A Machine Learning Approach to Optimize, Model, and Predict the Machining Factors in Dry Drilling of Nimonic C263. (16th June 2022)

• Record Type: Journal Article
• Authors: Lakshmana Kumar, S.; Jacintha, V.; Mahendran, A.; Bommi, R. M.; Nagaraj, M.; Kandasamy, Umamahesawari
• Other Names: Vijayan V. Academic Editor.
• Is Part Of: Advances in materials science and engineering. Volume 2022(2022)
• Publication Date: 2022-06-16
• Subjects: Materials science -- Periodicals; Materials science; Periodicals; 620.11
• DOI: 10.1155/2022/4856089
• Languages: English
• ISSNs: 1687-8434

Abstract

Abstract : In this present paper, the machine learning approach is used to optimize, model, and predict the factors during drilling Nimonic C263 under dry mode. Nimonic C263 is tough to machine aero alloys, and it is required to find a predictive model and to optimize the factors in drilling this alloy before the actual machining process. It helps to avoid the actual machining cost and material cost. Experimental trails are planned based on Taguchi analysis, and L27 orthogonal array was chosen. Speed, feed, and approach angle of drill were considered as controlling factors, and cutting force and surface roughness were considered as responses. The feed forward neural network (FFNN) was used to develop a predictive model. The prediction capability was validated with a predictive model developed by Taguchi analysis. Furthermore, ANOVA (analysis of variance) analysis was done to find out the most influence factor on the responses.