College of Elect. & Mech. Engineering Composites Manufacturing & Manufacturing Process Selection Strategy (the Ashby approach) Dept of Mechanical Engineering, NUST, College of E & ME, Rawalpindi, Pakistan Dr. Rizwan Saeed Choudhry [email protected]Material selection charts in this slide are copyright of Granata Design and should only be used for educational purpose
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Composites Manufacturing Manufacturing Process · PDF filePROCESS SELECTION! - COMPOSITES DRIVING FORCES Criteria on which composites are selected depend on the industry in
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Control on angles during layup Volume Fraction range Void Content achievable
• Batch Size
• Cost Model
• Production rate
• Documentation
PROCESS SELECTION
Translation of process
requirements
Function: What must the process do ? (e.g.
moulding? joining? finishing ?)
Constraints What technical limits must be met? (i.e.
Material and shape compatibility)
What quality limits must be met
(Precision, porosity/void content, volume
fraction, fibre orientation control …)
Objectives
What is to be maximized or minimized?
(Cost? Time ? Quality)
Free variables
Choice of process and process-operating
conditions
SCREENING USING
CONSTRAINTS
Process - Material
Compatibility
SCREENING USING
CONSTRAINTS
Process – Shape
Compatibility
SCREENING USING CONSTRAINTS Process – Mass Compatibility
SCREENING USING CONSTRAINTS Process – Section thickness Compatibility
SCREENING USING CONSTRAINTS Process – Tolerance Compatibility
SCREENING USING CONSTRAINTS Process – Surface Roughness Compatibility
RANKING – THE COST OBJECTIVE
The Cost function and economic batch size
m= component weight (mass)
f = scrap function
n = number of components
L = load factor
two = write-off time
ń = production rate (units per hour)
Int = integer value function
RANKING – THE COST OBJECTIVE Understanding economic batch size
The cost of sharpening a pencil plotted against batch size
RANKING – THE COST OBJECTIVE Process-vs-Economic batch size
COMPUTER AIDED PROCESS SELECTION
Cambridge Engineering Selector
The cost of sharpening a pencil plotted against batch size
CASE STUDY :
FORMING A FAN (FOR VACUUM CLEANERS)
CASE STUDY :
FORMING A FAN (FOR VACUUM CLEANERS)
CASE STUDY : FORMING A FAN (FOR VACUUM CLEANERS)
Process - Material
Compatibility
CASE STUDY : FORMING A FAN (FOR VACUUM CLEANERS)
Process – Shape
Compatibility
CASE STUDY : FORMING A FAN (FOR VACUUM CLEANERS)
Process – Mass
Compatibility
CASE STUDY : FORMING A FAN (FOR VACUUM CLEANERS)
Process – Section thickness
CASE STUDY : FORMING A FAN (FOR VACUUM CLEANERS)
Process – Tolerance
CASE STUDY : FORMING A FAN (FOR VACUUM CLEANERS)
Process – Roughness
CASE STUDY : FORMING A FAN (FOR VACUUM CLEANERS) Economic Batch Size
CASE STUDY : FORMING A FAN (FOR VACUUM CLEANERS) Final recommendation
Exploring the cost further
CASE STUDY : FORMING A FAN (FOR VACUUM CLEANERS)
Relative cost of moulding the fan
College of Electrical and Mechanical Engineering
Traditional and still the most prevalent approach - Trial
and Error based on historic data of usage and availability
Scientific approach: Most Popular theses days
Ashby approach – Cambridge Engineering Selector
Other scientific approaches include Matrix methods such
as Multiple Criteria Ranking Methods, Digital Logic
Method and Analytical Hierarchical Method (AHP)
All scientific approaches to material selection attempt to
ensure that the desired functionality is achieved while
satisfying the constraint(s) and maximizing the desirable
objective(s)
MATERIAL SELECTION PROCESS
College of Electrical and Mechanical Engineering
Function:
The desirable operation to be performed by the material; e.g. in mechanical design this can be usually translated into quantities that relate directly to material properties; for example a tie-rod resists axial loads and the functional requirement can be expressed in terms of both strength and stiffness.
Objective:
For example minimize mass and cost
Constraints:
E.g. Availability, minimum strength requirements, allergies
Defines the performance (p) for a design problem as functional p = p(F,G,M) where F = functional requirements; G = Geometric parameters; and M = material indices
THE ASHBY APPROACH[1-4]
College of Electrical and Mechanical Engineering
If this functional can be written in separable form such as
p = p1(F).p2(G).p3(M) then for a given set of F and G the
problem of Material selection reduces to the one of optimizing
M; i.e. the material indices.
Based on above the Material index is a combination of
materials properties that characterizes the Performance of a
material in a given application [1].
Function, Objective, and Constraint Index
Tie, minimum weight, stiffness E/r
Beam, minimum weight, stiffness E1/2/r
Beam, minimum weight, strength s2/3/r
Beam, minimum cost, stiffness E1/2/Cmr
THE ASHBY APPROACH
College of Electrical and Mechanical Engineering
College of Electrical and Mechanical Engineering
College of Electrical and Mechanical Engineering
College of Electrical and Mechanical Engineering
College of Electrical and Mechanical Engineering
College of Electrical and Mechanical Engineering
College of Electrical and Mechanical Engineering
CASE STUDY: MATERIAL FOR OARS
CASE STUDY: MATERIAL FOR OARS
Constraints:
Deflection limits:
Soft = 50 mm, Hard = 30 mm
Weight limit:
As light as possible:
Shape:
Hollow Shaft with variable diameter and flat spoon
Weight hung 2.05 m
from collar
CASE STUDY: MATERIAL FOR OARS
CASE STUDY: MATERIAL FOR OARS
CASE STUDY: MATERIAL FOR OARS
• Wooden oars made of laminated spruce wood
• Requires around 2 weeks to settle down after lamination and gluing
• Weighs between 4 to 4.3 kg
• Quality consistency also depends on availability of same grade of
wood and workers skill.
• CFRP is also better because
1. Possibility of faster production rates
2. More control over stiffness by precisely varying the fibre –
resin content
3. Weight can be easily lowered to 3.9 kg
4. More consistency of part quality
CASE STUDY: PROCESS FOR CFRP OARS
Process Requirements:
• Function Moulding (shapping)
• Constraints Material (CFRP)
Shape – Hollow/Solid 3D
Mass – less than 4 kg
Tolerance - ?
Roughness - ?
Control on angles < 2.5o variation ?
Volume fraction > 40% Void Content < 2%
Reinforcement Type –
Continuous (Multidirectional layup)
Batch Size – ? (1000)
Production Time - ? (less than 2 weeks)
Same Process for Spoon and Loom
• Objective Minimize cost
Free variables Choice of Process
Process parameters
CASE STUDY : FORMING A FAN (FOR VACUUM CLEANERS)
Process - Material
Compatibility
Oars
Process – Shape Compatibility
Process Loom Spoon
1. RTM ++ ++
2. VARI + ++
3. Vacuum
bagging Prep-preg
+++ +++
4. Spray-up +++ +++
5. Filament
Winding
+++ N/A
Process – Mass Compatability
All Five Processes
Process – Fibre Type and Layup
Compatibility
Process Loom Spoon
1. RTM ++ ++
2. VARI ++ ++
3. Vacuum
bagging Prep-preg
+++ +++
4. Spray-up N/A N/A
5. Filament
Winding
+++ N/A
Process – Production Time
Compatability
All Five Processes
CASE STUDY: PROCESS FOR CFRP OARS
Process – Batch Size
Compatibility
Process Loom Spoon
1. RTM +++ +++
2. VARI + +
3. Vacuum
bagging Prep-preg
++ +
4. Spray-up +++ +++
5. Filament
Winding
+++ +++
Process – Fibre Orientation
Control Compatibility
Process Loom Spoon
1. RTM + ++
2. VARI + +
3. Vacuum
bagging Prep-preg
+++ +++
4. Spray-up N/A N/A
5. Filament
Winding
+++ N/A
CASE STUDY: PROCESS FOR CFRP OARS
Process – Volume fraction /Void Content Compatibility
Process Loom Spoon
1. RTM ++ ++
2. VARI + +
3. Vacuum bagging Prep-preg +++ +++
4. Spray-up N/A N/A
5. Filament Winding N/A N/A
Process Shape Layup Vf/Void Orient.. Batc
h
Aggregate
1. RTM 4 4 4 3 6 21
2. VARI 3 4 2 2 2 13
3. Vacuum
bagging
Prep-preg
6 6 6 6 3 27
Cumulative Ranking after elimination of processes which were not applicable on one or more counts
CASE STUDY: PROCESS FOR CFRP OARS
Vacuum bagging with curing is better for the criteria
considered however it may require secondary curing using
oven or autoclave depending on design specifications
On rigorous cost analysis RTM may turn out to be cheaper