Parametric optimization and process capability analysis for machining of nickel-based superalloy

• DOI: 10.1007/s00170-019-03453-3
• Publication Date: 2019-03-04T00:26:18Z
• Authors: Munish Kumar Gupta; Mozammel Mia; Catalin I. Pruncu; Wojciech Kapłonek; Krzysztof Nadolny; Karali Patra; Tadeusz Mikolajczyk; Daniil Yu. Pimenov; Murat Sarikaya; Vishal S. Sharma
• Keywords: Inconel; Machinability

Abstract The manufacturing of parts from nickel-based superalloy, such as Inconel-800 alloy, represents a challenging task for industrial sites. Their performances can be enhanced by using smart cutting fluid approach considered sustainable alternative. Further, to innovate the cooling strategy, researchers proposed an improved strategy based on minimum quantity lubrication (MQL). It has advantage over flood because it allows better control its parameters (i.e., compressed air, fluid). In this study, machinability superalloy been investigated performing different turning tests under MQL conditions, where no previous data are available. To reduce numerous numbers tests, target objective was applied. This used in combination with response surface methodology (RSM) while assuming force input ( F c ), potential tool wear (VB max roughness R and length tool–chip contact L ) responses. Thereafter, analysis variance (ANOVA) embedded detect significance model understand influence each process parameter. optimize other speed machining, feed rate, side edge angle (cutting angle)), two advanced optimization algorithms were introduced particle swarm (PSO) along teaching learning-based (TLBO) approach). Both proved highly effective predicting machining responses, PSO being concluded best amongst two. Also, comparison methods made, found technique when compared dry cooling.