本研究旨為透過機器學習（Machine Learning）應用於塑膠射出成形建立拉伸衝擊試片重量、產品Z軸翹曲及最低碳排放參數之預測。先以Ｍoldex3D R16.0 進行模流分析，於半結晶性材料PP及非結晶性材料ABS的模擬結果探討重量及產品Z軸翹曲之趨勢並同時進行成形視窗實驗，其結果確立兩者材料之射出速度（Injection Velocity, Vinj）及熔膠溫度(Melt Temperature, Tmelt)。利用單一因子的實驗分析對試片重量最具影響的參數為背壓(Back Pressure, Bp)，而翹曲則為射出速度(Vinj）、模具溫度(Mold Temperature, Tmold)、保壓壓力(Packing pressure, Ppacking)及冷卻時間(Cooling Time, tcooling)，並將此參數作為田口方法(Taguchi Method)設計直交表進行實驗分析。運用類神經網路(Neural Network, NN)，對試片重量、翹曲與碳排放量進行建立模型及預測，在重量方面兩者材料預測準確率皆達98 %以上，PP翹曲預測準確率達96 %，碳排放量準確率達77 %，ABS翹曲預測準確率達68 %，碳排放量準確率為66 %，在田口方法最佳參數分析中，將碳排放量於PP降低 13.3 % ，ABS降低34 %，顯示本研究對於碳排放最佳化之成果，經由本研究結合田口方法及類神經網路(Neural Network, NN)未來可於低碳製程及能量耗損之評估模式中應用。

This research focuses on predicting plastic product weight, warpage, and the lowest carbon emission parameter of tensile and impacts specimens through application of machine learning to injection molding process. At the same time, the forming window experiment is performed. Results establish the Injection Velocity (Vinj)　and Melt Temperature (Tmelt) of the two plastic materials PP and ABS. The main effect factor of weight is Back Pressure (Bp) in a single factor experiment. However, the main effect factor of warpage includes the Injection Velocity (Vinj), Mold Temperature (Tmold), Packing Pressure (Ppacking), and Cooling Time (tcooling). Then these parameters are used by Taguchi Method to design orthogonal table for experimental analysis. Neural Network (NN) is used to model and predict the weight, warpage, and carbon emissions of the specimens. In terms of weight, the predicted accuracy of weight in PP and ABS both materials is more than 98 %, and the predicted warpage accuracy rate in PP material is 96 %, the prediction accuracy rate of carbon emission is 77 %, the prediction accuracy rate of ABS warpage is 68 %, and the carbon emission accuracy rate is 66 %. For the best parameter analysis of Taguchi Method, the carbon emission of PP can be reduced to 13.3 %, and ABS can be decreased to 34 %. Results show that the optimization of carbon emissions which combined with Taguchi method and Neural Network (NN) can be applied on the evaluation of low carbon processes and energy consumption for sustainable manufacturing in the future.

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