Plenary Talks

Emerging Trends in the Application of LLMs for Diagnostics, Prognostics and Maintenance of Manufacturing Equipment

Sagar Kamarthi

Associate Dean for Graduate Education, College of Engineering
Professor of Mechanical and Industrial Engineering
Northeastern University

Sagar Kamarthi

Abstract: Coming soon.

Biography: Coming soon.

Intelligent Steel Production through Digital Transformation

Takahiro Koshihara

Deputy General Manager, Cyber-Physical System R&D Department
Steel Research Laboratory, JFE Steel Corporation
JFE Steel Corporation

Takahiro Koshihara

Abstract:
JFE Steel is promoting company-wide digital transformation (DX) to stabilize steel production, improve productivity, and achieve decarbonization. The long-term vision is the realization of intelligent steelworks that can autonomously optimize operations by integrating cyber and physical domains.

A core element of this DX strategy is the deployment of cyber-physical systems (CPS), which combine operational data, sensor information, and accumulated process know-how with advanced modeling, data science, and artificial intelligence. CPS enables real-time analysis, prediction, and operational guidance, supporting faster and more accurate decision-making across major steelmaking processes. At JFE Steel, CPS has already been implemented in key processes such as blast furnaces, converters, coke ovens, and rolling mills, contributing to improved operational stability and energy efficiency.

In parallel, JFE Steel is advancing in-house development of robotics to address hazardous, labor-intensive, and skill-dependent tasks in harsh steelmaking environments. These robots are designed to complement CPS by enabling remote operation, automation, and the transfer of skilled expertise. This presentation introduces representative applications developed in-house: a control system for molten iron temperature of blast furnace; a guidance system for fuel and power management by model predictive control; an autonomous mobile robot for cleaning the top of the coke oven; and so on.

Through the integrated application of CPS and robotics, JFE Steel is accelerating the realization of intelligent steel production. The presented technologies demonstrate how digital transformation can simultaneously address operational excellence, workforce challenges, and sustainability in the steel industry.

Biography:
T. Koshihara is a Deputy General Manager at the Cyber-Physical System R&D Department of JFE-Steel Corporation. He has worked as a researcher in inspection and measurement techniques in the steel industry. He has also served as a visiting researcher at the Fraunhofer Institute, a person in charge of facility construction at a steel plant, a chief of the Research Planning Department, and a Deputy General Manager of the company-wide development department of DX technology at the head office. He received his Master of Engineering degree (1997) from the University of Tokyo.
Contact information: t-koshihara@jfe-steel.co.jp

Data-Driven Evolutionary Manufacturing: Leveraging Digital Twin and Generative AI for Autonomous System Optimization

Shintaro Iwamura

OMRON Corporation
Industrial Automation Company
Controller Div., Product Business Div. HQ,
Distinguished Specialist of Technology, Ph.D.

Shintaro Iwamura

Abstract: Coming soon.

Biography: Coming soon.

Physics-based AI-enhanced Sensing and Control of Manufacturing Processes

Jian Cao

Cardiss Collins Professor, Department of Mechanical Engineering (by courtesy)
Department of Materials Science and Engineering (by courtesy)
Department of Civil and Environmental Engineering
Director, Northwestern Initiative on Manufacturing Science and Innovation
Associate Vice President for Research Northwestern University

Jian Cao

Abstract:
Current research efforts at our manufacturing group aim to advance high-mix flexible manufacturing capability through co-design of materials and processes and the execution of digital twins. In this talk, I will demonstrate our work in the development of differentiable simulation tools, sensing, and process control to achieve effective and efficient predictions and control of a material’s mechanical behavior in metal additive and metal forming processes. Our solutions particularly target three notoriously challenging aspects of the process: long history-dependent properties, complex geometric features, and the high dimensionality of their design space.

Biography:
Cardiss Collins Professor Jian Cao (MIT’Ph.D, MIT’MS, SJTU’BS) specialized in innovative manufacturing processes and systems, particularly in the areas of deformation-based processes and laser additive manufacturing processes. She is the Founding Director of the Center on Manufacturing Science and Innovation at Northwestern, known as NIMSI. Prof. Cao is an elected member of the National Academy of Engineering (NAE) and of the American Academy of Arts and Sciences (AAA&S). She is a Fellow of American Association for the Advancement of Science (AAAS), ASME, the International Academy for Production Engineering (CIRP) and SME. Cao was the Editor-in-Chief of Journal of Materials Processing Technology. Prof. Cao now serves as an Associate Vice President for Research at Northwestern, Board of Directors of SME, and Board of mHUB – accelerator for hardtech innovation and manufacturing in Chicago.