AI AND METALLURGY: FAILURE PREDICTION AND PRODUCTION CHAIN OPTIMIZATION

Resumen

The integration of Artificial Intelligence (AI) into the metallurgical industry has significantly improved efficiency, reliability, and sustainability. AI-driven predictive maintenance enables early failure detection by analyzing vast datasets from sensors and operational logs, allowing for proactive interventions that minimize downtime and reduce costs. In addition, AI-based process optimization enhances production chain efficiency by dynamically adjusting key parameters such as temperature, pressure, and material flow. These advancements contribute to higher product quality, energy savings, and overall process stability. Despite these benefits, challenges remain in fully integrating AI into metallurgical processes. The industry faces issues such as data scarcity, high initial investment costs, and resistance to technological change. Furthermore, the complexity of metallurgical reactions requires highly specialized AI models capable of handling intricate, nonlinear interactions. Addressing these challenges demands a collaborative approach involving industry experts, researchers, and policymakers to develop tailored AI solutions and improve data accessibility. This paper explores the latest developments in AI applications for failure prediction and production chain optimization in metallurgy. A comprehensive review of recent studies highlights key advancements, including AI-based predictive maintenance, digital twins, machine learning models for material behavior prediction, and real-time process control systems. The findings emphasize the transformative potential of AI in metallurgy, offering insights into practical implementation strategies. By overcoming existing barriers, AI can revolutionize metallurgical manufacturing, making it more efficient, cost-effective, and sustainable.