The paradigm shift toward Industry 4.0 is not solely completed by enabling smart machines in a factory but also by facilitating human capability. Refinement of work processes and introduction of new training approaches are necessary to support efficient human skill development. This study proposes a new skill transfer support model in a manufacturing scenario. The proposed model develops two types of deep learning as the backbone: a convolutional neural network (CNN) for action recognition and a faster region-based CNN (R-CNN) for object detection. A case study using toy assembly is conducted utilizing two cameras with different angles to evaluate the performance of the proposed model. The accuracy for CNN and faster R-CNN for the target job reached 94.5% and 99%, respectively. A junior operator can be guided by the proposed model given that flexible assembly tasks have been constructed on the basis of a skill representation. In terms of theoretical contribution, this study integrated two deep learning models that can simultaneously recognize the action and detect the object. The present study facilitates skill transfer in manufacturing systems by adapting or learning new skills for junior operators.

向工业4.0的范式转变不仅可以通过在工厂中启用智能机器来完成，而且还可以通过提高人员能力来完成。必须完善工作流程并引入新的培训方法，以支持有效的人类技能发展。这项研究提出了一种在制造场景中的新技能转移支持模型。提出的模型开发了两种类型的深度学习作为主干：用于动作识别的卷积神经网络（CNN）和用于对象检测的基于区域的快速CNN（R-CNN）。通过使用两个具有不同角度的相机进行玩具组装的案例研究，以评估所提出模型的性能。CNN和更快的R-CNN的目标作业准确率分别达到94.5％和99％。假设已经基于技能表示构建了灵活的组装任务，则初级操作员可以由建议的模型指导。在理论贡献方面，本研究集成了两个可以同时识别动作和检测对象的深度学习模型。本研究通过适应或学习初级操作员的新技能，促进了制造系统中的技能转移。