A Digital Twin-Driven Guidance Method for Human-Machine Collaborative Assembly Operations Based on Machine Learning and Computer Vision

Ruman-machine collaborative assembly is commonly used in industrial assembly processes, while the existing assembly guidance methods have fixed processes, complex guidance procedures and low guidance accuracy. Based on this, in order to improve the success rate and quality of assembly, a digital twin-driven human-machine collaborative assembly guidance method system is proposed, and an inverse guidance mechanism for the assembly process is established, which realizes the on-line accurate identification of the parts to be assembled through the improved lightweight YOLO model with Biformer attention mechanism and CARAFE lightweight upsampling operator, and generates the geometric reference features of the parts for the assembly through OpenCV. The effectiveness of the proposed method is verified by taking the assembly process of a bipolar expansion helical cylindrical gear reducer as an example, and the advancement of the proposed method is verified by comparing it with the algorithms of the same type. The experimental results show that the proposed assembly guidance method is easy to implement, low-cost and high accurate, which is of great significance for improving the accuracy and flexibility of the human-machine collaborative assembly process.