在人工智慧(artificial intelligence, AI)和物聯網(Internet of Things, IoT)技術的驅動下，智慧製造(smart manufacturing)成為當今熱門的研究議題。製造品質(manufacturing quality, MQ)預測是智慧製造的基礎之一，對於某些無法快速測量品質，或是比較難測量品質的產品，希望可以基於生產前的靜態參數以及生產過程中收集的動態狀態時間序列資料，在產品生產後快速並且準確地預測其製造品質。
本論文提出兩種方法進行線切割放電加工(wire electrical discharge machining, WEDM)工件品質預測，明確地說是進行工件表面粗糙度(surface roughness)預測。第一種方法利用格拉姆角度域(Gramian angular field)轉換時間序列資料為二維圖像，並配合卷積長短期記憶(convolutional long short-term memory, CLSTM)神經網路預測工件表面粗糙度。第二種方法利用馬可夫轉換域(Markov transition field, MTF)轉換時間序列資料為二維圖像，並配合卷積長短期記憶神經網路預測工件表面粗糙度。實驗結果顯示，本論文提出的兩種方法在平均絕對百分比誤差(mean absolute percentage error, MAPE)方面皆優於一個近期提出的相關方法。

Driven by artificial intelligence (AI) and Internet of Things (IoT) technologies, smart manufacturing has become a hot topic today. Predicting manufacturing quality (MQ) is fundamental in smart manufacturing. For some manufactured products whose quality cannot be measured speedily or handily, it is desirable to fast and accurately predict the MQ based on static data, such as manufacturing parameters tuned before production, as well as dynamic data, such as manufacturing conditions gathered during production.
This paper proposes two methods to predict the MQ of wire electrical discharge machining (WEDM), specifically, to predict the workpiece surface roughness (SR). The first method uses Gramian angular field (GAF) to represent dynamic WEDM manufacturing conditions as images, and uses convolutional long short-term memory (CLSTM) neural networks to predict the workpiece SR. The second method uses Markov transition field (MTF) to represent dynamic WEDM manufacturing conditions as images, and uses CLSTM neural networks to predict the workpiece SR. Experiments are conducted to evaluate the performance of the proposed methods. As will be shown, the proposed methods outperform a related method proposed recently in terms of the mean absolute percentage error (MAPE).

中文摘要 I
Abstract II
誌謝 III
目錄 IV
圖目錄 VI
表目錄 VII
一、 緒論 1
1.1. 研究背景與動機 1
1.2. 研究目的與貢獻 1
1.3. 相關文獻研討 2
1.4. 論文架構 2
二、 背景知識 3
2.1. 線切割放電加工 3
2.1.1. 放電加工 3
2.1.2. 線切割放電加工 4
2.2. 分段聚合近似法 4
2.3. 格拉姆角度域 5
2.3.1. 格拉姆矩陣 5
2.3.2. 極座標表示法 5
2.3.3. 格拉姆角度域 6
2.4. 馬可夫轉換域 7
2.4.1. 馬可夫鏈 7
2.4.2. 馬可夫轉移矩陣 8
2.4.3. 馬可夫轉移機率矩陣 8
2.4.4. 馬可夫轉換域 9
2.5. 類神經網路 9
2.5.1. 類神經網路介紹 9
2.5.2. 前向傳播 12
2.5.3. 反向傳播 12
2.5.4. 激活函數 12
2.6. 深度學習 14
2.6.1. 深度學習介紹 14
2.6.2. 深度神經網路 14
2.6.3. 卷積神經網路 15
2.6.4. 遞歸神經網路 16
2.6.5. 長短期記憶神經網路 17
2.7. 相關方法 20
三、 問題定義 22
3.1. 問題定義 22
3.2. 標籤定義 23
四、 研究方法 25
4.1. 資料前處理 25
4.1.1. 資料縮放 25
4.1.2. 格拉姆角度域 26
4.1.3. 馬可夫轉換域 26
4.2. 模型架構 29
4.3. 模型優化 30
4.3.1. 提早停止 30
4.3.2. 交叉驗證 30
五、 實驗與效能評估 31
5.1. 實驗環境 31
5.2. 實驗結果 31
5.3. 前處理分析 33
六、 結論與未來展望 37
參考文獻 38

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