ANN modeling of the significance of constraints in turning superalloys using coated PCBN tools. (2022)

• Record Type: Journal Article
• Authors: Veeranaath, V.; Nandana Mohanty, Marut; Kumar, Aniket; Kumar, Pranjal
• Is Part Of: Materials today. Volume 65:Part 1(2022)
• Page Start: 20
• Page End: 28
• Publication Date: 2022
• Subjects: Orthogonal machining -- Ti6Al4V -- PCBN -- Taguchi Analysis -- ANN Modelling; Materials science -- Congresses -- Periodicals; 620.1
• DOI: 10.1016/j.matpr.2022.03.559
• Languages: English
• ISSNs: 2214-7853

Abstract

Abstract: In the current scenario, superalloys find a considerable volume of attention in aerospace and automobile fields, owing to their astonishing hardness and rigidity. The main concern with these superalloys is that these alloys are difficult to cut material because of their properties which may yield less insert life and increased tool wear. So, special tools need to be employed to overcome these problems, or else cooling fluid has to be used to decrease the temperature but usage of such fluids may degrade the environment. Hence, this paper is chiefly focused on machining the superalloy Ti6Al4V using TiN coated PCN insert by varying the three processing constraints in five levels via the L25 Taguchi array. The upshot of these constraints on the yield responses using Taguchi Analysis and optimal conditions are also determined using S/N plots of the analysis. The surface finish and insert weariness were analyzed as the output responses of the process. The ANN model is considered one of the best tools for simulation of cutting process and optimizing multiple responses system. Thus, in this article, the ANN model is developed using a controlled learning method to foresee the output responses based on the input factors. The training of the model is performed using the experimental data and the developed model is also tested and validated.