Multi-Response Optimization of Electrochemical Machining Parameters for Inconel 718 via RSM and MOGA-ANN

Author:

Saha Subhadeep1,Mondal Arpan Kumar1,Čep Robert2ORCID,Joardar Hillol3,Haldar Barun4ORCID,Kumar Ajay5ORCID,Alsalah Naser A.6ORCID,Ataya Sabbah4ORCID

Affiliation:

1. Department of Mechanical Engineering, National Institute of Technical Teachers Training and Research (NITTTR), Kolkata 700106, India

2. Department of Machining, Assembly and Engineering Metrology, Faculty of Mechanical Engineering, VSB-Technical University of Ostrava, 70800 Ostrava, Czech Republic

3. Department of Mechanical engineering, CV Raman Global University, Bhubaneswar 752054, India

4. Department of Mechanical Engineering, College of Engineering, Imam Mohammad Ibn Saud Islamic University, Riyadh 11432, Saudi Arabia

5. Department of Mechanical Engineering, School of Engineering and Technology, JECRC University, Jaipur 303905, India

6. Department of Industrial Engineering, College of Engineering, Imam Mohammad Ibn Saud Islamic University, Riyadh 11432, Saudi Arabia

Abstract

Inconel 718’s exceptional strength and corrosion resistance make it a versatile superalloy widely adopted in diverse industries, attesting to its reliability. Electrochemical machining (ECM) further enhances its suitability for intricate part fabrication, ensuring complex shapes, dimensional accuracy, stress-free results, and minimal thermal damage. Thus, this research endeavors to conduct a novel investigation into the electrochemical machining (ECM) of the superalloy Inconel 718. The study focuses on unraveling the intricate influence of key input process parameters—namely, electrolytic concentration, tool feed rate, and voltage—on critical response variables such as surface roughness (SR), material removal rate (MRR), and radial overcut (RO) in the machining process. The powerful tool, response surface methodology (RSM), is used for understanding and optimizing complex systems by developing mathematical models that describe the relationships between input and response variables. Under a 95% confidence level, analysis of variance (ANOVA) suggests that electrolyte concentration, voltage, and tool feed rate are the most important factors influencing the response characteristics. Moreover, the incorporation of ANN modeling and the MOGA-ANN optimization algorithm introduces a novel and comprehensive approach to determining the optimal machining parameters. It considers multiple objectives simultaneously, considering the trade-offs between them, and provides a set of solutions that achieve the desired balance between MRR, SR, and RO. Confirmation experiments are carried out, and the absolute percentage errors between experimental and optimized values are assessed. The detailed surface topography and elemental mapping were performed using a scanning electron microscope (SEM). The nano/micro particles of Inconel 718 metal powder, obtained from ECM sludge/cakes, along with the released hydrogen byproducts, offer promising opportunities for recycling and various applications. These materials can be effectively utilized in powder metallurgy products, leading to enhanced cost efficiency.

Funder

the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University

Publisher

MDPI AG

Reference44 articles.

1. Bhadeshia, H.K.D.H. (2023, October 07). Nickel Based Superalloys. Available online: https://www.phase-trans.msm.cam.ac.uk/2003/Superalloys/superalloys.html.

2. Machining nickel base superalloys: Inconel 718;Choudhury;Proc. Inst. Mech. Eng. Part B J. Eng. Manuf.,1998

3. Experimental research on the Electrochemical Machining of modern titanium- and nickel-based alloys for aero engine components;Klocke;Procedia CIRP,2013

4. Exploratory study of wire based ECM finishing of 316L stainless steel, implemented within a hybrid wire EDM-ECM platform;Qian;Procedia CIRP,2022

5. (1989). ASM Handbook Volume 16: Machining, ASM International.

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