Artificial Neural Network Modelling to Optimize Micro-Drilling Parameters of ECDM of Developed Novel Zn/(Ag+Fe)-MMC

Open Access

Year : 2023 | Volume : 11 | Special Issue : 01 | Page : 01-13
By

    Inderjeet Singh Sandhu

  1. Research Scholar, Punjab Engineering College, Punjab, India

Abstract

Several engineering fields have increased their use of metal matrix composites (MMCs) in the past few years. Due to the increase in composites, the demand for accurate machining has also become important. Specifically, pertaining to biomaterial applications, accuracy factor with desired surface finish is critical. While the near-net shape manufacturing process has advanced, MMCs frequently require post-mould machining to achieve surface quality, and dimensional tolerances. In the present study, a zinc MMC (ZMMC) is fabricated and its mechanical properties are tested. Taguchi’s orthogonal array (L18) is used to determine the machineability of novel Zn/(Ag+Fe)-MMC. As part of the current work, an artificial neural network (ANN) is implemented to model and optimize materials removal rate (MRR), overcut (Oc), and tool wear (TW) during electrochemical discharge machining (ECDM) of novel Zn/(Ag+Fe)-MMC. In order to obtain the response/output values, ECDM microdrilling experiments were conducted under different input control factors such as pulse-on-time, current, pulse-off-time, and feed rate. It identified that 4-16-3-3 was the best architecture for the ANN model. The root mean square error (RMSE) from the optimization model was used to evaluate performance. Based on regression coefficients between experimental and model predictions and the correlation coefficient (R-value) between the ANN predictions and experimental results, the performance of the model was evaluated. The overall R-index was assessed as 0.98722. During the experiment it was found that training, validation and testing results are 98.782%, 98.122% and 98.505%, respectively. ANN modelling and prediction analysis succeeded in replacing conventional method of regression analysis in field of machining hybrid materials.

Keywords: Artificial neural network, micro-drilling, optimization, metal matrix composite, electrochemical discharge machining, correlation coefficient, prediction

This article belongs to Special Issue Conference International Conference on Innovative, Sustainable Materials and Technologies (ICISMT-2022)

How to cite this article: Inderjeet Singh Sandhu Artificial Neural Network Modelling to Optimize Micro-Drilling Parameters of ECDM of Developed Novel Zn/(Ag+Fe)-MMC jopc 2023; 11:01-13
How to cite this URL: Inderjeet Singh Sandhu Artificial Neural Network Modelling to Optimize Micro-Drilling Parameters of ECDM of Developed Novel Zn/(Ag+Fe)-MMC jopc 2023 {cited 2023 Jun 19};11:01-13. Available from: https://journals.stmjournals.com/jopc/article=2023/view=111500

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Conference Open Access Original Research
Volume 11
Special Issue 01
Received December 21, 2022
Accepted March 19, 2023
Published June 19, 2023