基于GA-SVM的数控机床装配质量预测模型

Assembly quality prediction model of CNC machine tools based on GA-SVM

装配质量的好坏直接决定了数控机床的最终性能,为对数控机床装配质量进行事前预测,提高装配合格率,采用基于GA-SVM方法建立数控机床装配质量预测模型。首先,基于“功能—运动—动作”的结构化分解方法对机床进行元动作分解,将各级元动作内部影响因素作为装配影响因素,并以元动作链中最后一个元动作输出运动参数作为装配质量分析对象。然后,将GA-SVM模型运用到砂轮架X轴进给运动下的元动作链装配质量预测中,为证明该模型的实用性与有效性,将GV-SVM模型得到的预测结果与BP神经网络、常规SVM模型进行对比分析,结果表明:三者的预测结果的平均相对误差分别为3.83%、8.90%和10.16%,显然,GA-SVM模型较其他两种预测模型而言预测效果较好,为数控机床装配工艺进一步优化提供指导意义。

the final performance of CNC machine tools was directly determined by the quality of the assembly,in order to predict the assembly quality of CNC machine tools beforehand and improve the assembly qualification rate,the GA-SVM method is proposed to predict the assembly quality prediction model of CNC machine tools.First,CNC machine tools was decomposed into meta-actions based on“Function-Motion-Action”functional structural decomposition,the influential factors in meta-actions were regraded as the assembly influential factors,and the kinematics parameters of the terminal meta-action in meta-action chain were taken as the assembly quality analysis object.Then,the GV-SVM model was applied to assembly quality prediction of the meta-action chain under the X-axis feed motion of grinder wheel frame.To demonstrate the practicality and effectiveness of the proposed method,the prediction results obtained by the GV-SVM model were compared with those obtained by the BP neural network and the conventional SVM model,and the results show that the average relative errors of the three prediction results are 3.83%,8.90%and 10.16%,respectively.Obviously,the GA-SVM model has the best prediction effect than the other two prediction models,the proposed model provides theoretical guidance for optimizing the assembly process of CNC machine tools.