Pattern Recognition of Quality Control Chart of Multi-variety and Small-batch Production Mode Based on MC-GA Optimized BP

Abstract

Abstract To solve the problem of product quality control in multi-variety and small-batch production mode, a quality control chart recognition model of MC-GA optimized BP was established by using Monte Carlo method to simulate the product quality data characteristics of different varieties and combining the data processing capability of neural network and the adaptive global optimization search capability of genetic algorithm; then the MC(Monte Carlo method)-GA(Genetic Algorithm) optimized BP(Back Propagation Neuron Network) was compared with the traditional MC-BP is compared and analyzed in terms of accuracy, time and error in quality control graph pattern recognition, and it is concluded that the former is better in quality control graph pattern recognition. It can be seen that the MC-GA optimized BP model is more effective in identifying the quality status of products in the multi-variety and small-batch production mode, and can accurately predict the stability of the manufacturing process, so as to enhance the competitiveness of enterprises.