基于GRA-PSO算法的端铣工艺参数多目标优化

考虑生产实际的需求,综合最小变形误差、最大金属切除率和最大刀具耐用度建立端铣工艺参数多目标优化模型。通过对粒子群全局寻优能力和灰色理论的适应性综合分析,研究提出耦合粒子群算法（Particle Swarm Op-timization,PSO）和灰色关联（Gray Relevancy Analysis,GRA）的多目标工艺参数优化算法。该方法将多目标函数的优化问题转化为优化单项灰关联度,得到了多项工艺指标要求下的参数优化组合。将该方法应用在多目标工艺参数优化设计中取得了满意的结果,表明其具有很大的适应性。

Multi-objective Optimization of Face Milling Process Parameters Based on GRA-PSO Algorithm

According to the demand of manufacturing practice,a multi-objective optimization model of face milling process parameters is established by integrating minimum distortion error,maximum material removal rate and maximum tool durability.Based on the adaptability analysis for particle swarm algorithm and gray relevancy analysis,an effective multi-objective optimization algorithm is proposed by coupling Particle Swarm Optimization(PSO)algorithm and Gray Relevancy Analysis(GRA).This method makes the multi-objective optimization problem transformed into the optimization problem of single targeted gray relational degree,thus obtaining the optimized combination of parameters under the requirements of multiple technological indexes.The research indicates that the application of this method has got satisfactory results in the optimization design of multi-objective process parameters with prodigious adaptability.