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.


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    Titel :

    Smart Electrical Discharge Machining of Aerospace Alloys


    Weitere Titelangaben:

    Sustainable aviat.


    Beteiligte:
    Gürgen, Selim (Herausgeber:in) / Kumar, Namadi Vinod (Autor:in) / Abhilash, P. M. (Autor:in) / Chakradhar, D. (Autor:in)


    Erscheinungsdatum :

    09.08.2024


    Format / Umfang :

    22 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch