Integrating Machine Learning and Mathematical Programming for Optimisation of Electric Discharge of Machining Techniques

Aug 3, 2026

Upload by Aurora

This study explores how combining machine guidance with mathematical modelling and machine learning can improve precision and effectiveness in electric-discharge machining (EDM). It reviews quality control, energy efficiency, sustainability, optimisation-oriented modelling, and data-driven learning methods for EDM. The research identifies gaps in current scientific knowledge and proposes a pathway toward advanced EDM methods. Expected outcomes include enhanced material removal, improved energy efficiency, and overall process performance, supporting future work and more sustainable manufacturing practice.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages8

Reading Time20 min

AccessFree to download

Introduction

Project Specification

Aim and Objectives

Research Rationale

Literature Review

Machine Learning Applications in EDM Optimization

Mathematical Programming in EDM Process

Integrating Machine Learning and Mathematical Programming for Optimisation of Electric Discharge of Machining Techniques Ritu Gupta1 Former Associate Professor, Department of Applied Science and Humanities, Dr Akhilesh Das Gupta Institute of Technology and Management, New Delhi, India. Mail id: [r26jan@gmail.com](r26jan@gmail.com) Anshul Srivastava2 Associate Professor, School of Business, Galgotias University, Greater Noida, Uttar Pradesh, India\ Samreen Naqvi3 Assistant Professor, Department of Liberal Education Era University, Lucknow, Uttar Pradesh, India Tarun Lata Gupta4 Assistant Professor, Department of Applied Science and Humanities, Dr Akhilesh Das Gupta Institute of Technology and Management, New Delhi, India. Abstract: This study explores the combination of machine guidance and several developing approaches to enhance both precision and effectiveness during Electricity-discharged Machining (EDM) business operations. The studies on quality control, energy efficiency, sustainable development, mathematical modelling within EDM optimization, and machine learning applications in EDM optimisation are all examined in this study. It highlights significant gaps in scientific knowledge, providing a pathway for the development of state-of-the-art EDM methods. The outcomes show that material decrease, energy efficiency, along EDM technique optimisation can all be enhanced. This study offers valuable information for future research within the field and contributes to the ongoing conversation about advanced manufacturing techniques. This project intends to revolutionise EDM by merging mathematical programming and machine learning. Three primary topics are investigated machining parameter optimisation, efficiency improvement using machine learning and environmental effect assessment. The goals of the study are met by using the deductive method, which gives a formal setting in which to examine hypotheses. Descriptive research designs allow for in-depth analyses of previously published works, mathematical models and automated learning programs. Finding commonalities and trends in qualitative data is the goal of the thematic data analysis technique. The results of this study provide useful resources, standards and sustainable perspectives for enhancing EDM procedures in manufacturing settings. Keywords: EDM, Neural Network, ML, Extraction rate, Process optimization I. INTRODUCTION A. Project Specification The primary objective of the project is to combine machine learning and programming mathematics to optimize electrically terminated machining (EDM) processes [1] . This study manages the need to grow the effectiveness as well as efficiency regarding the EDM methodology by carefully looking at the relevant variables charming the operation of machining [2] . The aim is to develop a sweeping framework that utilizes mathematical optimization techniques in conjunction with datadriven artificial intelligence algorithms to ascertain the optimal EDM parameter settings [3] . Improving substance extraction rate, device wear, and overall EDM process efficiency are the main objectives of the research. Ultimately, the goal of this research effort is to provide insightful analysis and practical solutions that help the manufacturing industry. B. Aim and Objectives Aim The major goal of this analysis is to combine computational math as well as machine learning to enhance the effectiveness and efficiency of electrical Discharge Machining (EDM) guidelines. Objectives ● Examine the body of research on programming in mathematics and neural networks in relation to EDM improvement. ● Create models using mathematical programming to maximize the machining parameters. ● Analyze how well the combined strategy works to achieve increased sustainability and EDM efficiency. ● Provide useful guidelines for use in industry and make recommendations for directions to pursue further research in this area. C.

FAQs

What is the main objective of the project described in the document?

The document aims to integrate machine learning with mathematical programming to optimize electric-discharge machining (EDM) processes by determining optimal machining parameter settings.

Which outcomes are targeted by the optimisation approach?

The main objectives include improving material extraction rate, reducing tool/device wear, enhancing overall EDM efficiency, and supporting sustainability through energy and waste improvements.

How does the document explain machine learning usage in EDM optimisation?

It states that machine learning automates the selection of machining settings and uses models such as regression and neural networks to forecast outcomes like surface finish, corrosion/tool wear rate, and material removed (MRR).

Aug 3, 2026

Upload by Aurora