Abstract
The metallurgical industry has historically relied on heavy machinery, manual labor, and conventional process optimization techniques. However, with the advent of artificial intelligence (AI), the sector is undergoing an unprecedented transformation. This paper explores the integration of AI-driven smart automation in metallurgy, examining its impact on efficiency, sustainability, and economic viability. AI algorithms enhance predictive maintenance, defect detection, and quality control, reducing material waste and operational downtime. Machine learning (ML) models improve alloy composition prediction and process parameters, ensuring greater consistency and performance in metallurgical applications. Furthermore, the incorporation of AI-driven robotics minimizes human exposure to hazardous environments, increasing workplace safety. This study also highlights the challenges associated with AI adoption, including high initial investment costs, the need for skilled personnel, and potential ethical concerns related to automation replacing human jobs. The literature review evaluates six recent studies that demonstrate the effectiveness of AI applications in various metallurgical processes. The findings suggest that AI-driven automation significantly enhances productivity, reduces environmental impact, and contributes to the sustainability of metal production. This paper concludes that despite certain challenges, AI's role in metallurgy is indispensable for future industrial advancements, promoting a more efficient and sustainable metallurgical landscape.
Keywords
AI-driven automation, Metallurgical industry, Machine learning models, Sustainability in metal production, Workplace safety in metallurgy.
Citations
IRE Journals:
Richardson Cau
"Smart Automation in Metallurgy: How AI Is Revolutionizing the Metallurgical Industry" Iconic Research And Engineering Journals Volume 8 Issue 10 2025 Page 218-221
IEEE:
Richardson Cau
"Smart Automation in Metallurgy: How AI Is Revolutionizing the Metallurgical Industry" Iconic Research And Engineering Journals, 8(10)
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