Machine Learning Regression Approaches for Prediction Microhardness of Al-Y2O3 Composite

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2022 by IJETT Journal
Volume-70 Issue-4
Year of Publication : 2022
Authors : Sachin Rathore, Ratnesh Kumar Raj Singh, K.L.A. Khan
  10.14445/22315381/IJETT-V70I4P227

MLA 

MLA Style: Sachin Rathore, et al. "Machine Learning Regression Approaches for Prediction Microhardness of Al-Y2O3 Composite." International Journal of Engineering Trends and Technology, vol. 70, no. 4, Apr. 2022, pp. 303-315. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I4P227

APA Style: Sachin Rathore, Ratnesh Kumar Raj Singh, Khan., K.L.A.(2022). Machine Learning Regression Approaches for Prediction Microhardness of Al-Y2O3 Composite. International Journal of Engineering Trends and Technology, 70(4), 303-315. https://doi.org/10.14445/22315381/IJETT-V70I4P227

Abstract
The present study investigated the experimental analysis of microhardness of Al-Y2O3 composite material developed through the friction stir processing (FSP) route. The microhardness values were measured for a different set of experiments, and then these data were used for various machine learning (ML) models. All measured data sets were divided into two portions in which 75% of rules were used for training, and the remaining 25% of the data rules were used for testing the regression models. Experiments were carried out on various input parameters such as tool traverse speed (TS), Spindle speed (SS), number of passes and direction of rotation (tool). The micro-hardness data is considered as output response. The microhardness value increased 34.47% from BM, reaching a maximum of 147 HV on 1000rpm SS and 100 mm/min TS, with double pass FSP in which the direction of rotation of the tool is in the opposite direction. To predict microhardness values, various ML-regression algorithms have been considered, mainly: Fine tree (FT), Linear regression (LR), Interactions Linear regression (ILR), and Robust Linear regression (RLR), Stepwise Linear regression (SLR) and support vector machines (SVM). It is found that RLR generated appropriate prediction of microhardness with minimum errors based on measurement of Root Mean Square Error (RMSE), Error score (R2), Mean squared error (MSE) and mean absolute error (MAE).

Keywords
Friction stir processing, Machine learning, Support vector machine, Regression algorithm, Microhardness.

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