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

Gadagi, A. Amith; Adake, B. Chandrashekar

doi:10.22061/jcarme.2022.8684.2164

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	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
Journal of Computational & Applied Research in Mechanical Engineering (JCARME)
مقاله 1، دوره 12، شماره 2 - شماره پیاپی 24، فروردین 2023، صفحه 133-143 اصل مقاله (657.52 K)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22061/jcarme.2022.8684.2164
نویسندگان
A. Amith Gadagi^* ؛ B. Chandrashekar Adake
Department of Mechanical Engineering, KLE Dr. M. S. Sheshgiri College of Engineering & Technology, Belagavi, Karnataka, 590008, India
تاریخ دریافت: 21 آذر 1400، تاریخ بازنگری: 22 شهریور 1401، تاریخ پذیرش: 02 مهر 1401
چکیده
In a turning process, it is essential to predict and choose appropriate process parameters to get a component’s proper surface roughness (R_a). In this paper, the prediction of R_a 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 L27 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 R_a. It is observed that the mean absolute error (MAE) of MRA, ANN and random forest for the training cases are found to be 39.33%, 0.56%, and 24.88%, respectively whereas for the test cases MAE is 54.34%, 2.59%, and 24.88% for MRA, ANN, and random forest, respectively.
کلیدواژه‌ها
Machining؛ Neural networks؛ Machine learning؛ Regression analysis

مراجع
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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