金属加工（热加工）Issue(9)：13-20,8.
基于神经网络与遗传算法的钢的锻造工艺优化
Optimization of steel forging process based on neural network and genetic algorithm
摘要
Abstract
Based on neural network and genetic algorithm,an optimized neural network model for 40Cr steel forging process was constructed with heating method,forging pressure,initial forging temperature,final forging temperature,temperature of preheating,speed of forging and temperature of heating as seven input layer parameters,with tensile strength,yield strength,and elongation after fracture as three output layer parameters,moreover,the hidden and output layer were with tansig and purelin function,respectively.The model structure adopted a four-layer topology of 7 × 32 × 16 × 3.After training,prediction,and verification,it is found that the model converges after 9113 iterations,and the relative prediction errors of the tensile strength,yield strength,and elongation after fracture of model output are all less than 5％.The model has high prediction accuracy and predictive ability.Compared with the current forging process of 40Cr steel used by enterprises,the optimized forging process of 40Cr by the neural network-genetic algorithm results in an increase of 82MPa in tensile strength,88MPa in yield strength,and a slight increase in elongation after fracture within a small range.The neural network-genetic algorithm optimization model has better practical performance.关键词
神经网络/遗传算法/锻造工艺优化/力学性能/锻造工艺参数Key words
neural network/genetic algorithm/optimization of forging process/mechanical property/forging process parameter引用本文复制引用
杨小华,王仁本,佘斌,李建巍,魏金前..基于神经网络与遗传算法的钢的锻造工艺优化[J].金属加工（热加工）,2026,(9):13-20,8.基金项目
国家能源集团科技创新项目资助(CJNY-DDH-2024-023). （CJNY-DDH-2024-023）
