臺灣博碩士論文加值系統

摘要 i
Abstract ii
誌謝 iii
目錄 iv
表目錄 vii
圖目錄 ix
第1章 緒論 1
1-1 前言 1
1-2 科學化試模 2
1-3 二次料射出成型 3
1-4 人工智慧於射出成型應用 4
1-5 研究動機與目的 5
1-6 論文架構 6
第2章 文獻回顧 7
2-1 射出成型感測資訊 7
2-2 回收材料射出成型 7
2-3 類神經網路於射出成型品質預測 8
2-4 結合遷移式學習於射出成型 9
第3章 理論基礎 11
3-1 壓力曲線特徵擷取 11
3-2 類神經網路 13
3-3 貝式優化演算法 15
3-4 遷移式學習 17
第4章 研究方法 19
4-1 實驗設備與架構 19
4-2 實驗設計 24
4-2-1 科學化試模 25
4-2-2 田口實驗設計 27
4-2-3 全因子實驗 28
4-3 預測模型訓練 29
4-3-1 數據前處理 30
4-3-2 預訓練模型建立 32
4-3-3 目標品質預測模型建立 33
4-4 模型表現驗證 33
第5章 結果與討論 35
5-1 科學化試模結果 35
5-1-1 滿模行程實驗 35
5-1-2 找尋射出速度實驗 36
5-1-3 多段保壓時間設定 37
5-1-4 多段保壓壓力設定 39
5-2 新料田口實驗 41
5-3 新料與二次料全因子實驗 43
5-4 Target data濾除結果 47
5-5 模型架構及數據降維於遷移學習之分析 48
5-5-1 模型架構於遷移學習表現 49
5-5-2 數據降維於遷移學習表現 50
5-6 遷移學習對比傳統訓練手法之分析 52
5-6-1 訓練過程之損失值變化歷程 52
5-6-2 訓練時間及迭代次數 54
5-6-3 測試誤差評估 56
5-6-4 模型預測值分布趨勢 61
第6章 結論與未來展望 66
6-1 結論 66
6-2 未來展望 68
參考文獻 70
附錄 74

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