Abstract
Modeling the interrelationships between the input parameters and outputs (responses) in any machining processes is essential to understand the process behavior and material removal mechanism. The developed models can also act as effective prediction tools in envisaging the tentative values of the responses for given sets of input parameters. In this paper, the application potentialities of nine different regression models, such as linear regression (LR), polynomial regression (PR), support vector regression (SVR), principal component regression (PCR), quantile regression, median regression, ridge regression, lasso regression and elastic net regression are explored in accurately predicting response values during turning and drilling operations of composite materials. Their prediction performance is also contrasted using four statistical metrics, i.e., mean absolute percentage error, root mean squared percentage error, root mean squared logarithmic error and root relative squared error. Based on the lower values of those metrics and Friedman rank and aligned rank tests, SVR emerges out as the best performing model, whereas the prediction performance of median regression is worst. The results of the Wilcoxon test based on the drilling dataset identify the existence of statistically significant differences between the performances of LR and PCR, and PR and median regression models.
Highlights
Published: 6 November 2021A composite material usually consisting of a combination of two or more materials with varying physical and chemical properties has superior characteristics as compared to its individual constituents
In support vector machine (SVM), dimension of the classified vectors has less influence on its performance unlike other conventional regression models. It employs a set of training data to learn and develop a model in order to minimize the generalization error when its performance is validated with different sets of testing data. It is mainly applied for solving classification problems, but after the introduction of support vector regression (SVR), it has received a great interest among the research community in solving regression problems which are quite difficult to solve by the conventional models
If covariates are absent in the median regression model, the calculated intercept would be the usual estimate of the median [27]
Summary
A composite material usually consisting of a combination of two or more materials with varying physical and chemical properties has superior characteristics as compared to its individual constituents. To understand the process behavior and study the influences of the input parameters on the responses, development of a mathematical/statistical model is quite useful It can act as a prediction tool in envisaging the response values for given sets of input parameters and help in determining the optimal parametric intermix to achieve the target responses. In this direction, application of response surface methodology (RSM)-based meta-modeling has been quite popular among the researchers [7,8,9,10,11,12,13,14] due to its ability to derive higher order and interaction effects between the input parameters with a smaller number of experimental data. Two non-parametric tests in the form of the Friedman test and Wilcoxon test are performed to respectively identify the best performing regression model and statistically significant differences between those models
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