Diametral deviation and tool wear analysis in EDM of Fe-based shape memory alloy: An experimental study with microstructural analysis and advanced optimization. (December 2022)

• Record Type: Journal Article
• Authors: Singh, Ranjit; Singh, Ravi Pratap; Trehan, Rajeev
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 236:Number 23(2022)
• Page Start: 11282
• Page End: 11303
• Publication Date: 2022-12
• Subjects: Shape memory alloy (SMA) -- electrical discharge machining (EDM) -- tool wear rate (TWR) -- diametral deviation (DD) -- scanning electron microscope (SEM) -- Genetic Algorithm (GA) -- teacher learning based optimization (TLBO); Mechanical engineering -- Periodicals; 621.05
• DOI: 10.1177/09544062221113258
• Languages: English
• ISSNs: 0954-4062

Abstract

Shape memory alloys (SMAs) are an excellent choice for industrial applications such as orthopedic implacers, actuators, micro-tools, fitting and screening elements, aircraft components, military instruments, fabricating elements, and bio-medical devices, among others. Despite their remarkable characteristics, effective production of these alloys continues to be a problem for researchers worldwide. This paper has been aimed to examine the considered machining responses that is, tool wear rate (TWR) and diametral deviation (DD) in electrical discharge machining (EDM) of Fe-based shape memory alloy (SMA) using a Cu-electrode under the varying settings of input factors namely as; pulse on time (Ton ), pulse off time (Toff ), peak current (Ip ), and gap voltage (GV). The central composite design matrix has been employed for planning the main runs. The experimental results reflect the lowest and highest for TWR as; 0.0694 and 0.1357 g/min; and for DD as; 0.02 and 0.19 mm, respectively. The microstructure analysis of the EDMed work samples and electrode surfaces have also been conducted using scanning electron microscopy (SEM). The micrographs exposed the development of debris, craters, micro-cracks, and recast layer creation on the workpiece surface and electrode tool as well. The creation of debris develops as a result of large spark energy at the work piece-tool contact as a result of a high peak current and long pulse on time. Furthermore, single and multi-objective optimization of investigated responses (i.e. TWR and DD) were tried using the desirability approach, Genetic Algorithm (GA), and teacher learning based optimization (TLBO), techniques. The obtained multi-response optimized values for TWR and DD by using desirability approach, Genetic algorithm (GA), and Teacher Learning Based Optimization (TLBO) are as; 0.0815 g/min and 0.0527 mm; 0.1529 g/min and 0.2547 mm; 0.1675 g/min and 0.2533 mm., correspondingly.