Review on applications of machine learning for defect prediction and optimization in foundry processes
DOI:
https://doi.org/10.58368/MTT.24.1-2.2025.1-11Keywords:
Machine Learning, Casting Defects, Process Optimization, Industry 4.0, Defect PredictionAbstract
This review examines recent advancements in the application of machine learning (ML) techniques in foundry operations to predict and mitigate casting defects. Foundries face persistent challenges such as micro-shrinkages, porosity, and mechanical property deviations, which compromise product quality and increase costs. By integrating ML models like neural networks, decision trees, and ensemble methods, researchers have made significant strides in defect prediction, process optimization, and decision-making frameworks. This paper synthesizes methodologies, findings, and future directions from studies addressing defect classification, process parameter optimization, and the integration of Industry 4.0 technologies into casting operations.
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