Machine learning and AI driven digital twins in manufacturing o 2026 Next Ma

The advancements in manufacturing have been revolutionized by the implementation of machine learning (ML) and Artificial Intelligence (AI) approaches along with Digital Twins (DTs). This paper aims to provide an overview of how ML and AI-driven DTs enhance the manufacturing of alloys and compounds, focusing on intelligent monitoring, predictive maintenance, and data processing systems. The methodology includes a comprehensive analysis of novel advancements in ML and AI-based DT applications, discussing recent achievements from various research works. The results indicate that effective approaches for optimal defect reduction, predictive modeling of material properties, big data analysis, and optimized alloy production can be achieved through ML and AI-driven DTs. The review elaborates on the applications and challenges faced during alloy manufacturing, with insights on reducing processing costs, increasing alloy quality, optimizing production, and enhancing performance under working conditions. Furthermore, the study presents guidelines for future research, advocating for novel ideas such as explainable AI models and combined AI and ML models to further improve sustainability and productivity while minimizing waste and power consumption during alloy production.