Jan 1, 2022 · The attempt has been made to experimentally investigate the impact of the selected EDM process inputs and to optimize the considered responses by using the machine learning based algorithms such as; genetic algorithm (GA), and TLBO technique.

Aug 1, 2024 · In our study, we focus on cryogenically treated mold steel electrodes to investigate the potential of different machine learning algorithms to predict EDM wear.

Apr 11, 2023 · The techniques covered include statistical prediction models, Machine Learning (ML)-based prediction models, theoretical models, experimental models and thermal-electrical models using FEM developed by researchers.

Nov 1, 2024 · The effect of process parameters, including pulse on time, pulse off time, peak current, and gap voltage on tool wear rate and dimensional deviation, is calculated using advanced machine learning methods like Genetic Algorithms, Particle Swarm Optimization (PSO), and other meta-heuristics approaches.

This study optimizes EDM parameters for Inconel 718 using machine learning (ML) techniques. By leveraging ML algorithms, we aim to identify optimal parameters to improve material removal rates and machining efficiency.

Outcome of EDM operation is strongly influenced by various process parameters. The paper presents a framework based on machine learning algorithms to analyze the relationship between input process parameters and EDM response to build a predictive model of EDM operations.

This repository delves into the realm of energy efficiency optimization in manufacturing processes through the lens of machine learning. With its capacity to analyze extensive datasets and unveil valuable insights, machine learning emerges as a beacon of hope in this domain.

In this study, a machine learning (ML) based pulse classification based on the extracted discharge characteristics is proposed. The features are extracted from the raw voltage and current senor signals collected from the machining zone during the wire EDM operation.

This study attempts to make a model and optimize the complicated Electrical Discharge Machining (EDM) process using soft computing techniques. Artificial Neural Network (ANN) with back propagation algorithm is used to model the process.

Dec 6, 2021 · In this way, the current study achieves two goals: first, it proposes a predictive framework for determining the performance features of WEDM for unknown design points, and second, it reports...