Assembly is an important part of the product manufacturing process that consumes a lot of time and energy,affecting the entire life cycle of the product.In order to solve the problem of low product assembly efficiency,a quality prediction method of BP neural network optimized by genetic algorithm was proposed.Based on the three mass characteristics of the back-end of the center of mass,mass and length of the bomb bay physical quantity(referred to as DC physical quantity),the data were divided,the structure of the BP neural network was determined,the mean square error was used as the fitness function of the genetic algorithm,the optimal initial weight and threshold were found,the genetic algorithm was established to optimize the BP neural network model,and the prediction results were compared by combining the mean absolute percentage error MAE,mean square error(MSE)and root mean square error(RMSE).Experimental results show that compared with the traditional BP neural network,the BP neural network optimized by genetic algorithm has better precision and accuracy in quality prediction.
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