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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Main Author: Laosiritaworn,W.S.
Format: Conference or Workshop Item
Published: Springer Verlag 2015
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Online Access: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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Institution: Chiang Mai University
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spelling th-cmuir.6653943832-390562015-06-16T08:01:23Z Improving Multi-Panel Lamination Process Optimization using Response Surface Methodology and Neural Network Laosiritaworn,W.S. Control and Systems Engineering Computer Science (all) 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. 2015-06-16T08:01:23Z 2015-06-16T08:01:23Z 2015-01-01 Conference Paper 21945357 2-s2.0-84906542897 10.1007/978-3-319-08422-0_33 http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84906542897&origin=inward http://cmuir.cmu.ac.th/handle/6653943832/39056 Springer Verlag
institution Chiang Mai University
building Chiang Mai University Library
country Thailand
collection CMU Intellectual Repository
topic Control and Systems Engineering
Computer Science (all)
spellingShingle Control and Systems Engineering
Computer Science (all)
Laosiritaworn,W.S.
Improving Multi-Panel Lamination Process Optimization using Response Surface Methodology and Neural Network
description 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.
format Conference or Workshop Item
author Laosiritaworn,W.S.
author_facet Laosiritaworn,W.S.
author_sort Laosiritaworn,W.S.
title Improving Multi-Panel Lamination Process Optimization using Response Surface Methodology and Neural Network
title_short Improving Multi-Panel Lamination Process Optimization using Response Surface Methodology and Neural Network
title_full Improving Multi-Panel Lamination Process Optimization using Response Surface Methodology and Neural Network
title_fullStr Improving Multi-Panel Lamination Process Optimization using Response Surface Methodology and Neural Network
title_full_unstemmed Improving Multi-Panel Lamination Process Optimization using Response Surface Methodology and Neural Network
title_sort improving multi-panel lamination process optimization using response surface methodology and neural network
publisher Springer Verlag
publishDate 2015
url 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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