Predicton of MRR & Surface Roughness in Wire EDM Machining using Decision Tree and Naive Bayes Algorithm

• Published in: 2021 International Conference on Emerging Smart Computing and Informatics (ESCI) pp. 527 - 532
• Main Authors: L, Yogesh, Arunadevi, M, Prakash, C P S
• Format: Conference Proceeding
• Language: English
• Published: IEEE 05.03.2021
• Subjects: Decision Tree Algorithm; Decision trees; EDM; Machine Learning; Machine learning algorithms; Machining; Naϊve Bayes Algorithm; Optimization; Prediction algorithms; Rough surfaces; Surface roughness; Surface treatment
• DOI: 10.1109/ESCI50559.2021.9396857

Manufacturing industries facing problem in optimal selection of process parameters in machining process. Finding optimum process parameters for achieving maximum Material Removal Rate and minimum Surface Roughness is a challenging task and it requires lot of time and energy for experimentation trails or experience. It wastes lot of resources and money, sometimes ends up with negative results. To overcome the above issue, this paper presents an algorithm for prediction of Surface Roughness and Material Removal rate using Decision Tree Algorithm and Naive Bayes Algorithm without experimentation. Lot of resources and time can be saved using these machine learning algorithms. In this paper, Material removal rate and Surface roughness of EDM machining of Aluminum composites is predicted using Decision tree algorithm and Naive Bayes algorithm. Then the model can be used to predict the Material Removal Rate and Surface finish of any combination process parameters before machining process.