A deep learning-based method for reusing assembly processes of micro-components

The current assembly process planning of complex precision products is complicated and time-consuming, and there has been an underutilization of assembly knowledge from previous product cases. To solve this problem, a method for reusing the assembly process of microdevices based on deep learning is proposed. By constructing the design structure matrix (DSM) of the product, the assembly relationship is expressed using the way of graph structure. Node features that refer to multi-dimensional attribute information of the parts and the graph are simultaneously fed into a graph neural network, which can determine the similarity score of any two assembly connection diagrams with extensive training of the model. Then the new products can reuse the assembly process of products with higher scores. On the other hand, in the absence of a similar product case for reference, the similarity between the local structures of the new product and previous products can be identified through a mining algorithm based on Maximum Clique. The assembly information from previous product cases can be fully utilized to guide the assembly process planning for the new product, thereby enhancing the efficiency of the process planning.