Predict the modelling of electro chemical machining parameters for AA5083/MoS2 composites using Levenberg–Marquardt algorithm

¹ Department of Mechanical Engineering, Shri Vishnu Engineering College for Women, Bhimavaram.
² Department of Mechanical Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai.
³ Department of Mechanical engineering, Rajalakshmi Institute of technology, Chennai
⁴ Department of Mechanical Engineering, Bonam Venkata Chalamayya Engineering College (Autonomous), Odalarevu, Andhra Pradesh 533210, India.
⁵ Department of Mechanical Engineering , Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad.
⁶ University School of Social Sciences, Research & Incubation Centre, Research & Incubation Centre, Rayat Bahra University, Chandigarh-Ropar NH 205, Greater Mohali, Punjab, 140103, India
⁷ University Research Department, Bahra University, Waknaghat, Distt. Solan, HP-173234, India

^* Corresponding author: srinivasaraon@svecw.edu.in

Abstract

ECM is widely regarded as a highly promising and cost-effective manufacturing technique, especially for processing hard-to-machine materials that are challenging to shape using conventional methods. The machining operations were carried out using an ECM machine with a working voltage range of 0.6 to 1.0 V and a feed rate between 15 and 25 mm/min. A copper electrode was employed alongside an NaCl electrolyte solution for calculating material removal rate on AA5083/MoS2 composites. The Highest MRR is observed when voltage 1.0 V, feed rate 25 mm/min and Electrolyte Concentration 400 g/Lit. To improve the accuracy of the predicted output responses, an artificial neural network (ANN) model was designed using the Levenberg-Marquardt algorithm. The structure with a configuration of 3–10–1, confirmed strong regression fit outcomes, The overall correlation coefficients (R) calculated at 0.96348, confirmed a high level of consistency between the experimental data and the predicted value.