基于BP神经网络的20CrMo钢激光强化工艺参数优化控制

以激光功率P、光斑直径D、扫描速度V等为输入参数，非相变硬化处理、相变硬化处理和熔凝处理等为输出参数，对材料为20CrMo合金结构钢进行激光强化处理工艺控制优化研究。通过大量试验与计算机模拟分析和对比，建立了激光工作参数与材料表面强化关系的BP神经网络工艺优化模型。经过与实验数据的比较，运用该模型可以方便、准确地选择激光工艺参数，控制材料表面强化类别和保证工作表面的质量，真实反映了激光加工工艺规律。

Optimizing control of laser surface strengthening parameters for processing 20CrMo steel based on BP neural network

Research and optimizing on laser strengthening processing and controlling for processing 20CrMo alloy structure steel by taking laser power, laser processing beam diameter, laser scanning velocity as input parameters and non-transformation hardening, transformation hardening, melting as output parameters. BP neural network of processing optimal model between laser processing parameters and material surface strengthened quality is established by comparing experimental results and computering simulation analytic results. It is shown that laser processing parameters can be chosen conveniently and material surface quality can be controlled effectively by using BP neural network, furthermore, the proposed model provides accurate results for processing, and fits the experimental results well.