工業4.0的轉變模式並非僅在工廠中使用新技術，改進工作流程與引入新的培訓方法也是必須之改變項目，以利未來持續協助人類技術之發展和轉移。在此研究中提出一用於在製造業組裝任務中的技術轉移之協助模型。本研究提出之模型應用兩種深度學習之方法，其中包含卷積神經網路用於動作判讀與快速區域卷積神經網路。在此研究中將應用一個使用兩種不同攝影角度來記錄玩具組裝過程的個案以驗證提出之模型的成效。CNN和faster R-CNN分別的正確率為94.5%和99%。本方法提出之模型將指引尚不熟悉過程之操作者執行事前已制定好步驟的彈性組裝任務。在學術貢獻這部分，本研究結合兩種深度學習模型以達到同時記錄行為和偵測標的物。本研究之貢獻為在製造業的領域下提供一更為先進之訓練方法用於操作員接受新技術。

The shift paradigm toward Industry 4.0 is not solely completed by enabling smart machines in a factory, but enabling human capability. Refinement of work processes and introduction of new training approaches are needed to support efficient human skill development and transfer. This study proposes a new skill transfer support model in a manufacturing scenario. The proposed model uses two types of deep learning as the backbone: a convolutional neural network for action recognition and a faster region-based convolutional neural network for object detection. In this study, to evaluate the performance of the prosed model, a case study using toy assembly was conducted and they are recorded using two cameras with different angle. The accuracy for CNN and faster R-CNN is 94.5% and 99% respectively. A junior operator is guided by the proposed model as doing flexible assembly tasks based on the skill representation that has been constructed. In terms of theoretical contribution, this study integrated two deep learning models which can simultaneously recognize the action and detect the object. The contribution of the present study is to facilitate advanced training in manufacturing in terms of adapting new skills for their operators.

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