Alajmi Mahdi S, Almeshal Abdullah M
Department of Manufacturing Engineering Technology, College of Technological Studies, The Public Authority for Applied Education and Training, Safat 13092, Kuwait.
Department of Electronic Engineering Technology, College of Technological Studies, The Public Authority for Applied Education and Training, Safat 13092, Kuwait.
Materials (Basel). 2020 Jul 4;13(13):2986. doi: 10.3390/ma13132986.
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 = 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 = 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.
本研究提出了一种基于ANFIS-QPSO机器学习方法预测AISI 304不锈钢干式和低温车削表面粗糙度值的方法。ANFIS-QPSO在准确性、鲁棒性和向全局最优快速收敛方面结合了人工神经网络、模糊系统和进化优化的优势。模拟结果表明，对于干式车削过程，ANFIS-QPSO能够准确预测表面粗糙度，RMSE = 4.86%，MAPE = 4.95%，R = 0.984。同样，对于低温车削过程，ANFIS-QPSO预测的表面粗糙度RMSE = 5.08%，MAPE = 5.15%，R = 0.988，与测量值高度吻合。ANFIS-QPSO、ANFIS、ANFIS-GA和ANFIS-PSO之间的性能比较表明，ANFIS-QPSO是一种有效的方法，能够确保干式和低温车削过程表面粗糙度值的高预测精度。
