Using pre-manufacturing experience to assist building yield forecast model

碩士 === 國立成功大學 === 工業與資訊管理學系碩博士班 === 97 === Because of the age of global competition is coming, every manufacturer would like to provide low price to attract customers and get order in the market. For providing lower selling price or getting higher profit, manufacturer must be control the production...

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Main Authors: Cheng-jung Juang, 莊成中
Other Authors: Der-Chiang Li
Format: Others
Language:zh-TW
Published: 2009
Online Access:http://ndltd.ncl.edu.tw/handle/99459384124837717853
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spelling ndltd-TW-097NCKU50410072015-11-23T04:03:12Z http://ndltd.ncl.edu.tw/handle/99459384124837717853 Using pre-manufacturing experience to assist building yield forecast model 以先前製程經驗提升新製程良率預測精確度 Cheng-jung Juang 莊成中 碩士 國立成功大學 工業與資訊管理學系碩博士班 97 Because of the age of global competition is coming, every manufacturer would like to provide low price to attract customers and get order in the market. For providing lower selling price or getting higher profit, manufacturer must be control the production cost, not only finds cheaper material , but also improves the yield in manufacturing process to decrease the production cost relatively. In the beginning of new product of developing step, the experience and knowledge about manufacturing process is not sufficient. It is helpful for decreasing the researching time, if using the pre-manufacturing experience for new manufacturing process. This thesis researches a problem about forecasting yield in new manufacturing process. Because of it is a new manufacturing process, in the situation that datasets is small, we use mega trend diffusion according to the experience about pre-manufacturing process to construct the experience about new manufacturing process. We make some virtual sample that is similar to the data in new manufacturing process to train forecast model. The result shows that, comparing to other forecast model, our model is better than others in accuracy and stability. Der-Chiang Li 利德江 2009 學位論文 ; thesis 39 zh-TW
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description 碩士 === 國立成功大學 === 工業與資訊管理學系碩博士班 === 97 === Because of the age of global competition is coming, every manufacturer would like to provide low price to attract customers and get order in the market. For providing lower selling price or getting higher profit, manufacturer must be control the production cost, not only finds cheaper material , but also improves the yield in manufacturing process to decrease the production cost relatively. In the beginning of new product of developing step, the experience and knowledge about manufacturing process is not sufficient. It is helpful for decreasing the researching time, if using the pre-manufacturing experience for new manufacturing process. This thesis researches a problem about forecasting yield in new manufacturing process. Because of it is a new manufacturing process, in the situation that datasets is small, we use mega trend diffusion according to the experience about pre-manufacturing process to construct the experience about new manufacturing process. We make some virtual sample that is similar to the data in new manufacturing process to train forecast model. The result shows that, comparing to other forecast model, our model is better than others in accuracy and stability.
author2 Der-Chiang Li
author_facet Der-Chiang Li
Cheng-jung Juang
莊成中
author Cheng-jung Juang
莊成中
spellingShingle Cheng-jung Juang
莊成中
Using pre-manufacturing experience to assist building yield forecast model
author_sort Cheng-jung Juang
title Using pre-manufacturing experience to assist building yield forecast model
title_short Using pre-manufacturing experience to assist building yield forecast model
title_full Using pre-manufacturing experience to assist building yield forecast model
title_fullStr Using pre-manufacturing experience to assist building yield forecast model
title_full_unstemmed Using pre-manufacturing experience to assist building yield forecast model
title_sort using pre-manufacturing experience to assist building yield forecast model
publishDate 2009
url http://ndltd.ncl.edu.tw/handle/99459384124837717853
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