An Explainable Optimization Method for Assembly Process Parameter

Vibration out-of-tolerance is a common condition of unqualified engine inspection, and the cause of vibration out-of-tolerance is closely related to the quality of the engine assembly. When the engine is found to vibrate out of tolerance, it needs to be reassembled. This process is very time-consuming and labor-intensive. In order to improve the assembly quality and reduce the vibration value during the engine assembly process. An explainable optimization algorithm is proposed, which combines LightGBM, SHAP and PSO. First, a vibration value prediction model is trained using the LightGBM algorithm. Second, SHAP is used to explain the vibration value prediction model, and obtain the importance order of each assembly process parameter, which is used as the subsequent optimization order. Finally, according to the optimization order, PSO algorithm is used to iteratively optimize the assembly process parameters and it uses the vibration prediction model as the fitness function. It has been verified by experiments that the optimized assembly process parameters can effectively reduce the vibration value of the engine, which has positive guiding significance for the assembly of the engine.