Improving Multi-Panel Lamination Process Optimization using Response Surface Methodology and Neural Network
2k factorial experiment is the most general method of experimental design unusually used for factor screening. It helps to reduce the number of experiment trials by investigating multiple factors at the same time. As each factor in 2k factorial experiment contains 2 levels, it can only use to constr...
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| 格式: | Conference or Workshop Item |
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Springer Verlag
2015
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| 在線閱讀: | http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84906542897&origin=inward http://cmuir.cmu.ac.th/handle/6653943832/39056 |
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| 總結: | 2k factorial experiment is the most general method of experimental design unusually used for factor screening. It helps to reduce the number of experiment trials by investigating multiple factors at the same time. As each factor in 2k factorial experiment contains 2 levels, it can only use to construct first-degree polynomial model. If there is curvature in the system, other technique such as response surface has to be performed, which leads to additional cost in running more experiments. Instead of conducting actual experimentation, this paper proposes a new method of using neural network to construct a process model with factorial experiment data. This model is used to predict response for response surface experiments. A case study of multi-panel lamination process was used to demonstrate the proposed method. The result showed that optimization result achieved from response surface methodology via neural network model is better than the one from 2k factorial experiments. © Springer International Publishing Switzerland 2015. |
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