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系統識別號 U0026-2106201914150700
論文名稱(中文) 應用機械學習演算法預測醫材螺絲用線鍍膜厚度之研究
論文名稱(英文) Using Machine Learning Algorithms to Predict Coating Thickness of Medical Screws
校院名稱 成功大學
系所名稱(中) 工業與資訊管理學系碩士在職專班
系所名稱(英) Department of Industrial and Information Management (on the job class)
學年度 107
學期 2
出版年 108
研究生(中文) 郭叡蓁
研究生(英文) Ruei-Jhen,Euo
學號 R37061105
學位類別 碩士
語文別 中文
論文頁數 64頁
口試委員 指導教授-利德江
口試委員-戴文禮
口試委員-葉俊吾
中文關鍵字 醫材螺絲  機器學習  預測  生產參數 
英文關鍵字 Medical Screws  Machine Learning Algorithms  Prediction  production parameters 
學科別分類
中文摘要 隨著半導體工藝的快速發展,電腦的存儲容量和運行速度快速提高,機器學習的研究和應用也越來越普遍。在工廠製造端,機器學習也經常被應用在生產流程的改善,尋找最佳工藝參數等。本研究個案為傳統螺絲工廠,後因公司策略需求進而開發醫療用線,但在環保意識抬頭的今日以及醫療器材安全性的驅使下,進而開發出鍍X皮膜。由於鍍X皮膜製程之導入,而使原本穩定之製程參數產生許多不同於以往之設定與選擇,本研究則於此期間,擷取一年份之製程資料,並應用支援向量迴歸(support vector regression, SVR)、倒傳遞類神經網路(back propagation neural network, BPNN)、樹狀法則的M5’模式樹(M5’model tree)及多元線性迴歸(multiple linear regression, MLR)等四種機器學習方法來建模預測鍍膜厚度,並使用平均絕對誤差百分比和均方誤差平方根做為誤差評估指標。最後發現使用SVR預測的結果最為準確,因此使用SVR的訓練模式尋找最佳的生產參數,以利縮短研發時程及加快產線偵錯作業的流程。
英文摘要 With the rapid development of semiconductor technology, the storage capacity and running speed of the computer are increasing rapidly. The researches and applications of machine learning have been more and more common. For the manufacturing fields, machine learning is often used for determining the optimal parameters to improve processes. In this paper, we introduce the process of using machine learning algorithms in real case, and four algorithms containing support vector regression (SVR), back propagation neural network (BPNN), M5’ model tree (M5’), and multiple linear regression (MLR) are applied in dealing with a real case. The real case is a traditional screw factory that begin to develop medical screws for business transformation. Considering the environmental protection and medical material safety, the company developed a coated X film that is a new process whose parameter values are very different from the old process. In the experiments, we collect data of the last one year. In order to achieve the best coating thickness, machine learning algorithms are applied to predict the coating thickness with the history data. And we find that the SVR has the best performances compared with BPNN, M5’, and MLR with evaluation metrics mean absolute percent error and root-mean-square error. So that, the SVR will be used to find the best values of production parameters in the future.
論文目次 摘要 I
致謝 VIII
圖目錄 XI
表目錄 XII
第一章 緒論 1
第二章 文獻探討 7
2.2.1電鍍基本原理 9
2.2.2 化學鍍基本原理 10
2.3.1 多元線性迴歸 12
2.3.2 支援向量回歸 12
2.3.3 類神經網路 14
2.3.4 M5’模式樹 15
第三章 研究方法 19
3.2.1資料蒐集 27
3.2.2資料前處理 27
3.2.3屬性選擇 28
3.2.4 資料轉換 29
3.3.1 SVR支援向量迴歸 33
3.3.2多元線性迴歸 33
3.3.3 模式樹建構 34
3.3.4倒傳遞類神經網路建構 36
3.3.5 四種模式簡易比較 43
3.4.1 評估方式 45
3.4.2 評估工具 45
第四章 實例驗證 47
4.2.1 驗證流程 48
4.2.2 評估方法 49
4.2.3 軟體工具 50
4.3.1 RMSD評估結果 51
4.3.2 MAPE評估結果 54
第五章 結論 58
參考文獻 60
中文部分 60
英文部分 60
網站資料 63
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網站資料
經濟部統計處. (2016) 產業經濟統計簡訊. https://www.moea.gov.tw/mns/dos/bulletin/Bulletin.aspx?kind=9&html=1&menu_id=18808&bull_id=2698
行政院環保署.(2017) 放流水標準金屬. 基本工業、金屬表面處理業、電鍍業和印刷電路板製造業放流水水質項目及限值. https://oaout.epa.gov.tw/law/LawContent.aspx?id=FL015489
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