Process optimization and modeling using support vector regression in automatic flux cored arc welding for st 37 steel
This paper presents an integrated application of design of experiments (DoE), with support vector machine (SVM) for manufacturing process modeling in order to achieve a high accuracy model. The proposed method is as follows. Firstly, DoE is applied to indicate the critical parameters of the process....
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| Main Authors: | , |
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| Format: | Conference Proceeding |
| Published: |
2018
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| Subjects: | |
| Online Access: | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84906998298&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/60436 |
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| Institution: | Chiang Mai University |
| Summary: | This paper presents an integrated application of design of experiments (DoE), with support vector machine (SVM) for manufacturing process modeling in order to achieve a high accuracy model. The proposed method is as follows. Firstly, DoE is applied to indicate the critical parameters of the process. Then, support vector regression (SVR) was used to establish the nonlinear multivariate relationships between process parameters and responses. Data obtained from designed experiments were used in the training process. Finally, a grid search was adopted to the SVR model to find the optimum parameter setting. Data from real experiments of automatic flux cored arc welding (FACW) for ST 37 steel were used to demonstrate the proposed method. Other prominent approaches, namely response surface methodology (RSM) and artificial neural networks (ANN) learning with quick propagation algorithm (Quickprop), were conducted for comparison purpose. The experimental results suggested that the SVR was capable of high accuracy modeling and resulted in much smaller error in comparison with the results from ANN learning with quick propagation algorithm and RSM. © 2008 ICQR. |
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