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.

May 20, 2023 · This paper aims to optimize the various process inputs in the wire electrical discharge machining of Ti-alloy, Ti-6Al-7Nb, using machine learning (ML) algorithms. The present investigations report on examining the behavior of response factors with changes to the machine-controllable parameters.

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.

Jan 22, 2025 · One of the non-conventional machining processes is wire-cut electrical discharge machining (wire-cut EDM), which has emerged as a simple means of machining difficult-to-machine electric conductive materials like steels. It is an electro-thermal machining process capable of machining complex geometries and sharp edges with varying hardness.

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.

Dec 6, 2021 · To this end, we used the practically relevant noisy experimental dataset to construct the four different machine learning (ML) models (linear regression, regression trees, support vector machines, and Gaussian process regression) and compared their goodness of fit based on the corresponding R2 and RMSE values.

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.

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 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.

Educational Data Mining (EDM) is the fields where various techniques and tools are used within Data Mining domain for development manage analysis and extract information from the historical or present data of student. EDM is one of the emerging and exploring topics of Data Mining field.