基于PSO-BP神经网络的关节臂式坐标测量机长度误差补偿

摘要

针对关节臂式坐标测量机(AACMM)长度误差补偿问题,分析了误差来源,通过实验确定了影响其测量长度的误差参数.引入BP神经网络对长度误差补偿模型进行了建模,并通过粒子群化算法对BP神经网络的权值和阈值进行全局寻优,克服了BP神经网络收敛速度慢和易陷入局部极值的缺陷.在不同输入参数的条件下测量标准尺,获得了误差补偿模型的训练样本.进行了长度误差补偿验证,补偿后误差均值减小了0.014 mm,使AACMM的测量精度提高了31.8％.%In order to resolve the problem of the length error compensation for artieulated arm coordinate measuring machine (AACMM),the error sources were analyzed and the parameters of affecting the measurement length error for AACMM were determined by experiments.The AACMM length error compensation model was built up by BP neural network,and the particle swarm optimization algorithm was introduced to overcome the drawbacks of slow convergence and easily trapping in the local minimum values of BP neural network.For getting the training data of neural network,the standard ruler was measured on different error parameters,and the measurement compensation verification was also carried out.After compensation,the mean error reduced by 0.014mm and the measurement precision of AACMM increased by 31.8％.