激光熔覆成形薄壁金属制件精度的预测模型

为了控制激光熔覆成形薄壁金属制件的精度，分析了激光熔覆成形金属薄壁的工艺理论和影响因素。采用BP神经网络建立了激光功率、光斑直径、扫描速度和送粉率与金属零件壁厚的非线形关系模型和激光熔覆成形薄壁制件的精度控制系统。通过优化神经网络的权值和阈值，并引入动量因子和学习速率的自适应调整，克服了BP算法容易陷入局部最小值的问题。用实验参数作为训练样本对模型进行训练，并进行了误差分析。实验和仿真结果表明，训练样本和检验样本的最大相对误差分别为1.93％和1.19％，预测精度高。该网络模型可用于优化激光熔覆成形工艺参数和成形金属制件精度的在线实时控制。

Predictive Model of Thin-Wall Metal Parts Precision in Laser Cladding Forming

In order to control forming accuracy of laser cladding thin-wall metal parts, on the basis of analyzing technology theory and affecting factors of metal parts wall thickness in laser cladding, a nonlinear mapping between wall thickness of metal parts and laser power, spot diameter, scanning speed, powder feed rate is established by using BP artificial neural network. By using momentum coefficient, adaptive learn rates and optimized weights and bias, the problem of BP that easily falls into local minimum point is overcome. Experimental parameters are chosen as training samples, and BP neutral network is trained. Experimental and simulated results show that the maximum relative error of training and testing samples are 1.93% and 1.19% respectively. The optimized BP neutral network model can be used to process parameter optimization and predict forming accuracy of laser cladding metal thin-walled component for on-line control system.