The role of Artificial Intelligence for Process Optimization in Microwave-Assisted Casting Toward Enhanced Quality and Sustainability
DOI:
https://doi.org/10.62431/5r7zf693Keywords:
Artificial intelligence (AI), Energy efficient manufacturing, Microwave assisted casting (MAC), Process optimization, Sustainable manufacturingAbstract
As a novel non-traditional manufacturing route, Microwave-assisted casting (MAC) is gaining traction as a sustainable and efficient substitute for conventional casting techniques. The process uses microwave radiation to enable rapid, volumetric heating of materials, thereby reducing processing time and improving energy efficiency. Despite these benefits, industrial adoption is constrained by challenges in accurate temperature measurement and control, achieving melt homogeneity, preventing oxidation during processing, minimizing defect formation, and addressing scalability issues. Artificial Intelligence (AI) techniques, including machine learning and neural network models, provide viable solutions to these barriers. AI allows comprehensive analysis of critical interacting parameters such as microwave power, susceptor type, heating rate, material characteristics, and mold conditions. When AI is integrated with microwave-assisted casting systems, cast component quality improves while energy consumption and material waste decrease. Intelligent control strategies further permit dynamic regulation of microwave power and temperature profiles, ensuring uniform melting and consistent solidification behavior. AI also advances digital manufacturing by enabling predictive maintenance and real time, data-driven decision-making. The synergy between AI and microwave-assisted casting therefore holds strong potential to enhance process efficiency, product reliability, and overall sustainability in advanced manufacturing environments.
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