结合神经网络与遗传算法的磨抛工艺参数优化

Optimization of Processing Parameters in Grinding and Polishing Coupling Neural Networks with Genetic Algorithms

针对机器人磨抛系统工艺参数的自主选择与优化问题,提出一种基于神经网络与遗传算法的磨抛工艺参数优化方法,采用基于人工神经网络的工件表面粗糙度预测模型解决各工艺参数间复杂的非线性问题,结合粗糙度预测模型与磨抛效率公式,通过遗传算法对各工艺参数进行全局寻优解决加工质量和效率的双目标优化问题并得到最优工艺参数组合。在满足加工质量要求的前提下,加工效率提高了近三分之一,证明此工艺参数优化方法是可行有效的。

Aiming at the problem of autonomous selection and optimization of process parameters of robot grinding and polishing systems,an optimization method of the processing parameters in the grinding and polishing based on neural networks and genetic algorithms is proposed.Adopting artificial neural networks based workpiece surface roughness prediction model to solve complex non-linear problems among the processing parameters.Combining the roughness prediction model with the grinding and polishing efficiency formula,by using genetic algorithms to globally optimize each processing parameter to solve the dual-objective optimization problem of processing quality and efficiency and finally obtain the optimal processing parameter combination.On the premise of meeting the requirements of processing quality,the processing efficiency has been improved by nearly one third,which proves that this processing parameter optimization method is feasible and effective.