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Journal of Computational and Applied Research in Mechanical Engineering - JCARME، جلد ۱۲، شماره ۲، صفحات ۱۳۳-۱۴۳
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عنوان انگلیسی 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
چکیده انگلیسی مقاله 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 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 Ra. 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.
کلیدواژه‌های انگلیسی مقاله Machining, Neural Networks, Machine Learning models, Regression Analysis, DOE
نویسندگان مقاله Amith H Gadagi |
Department of Mechanical Engineering, KLE Dr. M. S. Sheshgiri College of Engineering and Technology, Belagavi, Karnataka, 590008, India

Chandrashekar Adake |
Department of Mechanical Engineering, KLE Dr. M. S. Sheshgiri College of Engineering and Technology, Belagavi, Karnataka, 590008, India

نشانی اینترنتی https://jcarme.sru.ac.ir/article_1782_68a1427045956427f37d823604103616.pdf
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