© Department of Materials Science and Technology, IIT Delhi
Materials Design and Process Optimization using AI/ML
Dr . Abhishek Kumar Thakur
Research & Development, Tata Steel Limited
Abstract
The integration of artificial intelligence and machine learning (AI/ML) in material design and process optimization offers transformative potential in metallurgical engineering, particularly in steel production. AI/ML models can be developed and applied across various sectors within the industry, enabling enhanced control and efficiency in steel processing. Leveraging physics-based, data-driven approaches, the developed model captures the effects of chemical composition and processing parameters. For example, by integrating thermodynamic and kinetic principles with extensive mill data, the AI/ML framework accurately predicts load requirements under varying processing conditions, accounting for microstructural evolution and mechanical property development. Furthermore, the model can be trained to simulate precipitation strengthening driven by microalloying elements like niobium, ensuring precise estimation of strength and ductility—critical for Advanced High Strength Steels (AHSS). Validation against industrial hot-strip mill data demonstrates high predictive accuracy, with load predictions within ±5% of measured values. This physics-informed AI/ML approach not only optimizes mill operations by reducing energy consumption and downtime but also enhances material design by linking process conditions to desired mechanical properties. The framework lays the groundwork for scalable, predictive tools in steel manufacturing, supporting the development of next-generation alloys with tailored performance.
Bio
Dr . Abhishek Kumar Thakur is a researcher at Tata Steel Limited with extensive expertise in metallurgical and materials engineering. He earned his bachelor’s degree in Metallurgical & Materials Engineering from NIT Jamshedpur , India. Following his undergraduate studies, he worked as a Senior Engineer in the Operations Department at Vedanta Limited, Goa, India. He later pursued a master’s degree in Metallurgical Engineering from IIT (BHU) Varanasi, India, and subsequently completed his Ph.D. in Materials Science & Engineering from the University of Arizona, Tucson, USA. Post-Ph.D., he worked at Freeport-McMoRan in Arizona, USA, contributing to innovative material solutions in the mining and metals sector . Currently, Dr . Thakur is a member of the Research and Development unit at Tata Steel in Jamshedpur , India, where he drives advancements in high-strength steels by leveraging AI/ML and thermodynamic modeling to optimize mechanical properties.
Abstract
The integration of artificial intelligence and machine learning (AI/ML) in material design and process optimization offers transformative potential in metallurgical engineering, particularly in steel production. AI/ML models can be developed and applied across various sectors within the industry, enabling enhanced control and efficiency in steel processing. Leveraging physics-based, data-driven approaches, the developed model captures the effects of chemical composition and processing parameters. For example, by integrating thermodynamic and kinetic principles with extensive mill data, the AI/ML framework accurately predicts load requirements under varying processing conditions, accounting for microstructural evolution and mechanical property development. Furthermore, the model can be trained to simulate precipitation strengthening driven by microalloying elements like niobium, ensuring precise estimation of strength and ductility—critical for Advanced High Strength Steels (AHSS). Validation against industrial hot-strip mill data demonstrates high predictive accuracy, with load predictions within ±5% of measured values. This physics-informed AI/ML approach not only optimizes mill operations by reducing energy consumption and downtime but also enhances material design by linking process conditions to desired mechanical properties. The framework lays the groundwork for scalable, predictive tools in steel manufacturing, supporting the development of next-generation alloys with tailored performance.
Bio
Dr . Abhishek Kumar Thakur is a researcher at Tata Steel Limited with extensive expertise in metallurgical and materials engineering. He earned his bachelor’s degree in Metallurgical & Materials Engineering from NIT Jamshedpur , India. Following his undergraduate studies, he worked as a Senior Engineer in the Operations Department at Vedanta Limited, Goa, India. He later pursued a master’s degree in Metallurgical Engineering from IIT (BHU) Varanasi, India, and subsequently completed his Ph.D. in Materials Science & Engineering from the University of Arizona, Tucson, USA. Post-Ph.D., he worked at Freeport-McMoRan in Arizona, USA, contributing to innovative material solutions in the mining and metals sector . Currently, Dr . Thakur is a member of the Research and Development unit at Tata Steel in Jamshedpur , India, where he drives advancements in high-strength steels by leveraging AI/ML and thermodynamic modeling to optimize mechanical properties.
Abstract
The integration of artificial intelligence and machine learning (AI/ML) in material design and process optimization offers transformative potential in metallurgical engineering, particularly in steel production. AI/ML models can be developed and applied across various sectors within the industry, enabling enhanced control and efficiency in steel processing. Leveraging physics-based, data-driven approaches, the developed model captures the effects of chemical composition and processing parameters. For example, by integrating thermodynamic and kinetic principles with extensive mill data, the AI/ML framework accurately predicts load requirements under varying processing conditions, accounting for microstructural evolution and mechanical property development. Furthermore, the model can be trained to simulate precipitation strengthening driven by microalloying elements like niobium, ensuring precise estimation of strength and ductility—critical for Advanced High Strength Steels (AHSS). Validation against industrial hot-strip mill data demonstrates high predictive accuracy, with load predictions within ±5% of measured values. This physics-informed AI/ML approach not only optimizes mill operations by reducing energy consumption and downtime but also enhances material design by linking process conditions to desired mechanical properties. The framework lays the groundwork for scalable, predictive tools in steel manufacturing, supporting the development of next-generation alloys with tailored performance.
Bio
Dr. Abhishek Kumar Thakur is a researcher at Tata Steel Limited with extensive expertise in metallurgical and materials engineering. He earned his bachelor’s degree in Metallurgical & Materials Engineering from NIT Jamshedpur , India. Following his undergraduate studies, he worked as a Senior Engineer in the Operations Department at Vedanta Limited, Goa, India. He later pursued a master’s degree in Metallurgical Engineering from IIT (BHU) Varanasi, India, and subsequently completed his Ph.D. in Materials Science & Engineering from the University of Arizona, Tucson, USA. Post-Ph.D., he worked at Freeport-McMoRan in Arizona, USA, contributing to innovative material solutions in the mining and metals sector . Currently, Dr . Thakur is a member of the Research and Development unit at Tata Steel in Jamshedpur , India, where he drives advancements in high-strength steels by leveraging AI/ML and thermodynamic modeling to optimize mechanical properties.
© Department of Materials Science and Engineering, IIT Delhi