Quality prediction model and sensitivity analysis of process parameters based on BP-PSO

The quality prediction of mechanical products can effectively improve the product qualification rate and improve the efficiency of the factory. Based on the quality traceability and production process data in the electric drive production process, the paper uses the data collected by the MES system of the electric drive production line as the training and test samples, and establishes the BP-PSO electric drive quality prediction model. According to the actual production data, the air tightness monitoring is established as the key quality characteristic, and the key station data is extracted as the input of this model after data correlation analysis, and the air tightness detection value is used as the output. Comparative experiments show that the model has a good improvement over the traditional BP model in terms of average absolute error and average absolute error percentage, and can accurately predict the production quality. On the basis of this model, the total sensitivity and first-order sensitivity of production process parameters are obtained and sorted by Sobol global sensitivity analysis method, and the influence of production process parameters on production quality is analyzed.