Machine learning assisted optimization of tribological parameters of Al–Co–Cr–Fe–Ni high-entropy alloy

References

• Gopinath, V. M.; Arulvel, S. A Review on the Steels, Alloys/High Entropy Alloys, Composites and Coatings Used in High Temperature Wear Applications. Materials Today: Proceedings, 2021, 43, 817–823. DOI: 10.1016/j.matpr.2020.06.495.
   Google Scholar
• Gao, M. C.; Yeh, J. W.; Liaw, P. K.; Zhang, Y. High-Entropy Alloys; Springer International Publishing: Cham, 2016. DOI: 10.1007/978-3-319-27013-5.
   Google Scholar
• Lu, Z. P.; Wang, H.; Chen, M. W.; Baker, I.; Yeh, J. W.; Liu, C. T.; Nieh, T. G. An Assessment on the Future Development of High-Entropy Alloys: Summary from a Recent Workshop. Intermetallics. 2015, 66, 67–76. DOI: 10.1016/j.intermet.2015.06.021.
   Web of Science ®Google Scholar
• Chikumba, S.; Rao, V. V. High Entropy Alloys: Development and Applications. In Proceedings of the 7th International Conference on Latest Trends in Engineering & Technology (ICLTET’2015), 2015, 1–5. DOI: 10.15242/IIE.E11150005.
   Google Scholar
• Zhang, Y.; Zuo, T. T.; Tang, Z.; Gao, M. C.; Dahmen, K. A.; Liaw, P. K.; Lu, Z. P. Microstructures and Properties of High-Entropy Alloys. Prog. Mater. Sci. 2014, 61, 1–93. DOI: 10.1016/j.pmatsci.2013.10.001.
   Web of Science ®Google Scholar
• Zhou, J. L.; Yang, J. Y.; Zhang, X. F.; Ma, F. W.; Ma, K.; Cheng, Y. H. Research Status of Tribological Properties Optimization of High-Entropy Alloys: A Review. J. Mater. Sci. 2023, 1–35. DOI: 10.1007/s10853-023-08255-3.
   Web of Science ®Google Scholar
• Kasar, A. K.; Scalaro, K.; Menezes, P. L. Tribological Properties of High-Entropy Alloys Under Dry Conditions for a Wide Temperature Range—A Review. Materials. 2021, 14(19), 5814. DOI: 10.3390/ma14195814.
   PubMed Web of Science ®Google Scholar
• Marian, M.; Tremmel, S. Current Trends and Applications of Machine Learning in Tribology—A Review. Lubricants. 2021, 9(9), 86. DOI: 10.3390/lubricants9090086.
   Web of Science ®Google Scholar
• Hasan, M. S.; Nosonovsky, M. Triboinformatics: Machine Learning Algorithms and Data Topology Methods for Tribology. Surface Innovations. 2022, 10(4–5), 229–242. DOI: 10.1680/jsuin.22.00027.
   Web of Science ®Google Scholar
• Mamun, O.; Wenzlick, M.; Hawk, J.; Devanathan, R. A Machine Learning Aided Interpretable Model for Rupture Strength Prediction in Fe-Based Martensitic and Austenitic Alloys. Sci. Rep. 2021, 11(1), 1–9. DOI: 10.1038/s41598-021-83694-z.
   PubMed Web of Science ®Google Scholar
• Xiong, J.; Shi, S. Q.; Zhang, T. Y. A Machine-Learning Approach to Predicting and Understanding the Properties of Amorphous Metallic Alloys. Mater. Des. 2020, 187, 108378. DOI: 10.1016/j.matdes.2019.108378.
   Web of Science ®Google Scholar
• Sardar, S.; Dey, S.; Das, D. Modelling of Tribological Responses of Composites Using Integrated ANN-GA Technique. J. Compos. Mater. 2021, 55(7), 873–896. DOI: 10.1177/0021998320960520.
   Web of Science ®Google Scholar
• Diao, Y.; Yan, L.; Gao, K. Improvement of the Machine Learning-Based Corrosion Rate Prediction Model Through the Optimization of Input Features. Mater. Des. 2021, 198, 109326. DOI: 10.1016/j.matdes.2020.109326.
   Web of Science ®Google Scholar
• Zhao, Q.; Yang, H.; Liu, J.; Zhou, H.; Wang, H.; Yang, W. Machine Learning-Assisted Discovery of Strong and Conductive Cu Alloys: Data Mining from Discarded Experiments and Physical Features. Mater. Des. 2021, 197, 109248. DOI: 10.1016/j.matdes.2020.109248.
   Web of Science ®Google Scholar
• Thankachan, T.; Soorya Prakash, K.; Kamarthin, M. Optimizing the Tribological Behavior of Hybrid Copper Surface Composites Using Statistical and Machine Learning Techniques. J. Tribol. 2018, 140(3). DOI: 10.1115/1.4038688.
   Web of Science ®Google Scholar
• Hasan, M. S.; Kordijazi, A.; Rohatgi, P. K.; Nosonovsky, M. Triboinformatics Approach for Friction and Wear Prediction of Al-Graphite Composites Using Machine Learning Methods. J. Tribol. 2022, 144(1). DOI: 10.1115/1.4050525.
   Web of Science ®Google Scholar
• Hayajneh, M.; Hassan, A. M.; Alrashdan, A.; Mayyas, A. T. Prediction of Tribological Behavior of Aluminum–Copper Based Composite Using Artificial Neural Network. J. Alloys Compound. 2009, 470(1–2), 584–588. DOI: 10.1016/j.jallcom.2008.03.035.
   Web of Science ®Google Scholar
• Genel, K.; Kurnaz, S. C.; Durman, M. Modeling of Tribological Properties of Alumina Fiber Reinforced Zinc–Aluminum Composites Using Artificial Neural Network. Mater. Sci. Eng. A. 2003, 363(1–2), 203–210. DOI: 10.1016/S0921-5093(03)00623-3.
   Web of Science ®Google Scholar
• Stojanović, B.; Vencl, A.; Bobić, I.; Miladinović, S.; Skerlić, J. Experimental Optimisation of the Tribological Behaviour of Al/SiC/Gr Hybrid Composites Based on Taguchi’s Method and Artificial Neural Network. J. Braz. Soc. Mech. Sci. Eng. 2018, 40, 1–14. DOI: 10.1007/s40430-018-1237-y.
   Web of Science ®Google Scholar
• Tyagi, L.; Butola, R.; Kem, L.; Singari, R. M. Comparative Analysis of Response Surface Methodology and Artificial Neural Network on the Wear Properties of Surface Composite Fabricated by Friction Stir Processing. J. Bio- Tribo-Corros. 2021, 7, 1–14. DOI: 10.1007/s40735-020-00469-1.
   Google Scholar
• Nirmal, U.; Hashim, J.; Ahmad, M. M. A Review on Tribological Performance of Natural Fibre Polymeric Composites. Tribol. Int. 2015, 83, 77–104. DOI: 10.1016/j.triboint.2014.11.003.
   Web of Science ®Google Scholar
• Gyurova, L. A.; Friedrich, K. Artificial Neural Networks for Predicting Sliding Friction and Wear Properties of Polyphenylene Sulfide Composites. Tribol. Int. 2011, 44(5), 603–609. DOI: 10.1016/j.triboint.2010.12.011.
   Web of Science ®Google Scholar
• Paturi, U. M. R.; Palakurthy, S. T.; Reddy, N. S. The Role of Machine Learning in Tribology: A Systematic Review. Arch. Comput. Methods Eng. 2022, 1–53. DOI: 10.1007/s11831-022-09819-3.
   PubMedGoogle Scholar
• Mahanta, B. K.; Chakraborti, N. Tri-Objective Optimization of Noisy Dataset Inblastfurnace Iron-Making Process Using Evolutionary Algorithms. Mater. Manuf. Processes. 2020, 35(6), 677–686. DOI: 10.1080/10426914.2019.1643472.
   Web of Science ®Google Scholar
• Mahanta, B. K.; Sarkar, S.; Sen, P. K.; Chakraborti, N. Consequence of Natural Gas Injection in Blast Furnace: A Critical Appraisal Using a Thermodynamic and Evolutionary Computation Approach. Can. Metall. Q. 2022, 61(1), 1–13. DOI: 10.1080/00084433.2021.2016344.
   Web of Science ®Google Scholar
• Chugh, T.; Jin, Y.; Miettinen, K.; Hakanen, J.; Sindhya, K. A Surrogate-Assisted Reference Vector Guided Evolutionary Algorithm for Computationally Expensive Many-Objective Optimization. IEEE Trans. Evol. Comput. 2018, 22(1), 129–142. DOI: 10.1109/TEVC.2016.2622301.
   Web of Science ®Google Scholar
• Chugh, T.; Chakraborti, N.; Sindhya, K.; Jin, Y. A Data-Driven Surrogate-Assisted Evolutionary Algorithm Applied to a Many-Objective Blast Furnace Optimization Problem. Mater. Manuf. Processes. 2017, 32(10), 1172–1178. DOI: 10.1080/10426914.2016.1269923.
   Web of Science ®Google Scholar
• Chakraborti, N. Data-Driven Evolutionary Modeling in Materials Technology. CRC Press. 2022. DOI: 10.1201/9781003201045.
   Google Scholar
• Mahanta, B. K.; Chakraborti, N. Evolutionary Data Driven Modeling and Multi Objective Optimization of Noisy Data Set in Blast Furnace Iron Making Process. Steel Res. Int. 2018, 89(9), 1800121. DOI: 10.1002/srin.201800121.
   Web of Science ®Google Scholar
• Chakraborti, N. Strategies for Evolutionary Data Driven Modeling in Chemical and Metallurgical Systems. Applications Of Metaheuristics In Process Engineering. 2014, 89–122. DOI: 10.1007/978-3-319-06508-3_4.
   Google Scholar
• Pettersson, F.; Chakraborti, N.; Saxén, H. A Genetic Algorithms Based Multi-Objective Neural Net Applied to Noisy Blast Furnace Data. Appl. Soft Comput. 2007, 7(1), 387–397. DOI: 10.1016/j.asoc.2005.09.001.
   Web of Science ®Google Scholar
• Kumar, A.; Chakrabarti, D.; Chakraborti, N. Data‐Driven Pareto Optimization for Microalloyed Steels Using Genetic Algorithms. Steel Res. Int. 2012, 83(2), 169–174. DOI: 10.1002/srin.201100189.
   Web of Science ®Google Scholar
• Roy, S.; Saini, B. S.; Chakrabarti, D.; Chakraborti, N. Mechanical Properties of Micro-Alloyed Steels Studied Using a Evolutionary Deep Neural Network. Materials And ManufacturingProcesses. 2020, 35(6), 611–624. DOI: 10.1080/10426914.2019.1660786.
   Web of Science ®Google Scholar
• Radhika, N.; Raghu, R. Investigation on Mechanical Properties and Analysis of Dry Sliding Wear Behavior of Al LM13/AlN Metal Matrix Composite Based on Taguchi’s Technique. J. Tribol. 2017, 139(4). DOI: 10.1115/1.4035155.
   Web of Science ®Google Scholar
• Dama, K.; Prashanth, L.; Nagaral, M.; Mathapati, R.; Hanumantharayagouda, M. B. Microstructure and Mechanical Behavior of B4C Particulates Reinforced ZA27 Alloy Composites. Mater. Today Proc. 2017, 4(8), 7546–7553. DOI: 10.1016/j.matpr.2017.07.086.
   Google Scholar
• Shaikh, M. B. N.; Raja, S.; Ahmed, M.; Zubair, M.; Khan, A.; Ali, M. Rice Husk Ash Reinforced Aluminium Matrix Composites: Fabrication, Characterization, Statistical Analysis and Artificial Neural Network Modelling. Mater. Res. Express. 2019, 6(5), 056518. DOI: 10.1088/2053-1591/aafbe2.
   Web of Science ®Google Scholar
• Raaft, M.; Mahmoud, T. S.; Zakaria, H. M.; Khalifa, T. A. Microstructural, Mechanical and Wear Behavior of A390/Graphite and A390/Al2O3 Surface Composites Fabricated Using FSP. Mater. Sci. Eng. A. 2011, 528(18), 5741–5746. DOI: 10.1016/j.msea.2011.03.097.
   Web of Science ®Google Scholar
• Bellemare, S. C.; Dao, M.; Suresh, S. Effects of Mechanical Properties and Surface Friction on Elasto-Plastic Sliding Contact. Mech. Mater. 2008, 40(4–5), 206–219. DOI: 10.1016/j.mechmat.2007.07.006.
   Web of Science ®Google Scholar
• Kumar, R.; Dhiman, S. A Study of Sliding Wear Behaviors of Al-7075 Alloy and Al-7075 Hybrid Composite by Response Surface Methodology Analysis. Mater. Des. 2013, 50, 351–359. DOI: 10.1016/j.matdes.2013.02.038.
   Web of Science ®Google Scholar
• Tu, C. F.; Fort, T. A Study of Fiber–Capstan Friction. 1. Stribeck Curves. Tribol. Int. 2004, 37(9), 701–710. DOI: 10.1016/j.triboint.2004.02.008.
   Web of Science ®Google Scholar
• Ing, T. C.; Mohammed Rafiq, A. K.; Azli, Y.; Syahrullail, S. The Effect of Temperature on the Tribological Behavior of RBD Palm Stearin. Tribol. Trans. 2012, 55(5), 539–548. DOI: 10.1080/10402004.2012.680176.
   Web of Science ®Google Scholar