Computational performance comparison of multiple regression analysis, artificial neural network and machine learning models in turning of GFRP composites with brazed tungsten carbide tipped tool

In a turning process, it is essential to predict and choose appropriate process parameters to get a component’s proper surface roughness (Ra). In this paper, the prediction of Ra through the artificial neural network (ANN), multiple regression analysis (MRA), and random forest method (machine learning) are made and compared. Using the process variables such as feed rate, spindle speed, and depth of cut, the turning process of glass fiber-reinforced plastic (GFRP) composite specimens is conducted on a conventional lathe with the help of a single-point HSS turning tool brazed with a carbide tip. The surface roughness of turned GFRP components is measured experimentally using the Talysurf method.  By utilizing Taguchi's L۲۷ array, the experiments are carried out and the experimental results are utilized in the development of MRA, ANN, and random forest method models for predicting the Ra. It is observed that the mean absolute error (MAE) of MRA, ANN and random forest for the training cases are found to be ۳۹.۳۳%, ۰.۵۶%, and ۲۴.۸۸%, respectively whereas for the test cases MAE is ۵۴.۳۴%, ۲.۵۹%, and ۲۴.۸۸% for MRA, ANN, and random forest, respectively.

Keywords:

Machining , Neural Networks , Machine Learning models , Regression Analysis , DOE