臺灣博碩士論文加值系統

熔融沉積成型 (Fused Deposition Modeling, FDM) 3D 列印是增材製造 (Additive Manufacturing, AM) 的一種，它基於以預定方式專門保存軟化材料並利 用以纖維的熱塑性聚合物逐層製造 3D 模型。 FDM 已經發展了很長時間，儘管 如此，它在列印過程中可能存在缺陷。 因此，本文提供了一種通過 3D 列印幾何 進行品質檢測的方法。利用融合預訓練模型作為特徵提取器的遷移學習和集成學 習，用在熔融沉積成型中進行監控和品質檢測。實驗結果表明，在大多數情況底下 VGG16 以及 VGG19 的算法組合都會給出最大的準確度，而 EfficientNetB0 以及 EfficientNetV2L 的算法組合都會給出較低的準確度。此外，遷移學習加上集成學習 的算法組合能夠有效的檢測逐層品質，從而減少時間以及耗材的浪費以至於提高 製造品質。

Fused Deposition Modeling (FDM) 3D printing is one of the Additive manufacturing (AM) which fabricates 3D models layer by layer based on specifically saving softened material in a foreordained way and utilizing thermoplastic polymers that come as fibers. FDM has been in development for a long time, nevertheless, it can have defects in the printing process. Consequently, this paper offers an approach to fault detection by 3D printing geometry for utilizing a gathering of transfer learning using pre-trained model as a feature extractors and ensemble learning. The experimental results show that the algorithm combination of VGG16 and VGG19 will give the maximum accuracy in most cases, while the algorithm combination of EfficientNetB0 and EfficientNetV2L will give lower accuracy. In addition, the algorithm combination of transfer learning and ensemble learning can effectively detect the layer-by-layer quality, thereby reducing the waste of time and consumables and improving the manufacturing quality

論文審定書 i
摘要 ii
Abstract iii
目 錄 iv
第一章 緒論 1
第一節 研究背景 1
第二節 研究動機與目的 1
第二章 文獻探討 2
第一節 文獻回顧 2
第二節 VGG & Inception 2
第三節 Resnet 4
第四節 EfficientNet 5
第五節 相關文獻 6
第三章 研究架構與方法 8
第一節 研究架構 8
第二節 分類原則 9
第三節 卷積神經網路 12
一、 VGG16 12
二、 VGG19 13
三、 InceptionV3 13
四、 Resnet50 14
五、 EfficientNetB0 15
六、 EfficientNetV2L 15
第四節 集成學習 16
一、 Bagging 16
1. Random Forest 16
二、 Boosting 16
1. AdaBoost 16
2. GBDT 17
3. XGBoost 17
4. LightGBM 18
5. CatBoost 18
第四章 研究成果 19
第一節 六種幾何圖形辨識比較 19
第二節 顏色差異對於辨別準確度比較 23
第五章 研究討論與結論 31
第一節 研究討論 31
第二節 研究結論 32
參考文獻 34
附錄一、灰色幾何圖形準確度 43
附錄二、綠色幾何圖形準確度 45
附錄三、藍色幾何圖形準確度 47
附錄四、基線準確度 49
附錄五、英文縮寫及中文對照表 49

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