A Critical review on Artificial Intelligence in EDM: Advancements in Process Optimization, Monitoring, and Predictive Control
DOI:
https://doi.org/10.37255/jme.v21i1pp001-005Keywords:
Electrical Discharge Machining, Artificial Intelligence, Machine Learning, Neural Networks, Fuzzy Logic, Parameter OptimizationAbstract
Electrical Discharge Machining (EDM) is a widely used non-traditional machining process that removes material from a workpiece by a series of repetitive discharges. EDM is widely used for machining hard, brittle, and heat-resistant materials, often achieving high precision. The EDM process is influenced by various factors, including discharge energy, pulse duration, and electrode wear. These factors can be challenging to optimize due to their interdependencies and the highly nonlinear behaviour of the process. Traditionally, the optimization process has been conducted through trial and error or operator expertise, both of which are time-consuming and costly. Artificial Intelligence (AI), including machine learning (ML), neural networks (NN), fuzzy logic, and real-time process monitoring, offers powerful tools to automate and optimize these processes. This paper explores how AI is applied in EDM for process optimization, quality enhancement, predictive modeling, and real-time adjustments, with a focus on practical applications, methodologies, and challenges.
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