Quantitative evaluation of aerospace product manufacturing readiness based on BP neural network

Abstract

In order to manage the aerospace product manufacturing, a quantified evaluation of product manufacturing readiness based on BP neural network is proposed. In view of the unique problems of Chinese aerospace product manufacturing, the risk factors of aerospace product manufacturing are analyzed, and each manufacturing factor is decomposed hierarchically to establish a three-level indicator hierarchy for the aerospace product manufacturing readiness evaluation. According to the evaluation indicator of manufacturing readiness, the qualitative indicators and the quantitative indicators are quantified according to the demand satisfaction and fuzzy mathematics membership function. Based on the BP neural network, a quantitative evaluation of Chinese aerospace product manufacturing readiness is modeled. The comprehensive scores of manufacturing readiness is calculated by BP neural network according to the indicator evaluation scores, then the manufacturing readiness level is evaluated quantitatively and objectively. In order to optimize the evaluation model for the aerospace product manufacturing readiness, trainrp and trainlm are selected as the training function respectively for training. The error analysis experiments show that the average relative error of the manufacturing readiness evaluation model using trainlm as the training function is small, which can provide a scientific method for the objective evaluation of the aerospace product manufacturing readiness.