Optimization of WEDM parameters for Machining Inconel 800 by ANN based Bayesian hybrid algorithm. (2022)

• Record Type: Journal Article
• Authors: Choudhuri, Bikash; Sen, Ruma
• Is Part Of: Materials today. Volume 62:Part 2(2022)
• Page Start: 1098
• Page End: 1101
• Publication Date: 2022
• Subjects: WEDM -- Inconel 800 -- Bayesian optimization -- ANN; Materials science -- Congresses -- Periodicals; 620.1
• DOI: 10.1016/j.matpr.2022.04.318
• Languages: English
• ISSNs: 2214-7853

Abstract

Abstract: Superalloys like Inconel 800 are highly preferable in aerospace industries due to their property like high strength, high thermal resistivity, and non-corrosive behavior. Machining such difficult-to-machine material with high accuracy is still scope for research. In the present investigation, experiments have been done on Inconel 800 with the help of a wire electrical discharge machining process. The process parameter of this advanced machining must be optimized as they greatly influence the performance characteristics like cutting rate, surface roughness, and wire consumption rate. ANN-based Bayesian hybrid optimization algorithm was applied to determine the best parametric combination of the above machining process. The discharge spark produces porosities and other surface defects over the machined surface. The surface integrity of the optimum surface has been compared with initial experiments with the help of a scanning electron microscope.