References
- Hirsch, J.; Alsamman, T. Superior Light Metals by Texture Engineering: Optimized Aluminum and Magnesium Alloys for Automotive Applications. Acta Mater. 2013, 61(3), 818–843. DOI: https://doi.org/10.1016/j.actamat.2012.10.044.
- Ramesh, S.; Viswanathan, R.; Ambika, A. S. Measurement and Optimization of Surface Roughness and Tool Wear via Grey Relational Analysis, TOPSIS and RSA Techniques. Measurement. 2016, 78, 63–72. DOI: https://doi.org/10.1016/j.measurement.2015.09.036.
- Kleiner, M.; Geiger, M.; Klaus, A. Manufacturing of Lightweight Components by Metal Forming. Cirp Annals. 2003, 52(2), 521–542. DOI: https://doi.org/10.1016/S0007-8506(07)60202-9.
- Akhtar, K.; Kalipada, M. Application of MCDM-Based TOPSIS Method for the Optimization of Multi Quality Characteristics of Modern Manufacturing Processes. International Journal of Engineering Research in Africa. 2016, 23, 33–51. DOI: https://doi.org/10.4028/scientific.net/JERA.23.33.
- Maher, I.; Eltaib Ahmed, M. E. H.; Sarhan, A. D.; EI-Zahry, R. M. Cutting Force-Based Adaptive Neuro-Fuzzy Approach for Accurate Surface Roughness Prediction in End Milling Operation for Intelligent Machining. Int. J. Adv. Manuf. Technol. 2015, 76(5–8), 1459–1467. DOI: https://doi.org/10.1007/s00170-014-6379-1.
- Ramesh, S.; Karunamoorthy, L.; Palanikumar, K. Fuzzy Modeling and Analysis of Machining Parameters in Machining Titanium Alloy. Mater. Manuf. Processes. 2008, 23(4), 439–447. DOI: https://doi.org/10.1080/10426910801976676.
- Alata, M.; Demirli, K. Prediction Model for Bta Deep-hole Machining Using Fuzzy Clustering Approach: Experimental Study. Mater. Manuf. Processes. 2004, 19(6), 1103–1119. DOI: https://doi.org/10.1081/AMP-200035257.
- Sen, B.; Mondal, U. K.; Mondal, S. P. Advancement of an Intelligent System Based on ANFIS for Predicting Machining Performance Parameters of Inconel 690 – A Perspective of Metaheuristic Approach. Measurement. 2017, 109, 9–17. DOI: https://doi.org/10.1016/j.measurement.2017.05.050.
- Ramesh, S.; Karunamoorthy, L.; Palanikumar, K. Fuzzy Modeling, and Analysis of Machining Parameters in Machining Titanium Alloy. Mater. Manuf. Processes. 2008, 23, 39–447. DOI: https://doi.org/10.1080/10426910801976676.
- Jang, J. S. R.; Sun, C. T.; Mizutani, E. Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence, IEEE- Transactions on Automatic control, Prentice-Hall. Upper Saddle River. NJ. 1997, 42(10), 1482-1484. doi:https://doi.org/10.1080/10426914.2020.1843664
- Jafarian, F.; Taghipour, M.; Amirabadi, H. Application of Artificial Neural Network and Optimization Algorithms for Optimizing Surface Roughness, Tool Life and Cutting Forces in Turning Operation. J. Mech. Sci. Technol. 2013, 27(5), 1469–1477. DOI: https://doi.org/10.1007/s12206-013-0327-0.
- Shivakoti, I.; Kibria, G.; Pradhan, P. M. ANFIS Based Prediction and Parametric-based Prediction and Parametric Analysis during Turning Operation of Stainless Steel 202. Materials and Manufacturing. 2019, 34, 112–121. DOI: https://doi.org/10.1080/10426914.2018.1512134.
- Manikandan, N.; Balasubramanian, K.; Palanisamy, D.; Gopal, P. M.; Arulkirubakaran, D.; Binoj, J. S. Machinability Analysis and ANFIS modelling on Advanced Machining of Hybrid Metal Matrix Composites for Aerospace Applications. Materials and Manufacturing Processes. 2019, 34(16), 1866–1881. DOI: https://doi.org/10.1080/10426914.2019.1689264.
- Valarmathi, T. N.; Palanikumar, K.; Sekar, S.; Latha, B. Investigation of the Effect of Process Parameters on Surface Roughness in Drilling of Particleboard Composite Panels Using Adaptive Neuro Fuzzy Inference System. Materials and Manufacturing Processes. 2019, 35(4), 469–477. DOI: https://doi.org/10.1080/10426914.2020.1711931.
- Trinh, N. D.; Shaohui, Y.; Tan, N. N.; Son, P. X.; Duc, L. A. A New Method for Online Monitoring When Grinding Ti-6al-4v Alloy. Materials and Manufacturing Processes. 2019, 34(1), 39–53. DOI: https://doi.org/10.1080/10426914.2018.1532587.
- Nain, S. S.; Garg, D.; Kumar, S. Performance Evaluation of the WEDM Process of Aeronautics Super Alloy. Materials and Manufacturing Processes. 2018, 33(16), 1793–1808. DOI: https://doi.org/10.1080/10426914.2018.1476761.
- Kumar, S.; Ghoshal, S. K.; Arora, P. K.; Nagdeve, L. Multi-Variable Optimization in Die-Sinking EDM Process of AISI420 Stainless Steel. Mater. Manuf. Process. 2020. DOI: https://doi.org/10.1080/10426914.2020.1843678.
- Manikandan, N.; Arulkirubakaran, D.; Palanisamy; Raju, R. Influence of Wire-EDM Textured Conventional Tungsten Carbide Inserts in Machining of Aerospace Materials (Ti–6al–4v Alloy). Mater. Manuf. Process. 2018. 34,103-111. doi:https://doi.org/10.1080/10426914.2018.1544712
- Jaypuria, S.; Mahapatra, T. R.; Jaypuria, O. Metaheuristic Tuned ANFIS Model for Input-Output Modeling of Friction Stir Welding. Mater. Today Proc. 2019, 18, 3922–3930. DOI: https://doi.org/10.1016/j.matpr.2019.07.332.
- Singh, N. K.; Singh, Y.; Kumar, S.; Sharma, A. Predictive Analysis of Surface Roughness in EDM Using Semi-empirical, ANN and ANFIS Techniques: A Comparative Study. Mater. Today Proc. 2020, 25, 735–741. DOI: https://doi.org/10.1016/j.matpr.2019.08.234.
- Yadav, D.; Chhabra, D.; Gupta, R. K.; Phogat, A.; Ahlawat, A. Modeling and Analysis of Significant Process Parameters of FDM 3D Printer Using ANFIS. Materials Today: Proceedings. 2020, 333 61, 1592–1604.
- Kirby, E. D.; Zhang, Z.; Chen, J. C. Development of an Accelerometer–Based Surface Roughness Prediction System in Turning Operations Using Multiple Regression Techniques. Journal of Industrial Technology 2004, 20(4), 1–8.
- Dweiri, F.; Al-Jarrah, M.; Al-Wedyan, H. Fuzzy Surface Roughness Modeling of CNC down Milling of Alumic-79. Journal of Materials Processing Technology. 2003, 52(3), 266–275. DOI: https://doi.org/10.1016/S0007-8506(07)60202-9.
- Abd El-Raaouf, A. M.; Osman, M. S.; El-Axir, M. H.; Elshanawani, A. A. Applicability of Fuzzy Approach for the Optimization and Analysis of Surface Roughness in Orthogonal Cutting, Seventh International Conference On Production Engineering, Design, and Control, Alexandria University-Egypt, 2001, 1261–1272.
- Samhouri, M. S.; Surgenor, B. W. Surface Roughness In Grinding: On-Line Prediction With Adaptive Neuro-Fuzzy Inference System, Proceeding of thirty-third Annual North American manufacturing research conference, (NAMRC 33), Columbia University, New York, USA, 2005, 57-64.
- Ozel, T.; Karpat, Y. Predictive Modeling of Surface Roughness and Tool Wear in Hard Turning Using Regression and Neural Networks. Int. J. Mach. Tools Manuf. 2005, 45(4–5), 467–479. DOI: https://doi.org/10.1016/j.ijmachtools.2004.09.007.
- Hamdan, A.; Sarhan, A. A. D.; Hamdi, M. An Optimization Method of the Machining Parameters in High-Speed Machining of Stainless Steel Using Coated Carbide Tool for Best Surface Finish. The International Journal of Advanced Manufacturing Technology. 2012, 58(1–4), 81–91. DOI: https://doi.org/10.1007/s00170-011-3392-5.
- Dweiri, F.; Al-Jarrah, M.; Al-Wedyan, H. Fuzzy Surface Roughness Modeling of CNC down Milling of Alumic-79. Journal of Materials Processing Technology. 2003, 133(3), 266–275. DOI: https://doi.org/10.1016/S0924-0136(02)00847-6.
- Miriyala., S. S.; Mittal, P.; Majumdar, S.; Mitra, K. Comparative Study of Surrogate Approaches while Optimizing Computationally Expensive Reaction Networks. Chem. Eng. Sci. 2016, 140, 44–61. DOI: https://doi.org/10.1016/j.ces.2015.09.030.
- Miriyala, S. S.; Mitra, K. Multi-objective Optimization of Iron Ore Induration Process Using Optimal Neural Networks. Mater. Manuf. Processes. 2020, 35(5), 5. DOI: https://doi.org/10.1080/10426914.2019.1643476.
- Anitha, M.; Prateek, M.; Saptarshi, M.; Mitraa, K. Kriging Surrogate Based Multi-objective Optimization of Bulk Vinyl Acetate Polymerization with Branching. Mater. Manuf. Processes. 2015, 30(4), 394–402. DOI: https://doi.org/10.1080/10426914.2014.921709.
- Pantula, P. D.; Miriyala, S. S.; Mitra, K. KERNEL: Enabler to Build Smart Surrogates for Online Optimization and Knowledge Discovery. Mater. Manuf. Processes. 2017, 32(10), 1162–1171. DOI: https://doi.org/10.1080/10426914.2016.1269918.
- Pantula., P. D.; Mitra, K. A Data-driven Approach Towards Finding Closer Estimates of Optimal Solutions under Uncertainty for an Energy Efficient Steel Casting Process. Journal of Energy 2019, 189. DOI: https://doi.org/10.1016/j.energy.2019.116253.
- Pantula, P. D.; Kishalay, M. Towards Efficient Robust Optimization Using Data Based Optimal Segmentation of Uncertain Space. Reliability Engineering and System Safety. 2020, 197, 106821.
- Astakhov, V.;. Metal Cutting Mechanics; CRC Press: USA, 1999; pp 245–255.
- Stephenson, D.;. Tool-Work Thermocouple Temperature Measurements—Theory and Implementation Issues. Journal of Engineering for Industry. 1993, 115(4), 432–437. DOI: https://doi.org/10.1115/1.2901786.
- Abdullah, A. B.; Chia, L. Y.; Samad, Z. The Effect of Feed Rate and Cutting Speed to Surface Roughness. Asian Journal of Scientific Research. 2008, 1(1), 12–21. DOI: https://doi.org/10.3923/ajsr.2008.12.21.
- Jang, J. S.; Sun, C. T. Neuro-fuzzy modeling and Control. Proceedings of the IEEE. 1995, 83(3), 378–406. DOI: https://doi.org/10.1109/5.364486.
- Sahin, M.; Erol, R. A Comparative Study of Neural Networks and ANFIS for Forecasting Attendance Rate of Soccer Games. Math. Comput. Appl. 2017, 22(4), 43. DOI: https://doi.org/10.3390/mca22040043.
- Alimam, H.; Hinnawi, M.; Pradhan, P.; Alkassar, Y. ANN & ANFIS Models for Prediction of Abrasive Wear of 3105 Aluminium Alloy with Polyurethane Coating. Tribol. Ind. 2016, 38, 221–228.