Comparision of both methods psi and curli: applied in solving multi-objective optimization problem of turning process

Q3 Engineering
Do Duc Trung, Nguyen Thi Phuong Giang, Nguyen Hoai Son
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Abstract

Solving a multi-objective optimization problem involves finding the best solution to simultaneously satisfy multiple predefined objectives. Currently, various mathematical methods are available for solving optimization problems in general, and multi-objective optimization in particular. The comparison of mathematical methods when addressing the same problem has been explored in numerous studies. In this study, let’s conduct a comparison of two multi-objective optimization methods: the PSI method and the CURLI method. These two methods were applied collectively to tackle a multi-objective optimization problem related to a turning process. Experimental data were borrowed from a previous study, and a total of sixteen experiments were conducted. Roughness average (Ra), Roundness Error (RE), Tool Wear (VB), and Material Removal Rate (MRR) were the four output parameters measured in each experiment. The objective of solving the multi-objective optimization problem was to identify an experiment among the sixteen existing experiments that simultaneously minimized the three parameters of Ra, RE, and VB while maximizing MRR. The optimal results determined using the PSI and CURLI methods were also compared with the optimal results obtained through other methods (COCOSO, MABAC, MAIRCA, EAMR and TOPSIS) in published documents. The comparison results indicate that the optimal experiment found using the CURLI method consistently matches that of other methods. In contrast, the optimal results obtained through the PSI method differ significantly from those obtained through other methods. The Spearman correlation ranking coefficient between CURLI and the five methods COCOSO, MABAC, MAIRCA, EAMR, and TOPSIS is very high, ranging from 0.9 to 1. In contrast, this coefficient is very small when comparing PSI with the aforementioned five methods, falling within the range of –0.6088 to –0.3706 in this case. Ultimately, this study concludes that the CURLI method is suiTable for solving the multi-objective optimization problem in the turning process, whereas the PSI method is deemed unsuitable
psi 和 curli 两种方法的比较:应用于解决车削工艺的多目标优化问题
解决多目标优化问题需要找到同时满足多个预定目标的最佳解决方案。目前,有多种数学方法可用于解决一般优化问题,特别是多目标优化问题。许多研究都探讨了在解决相同问题时数学方法的比较。在本研究中,我们将对 PSI 方法和 CURLI 方法这两种多目标优化方法进行比较。这两种方法共同用于解决与车削工艺相关的多目标优化问题。实验数据借自之前的一项研究,共进行了 16 次实验。平均粗糙度 (Ra)、圆度误差 (RE)、刀具磨损 (VB) 和材料去除率 (MRR) 是每次实验测量的四个输出参数。解决多目标优化问题的目标是在现有的 16 个实验中找出一个实验,使 Ra、RE 和 VB 这三个参数同时最小化,而 MRR 最大化。使用 PSI 和 CURLI 方法确定的最优结果还与已发表文献中通过其他方法(COCOSO、MABAC、MAIRCA、EAMR 和 TOPSIS)获得的最优结果进行了比较。比较结果表明,使用 CURLI 方法得出的最佳实验结果与其他方法一致。相比之下,通过 PSI 方法得到的最优结果与其他方法得到的结果有很大差异。CURLI 与 COCOSO、MABAC、MAIRCA、EAMR 和 TOPSIS 五种方法之间的 Spearman 相关排名系数非常高,在 0.9 到 1 之间。最终,本研究得出结论,CURLI 方法适合解决车削过程中的多目标优化问题,而 PSI 方法则被认为不合适。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
EUREKA: Physics and Engineering
EUREKA: Physics and Engineering Engineering-Engineering (all)
CiteScore
1.90
自引率
0.00%
发文量
78
审稿时长
12 weeks
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