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引用次数: 2
摘要
目前的研究包括通过GRA和PSO对Calmax-635模具钢精加工过程中的表面质量进行优化。GRA将多目标转化为单目标域。然而,它在问题空间内产生离散的参数组合,并获得拟最优解。而当适应度函数可用时,粒子群算法得到最优解。为了得到Calmax-635模具钢的适应度函数,对主轴转速、进给速度和切削深度等参数进行了三层次的全因子DOE分析。通过方差分析,得到了问题空间内的适应度函数。因此,当PSO与GRA耦合时,优化后的工艺参数分别为5660.6 rpm、579.4 mm/min和0.105 mm, Ra、Rmax和Rz的粗糙度值分别为0.862µm、6.591µm和4.638µm。因此,所提出的方法揭示了在缺乏适应度函数的情况下优化的途径。
Concurrent parametric optimisation of single pass end milling through GRA coupled with PSO for Calmax-635 die steel
The present investigation includes optimisation for enhanced surface quality during finishing of Calmax-635 die steel through GRA coupled with PSO. GRA converts multiple objectives into single objective domain. However, it yields discrete parametric combination within the problem space and fetches quasi-optimal solution. Whereas, PSO obtains optimal solution if the fitness function is available. To obtain the fitness function for Calmax-635 die steel, a full factorial DOE was conducted for parameters like, spindle speed, feed rate and depth of cut all at three levels. With the help of ANOVA, a fitness function was obtained within the problem space. Thus, when PSO was coupled with GRA, the optimised process parameters became 5,660.6 rpm, 579.4 mm/min, 0.105 mm, respectively and the roughness values obtained were 0.862 µm, 6.591 µm and 4.638 µm for Ra, Rmax and Rz, respectively. Therefore, the proposed methodology reveals an avenue for optimisation in absentia of fitness function.
期刊介绍:
The mission of the International Journal of Swarm Intelligence Research (IJSIR) is to become a leading international and well-referred journal in swarm intelligence, nature-inspired optimization algorithms, and their applications. This journal publishes original and previously unpublished articles including research papers, survey papers, and application papers, to serve as a platform for facilitating and enhancing the information shared among researchers in swarm intelligence research areas ranging from algorithm developments to real-world applications.