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Gadagi, Amith, Adake, Chandrashekar. (1401). 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. فناوری آموزش, (), -. doi: 10.22061/jcarme.2022.8684.2164
Amith H Gadagi; Chandrashekar Adake. "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". فناوری آموزش, , , 1401, -. doi: 10.22061/jcarme.2022.8684.2164
Gadagi, Amith, Adake, Chandrashekar. (1401). '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', فناوری آموزش, (), pp. -. doi: 10.22061/jcarme.2022.8684.2164
Gadagi, Amith, Adake, Chandrashekar. 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. فناوری آموزش, 1401; (): -. doi: 10.22061/jcarme.2022.8684.2164

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)
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 02 مهر 1401  XML اصل مقاله (599.81 K)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22061/jcarme.2022.8684.2164
نویسندگان
Amith H Gadagi email ؛ Chandrashekar Adake
Department of Mechanical Engineering, KLE Dr. M. S. Sheshgiri College of Engineering and Technology, Belagavi, Karnataka, 590008, India
تاریخ دریافت: 21 آذر 1400،  تاریخ بازنگری: 22 شهریور 1401،  تاریخ پذیرش: 02 مهر 1401 
چکیده
In any machining process, it is important to predict and select the appropriate process parameters to get the proper Surface roughness (Ra) of a component. In this paper, the prediction of Ra through Multiple regression Analysis (mra), Artificial neural network (ann) and Random forest method (Machine Learning) are done and compared. Using the process parameters namely spindle speed, feed and depth of cut, the turning of the GFRP composites was carried out on a conventional lathe using single point HSS cutting tool with brazed carbide tip. Surface Roughness of the turned components is measured experimentally using Talysurf method. By employing the Taguchi's L27 array (3 Level), the experiments were conducted and the experimental results were utilized in the development of mra, ann and Random forest method models for the prediction of Surface roughness (Ra). It is observed that 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 are 54.34%, 2.59% and 24.88% for mra, ann and Random forest respectively.
کلیدواژه‌ها
Machining؛ Neural Networks؛ Machine Learning models؛ Regression Analysis؛ DOE
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