Prediction and Optimization of Surface Roughness in a Turning Process Using the ANFIS-QPSO Method

被引：57

作者：

Alajmi, Mahdi S. ^{[1
]}

Almeshal, Abdullah M. ^{[2
]}

机构：

[1] Publ Author Appl Educ & Training, Coll Technol Studies, Dept Mfg Engn Technol, Safat 13092, Kuwait

[2] Publ Author Appl Educ & Training, Coll Technol Studies, Dept Elect Engn Technol, Safat 13092, Kuwait

来源：

MATERIALS | 2020年 / 13卷 / 13期

关键词：

adaptive neuro-fuzzy inference system; turning process; surface roughness; machine learning; quantum particle swarm optimization; ANFIS-QPSO; ANN; PARTICLE SWARM OPTIMIZATION; MULTIOBJECTIVE OPTIMIZATION; MODEL; ALGORITHM; STEEL; PERFORMANCE; PARAMETERS; TAGUCHI; RSM;

D O I：

10.3390/ma13132986

中图分类号：

O64 [物理化学（理论化学）、化学物理学];

学科分类号：

070304 ; 081704 ;

摘要：

This study presents a prediction method of surface roughness values for dry and cryogenic turning of AISI 304 stainless steel using the ANFIS-QPSO machine learning approach. ANFIS-QPSO combines the strengths of artificial neural networks, fuzzy systems and evolutionary optimization in terms of accuracy, robustness and fast convergence towards global optima. Simulations revealed that ANFIS-QPSO results in accurate prediction of surface roughness with RMSE = 4.86%, MAPE = 4.95% and R-2= 0.984 for the dry turning process. Similarly, for the cryogenic turning process, ANFIS-QPSO resulted in surface roughness predictions with RMSE = 5.08%, MAPE = 5.15% and R-2= 0.988 that are of high agreement with the measured values. Performance comparisons between ANFIS-QPSO, ANFIS, ANFIS-GA and ANFIS-PSO suggest that ANFIS-QPSO is an effective method that can ensure a high prediction accuracy of surface roughness values for dry and cryogenic turning processes.

引用

页码：1 / 23

页数：23

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