbull The aim of this study is to determine factors that affect water turbidity and also to determine the optimum combina -

tion of settings of the factors that can produce the level of minimum turbidity The specific objectives of this study are to determine factors that affect water turbidity in the coagulation-flocculation process and the optimum value of each factor to obtain the minimum level of water turbidity in coagulation-flocculation process The study will be conducted in a water treatment plant in Tangerang Indonesia using Design of Experiments (DOE) and Response Surface Method (RSM)

bull This study uses a full 3k Factorial Design with three replications With three different levels of each factor it allows the relationship between response and the controllable factors can be modeled in a non-linear term 3k Factorial Design method is usually used for optimization problems The next step is to use of Response Surface Method (RSM) to determine the optimal setting of the controllable factors to get the lowest water turbidity level

bull Table 1 presents the list of controllable factors and their settings Response variable in this study is water turbidity level which is measured using Turbidimeter Measurement unit for turbidity level of water is expressed in units of NTU (Nephelometer Turbidity Unit)

bull The desired response variable is the lowest value of water turbidity that is in the direction of the minimum decrease in the response (Montgomery 2005)

bull Using α = 5 Table 3 shows that all factors significantly affect the water turbidity and there is no interaction among factors

bull This study aims to analyze the influence of coagulant dose (ie a mixture of alum and alum sludge) stirring speed and stirring time on the water turbidity and also to determine the optimal combination of these factors using Response Sur-face Method Where the lowest level of water turbidity is obtained at 52 alum 48 alum sludge with stirring speed of 91 rpm and 12 minutes of stirring times

bull This study shows that in an effort to run an environmentally friendly production processes and to improve effective-ness in reducing water turbidity it is indispensable to reduce the use of alum and mix it with alum sludge

Teaching the Taguchi method to industrial engineers

bull DOE is a powerful statistical technique for determining the optimal factor settings of a process and thereby achieving improved process performance reduced process variability and improved manufacturability of products and processes

bull Bendell et al (1989) and Rowlands et al (2000) report success of the Taguchi method in the automotive plas -tics semiconductors metal fabrication and foundry industries

bull Taguchi method steps- Step1 formulation of the problem plusmn the success of any experiment is dependent on a full understanding of the

nature of the problem- Step 2 identification of the output performance characteristics most relevant to the problem- Step 3 identification of control factors noise factors and signal factors (if any) Control factors are those which

can be controlled under normal production conditions Noise factors are those which are either too difficult or too expensive to control under normal production conditions Signal factors are those which affect the mean perfor-mance of the process

- Step 4 selection of factor levels possible interactions and the degrees of freedom associated with each factor and the interaction effects

- Step 5 design of an appropriate orthogonal array (OA)- Step 6 preparation of the experiment

- Step 7 running of the experiment with appropriate data collection- Step 8 statistical analysis and interpretation of experimental results- Step 9 undertaking a confirmatory run of the experiment

bull To determine DoF (Degree of Freedom) is the number bigger than (number of attributesfactors + number of interaction between attributesfactors) Ex number of factors = 6 (with 2 levels recently) and there are 3 interactions then DoF must be bigger than (6 + 3 = 9) and the closest of the number of experiments is 2^4 = 16

Quality Improvement in Plastic Painting Production Line

bull Applied in motorcycle industry in Indonesia bull The objective of this study is to improve quality in plastic painting line of motorcycle

production using Taguchi method This is based on the fact that defect rate in the painting production line is still high In this case Taguchi method allows us to reduce costs by reduc-ing variation so that performance and quality will automatically improve The experiments were carried out on nine types of defects that occur in the plastic painting production line

bull The result of this study found the optimal settings for the controllable factors to reduce the defect rate Applying the optimal setting of the controllable factors can reduce the defect rate by 09 and cost saving of IDR 537000000 per year for the company

bull The tight competition in the motorcycle industry is making every company to improve product quality and reduce costs in order to gain competitive advantage

bull High defect rate in the painting lines is likely caused by many types of defect that may occur such as dirt pervade dust thin melt oily scratch water dots and orange peel This has caused high cost of rework

bull The objective of this research is to investigate controllable factors that affect the defect product in plastic painting line using Taguchi method Hence it can be determined the opti-mal settings of those factors in order to reduce rejection rate in the plastic painting line

bull Taguchi method is one of quality engineering tools that can be used in VE whereby the application of Taguchi method is useful not only to reduce costs but also improves quality by reducing variation thus increasing the value of the product

bull The experiments were carried out on nine types of defects that occur in the plastic painting production line The response variable chosen for the experiment was the defect rate Since the highest production rate in the painting lines is black colored parts (ie black is the preferred color by the consumer) but with the highest rejection rate hence analysis will be focused on black color

bull From the brainstorming and CEA it was determined three controllable factors for the experiment which are conveyor speed paint viscosity and oven temperature (in top coat process)

bull The choice of a suitable OA design is critical for the success of an experiment and de-pends on the total degrees of freedom required to study main and interaction effects the goal experiment resource and budget available and time constraints Orthogonal array allow one to compute the main and interaction effects via a minimum number of experimental tri-als (Ross 1988)

bull In this experiment the degree of freedom for studying the three main effects is equal to three In this case the number of degrees of freedom for studying the three interaction ef-fects is equal to three Therefore the total degree of freedom is equal to six (ie 3 + 3) It is important to notice that the number of experimental trials must be greater than the total de-grees of freedom required for studying the effects

bull The experiment was successfully in terms of reducing defect rate in the plastic painting production line Due to the significant reduction in defect rate the costs due to rework have reduced by IDR 537000000 per year

bull This study also shows that Taguchi method is in line with Value Engineering where the application of Taguchi method in this study allows us to reduce costs by reducing variation so that performance and quality will automatically improve

A factorial design study to determine the significant parameters of fresh con-crete lateral pressure and initial rate of pressure decay

bull Formwork shape coarse aggregate concentration and concrete impact have a minor effect in maximum lateral pressure while temperature shows an inverse relationship with the pressure but not to a sufficient degree to be considered a significant parameter

bull On the other hand formwork size has a major effect on the pressure narrow sections generate less lateral pressure than higher ones This is attributed to the friction forces between concrete and form-work which are much more important in small sections

bull Formwork shape and size present a major influence in the initial rate of pressure decay While circular formworks present a higher value than squares ones smaller cross sections present a lower value than larger ones On the other hand coarse aggregate concentration has a minor effect on this parameter

bull The key issue for designing vertical formwork is to determine the maximum horizontal pres-sure exerted by fresh concrete during casting since an overestimation of this value results in an increase of formwork cost On the other hand an underestimation of the pressure generates pieces made of poor quality which may delay construction causing economic and time losses

bull The objective of this work is to determine the influence of five factors on fresh concrete maxi-mum lateral pressure formwork size and shape coarse aggregate concentration concrete temper-ature and concrete impact and also the influence of the first four in the initial rate of pressure de-cay (the time needed to reduce a given percentage of hydrostatic pressure)

bull A full factorial design 2^3 was used initially to determine the influence of formwork size and shape and coarse aggregate concentration in maximum lateral pressure and in the initial rate of pressure decay

bull Moreover temperature was analysed as a co-variable since it was difficult to control in the fieldbull The design is able to estimate each of the main effects independently even if the interaction among them be-

comes convolutedbull Finally two tests were performed in which the end of the hose level increases with concrete level The objec-

tive of these tests was to determine the influence of concrete impact on maximum lateral pressure

bull An effect is usually considered significant when the p-value is less than 005 as suggested by Tanco et al [34]

bull The p-value for the model states that there is only a 515 probability that this result could occur due to noisebull As explained above two extra tests of the full factorial design were performed to study the effect of concrete

impact on lateral pressure Tests B6 and B7 as shown in Table 3 have a maximum pressure of 6114 and 6519kPa respectively lower than the 6237 and 6548 kPa presented by tests A6 and A7 The differences between the tests per-formed with the holes in different positions was 04ndash25 pointing out that when the hole is maintained at the same level of the formwork top the lateral pressure is higher than when the hole level is elevated with concrete This con-clusion is consistent with the results presented by CIRIA Report 108 [5] and Harrison [22]

bull Conclusion Formwork shape had little influence on fresh concrete lateral pressure On the other hand this pa -rameter has a major influence on the initial rate of pressure decay Formwork size is a significant factor in fresh con -crete lateral pressure and in the initial rate of pressure decay Coarse aggregate concentration presented little influ-ence in lateral pressure and in the initial rate of pressure decay Concrete temperature has an inverse relationship with fresh concrete lateral pressure and it is not a significant parameter Therefore concrete temperature can be consid -ered as a non-significant parameter with respect to the initial rate of pressure decay Concrete impact has very little influence on fresh concrete lateral pressure

A methodology for product reliability enhancement via saturatedndashunreplicated fractional factorial designs

bull A case study based on aluminum milling operations is utilized to illustrate how the method presented here is adopted in screening through a can-stock product in order to achieve optimal levels of reliability

bull Reliability remains a product quality indicator of paramount importance in competitive manufacturing opera-tions

bull A case study dedicated to the can-making sector targets manufacturing product reliability on aluminum can-stock containers intended for the soft-drink and beverage industry Buckle strength data obtained from ldquodrawing-and-ironingrsquorsquo forming operations have been programmed according to Taguchirsquos L9(3^4) saturated orthog-onal array (OA) Resulting responses transformed to two-parameter Weibull model are confronted as though they were two sets of unreplicated quality characteristics The method stresses the embodied convenience and built-in ro-bustness in carrying out reliability improvement studies while eliminating data distribution concerns appearing due to anticipated shape and scale variations

bull A particularly interesting aspect of DOE data collecting schemes are the fractional factorial designs [4] Frac-tionated designs have been shown to be useful for economical and timely product testing

bull Taguchi methods have been employed to improve reliability of molded 225 plastic ball grid arrays and chip scale packages [1415]

bull The Weibull model requires three parameters in order to provide a quantification of the failure tendency pos-sessed by a product trait This tendency is easily discerned to a three-phase product life-time behavior as captured by the well-known lsquolsquobath-tubrsquorsquo model The three parameters involved in the Weibull model are (1) the shape b (2) the scale n and (3) the threshold g

bull Step 1 Collectively they select possible control factors that may influence reliability levels of the product or process under investigation Step 2 An orthogonal array is selected to accommodate the appropriate number of con-trol factors decided on the previous step Step 3 Experiments are conducted based on the prescribed factor setting combinations from the previous step Step 4 The data collected and transformed in the previous step may be ana-lyzed by standard comparison tests for two-level fractional factorial designs in concert with the resulting unreplicated super-rank response Step 5 In this step information arising from data analysis is put to test in order to confirm that the predicted responses are sufficient to describe the product (or process) reliability characteristic outcomes after opti -mization

bull In aluminum can-making a crucial quality characteristic is the buckle strength (BS) Production managers re-quested an economical three-level estimation of four- parameter influences on the enhancement of BS reliability due to the enormous expense inherent to bulky aluminum sheet orders Accelerated reliability measurements were issued for testing the possible non-linear influence of key production parameters such as aluminum-alloy content in man-ganese (Mn) and magnesium (Mg) as well as hot mill pass counts (HMPC) and cold mill reduction rate (CMR)

bull The technique proposes a combination of fractional factorial designs for efficient and balanced trial sampling in concert with non- parametric reliability analysis The settings for the four control factors were accommodated on a experimental plan dictated by an L9 (3^4) orthogonal array In Table 1 it is shown the selected factor settings in concert with data collected for five replicates of monitoring time to reach to the point of the yield (TY) In the same table there are computed values for the signal-to-noise ratio (SNR) and the mean value for the five outcomes per trial run By substituting Taguchirsquos SNR concept with the lsquoshape-and-scalersquo parameter responses we approached the opti-mization problem in a novel fashion by placing direct importance to the two metrics that make sense exclusively for reliability assessment studies

bull At a level of significance of 010

A semi-parametric approach to robust parameter design

bull In the mid 1980s Japanese quality consultant Genichi Taguchi popularized a cost-efficient approach to quality improvement known as robust parameter design (RPD) Taguchi postulated that there are two types of factors which operate on a process control factors and noise factors

bull The goal of RPD is to determine levels of the control factors which cause the response to be robust to changes in the levels of the noise variables A popular design for studying both the impact of control factors and noise factors on a process is the crossed array A 2^2 times 2^2 crossed array is shown in Fig 1

bull In the printing ink example the semi-parametric fit was observed to be superior to its parametric and non-parametric counterparts in terms of SEL In this section we compare the three methods more generally in terms of fit via a simulation study The performance of the semi-parametric approach will be compared to the parametric and nonparametric approaches in four scenarios the researcher correctly specifies the forms of both the underlying mean and variance functions the researcher correctly specifies the form of the underlying variance function but misspeci -fies the means model the researcher correctly specifies the form of the means model but misspecifies the variance model and the researcher incorrectly specifies the forms of both the underlying mean and variance functions For each scenario Monte Carlo simulations will be used to generate 500 data sets each of which are based on the follow-ing underlying dual model

bull In all scenarios factors x1 and x2 have four levels with values are taken to be 0 13 23 and 1 for each factor The data will be generated as if a 4^2 complete factorial experiment was run with three replicates at each design point for a total of 16 design points and 48 experimental runs

bull Fig2 shows the response surface for the true underlying means model when γμ

= 000 and the response sur-

faces of the mean function for the varying degrees of model misspecification (γμ

= 025 050 075 and 100) appear

in Figs 3ndash6 respectively Note that as γμ

increases the curvature of the mean surface becomes much more pro-

nounced Fig 7 shows the response surface for the true underlying variance model when γσ

= 000 and the response

surfaces of the variance function for the varying degrees of model misspecification (γσ

= 025 050 075 and 100)

appear in Figs 8ndash11 respectively Again note that as γσ

increases the curvature of the variance surface becomes

much more pronouncedlarrlarrA study on optimal compensation cutting for an aspheric surface using the Taguchi method

bull In this research the optimization of compensation cutting for eliminating the residual form error of an aspheric surface using the Taguchi method was performed Three parameters of cutting depth revolution of work spin-dle and compensation ratio were considered as control factors and the experimental trials based on the L9(33) orthogonal array were carried out

bull With the growing market for 3C (computer consumer and communication) products the manufacturing of precision optical components is becoming increasingly important [1]

bull The Taguchi method is an efficient and systematic approach that can reduce the experimental trials necessary to determine the optimal conditions and has been broadly applied in many different fields

bull the Taguchi method makes use of a special design of orthogonal array (OA) to examine the quality characteris-tics through a minimal number of experiments The experimental results based on the OA are then transformed into S N ratios to evaluate the performance characteristics Therefore the Taguchi method concentrates on the effects of variations on quality characteristics rather than on the averages That is the Taguchi method makes the process per-formance insensitive to the variations of uncontrollable noise factors

bull In this study the Taguchi method is employed to determine the optimal operational parameters of the compen-sation cutting for precisely manufacturing an aspheric surface The technique is summarized here and includes the following steps (1) identify the objective of the experiment (2) identify the quality characteristic (3) identify the control factors that may influence the quality characteristic (4) select an appropriate OA and assign the factor levels (5) perform experimental trials based on the configured OA (6) determine the optimal levels of control factors based on the SN ratios (7) determine the significant factors that mainly affect the quality characteristic by the analysis of variance (ANOVA) (8) verify the optimal operational parameters through confirmation experiments

bull In this study three parameters namely cutting depth revolution of work spindle and compensation ratio are determined as the control factors which mainly affect the performance of compensation cutting

bull The configuration of OA is based on the total degree-of-freedom (DFT) of the objective function Since there are three control factors and three levels in this study the DFT is given as eight To make the performance compar-isons among different combinations of control factors it is necessary to select an OA having at least nine experimen -tal trials (DFT + 1)

bull The signal-to-noise (SN) ratio is used to evaluate the quality characteristic lsquolsquoSignalrsquorsquo means the desired or expected output characteristic of the process whereas lsquolsquonoisersquorsquo means the undesired characteristic The SN ratio is therefore used to measure the deviation error from the desired quality characteristic The smaller the deviation error the higher the stability of the quality characteristic obtained

bull Since a higher SN ratio means a better quality characteristic (ie smaller PV form error) the optimal combi -nation of control factor levels is therefore determined as A3B3C2 ie A3 = 9 mm B3 = 600 rpm C2 = 1 From the responses caused by the cutting depth (A) it is worth noting that a smaller cutting depth would not guarantee a better quality characteristic but rather than the over cutting ie A3 resulted in the best quality characteristic among the three levels This can be considered as the reason why less cutting depth required multi feeding cycles of compensa -tion cutting and thus with increased uncertainty On the other hand the compensation ratio having the value of 1 ie C2 could obtain the best quality characteristic compared to the other two values of 05 and 15

bull As a result of the pooling the percentage contributions of factors B and C are 3522 and 4937 respec-tively and the combined error is 1541 Therefore the control factor C the compensation ratio is the most signifi-cant factor in affecting the quality characteristic According to the results of ANOVA since the percentage contribu-tion of the combined error is as small as only 1541 it can be concluded that no further important factors were missed in the Taguchi approach and thus the arrangement of the experimental trials given in this study is successful

bull The confirmation experiment conducted in the above section was based on the optimal levels of factors being A3 = 9 mm B3 = 600 rpm and C2 = 1 The initial accuracy of 581 mm obtained by rough cutting could be signifi -

cantly improved to 153 mm The improved efficiency due to the optimal condition is 7376 One the other hand compared to one of the initial trials A1B1C1 the accuracy is improved from 260 mm to 153 mm with an improved efficiency of 4123 Through these examinations it is experimentally demonstrated that compensation cutting using the Taguchi method could significantly improve the cutting performance

bull The measured PV form errors are compared to the results of rough and compensation cutting as listed in Table 9 In the first trial of re-compensation cutting the PV form error is reduced from 153 mm to 146 mm Since the effi -ciency is only 458 further improvement in accuracy is insignificant In the second trial of re-compensation cutting however an over-compensation is recorded as the PV form error increasing to 176 mm with an efficiency of -2055 Through the confirmation experiment the rough machining accuracy of 581mm could be significantly im-proved to 153mm with a percentage improvement of 7367 In addition compared to the machining accuracy of 260 mm obtained by one of the initial combinations the form accuracy is improved to 4115 and the quality loss is only 3436 of the initial trial

bull Though only a micron order of compensation accuracy for the aspheric surface was obtained the method pre-sented in this paper can be applied to various applications of meso-machining such as micro-tools micro-fluidic channels or more complex three-dimensional (3D) features

larrlarrA Taguchi approach for optimization of design parameters in a tube with coiled wire inserts

bull The goal of this study is to reach maximum heat transfer (ie Nusselt number) and minimum pressure drop (ie friction factor)

bull The goal of a study made to enhance heat transfer is to create new designs that provide higher heat trans-fer rates

bull The Taguchi method a leading optimization technique reducing the experimental cost enables us to mini-mize the variability around the target when bringing the performance value to the target value Another advantage is that optimal working conditions determined from the experimental work can also be reproduced in real applications

bull Therefore this article is focused on the determination of the optimum values of design parameters in a tube with coiled wire inserts placed separately from the tube wall by using the Taguchi method to fill the gap in the studies on optimization of design parameters of coiled wire inserted tubes in literature

bull In Taguchi method there are three design stages system parameter and tolerance designs The goal of param-eter design is the identification of settings that minimize variation in the performance characteristic and adjust its mean to an ideal value Tolerance design is used to determine the best tolerances for the parameters SNR (SN) is a measure of the performance variability of parameters selected in the presence of noise factors SNR is a performance criterion defined as the signal to noise ratio in that S (signal) stands for mean and N (noise) stands for standard devi -ation As SNR gets higher the quality of product improves The principal aim is to maximize the SNR In the Taguchi analysis there exist three types of quality character- istics concerning the target design These are ldquohigher is the betterrdquo ldquonominal is the bestrdquo and ldquolower is the betterrdquo

bull Step 1-Identification of the objectives The objective of this work is to determine the optimum values of design parameters in a tube with coiled wire inserts (parameter design) Step 2-Selection of characteristics The characteris-tics are classified into three types higher is better nominal is the best and lower is better There are two objectives in this paper The first objective is maximizing Nusselt number therefore it is a higher-the-better problem The second objective is minimizing pressure losses a lower-the-better problem Step 3-Selection of the controllable factors and noise factors the controllable factors are the ratio of the distance between the test tube wall and coiled wire to tube diameter (sD) pitch ratio (PD) the ratio of side length of equilateral triangle to tube diameter (aD) and Reynolds number(Re) as depicted in Table 1 The noise factors are the heat transfer and pressure drop Step 4-Selection of an orthogonal array To select an appropriate OA for the experiments the total degrees of freedom need to be computed The choice of a suitable OA is critical for the success of an experiment and depends on the total degrees of freedom

required to the study the main and interaction effects the goal of the experiment resources and budget available and time constraints OAs allow one to compute the main and interaction effects via a minimum number of experimental trials The number of degrees of freedom is one less than the number of levels associated with the factor In the present study the interaction between the design parameters was neglected There are four factors with three levels Therefore there are 8 (8 = 4x2) degrees of freedom owing to there being four design parameters For four parameters at three levels each the traditional full factorial design would require 3^4 or 81 experiments Note that this design re -duces 81 configurations to 9 experimental eval- uations This array reduced the total cost of experiments The total testing time of experiments are shortened significantly by this array Generally L9 OA was used for four factors three levels in the literature [333536] Therefore we have chosen L9 instead of L27

bull The optimum values of the parameters for maximum heat transfer condition are as follows sD = 0026785 (A2) PD = 1 (B1) aD = 01071 (C3) and Re = 19800(D3) Consequently A2B1C3D3 is defined as the optimum condition of design parameters related to the heat transfer according to the ldquothe higher is the betterrdquo situation for Nus-selt number

bull The optimum values of the parameters for minimum friction factor condition as follows sD = 00357 (A3) PD = 3 (B3) aD = 00714 (C1) and Re = 19800 (D3) (see Fig 5) Hence according to the ldquothe lower is the betterrdquo situation A3B3C1D3 is the optimum condition of design parameters related to the friction factor

bull The R is the difference of maximum and minimum of the SNR for every factor The contribution ratio is equal to the ratio of the R values of each factor to the total R value of all factors as presented in Tables 3 and 4 Rank row is the order of factors according to the effectiveness The contribution ratio of each parameter to Nusselt number is shown in Fig 6 It is obvious from the figure that the pitch ratio has 4260 percentage of the total effect This means that parameter B is the most effective one on heat transfer

bull As seen from Fig 7 the parameter C is the most effective param- eter on friction factor with a contribution ra -tio of 4509 percentage of the total effect If Fig 7 is examined carefully it is clearly found that the second most ef-

fective parameter is B with a contribution ratio of 2831 and the effects of parameters A and D on friction factor are contribution ratio of 1596 and 1064 respectively

bull In this study the optimal parameters have been designed to maximize the heat transfer (ie Nusselt number) and minimize the pressure drop (ie friction factor) by Taguchi method

bull When the whole system is optimized by considering with maximum heat transfer and minimum friction factor the optimum condition of the design parameters isA3B1C1D3 This condition includes the effects of all performance goals Based on this condition the optimal values are found to be sD = 00357 PD = 1 aD = 00714 and Re = 19800

larrlarrAnalysis of operating parameters considering flow orientation for the performance of a proton exchange membrane fuel cell using the Taguchi method

bull This study has applied the L18 2 times 3^7 orthogonal array of the Taguchi method to determine the optimal combination of six primary operating parameters (flow orientation temperature of fuel cell anode and cathode humidification temperatures anode and cathode stoichiometric flow ratios) of a PEM fuel cell

bull The results for the experiment indicate that flow orientation temperature of fuel cell and anode and cathode humidification temperatures are significant factors for affecting the performance

bull The major advantages of proton exchange membrane fuel cell (PEMFC) are low-temperature operation high efficiency no emission low noise and quick starting to high energy and current density under room temperature The performance of the fuel cell depends on the kinetics of the electrochemical process and performance of the compo-nents

bull In the first stage they adopted a 2kminus2 fractional factorial design of the DOE to determine whether these fac-tors significantly influence a response and the interactions between various parameters Second the L27 (3^13) or-thogonal array of the Taguchi method was employed to determine the optimal combination of factors for a fuel cell

bull Hence the objective of this paper is to apply the Taguchirsquos orthogonal array L18 (2times3^7) with experiment to determine the optimal operating condition of these operating parameters and then to realize the transport phenomenon by two-dimensional simulation models for the fuel cell performance at the optimal operating condition The results of this paper may be of interest to engineers attempting to develop the optimization of a PEM fuel cell performance and to researchers interested in the transport phenomenon corresponding to the optimization condition in the PEM fuel cell

bull The experimental design factors considered are (A) flow orientation (B) temperature of fuel cell (C) anode humidification temperature (D) cathode humidification temperature (E) stoichiometric flow ratio of hydrogen and (F) stoichiometric flow ratio of oxygen

bull An orthogonal array is a fractional factorial design with pair wise balancing property In this paper we used the L18 (2 times 3^7) the minimum orthogonal array for six parameters To observe the data reliably on this experiment this study repeated each one two times with same conditions

bull The greatest SN from these levels obtained is the primary optimal combination of the control factor levels A1 (the co-flow) B2 (temperature of 333 K) C3 (the anode humidification temperature of 353 K) D2 (the cathode hu-midification temperature of 333 K) E2 (the hydrogen stoichiometric flow ratio of 2) and F3 (the oxygen stoichio-metric flow ratio of 3)

bull When the F-test values of factors and interactions between the factors are smaller than 94 confidence inter -val these values are classified as insignificant factors and can be regarded as errors Therefore the control factors with the significant effect for acquiring maximal electrical power are A (flow orientation) B (temperature of fuel cell) C (anode humidification temperature) and D (cathode humidification temperature)

bull Moreover the ANOVA result indicates that the anode and cathode humidification temperatures cell tempera-ture and flow orientation are the significant factors in affecting the PEM fuel cell performance

larrlarrDetermining the effect of cutting parameters on surface roughness in hard turning using the Taguchi method

bull This study focuses on optimizing turning parameters based on the Taguchi method to minimize surface roughness (Ra and Rz)

bull The developed model can be used in the metal machining industries in order to determine the optimum cut-ting parameters for minimum surface roughness

bull Hard turning is a turning process in which work pieces whose hardness ranges between 50 and 70 HRC are machined by using single point cutting tools which have high hardness and wear resistance

bull Cutting parameters may be specified according to hardness of materials and roughness of the surface of a work piece The advantages in machining materials with higher hardness are decreasing machining costs saving time improving surface quality and eliminating of deformities in parts caused by temperature [34]

bull The Taguchi method and L9 Orthogonal Array were used to reduce number of the experiments The design of experiments (DOE) and measured RaRz values are shown in Table 2 The experiments were conducted with three replicates The Taguchi method is an experimental design technique which is useful in reducing the number of exper-iments dramatically by using orthogonal arrays and also tries to minimize effects of the factors out of control The ba-

sic philosophy of the Taguchi method is to ensure quality in the design phase The greatest advantages of the Taguchi method are to decrease the experimental time to reduce the cost and to find out significant factors in a shorter time period [23]

bull Thus the optimum cutting condition was found as -232 and -1875 SN ratios for Ra and Rz respectively in L9 orthogonal array in Table 2

bull According to Table 6 three depth levels have no effect on surface roughness at the reliability level of 95 As a result any interaction between the SN-cutting speed and the SN-depth of cut was not observed However a signif -icant correlation between the SN and the feed rate was observed

bull Table 8 shows the interactions between the SN and cutting speed do not produce a significant level within the reliability interval of 95 According to Table 9 P value is effective for every three feed rate levels at the reliability level of 95 because the results are lower than 005

bull Optimum cutting conditions-which correspond to maximum -232 SN value of the smaller Ra value for the smaller surface roughness in hard turning operation (2 1 2)-were found to be 120 mmin for the cutting speed 018 mmrev for the feed rate and 04 mm for the depth of cut Optimum cutting conditions-which correspond to maximum -1875 SN value of Rz value (3 1 1)-were found to be 120 mmin for the cutting speed 018 mmrev for the feed rate and 04 mm for the depth of cut

larrlarr

DOE applied to optimization of aluminum alloy die castings (full factorial design dan otomotif)

bull This paper discusses the application of a design of experiments (DOEs) experimental method for analysing the influence of three injection parameters (slow shot fast shot and up set pressure) on the internal quality of die casting SAE 305 alloy parts

bull In manufacturing processes there are various parameters with different adjustment levels which may influ-ence the final characteristics of the product To optimize a manufacturing process the trial and error method is used to identify the best parameters to manufacture a quality product

bull These experimental methods may be employed to solve problems related to a manufacturing process to substi-tute a process for another one to develop different products and to understand the influence of various factors on the final quality of a given product

bull The design of experiments (DOEs) is an experimental technique that helps to investigate the best combinations of process parameters changing quantities levels and combinations in order to obtain results statically reliable

bull Usually the main controlled variables are mold temperature dosage volume slow and fast shots commuta-tion spots injection pressure up set pressure as well as chemical composition and liquid metal temperature

bull In this paper the methodology DOE is employed to study the influence of some machine parameters on the quality of die casting parts using the SAE305 aluminum alloy

bull A total of 18 different combinations were tried and 5 parts were cast for each combination which totals 90 die casting samples

bull the best results in terms of the density of the die casting part were obtained when low slow (014 ms) and fast (133 ms) shots and high up set pressure (300 MPa) were used

larrlarrImproving the fine-pitch stencil printing capability using the Taguchi method and Taguchi fuzzy-based model

bull This paper presents industrial applications for improving the capability of the fine-pitch stencil printing process (SPP) based on the DMAIC framework and using Taguchi-based methodologies The performance of these two approaches is compared through the process capability metric and the material and factors significantly affecting the fine-pitch SPP performance are reported

bull Surface mount technology (SMT) is a significant development in the electronics industry and is now used to fabricate many types of electronics products The surface mount assembly (SMA) process is comprised of solder paste stencil printing component placement (Pick and place PampP) and solder reflow process steps

bull Studies have reported that approximately 60 of soldering defects originate from poor SPP performance [34] because of nonlinearity

bull The closer the amount of solder paste is to a nominal value the better the stencil printing performance The presence of printing defects (such as bridges slumping incompleteness or shifting) also has a significant influence on the SPP performance and solderability [5]

bull The Taguchi method is a cost-effective quality improvement methodology which has been widely applied in a variety of industries for the purpose of achieving robustness in manufacturing processes and design [16ndash19] The Taguchi method provides a systematic scheme for determining the effects of various factors and their possible inter-actions The results can help to design a process for achieving the particular output and quality characteristics Two important tools provided by the Taguchi design are the orthogonal array (OA) and the signal-to-noise (SN) ratio

bull The acronym Define-Measure-Analyze-Improve-Control (DMAIC) is used for projects aimed at improving an existing process The basic DMAIC deployment roadmap consists of the following five steps [28] (1) Define define the related improvement goals pertaining to customer demands and the enterprise strategy (2) Measure measure the current process capability and collect relevant data (3) Analyze analyze the process data to ascertain the cause-and- effect relationships of interest and ensure that all system factors have been considered (4) Improve optimize the process based on data analysis using DOE process capability analysis and other statistical techniques (5) Control conduct pilot runs to achieve desired process capability and make advances to the mass production establish long-term process control mechanisms and execute continuous monitoring of the process

bull In this study we work with the DMAIC framework to improve the SPP capability as shown in Fig 2 This main effort is to reduce variation in the volume of solder paste deposits from a nominal value and eliminate printing defects under the considerations of single and multiple performance indices gtgt Poor SPP performance can cause sig -nificant productivity and quality losses A large variation in the solder paste volume from the nominal value and sig-nificant amount of printing defects have the potential to produce soldering failures that can significantly increase manufacturing costs to correct the defects The sub-goals include the following (1) comparing the optimization per -formance attained by the Taguchi method and Taguchi fuzzy-based model (2) identifying the factors having signifi -cant effects on the fine-pitch SPP performance and (3) enhancing the SPP capability for fine-pitch SMCs with mini -mal defects

bull Full factorial experimental design requires a number of experimental runs which makes the process time- con-suming and cost-ineffective To effectively ascertain the key stencil printing factors a fractional factorial experimen-tal design uses a portion of the full factorial columns to estimate the main factor effects and their interactions For ex -ample an examination of eight factors (2^1 x 3^7) requires a total of 4374 trials Taguchi OA L18 is selected due to fitting the experimental requirements and only needing 18 experimental runs

bull Some of the noises in this experiment arise from (1) working temperature and humility (2) variations in the stencil printer and (3) dust

bull In the preliminary ANOVA analysis the factors paste particle size (B) squeegee speed (D) and snap-off height (E) are insignificant since their F ratios have a confidence significance of less than 95 Consequently these three factors are pooled and considered error terms The summary of the ANOVA analysis of the SN ratios and the results is shown in Table 5 The component lead-pitch (A) stencil aperture area (F) and stencil thickness (G) account for approximately 8366 of the total variation in the SPP Specifically the lead-pitch factor represents about 55 of the variation This indicates that the product configuration and stencil design have a significant effect on the amount of the solder paste deposits and the overall printing performance considering the single characteristic performance

bull The conventional Taguchi method is employed in order to optimize the SPP with a single response using the experimental results shown in Table 4 The variation is reduced by selecting the factor levels in order to maximize the SN ratios During ANOVA analysis of the SN ratios the main contributors are factors A F and G The lsquolsquoArsquorsquo factor (lead-pitch) is categorized into 04 mm (ultra-fine-pitch) and 05 mm (fine-pitch) printing processes which identify the different lead-pitch printing requirements Consequently for the 04mm lead-pitch process the optimal combina-tion of factor levels will be A1C3F3G3H3 For the 05 mm lead-pitch process the optimal combination of factor lev -els will be A2C3F3G3H3

Investigations of the effects of temperature and initial sample pH on natural or-ganic matter (NOM) removal with electrocoagulation using response surface method (RSM)

bull In water treatment plant

bull The removal of natural organic matter (NOM) from surface water by electrocoagulation (EC) was studied us-ing response surface method (RSM) Factors used in the empirical model were electric charge per liter initial pH and temperature

bull Based on analysis of variance (ANOVA) the model fitted well with dissolved organic carbon (DOC) reduc -tion aluminum dissolving and pH changes According to results temperature affected significantly the dissolving rate of aluminum electrodes

bull Initial DOC concentration of the surface water was 1835 mgl Maximum DOC removal of 804 was ob-tained when high electric charge per liter (144 Cl) low pH (43) and high temperature (29515 K) were used How -ever high DOC removal of 762 was predicted also when water temperature was only 27515 K

bull Effect of temperature on NOM removal was minor as compared to the effects of electric charge per volume and the initial pH According to the results EC can be used for NOM removal during cold water period in Nordic countries

bull Surface water in Nordic region typically contains a high concentration of natural organic matter (NOM) which gives water its brown color Efficient removal of NOM is required for drinking and industrial water applications In the drinking water high NOM concentration causes formation of disinfection by-products (DBP) that are carcino-genic In the industrial applications such as paper making process NOM can cause fouling of the surfaces resulting in the defects in the end-product quality [1] NOM is a complex mixture of different organic materials such as bacte-ria viruses humic acids fulvic acids polysaccharides and proteins Dissolved organic matter (DOM) is defined to be a part of the NOM that passes through 0451113146m pore size membrane

bull Design of the experiments was done using full factorial design Factors of the RSM model were electric charge added per volume temperature and initial pH Electric charge per volume controls the amount of electrochemical re-actions taking place on the electrode surface Initial pH was selected because pH has significant effect on coagulation efficiency and optimum coagulation pH may vary with temperature as stated in Section 1 Effect of temperature is important factor because water temperature changes significantly especially in the Nordic countries

bull Responses included in the model were removal efficiency (R) of DOC and UV 254 nm absorbance total alu-minum added pH change during the EC treatment and zeta-potential of the filtered samples after treatment

bull According to the model quality values RSM was suitable method to study the effects of electric charge per volume temperature and initial pH on NOM removal during the EC treatment

bull Temperature had significant effect on the dissolving speed of the aluminum Dissolving speed of the aluminum electrodes increased when temperature of the samples increased

bull Change of the pH during EC treatment was mainly affected by the initial pH Final pHs of the samples were in the range of 56ndash73

bull Electrocoagulation had little impact on the conductivity of the water Conductivities of the samples varied from 29mSm to 52 mSm

bull High NOM removal was obtained in EC treatment Maximum NOM removal according to DOC and UV 254 nm measurements was achieved when low pH of 43 high temperature of 29515 K and high electric charge of 144 Cl were applied Energy consumption in maximum DOC and UV 254 nm absorbance removal in the factors range was 04 kWhm3 and aluminum consumption was 40 mgl

bull Temperature had minor effect on the removal of NOM in studied range when compared to effect of electric charge per liter and initial pH

bull According to results EC treatment can be used for efficient removal of NOM from surface waters also during the cold water period

larrlarrOptimization of design parameters using a response surface method in a cold cross-wedge rolling

bull In automotive industry

bull Cold cross-wedge rolling process is utilized to enhance the surface hardness and the strength in a round shaped part Main geometrical design parameters in a cold cross-wedge rolling die are the forming angle the spreading angle and the die height

bull General defects in this process are an excessive slip on the surface and a hole generation at the center point which is usually called as the Mannesmann hole defect The friction coefficient and geometrical design variables af -fect them

bull In this paper effects of the forming angle and the friction coefficient on the initiation of the Mannesmann hole defect are analyzed by using a response surface method

bull Optimization of design parameters for prevention of the Mannesmann hole defect initiation is carried out using a response surface method As results that the forming angle of 25 and the spreading angle of 1 can be proper de-sign conditions with the prevention of occurrences of internal hole defect and an excessive slip

bull Many kinds of micro-alloyed steels have been developed and improved for hot forming cold forming or cut -ting process Recently practical manufacturing processes for the micro- alloyed material have been developed in order to apply for the component of automotive parts and fasteners

bull Cross-wedge rolling (CWR) process is the specialized incremental forming process to make axisymmetric products Main design variables of CWR process are forming angle (shoulder angle) spreading angle (stretching an -gle) and area reduction ratio (Lee et al 2006) In case of cold working an excessive slip causes to reduce forming angle within the range from 20 to 25 and also to reduce spreading angle below 2 The frequently appeared defects in CWR process are the excessive slip in rolling process resulted from the low friction coefficient between dies and material and central hole initiation due to the repeated compression and tension

bull The result of these experiments can also support the same threshold value of the Mannesmann hole initiation

bull The significant parameters in the response surface regression are constant term liner term of the forming an-gle linear and quadratic terms of the friction coefficient Accuracy of the regression equation (R2) is 997 and effi -ciency of the equation (R2 adj) is 995 This means that design variables of the forming angle and the friction coef-ficient are properly selected to represent the response variable that is the effective plastic strain

bull Excessive slip occurs for the whole model according to the forming angle at the friction coefficient below 02 for the spreading angle of 15 But models of the spreading angle of 10 give a successful rolling at the friction coef-ficient over 013 except the model of the forming angle of 18 Considering a safety factor of 10 the forming angle of 25 gives a successful cold cross-wedge rolling without depending on the friction coefficient Therefore the rec-ommended design parameters in the cold CWR process for developed micro-alloyed steel can be determined at the forming angle of 25 the spreading angle of 10 and the friction coefficient of 015

Optimization of dry machining parameters for high-purity graphite in end milling process via design of experiments methods

bull This investigation applied the designs of experiments (DOE) approach to optimize parameters of a computer numerical control (CNC) in end milling for high-purity graphite under dry machining

bull Quality objective The groove difference (ie dimensional accuracy of groove width) and the roughness aver-age at the bottom plane of the inside groove (ie the plane of end milling) were studied

bull Planning of experiment was based on a Taguchi orthogonal array table The analysis of variance (ANOVA) was adapted to identify the most influential factors on the CNC end milling process

bull Factors cutting speed feed rate and depth of cut which influence the machining process to great extent in end milling of high-purity graphite under dry machining The feed rate is found to be the most significant factor af -fecting the groove difference and the roughness average in end milling process for high-purity graphite

bull Electronic products have been developed rapidly in 21st century gtgt and one of the key factors is the progress on semiconductor manufacturing processes gtgt in order to meet the rapid growth of semiconductor technology many new applications of materials such as using boron nitride ceramics and quartz for insulation or applying graphite for conductor had been developed This paper will concentrate on optimizing the cutting parameters of dry machining of high-purity graphite gtgt electronics industry

bull The impurities of graphite can contaminate environment of semiconductor manufacturing and a high powder particle ratio will reduce the quality and reliability of semiconductors Therefore high-purity graphite is necessary for semiconductor manufacturing processes Furthermore high-purity graphite must be high thermal and chemical resis -tance excellent resistance to the temperature change good electrical conductivity high thermal conductivity increas-ing strength with higher temperature and easy to be machined (Sglcarbon 2000)

bull In this study further processing procedure for dry machining of high-purity graphite is experimented using the end milling

bull In practice the DOE method has been used quite successfully in several industrial applications as in optimiz-ing manufacturing processes or designing electricalmechanical components

bull In order to reduce the numbers of experiments and maintain the resolution of the results a two-level factorial design with three factors which are the cutting speed (mmin) the feed rate (mmrev) and the depth of cut (mm) was implemented

bull The aim of this analysis is to identify the machining parameters and the interactions of each parameter that have significant affect on the outputs the

Δand the Ra by investigating the percentage of contributions of the sum

of squares for each model term relative to the total sum of squares of a model According to the tables the feed rate (mmrev) is the major contributing factor that influences the

Δand the Ra

bull Similarly an ldquoF-Valuerdquo on any individual factor terms is calculated from a term mean square divided by a residual mean square It is a test that compares a term variance with a residual variance If the variances are close to the same the ratio will be close to one and it is less likely that the term has a significant effect on the response Fur-thermore if a ldquoP-Valuerdquo of any model terms is very small (less than 005) the individual terms in the model have a significant effect on the response

bull Itrsquos indicating that the model term ldquoBrdquo is significant for Δ

and the Ra

bull The objective of this study is to minimize the Δ

and the Ra simultaneously

bull After identifying the most effective parameters the final phase is to verify the regression models of the Δ

and

the Ra by conducting few confirmation experiments and comparing the results of these validation runs with respect to the model predicted values From Table 7 the first and the second test are the confirmation runs the third case is the optimized cutting parameters based on the first-order regression models by minimizing the

Δ and the Ra simultane-

ously According to Table 7 the first-order regression model for the Ra yields reasonable results with less than 3 errors between the predicted values and the experiment data In addition the first-order regression model for the

Δgives the results with 695 average errors The prediction of the Ra is better than the

Δbecause the Ra is an aver-

age value that can reduce some undesirable noise from the experiments

bull The cutting parameter the feed rate is the most important factor to attain a better surface finish and to improve the groove difference based on the first-order model

bull DOE Puertas and Luis (2004) applied the DOE method to optimize the machining parameters for electrical discharge machining of boron carbide Alagumurthi et al (2006) applied the DOE method to determine optimal set -tings of grinding conditions and grinding cycle time for which results were compared and analyzed At the same time the ANOVA analysis was carried out for the interpretation and for obtaining insight into the process Sofuoglu (2006) investigated the effect of different process parameters on the flow behavior of plasticine and to insure its validity as a modeling medium of metal forming processes Yang (2006) also used the design of DOE approach to determine the optimal parameters of photo resist (PR) coating process for photolithography in wafer manufacturing

Optimization of ferrous biooxidation rate in a packed bed bioreactor using Taguchi approach

bull The biological oxidation of ferrous ion by iron-oxidizing bacteria is potentially a useful industrial process for removal of H2S from industrial gases desulphurization of coal removal of sulfur dioxide from flue gas treatment of acid mine drainage and regeneration of an oxidant agent in hydrometallurgical leaching operations

bull The main purpose of this study was to find optimum values of the process parameters on the ferrous biooxida-tion rate by immobilization of a native Sulfobacillus species on the surface of low density polyethylene (LDPE) parti-cles in a packed-bed bioreactor using Taguchi method

bull Five control factors including temperature initial pH of feed solution dilution rate initial concentration of Fe3+ and aeration rate in four levels are considered in Taguchi technique L16 orthogonal array has been used to de-

termine the signal to noise (SN) ratio Analysis of variance (ANOVA) was used to determine the optimum conditions and most significant process parameters affecting the reaction rate

bull Analysis of the experiments using Taguchi method indicated that pH of feed solution has the most contribution in the biooxidation rate of ferrous ion

bull Industry gtgt industrial operations such as coke production viscose rayon production wastewater treatment wood pulp production using sulfate process oil refining process tanning of leather and during combustion of fossil fuels containing sulfur gtgt The process of microbiological desulphurization has been applied for the quality improve-ment of coals used as a fuel or a raw material in the chemical industry

bull This paper describes a case study investigating the parameters that influence biooxidation rate of ferrous iron using a native Sulfobacillus species in a packed-bed bioreactor Concentration of Fe3+ in effluent of bioreactor and rate of biological ferrous oxidation are the key factors for evaluating the performance of bioreactor Factors such as temperature initial pH dilution rate initial Fe3+ concentration and rate of aeration affect the biooxidation rate of fer-rous ion The main objective was using the Taguchi approach to find a combination of effective parameters to achieve high ferrous biological oxidation rate

bull The appropriate orthogonal array for the experiment was determined by the software The Taguchi technique applies fractional factorial experimental designs called orthogonal arrays to reduce the number of experiments and meanwhile obtaining statistically meaningful results

bull in Table 2 the number of experiments required can be drastically reduced to 16 It means that 16 experiments with different combinations of the factors should be conducted in order to study the main effects and inter- actions which in the classical combination method using full factorial experimentation would require 4^5 = 1024 number of experiments to capture the influencing parameters However in general Taguchi design is preferred because it re-duces the number of experiments significantly

bull According to the ANOVA results in Table 3 the initial pH has the largest variance and the initial Fe3+ concen-tration indicated the second place Therefore it can be concluded that the most influential factor was in the order of the pH

Optimization of oil removal from oily wastewater by electrocoagulation using response surface method

bull Electrocoagulation process with sacrificial aluminium anode was used to separate oil from oily wastewater emulsion A preliminary experimental study was performed to evaluate the most accurate operating parameters which are then used for the determination of oil removal efficiency An experimental design using response surface method (RSM) was then applied and oil separation was estimated by measuring turbidity and chemical oxygen de-mand (COD)

bull The experimental results indicated that electrocoagulation was very efficient and able to achieve 99 turbidity and 90 chemical oxygen demand (COD) in less than 22 min and current density of 25 mA cmminus2

bull The mechanical and metallurgical industries generate great quantities of oily wastewater which in the ma-jority of cases are rejected into nature because of non-adaptation of the processes of treatment

bull Appropriate treatment of these wastewaters is necessary in order to reduce the impact of their discharge A cer-tain number of studies which show the success of the oil separation from oily rejections by using electroflotation [78] The principal disadvantage of this method is the limitation of separation efficiency by the oil concentration in the emulsion To reduce this limitation other techniques based on the combination of the electroflotation with floccu-lation were used successfully [9]

bull response surface method (RSM) was proposed to determine the influences of individual factors and their inter -active influences RSM is a statistical technique for designing experiments building models evaluating the effects of several factors and searching optimum conditions for desirable responses [18] The main advantage of this method of other statistical experimental design methods is the reduced number of experiments trials needed to evaluate multiple parameters and their interactions [19] Recently this method has been used to determine optimum parameters in dif-ferent processes [2021]

bull In order to evaluate the decrease of turbidity and increase of oil removal from the emulsion three important electrochemical factors were investigated current density initial pH electrocoagulation time The responses fixed as objectives in this process are turbidity removal (Y1) and COD removal (Y2) One of the most important charac-teristic of the oil-in-water emulsion is a high turbidity and COD

bull The ANOVA of these responses demonstrated that the model is highly significant as is evident from the value of Fstatistic (the ratio of mean square due to regression to mean square to real error) (Fmodel(a) = 347096 and Fmodel(b) = 72955) and a very low probability value (P = 00001) P value lower than 001 indicated that the model is consid-ered to be statistical significant [32]

bull As we can see these results closely agree with the experimental results confirming that the RSM could be ef -fectively used to optimize the process parameters in complex process using the statistical design of experiments

bull Analysis of variance showed a high coefficient of determination value (R2 = 0998) thus ensuring a satisfac-tory adjustment of the second-order regression model with the experimental data Under optimal values of process pa-rameters (current density=25mAcmminus2 initial pH 7 electrolysis time=22min) more than 99 removal turbidity and 8963 removal COD were obtained

Optimization of parameters for an aluminum melting furnace using the Taguchi approach

bull To achieve high thermal efficiency less pollutant emission and high quality products a study on a regenerative aluminum melting furnace was carried out The effects of the vertical angle of burner (A) height of burner (B) sec -ondary flue (C) swirl number (D) horizontal angle between burners (E) air preheated temperature (F) natural gas mass flow (G) and air-fuel ratio (H) on the performance of aluminum melting furnaces were investigated RSD (rela -tive standard deviation) of aluminum temperature (Y1) melting time (Y2) and RSD of furnace temperature (Y3) were designed for evaluation criteria The optimum condition which may be used to reduce energy consumption and pollutant emission is A2B3C3D3E2F1G3H1

bull The core of the Taguchi method is the parameter design whose main feature is to transform quality loss func-tion into SN ratio (signal-to-noise ratio) to evaluate the characteristics of product quality

bull Aluminum industry is an industry of high-energy-consumption and high-pollution

bull Therefore there are sixteen degrees of freedom owing to the eight melting parameters with three levels Once the degrees of freedom required are known the next step is to select an appropriate orthogonal array to fit the specific task As per Taguchirsquos method the total DOF (degree of freedom) of selected orthogonal array must be greater than or equal to the total DOF required for the experiment As a result an orthogonal table L27(38) was selected as CFD experimental plan

bull The goal of this study is to optimize the parameters for an aluminum melting furnace to get better performance Thus RSD of aluminum temperature (Y1) melting time (Y2) and RSD of furnace temperature (Y3) are selected as evaluation criteria for the SN ratio of static characteristics The objective function is to minimize RSD of aluminum temperature melting time and RSD of furnace temperature

Optimizing process parameters of hot-bar soldering process through quality function deployment and Taguchi method

bull As quality values failed to meet customersrsquo requirements when the hot-bar soldering process (HBSP) was first introduced in the electronic manufacturing service (EMS) company it is the intention of this study to combine quality function deployment (QFD) and the Taguchi method to analyze the produced quality characteristics and to optimize the process parameters

bull The product from HBSP is a gate board that transmits vertical signals in thin film transistor liquid crystal dis-play (TFT-LCD) modules To produce a gate board through HBSP a film of flexible printed circuit (FPC) is soldered onto the pad of a printed circuit board (PCB) with the hot-bar (HB) which is heated by a pulse heater

bull The defect rate of HBSP has reached an extraordinary height at level of 3000ndash4000 ppm in present industrial applications This level is far below the quality requirement 60 ppm of defect rate which is acceptable to customers Hence there is a need to perform robust design to improve the level of quality so that customersrsquo requirements can be satisfied

bull This technology is now widely applied in the production of multi-function electronic goods in the high tech -nology industry

bull The response value the strength of solder joints is influenced by a number of factors such as material of the film 1st temperature 1st heating time 2nd temperature 2nd heating time pressure of the HB cooling temperature and the contact area of HBSP Based on the process engineersrsquo previous experience and a preliminary investigation for defective gate boards material of the film and the contact area of HBSP are also control factors influencing the strength of solder joints The uncontrollable variables are considered as noise impact in the present design system They are thickness of solder on PCB thickness of solder on film and time spanned from initiation of the HB until the present

bull Because two levels for one of the control factors and three levels for the remaining control factors were re -quired in the experiment the degree of freedom was 15 (=1 x 1 + 2 times 7) Therefore the appropriate inner orthogonal array is L18 (2^1 times 3^7)

bull Analysis of variance (ANOVA) of SN ratio The initial degree of freedom for the error terms is 2 As factors B and G were not significant their SN ratios were pooled into the error terms Therefore the degree of freedom of error terms after pooling was 6 (=2 + 2 times 2) and the sum of square became 392 The most significant contribution was from factors D (2nd temperature) 2553 and B (1st temperature) 2500 The sum of contribution percentage due to error was approximately 1984

bull In using the Taguchi method the optimal parameter levels are A1 B3 C3 D3 E3 F2 G3 H3

bull Through Eq (8) the 95 confidence interval for the SN ratio η

confirm of the experimental verification was

6813plusmn195 after plugging into known values As η

confirm which is equal to 6643 shown in Table 8 lies within

the 95 confidence interval the additive model and associated optimal parameter levels are valid for this experimentbull When engineers have had exposure to experimental design typically their reaction is to deem the approach

too costly and time consuming because of full factorial designs It is unfortunate that people are not aware of the po-tential savings in test time and in money offered by more efficient testing strategies In this regard Dr Taguchi devel-oped a simple design of experiment that can be efficiently used to investigate optimal parameters of a system with the Taguchi method employed to get optimal process parameters of HBSP

bull In addition to identifying the key factors derived from the QFD method a statistical method is also needed to improve the quality of manufactured goods and to further optimize those parameters of the process which were

ranked in influencing the quality Because the knowledge of a proper testing strategy is usually limited in the begin -ning of product or process development and the approach of full experimental factorial designs is too costly and time consuming the Taguchi method is introduced in this study

larrlarrRobust cutting parameters optimization for production time via computer experi-ment

bull This study intends to determine the optimal cutting parameters required to minimize the cutting time while maintaining an acceptable quality level

bull The equation for predicting cutting time can be determined by studying the output cutting times vs input cut -ting parameters through CATIA software with assistance from the statistical method response surface methodology (RSM)

bull A properly designed machining process can significantly affect overall production costs To minimize the cost of work- piece machining cutting parameters must permit the reduction of cutting time and costs to the lowest levels

bull The purposes of this experimental approach are to find the regression function through regressing the response value vs the controllable variables to determine which variables significantly affect the quality functionality and cost of a product or manufacturing This experimental approach can lead to the development of designs with en-hanced quality lower costs and shorter design and development cycles

bull Thus many designs can now routinely proceed with the aid of computer experiments The other reasons for re-placing physical experiments with computer experiments are to reduce the cost of experimentation and perhaps more importantly to speed up design activity

bull With this method the cutting time equation an exact functional relationship between inputs of cutting parame-ters and outputs of cutting time and the importance ranking can be obtained from statistical analysis

bull Based on the RSM results the design engineer can select the critical process controllable factors for reducing the variation in quality value significantly The eventual goal of RSM is to determine the optimal factor levels and to form the prediction function in the system

bull U1 and t1 are the mean and tolerance values of the feed rate (mmrev) U2 and t2 are the mean and tolerance values of the cutting speed (mmin) U3 and t3 are the mean and tolerance values of depth of the cut (mm) and U4 and t4 are the mean and tolerance values of the percentage of tool diameter

bull The optimal cutting time is CT = 49039093 The optimal cutting conditions were U1 = 0059160 U2 = 150000000 U3 = 4805285 U4 = 41880932 t1 = 0009000 t2 = 6000000 t3 = 0150000 t4 = 1000000 The associated surface roughness under the optimal cutting conditions Ra = 0000007 The critical cutting parame-ters in the robust optimization of CT were U1 and U3

bull the disadvantages of the Taguchi method are (a) the Taguchi method fails to deal with interaction effects (b) the solutions found are only near-optimal (c) because the Taguchi method does not provide a cutting time equation the repeated application of the cutting time equation for the evolutionary design process becomes impossible (d)

lacking a cutting time equation we were unable to formulate the problem in MP as we could with the proposed ap-proach Thus for instance to add Eq (18) in MP for handling multiple objective problems was impossible and (e) physical experiments are required that may be subject to cost involvement

bull RSM is a design of experiment (DOE) which is a statistical and mathematical optimization technique Because RSM can overcome the above a and b deficiencies Lee et al [12] Fuh and Chang [13] and EI-Axir [14] used re-sponse sur- face methodology (RSM) to discover the optimal parameter values in cutting operations Similar to the Taguchi method costly physical experiments were required The only disadvantage as compared to the Taguchi method was that RSM took substantial experimental runs which were costly and time consuming However EI-Axir did not take advantage of using the found cutting equation from RSM an advantage unavailable in the Taguchi method to formulate the problem in the form of MP in the proposed approach MP could integrate numerous objec-tives in one formulation Therefore only for the RSM approach without integrating other objectives via MP for prob -lem formulation the optimal parameter values found were less able to describe the problem of interest due to the complex nature of the cutting processes

larrlarrSix Sigma based approach to optimize radial forging operation variables

bull The present competitive market is focusing industrial efforts on producing high-quality products with the low-est possible cost The work in this paper focuses on implementing the DMAIC (Define Measurement Analyze Im-prove and Control) based Six Sigma approach in order to optimize the radial forging operation variables

bull Analysis of various critical process parameters and the interaction among them was carried out with the help of Taguchirsquos method of experimental design To optimize the results obtained and to make the analysis more precise and cost effective response surface methodology (RSM) was also incorporated

bull Radial forging is a unique process used for the precision forging of round and tubular components with or without an internal profile

bull The foremost application of this process is the manufacturing of high-pressure tubes for deep-sea oil and gas pipelines

bull In this work the prime focus is on minimizing the residual stress developed in components manufactured by the radial forging process

bull Radial forging is a unique process for the precision forging of round and tubular components with or without internal profiles and for reducing the diameter of ingots and bars Deformation in radial forging results from a large number of short stroke and high-speed pressing operations by two three or four hammer dies arranged radially around the workpiece

bull In this context we have considered the radial forging of gun tubesbull the most significant contributors considered in the current research are the friction factor length of die land in-

let angle percentage reduction and corner fillet radiusbull The response variable was as discussed residual stressbull The total number of degrees of freedom (DOF) for four factors at three levels and one factor at two levels and

three interactions are found to be lsquo22rsquo There- fore a three level orthogonal array with at least lsquo22rsquo DOF was selected From the Taguchirsquos orthogonal inner arrays the L27 (313) design for controllable factors as shown in Table 6 was se-lected

bull From the response graph the optimum levels of parameters were found as inlet angle (A3) friction coefficient (B1) percentage reduction (C3) die land length (D1) and corner fillet (E2) respectively

bull An effectrsquos degrees of freedom should also be considered ie an effectrsquos mean square (MS) should determine whether an effect is weakmdashthe smaller an MS the weaker the effect

bull Table 9 shows that the inlet angle is the most critical parameter contributing up to 7388 Next is the friction coefficient contributing up to 928 Also Table 9 illustrates the percentage contributions of the various interaction effects For example it can be concluded that as compared to interaction effect A times B A times C and B times C are more significant

bull Since the interaction factor X1 times X2 or (A times B) is not significant as compared with X1 timesX3 or (AtimesC) and X2 timesX3 or (BtimesC) this interaction has not been included in the regression equation to simplify the analysis

bull In this case study we have implemented DMAIC based Six Sigma approach to optimize the operation vari -ables of a radial forging operation The Taguchi method of experimental design was applied to analyze the optimum levels of individual process parameters Table 11 shows the results obtained from factorial design and provides an in-sight into the process parameters affecting the forging process Thus from the ANOVA analysis it can be concluded that inlet angle has emerged as the most crucial and influential parameter the friction coefficient is the second most significant parameter the interaction effects between inlet angle friction coefficient and percentage reduction are also quite significant and must be taken into account when designing further experiments

bull Furthermore response surface methodology was employed to optimize the set of parameters to ensure a mini-mum residual stress Table 12 gives the optimum conditions found by RSM for the radial forging operation The opti-mum parameter values were then applied to the process and Table 13 shows the process sample data that was gath-ered from the shop floor over a period of time A control chart drawn for the improved state illustrates that there is a considerable amount of reduction in residual stress as shown in Fig 9

Warpage and structural analysis of thin shell plastic in the plastic injection molding

bull In this study the cell thin shell phone cover produced with polycarbonateacrylonitrile butadiene styrene (PCABS) thermoplastic were decided as a model First the effects of the injection parameters on warpage for differ -ent thickness values were examined using Taguchi method

bull Second to determine the forces that cause the plastic part to fail at the points determined over the top surface of the cell phone cover CATIA V5R12 (general structural analysis) was used The structural analysis of ABS PC re-inforced ABS reinforced PCABS thermoplastic materials in addition to PCABS material used in telephone manu-facture were done in order to determine the performance When we look at the structural analysis the strongest mate-rials are 15 carbon fiber reinforced PCABS 15 carbon fiber reinforced ABS PC PCABS and ABS respec -tively

bull In plastic injection molding the production of the thin walled parts is very difficult Itrsquos hard because melted plastic cannot easily fill the mold cavity Because of this the most important problem in thin walled parts is warpage

bull Huang and Tai [1] have examined the influential effects over warpage that is seen in thin walled parts pro-duced by injection molding

bull The experimental tests were built on Taguchi experimental method Cycolone scanner polycad and polyworks were used to measure the shrinkage and warpage values

bull The warpage analyses were done with the assistance of the table which was built with the help of Taguchi ex-perimental method After getting the warpage values by the results of the simulations ANOVA analyses were done with the MINITAB V14 software to see the effect of every parameter on warpage

bull In this study the injection parameters are taken as packing pressure mold temperature melt temperature packing time

bull In this study L27 (34) orthogonal array is shown on Table 3 Analyses were done by locating the values from Table 2 to Table 3

bull If the R2 value obtained form the regression analysis is bigger than 80 we can say that it is acceptable [22]

bull In this study the suggested parameter values for the minimization of the warpage for PCABS material are packing pressure 30 MPa melt temperature 300 degC packing time 10 s mold temperature 60 degC

bull For PCABS material the most effective factor for the warpage is the packing pressure (852) The highest warpage value is seen with the smallest value of the packing pressure and the smallest warpage value is seen with the highest value of the packing pressure If the melt temperature value which is the second effective parameter (1266) is considered the increase of the melt temperature causes a decrease on warpage It is seen that packing time (196) is not effective for PCABS The result for PCABS material is the most influential parameters on warpage are pack-ing pressure and melt temperature

bull While the warpage analyses were done four injection parameters were used These parameters are melt tem-perature mold temperature packing pressure and packing time After getting the warpage values the effect of each injection parameter on each material was examined ANOVA analysis was used for this examination As a result of analyses the most influential parameter for PCABS material is packing pressure

bull When we look at the structural analysis the strongest materials are 15 carbon fiber reinforced PCABS 15 carbon fiber reinforced ABS PC PCABS and ABS respectively The strongest points over the top surface of the cell phone cover are Points 4 3 2 1 in order This result is the same for every material

Analyses of surface roughness by turning process using Taguchi method

bull Purpose The purpose of this research paper is focused on the analysis of optimum cutting conditions to get lowest surface roughness in turning SCM 440 alloy steel by Taguchi method

bull Designmethodologyapproach Experiment was designed using Taguchi method and 18 experiments were designed by this process and experiments conducted The results are analyzed using analysis of variance (ANOVA) method

bull Findings Taguchi method has shown that the depth of cut has significant role to play in producing lower sur-face roughness followed by feed The Cutting speed has lesser role on surface roughness from the tests

bull Research limitationsimplications The vibrations of the machine tool tool chattering are the other factors which may contribute poor surface roughness to the results and such factors ignored for analyses

bull The dependent variable is surface roughness

bull The purpose of turning operation is to produce low surface roughness of the parts Surface roughness is an-other important factor to evaluate cutting performance Proper selection of cutting parameters and tool can produce longer tool life and lower surface roughness

bull From the ANOVA table 4 it is evident that 14467 depth of cut C is contributing on surface roughness than other two cutting parameters The feed is the next contributing factor having 9764 on surface roughness and Cut-ting speed has very little role to play

bull (1) From the ANOVA Table 4 and the P value the depth of cut is the only significant factor which contributes to the surface roughness ie 14467 contributed by the depth of cut on surface roughness (2) The second factor which contributes to surface roughness is the feed having 9764 (3) The Validation experiment confirms that the error occurred was less than 10 between equation and actual value (4) It is recommended from the above results that depth of cut of 1 to 15 mm can be used to get lowest surface roughness

Application of Taguchi Method in the Optimization of Injection Moulding Pa-rameters for Manufacturing Products from Plastic Blend

bull This paper presents a study in which an attempt has been made to improve the quality characteristic (shrink-age) of an injection molding product (plastic tray) made from blends plastic (75 polypropylene (PP) and 25 low density polyethylene (LDPE)) by optimizing the injection molding parameters using the Taguchi method

bull The analysis of the results shows that the optimal combination for low shrinkage are low melting temperature high injection pressure low holding pressure long holding time and long cooling time

bull Injection moulding is the most important method for manufacturing varieties of plastic componentsbull Various studies have been carried out to improve or to optimise the quality characteristic so as to produce high

quality commercial plastic product on injection moulding machine [4]-[7] However most of the researchers did not consider the effect of interaction between parameters on the quality characteristic of the products although it could be quite significant

bull This paper attempts to describe the optimisation of the injection moulding process parameters for optimum shrinkage performance of a plastic tray which is made from polymer blends or polyblends

bull Response the effect of six injection moulding parameters namely injection speed melting temperature injec-tion pressure holding pressure holding time and cooling time on shrinkage of plastic tray was investigated to obtain significant parameters

bull Variables effect of five significant parameters namely melting temperature injection pressure holding pres-sure holding time and cooling time and two interactions between melting temperature and injection pressure be -tween injection pressure and holding pressure was explored to determine optimal combination of parameters for low plastic tray shrinkage

bull Taguchi a result time cost and labour saving can be achieved Therefore the optimal level of the process pa -rameters is the level with the greatest SN ratio Furthermore a statistical analysis of variance (ANOVA) is per-formed to see which process parameters are statistically significant

bull In this study since each parameter has three levels except injection speed which has two levels the total de-grees of freedom (DOF) for the parameters are equal to 11 Basically the degrees of freedom for the OA should be greater than or at least equal to those for the process parameters Therefore an L18 (21 times 37) orthogonal array with eight columns and eighteen rows was appropriate and used in this study

bull For example the mean SN ratio for the melting temperature at levels 1 2 and 3 can be calculated by averag-ing the SN ratios for the experiments 1ndash3 and 10ndash12 4ndash6 and 13ndash15 and 7ndash9 and 16ndash18 respectively

bull Thus based on the SN ratio and ANOVA analyses the optimal combination of parameters and their levels for achieving minimum shrinkage is A2B3C2D1E1F3 ie injection speed at level 2 melting temperature at level 3 injec-tion pressure at level 2 holding pressure at level 1 holding time at level 1 and cooling time at level 3

bull The contribution from these parameters were melting temperature (524) holding pressure (36) holding time (09) and cooling time (69)

bull The confirmation test is used to verify the estimated result with the experimental results If the optimal combi-nation of parameters and their levels coincidently match with one of the experiments in the OA then the confirmatory test is not required

bull From the Table 5 it is observed that the injection speed is one of the least significant factors to influence the dimensional stability of the plastic tray Therefore only five remaining factors were considered in the design of ex -

periments with interaction Among these factors injection pressure was considered to interact with melting tempera-ture and holding pressure Two levels of each factor were considered L8 Orthogonal Array was used in this case

bull Consider the initial optimum which excluded the effects of the interaction (A2 B2 C1 D2 and E2) and it was found to be 01543 cm Similarly for the revised optimum considering interaction the estimated shrinkage at opti -

mum condition (A1 B2 C1 D2 and E2) was found to be 01462 cm This shows that the revised optimum yields a smaller value (the-smaller-the-better) of shrinkage and thus it confirms that the revised optimum is better

bull A small difference (0006 cm) can be observed between these values This indicates that the experimental value is close to the estimated value Therefore this verifies that the experimental result is highly correlated with the estimated result

bull The optimum conditions are A2 B3 C2 D1 E1 F3 ie injection speed (90 rpm) melting temperature (240degC) injection pressure (110 bar) holding pressure (96 bar) holding time (5 sec) and cooling time (10 sec)

bull The optimum total shrinkage is 01645 cm bull Melting temperature is the most significant parameterbull The contribution of parameters is melting temperature (524) holding pressure (36) holding time (09)

and cooling time (69)

Optimum parameters with interaction effectbull The optimum conditions are A1 B2 C1 D2 and E2 ie melting temperature (220degC) injection pressure (120

bar) holding pressure (80 bar) holding time (10 sec) and cooling time (10sec)bull The optimum total shrinkage is only 01521 cm bull Interaction between melting temperature and injection pres-

sure is higher than the interaction between holding pressure and injection pressurebull The suspected interaction between factors injection pressure and holding pressure is not found whereas the in-

teraction between melting temperature and injection pressure does existbull The contributions of parameters are injection pressure (1049) holding time (7145) and cooling time

(977)

Statistical analysis of surface roughness and cutting forces using response surface methodology in hard turning of AISI 52100 bearing steel with CBN tool

bull The main objectives are firstly focused on delimiting the hard turning domain and investigating tool wear and forces behaviour evolution versus variations of workpiece hardness and cutting speed Secondly the relationship be-tween cutting parameters (cutting speed feed rate and depth of cut) and machining output variables (surface rough -ness cutting forces) through the response surface methodology (RSM) are analysed and modeled

bull Results show how much surface roughness is mainly influenced by feed rate and cutting speed Also it is underlined that the thrust force is the highest of cutting force components and it is highly sensitive to workpiece hardness negative rake angle and tool wear evolution Finally the depth of cut exhibits maximum influence on cutting forces as compared to the feed rate and cutting speed

bull Hard turning is a lsquolsquosingle pointrdquo cutting process which is largely adopted in actual industrial production It concerns the removal of materials whose hardness is higher than 45 HRC

bull The new cutting applications in various industries like aerospace automobile die and mould manufacturing re-quires everlasting researches for products with very fine surface finish

bull In this framework the present study is focused on delimiting accurately the working parametersrsquo domain of hard turning Also the effect of workpiece hardness and cutting speed on the cutting forces for different values of the tool wear is investigated Moreover this work treats the effect of cutting speed feed rate and cutting depth on surface roughness and cutting forces in turning of AISI 52100 bearing steel hardened to 64 HRC with CBN tool using the RSM

bull In this research an L27 Taguchi standard orthogonal array is adopted as the experimental design The parame-ter levels were chosen within the intervals recommended by the cutting tool manufacturer The parameters to be stud -ied and the attribution of the respective levels are indicated in Table 1 The L27 Taguchi standard orthogonal array has 27 rows corresponding to the number of tests (26 degrees of freedom) with 13 columns at three levels The fac -tors and the interactions are assigned to the columns The first column of the table was assigned to the cutting speed (Vc) the second to the feed rate (f) the fifth to the cutting depth (ap) and the remaining were assigned the interac -tions [18]

bull arithmetic average of absolute roughness Ra maximum height of the profile Rt and average maximum height of the profile Rz

bull The surface roughness parameters (Ra Rt and Rz) and cutting force components (Fa Fc and Fp) have been measured after the straight turning operation

bull Values are given in Table 3 (surface roughness) and Table 4 (force component) When P-values are less than 005 (or 95 confidence) the obtained models are considered to be statistically significant When R2 approaches to unity the better the response model fits the actual data

bull From Tables 4 it can be seen that the Vc f and the products Vc2 f2 Vcf are significant terms on surface roughness parameters arithmetic average of absolute roughness Ra maximum height of the profile Rt and average maximum height of the profile Rz The most significant factor on the parameters Ra and Rt is feed rate (f) which ex -plains respectively 592 and 5626 contributions of the total variation For Rz (Table 3c) f is in the second place and explains 3934 of the evolution The next largest contribution on Ra and Rt comes from the cutting speed Vc with the contributions 316 and 3392 respectively For Rz Vc is in first position with a contribution at 5335 For the depth of cut (ap) and its interactions with cutting speed (Vc ap) and feed rate (f ap) influence value is less than 02 It does not present a statistical significance on surface roughness parameters

bull Tables 5 are ANOVA tables corresponding to the cutting force components These tables show that the main effects of cutting speed (Vc) feed rate and depth of cut (ap) are all significant with respect to the thrust feed and tan -gential cutting forces Table 4a and b shows that the main effects of the terms Vc f ap Vc2 f2 ap2 Vc ap and f ap are significant with respect to the feed and tangential cutting forces The depth of cut has maximum influence on the cutting force components Fa Fc and Fp with the contributions 6334 5359 and 6351 respectively fol -lowed by feed rate (1847 2372 and 755) and cutting speed (563 39 and 142) The cutting speed has decreasing control on cutting forces components (Fa Fc and Fp) decreases

bull The optimum cutting parameters obtained in Table 5 are found to be cutting speed of 246 mmin feed rate of 008 mmrev and cutting depth of 015 mm The optimized surface roughness parameters are Ra = 0186 lm Rt = 1184 lm and Rz = 0749 lm In addition the optimized cutting force components are Fa = 25703 N Fp = 140028 N and Fc = 39870 N

bull the higher the feed rate and cutting depth the higher the cutting force whereas the higher the cutting speed the lower the cutting force The depth of cut exhibits maximum influence on cutting forces as compared to the feed rate and cutting speed Moreover it is noted that the thrust force is the largest force component regardless the cutting con-ditions and it is most sensitive to workpiece hardness negative rake angle and tool wear evolution According to pre -sented results the surface roughness is highly affected by feed rate whereas the cutting speed has a negative effect and the depth of cut a negligible influence Consequently when implementing process planning measures shall be taken to maximize the cutting speed and the cutting depth yet minimize the feed rate The aim is to secure an optimal surface roughness value and an optimal metal-removal rate The using of the response surface optimization and com-posite desirability show that the optimal setting values of machining parameters are (246 mmin 008 mmrev 015 mm) for cutting speed feed rate and cutting depth respectively