Affiliation:
1. Vijayanagara Sri Krishnadevaraya University
Abstract
Abstract
Wear rate prediction is most important in industrial applications. Machine learning (ML) has made an admirable contribution to the field of tribology. Standard ML models are extremely dependent on the parameter values; hence, tuning plays a crucial role in enhancing predictive performance. ML models largely work empirically, based on the data availability and application domain, the parameter tuning process effectively attains the desired accuracy of the models. The main aim of this study is to develop optimized ML models which render better accuracy than the previous study by using a grid search hyperparameter optimization technique. Five ML models namely Random Forest (RF), Support Vector Machine (SVM), K- Nearest Neighbor (KNN), Gaussian Process Regression (GPR), and Linear Regression (LR) are designed by tuning the parameters which lead to the optimization of models concerning the prediction accuracy.
Publisher
Research Square Platform LLC
Reference32 articles.
1. 1. Menezes PI, Nosonvsky M, Ingole SP, Kailas SV, Lovell MR, Tribology for scientists and engineers, NY: Springer; 2013.
2. 2. Kordijazi A, Roshan HM, Dhingra A, Povolo M, Rohatgi PK, Nosonvsky M, Machine-learning methods to predict the wetting properties of iron-based composites. Surf Innov 2021; 111-9.
3. 3. Amit K Gupta, Deep Narayan Mishra, An experimental investigation of the effect of carbon content on the wear behavior of plain carbon steel, IJSR 2013; 2(7): 222–224.
4. 4. Sharanabasappa M, VR Kabadi, Veerabhadrappa Algur, Some investigation on Dry Sliding Wear Behaviour of Ultra High Carbon Steel, Int Journ of Mech Engg Reser 2014; 4(1): 75–82.
5. 5. Ling Qiao, Jingchuan Zhu, YuanWang, Machine learning- Aided process design: modeling and prediction of transformation temperature for pearlitic steel, steel research international 2022; 93.