Smart electrical discharge machining of aerospace alloys

Vinod Kumar, Namadi and Abhilash, P.M. and Chakradhar, D.; Gürgen, Selim, ed. (2024) Smart electrical discharge machining of aerospace alloys. In: Advances in Manufacturing for Aerospace Alloys. Sustainable Aviation . Springer, Cham, pp. 77-98. ISBN 9783031644559 (https://doi.org/10.1007/978-3-031-64455-9_4)

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Abstract

Smart electrical discharge machining (EDM), the integration of artificial intelligence (AI) into EDM in aerospace engineering, signifies a transformative leap that offers unprecedented precision, efficiency, and adaptability. This integration involves learning algorithms, real-time decision-making, predictive insights, and adaptive optimization and fostering enhanced precision, efficiency overhaul, cost reduction, and versatility in complexity. Notable studies showcase the evolution of AI-driven smart EDM, utilizing techniques such as artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS), convolutional neural networks (CNN), deep neural networks (DNN), and support vector regression (SVR), for optimizing critical parameters in aerospace alloy machining. A comprehensive case study on smart wire EDM workpiece height estimation highlights the experimental validation of AI integration, employing CNN for accurate predictions. Results indicate an overall accuracy of 86% in estimating workpiece height across different classes. Future prospects include exploring alternative sensors, real-time monitoring, and multisensor fusion, promising to elevate precision manufacturing in aerospace engineering. In conclusion, smart EDM, with the integration of AI, represents technological convergence and charts a transformative journey that reshapes the landscape of aerospace alloy machining with adaptability and flexibility.

ORCID iDs

Vinod Kumar, Namadi, Abhilash, P.M. ORCID logoORCID: https://orcid.org/0000-0001-5655-6196 and Chakradhar, D.; Gürgen, Selim
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