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研究生(外文):HSU, PEI-WEN
論文名稱(外文):Implement Machine Learning for Quality Identification of Injection Molding Process
指導教授(外文):SU, CHWEN-TZENG
外文關鍵詞:Injection MoldingRandom ForestLightGBMXGBoost
  • 被引用被引用:0
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The reasons for the defective injection molding products include operators, machine equipment, raw material types, parameter settings, production environment, etc., among which machine parameter settings are key factors, and injection parameter settings include pressure, speed, temperature and time. In the past, it was on site. When personnel adjust parameters, errors often occur and result in the failure of the entire batch of finished products. In order to effectively solve the above problems, it is necessary to adjust the parameters in a non-manual way to maintain the yield of the finished product, reduce the cost of injection molding industry waste, and enhance industry competition.
The research proposes a machine learning framework to improve the yield of finished products., mainly using the important parameters in the machine parameters of the machine learning, with the effect of the model prototype, making the production, building Random Forest, LightGBM, XGBoost, performance evaluation indexs are AUC, ACC, Recall , Precision , F-Measure. The research results show that after filtering the features, the models have effectively completed the models better than have not completed feature selection, LightGBM outperforms Random Forest and XGBoost in AUC, ACC, Precision, and F-Measure, XGBoost has the best performance in Recall. The results show that feature selection combine machine learning can effectively improve model computing efficiency and accuracy.

摘要 i
Abstract ii
目錄 iii
表目錄 v
圖目錄 vi
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 2
1.3 研究架構 3
第二章 文獻探討 5
2.1 射出成型定義與特性 5
2.2 影響射出成型良率因素 6
2.3 特徵篩選 6
2.4 射出成型良率預測方法 9
2.4.1 機器學習 10
2.4.2 隨機森林 12
2.4.3 輕量化梯度提升演算法 13
2.4.4 極限梯度提升演算法 14
2.5 小結 17
第三章 研究方法 19
3.1 研究架構 19
3.2 資料介紹 20
3.3 資料前處理 21
3.3.1 資料清洗 21
3.3.2 特徵篩選 24
3.3.3 資料正則化 25
3.4 模型建立 26
3.4.1 隨機森林 26
3.4.2 輕量化梯度提升演算法 27
3.4.3 極限梯度提升演算法 29
3.5 績效評估 30
3.5.1 混淆矩陣 30
3.5.2 F-Measure 32
3.5.3 ROC曲線 32
第四章 結果探討 34
4.1 特徵篩選結果 34
4.1.1 隨機森林特徵篩選結果 34
4.1.2 輕量化梯度提升演算法特徵篩選結果 36
4.1.3 極限梯度提升演算法特徵篩選結果 37
4.1.4 特徵篩選結果比較與小節 39
4.2 模型建立 39
4.2.1 隨機森林 39
4.2.2 輕量化梯度提升演算法 40
4.2.3 極限梯度提升演算法 41
4.3 模型比較與小結 42
第五章 結論與建議 45
參考文獻 46

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