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This review paper provides a comprehensive investigation on problems encountered in casting practices, integrating insights from diverse research areas within the field of sand casting along with the most probable and effective remedies of those problems. The study revolves around three main themes: the optimization of sand-casting processes, the selection of binders for sand-casting, and the transformative impact of artificial intelligence (AI) and advanced control systems in foundry and steel casting processes. Our paper also highlights the significance of both digital and manual optimization techniques in sand casting processes, emphasizing the transformative impact of AI technologies like Artificial Neural Networks (ANN), Fuzzy Logic, and Genetic Algorithms for enhancing efficiency, accuracy, and automation. These digital methods enable data-driven decision-making and continuous process improvement, leading to higher quality and reduced defects. Meanwhile, manual optimization, relying on the expertise and adaptability of foundry professionals, complements digital approaches by providing hands-on problem-solving and flexibility in complex scenarios. Manual optimization in sand casting involves hands-on activities that leverage the expertise and experience of foundry workers. Examples include adjusting the mold packing density to ensure uniformity, tuning the sand mixture composition for optimal mold strength and permeability, use of the binders to enhance the ratio of sand mixtures and placing sprues and risers to control metal flow and minimize defects. Together, these approaches enhance casting quality, reduce costs, promote sustainability, and maintain industry competitiveness, offering a comprehensive framework for modernizing sand casting. The main objective of this study is to offer a comprehensive understanding of how these areas converge to reshape and elevate the quality, efficiency, and sustainability of casting processes. The first section explores the optimization of sand-casting processes, that focus on critical factors such as mold types, molding sand composition, and process parameters. By identifying common grounds, including the influence of mold type on mechanical properties and the optimization of molding sand composition for reduced defects, this segment provides practical insights for achieving high-quality sand castings. The second section explores the choice of binder for sand molding, combining research from different studies that examined different natural and synthetic binders. Key findings, such as the optimization of binder content for specific casting requirements and the potential of natural binders as eco-friendly alternatives, offer guidance for practitioners seeking to enhance sand mold properties and overall casting quality. The third and final part explores the application of AI and advanced control systems in foundry and steel casting. The review highlights the transformative impact of AI techniques, including neural networks, fuzzy logic, genetic algorithms, and particle swarm optimization. These AI-driven approaches optimize customer order management, enhance supply chain efficiency, and improve green sand properties, contributing to the realization of Industry 4.0 concepts in casting processes. [ABSTRACT FROM AUTHOR] |
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