EXTENDED ABSTRACT: Virtual characterization such as computer simulations was successfully applied to characterize various manufacturing processes especially performed in extreme conditions, e.g. under high temperatures and pressure. One example of the crucial process in manufacturing ceramic products is the sintering process. The sintering process involves the heating of ceramic powder to form a solid mass. Designing an efffcient sintering furnace involves considerations of heat transfer, temperature proffles, and optimizing the overall process. Combining computational ffuid dynamics (CFD), heat transfer, electromagnetic simulations, and artiffcial intelligence (AI) for real time temperature proffle prediction can signiffcantly enhance the design and control of the sintering furnace. AI algorithms, particularly machine learning, can be trained using virtual (computational) data and experimental data from the sintering process. This digital twin model can be used to optimize the sintering process by adjusting parameters such as temperature, airffow, and material composition in real time to achieve desired outcomes. This integrated approach contributes to energy efficiency, process optimization, and improved product quality in industries that rely on the sintering process. In this work, we introduce an automatic generation platform of virtual data for the sintering process under various manufacturing conditions to achieve optimal temperature profiles. We developed a digitized and automatic workflow accompanying the multiscale and multiphysics simulations and AI algorithms to characterize and optimize the temperature profile in the process. The accelerated digital twin model can predict the temperature profile signiffcantly faster than the actual computer simulations. In the wide range of manufacturing technology based on ceramic materials, this multiscale and data-driven approach can be used as a real time characterization and monitoring tool for optimizing processes for complex geometry and operating conditions. The ceramics manufacturing DX platform (VECTOR) is developed to provide optimization digital tools customized for fundamental study as well as industrial needs.
Keywords: ceramic manufacturing process, real time digital twin model, multiphysics computer simulation, AI
Sangil Hyun has completed his PhD in theoretical condensed matter physics from Michigan State University and postdoctoral research at Princeton University and Johns Hopkins University. He is now a chief research scientist at the Korea Institute of Ceramic Eng. & Tech (KICET) in Korea. He is also the principal investigator of the research project for a virtual engineering platform for ceramics supported by MOTIE in Korea. His main research interests have been on computational studies for materials design, process characterization, and device optimizations in many length scales. His current interest is mainly on the digital transformation (DX) of materials, devices, and manufacturing in the field of fundamental research and industrial application.