Performance Enhancement of CNC Milling Process using Different Machine Learning Techniques
Authors
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Department of Mechanical Engineering, Dayananda Sagar College of Engineering, Bengaluru-78, Karnataka ,IN
M. Arunadevi -
BE-Student, Department of Mechanical Engineering, Dayananda Sagar College of Engineering, Bengaluru-78, Karnataka ,INShreeram P. B.
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BE-Student, Department of Mechanical Engineering, Dayananda Sagar College of Engineering, Bengaluru-78, Karnataka ,INThanoj Kumar K.
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BE-Student, Department of Mechanical Engineering, Dayananda Sagar College of Engineering, Bengaluru-78, Karnataka ,INUday M. Gowda
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Lecturer, Department of Mechanical Engineering, Govt. Polytechnic, Kustagi - 583277 ,INDeepika C.
DOI:
https://doi.org/10.18311/jmmf/2023/33377Keywords:
ANN, KNN, Linear RegressionAbstract
Towards the requirement of technological development, it is necessary to optimize the machining parameters in the manufacturing process. Nowadays machine learning algorithms have proven their potential towards a performance enhancement process compared to conventional methods. In the present study, the different machine learning methods such as Linear Regression, Decision tree algorithm, ANN are used for prediction of process parameters of CNC milling process. Performance Enhancement can be achieved through predicting the proper combination of cutting speed, feed and depth of cut with the objectives of Minimum machining time, maximum material removal rate, minimum surface roughness and maximum tool life. Mathematical models are developed using a regression tool in MINITAB. Modelling of machine learning algorithms is done using python codes. Comparison of different machine learning algorithms are done to select the best optimization tool for this process enhancement procedure.
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
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