Product design model for Impact Toughness Estimation in Steel Plate Manufacturing Satu Tamminen ISG - Data Mining Group, Department of Electrical and Information.

Product design model for Impact Toughness Estimation in Steel Plate Manufacturing

Satu TamminenISG - Data Mining Group,

Department of Electrical and Information Engineering, University of Oulu, Finland

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TOC

Introduction Data Model Conclusions

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Introduction

Impact toughness (notch toughness) describes how well does the steel resist fracturing at predefined temperature, when hard impact suddenly hits the object.

The property is crucial for steel products that are used in cold and harsh environments e.g. ships, derricks and bridges.

The harder the steel the lower the impact toughness.

In a room temperature the steel can perform well in the impact toughness test, but when the temperature falls, the performance weakens.

In this research, the most demanding steel qualities are tested as low as in -100˚C.

Transition behavior (ductile-to-brittle transition in certain temperature) is typical for ferritic steel qualities.

Factors that rise transition temperature have a negative effect on impact toughness.

The complicated interactions between these factors bring challenge to the modelling (a harmful elements can produce a desirable effect together with another component).

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Impact toughness is defined by Charpy-V impact toughness test (CVT).

The test piece is broken with a pendulum and the energy absorbed in fracturing is measured.

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The test is performed for three different samples from every steel plate, and it will be accepted if the average of the measurements is higher than the requirement. In addition, only one of the measurements is allowed to be not more than 30% under the requirement.

The average of the measurements does not serve well as the target of the model. Instead, the target is

3

1210

1

3

1log

i imLIB

The difference between the average of the measurements (left) and the lib transformation (right).

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100 J

Data The data was collected at Ruukki's steel plate mill in

Raahe, Finland during 2002-2007 and it consists of information about over 200 000 low-alloy steel plates and over 70 variables.

After careful pre-processing, the final data included 202 667 observations and 42 variables.

The variables included information about the shape and position of the test bar, the test temperature, chemical composition and process parameters.

63% of plates rejected in CVT were rejected because of one too-low measurement.

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Model The model is developed for product design group, who

plans the chemical composition, possible treatments during melting and some production requirements for heating, working and thermomechanical treatments.

The model predicts the rejection probability in CVT. The model will guide designers in producing desired

properties in the product at lower cost. The working allowance that keeps the product within

tolerance can be decreased with the model. Ruukki competes with high quality, short delivery time,

and a large product range.

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Target values for chemical composition and for the rest of the process variables were used.

MLP (multilayer perceptron) networks were trained with MATLAB R2007a.

Half of the data was used for training and one quarter for validating and one quarter for model selection.

The independence of the data sets was verified by not allowing plates from the same melting to belong to different sets.

The network for LIB-model had two hidden layers with 39 and 5 neurons.

For comparison a network for mean of the three measurements was trained as well (AVG-model).

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Over 96%of plates

Scatter plot between the predicted LIB and the model error.

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MSE (LIB) R (LIB) R (AVG)

Train 0.0487 0.8747 0.9056

Test 0.0502 0.8676 0.9016

ˆ

ˆ ii

LP

The rejection probability can be calculated with cumulativeNormal distribution function

iwhere L is the rejection limit, is the estimated LIB and is the calculated sample deviation.

The performance of the LIB-model and AVG-model.

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ROC-analysis of the results show that the LIB transformation discriminate better the rejected plates (LIB-model solid, AVG-model dashed).

Rejection level LIB model AVG model

20J 0.9860 0.9586

27J 0.9850 0.9644

40J 0.9452 0.9192

100J 0.9110 0.9049

120J 0.9125 0.9104

Area under ROC curve

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Conclusions

Analysis showed that most of the rejections could have been recognized with the model, and thus, it is expected that the number of rejections will be reduced when the model is entered into the product design.

The LIB-transformation improves the prediction result. At the moment, the model is in test use at the product

design department in Rautaruukki, Raahe. The assumption of independence between model error

and the variables is not valid, and the results can be improved further with the use of a variance model (heteroscedastic regression).

THANK YOU!

[email protected]

[email protected]

http://www.ee.oulu.fi/research/isg/

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