Our manufacturing research leverages advanced AI-driven approaches to revolutionize the design, optimization, and production of next-generation manufacturing systems. By combining experimental testing, finite element modeling, machine learning-based surrogate modeling, generative design, and process optimization, we enable the development of advanced manufacturing processes across additive manufacturing, battery manufacturing, and semiconductor manufacturing. Our work spans from material development to process optimization, addressing applications in energy storage, electronics, and advanced materials.

We develop advanced AI-driven approaches for additive manufacturing, including 3D printing algorithm development, material development using AI, and process optimization. Our research focuses on digital light processing (DLP) techniques and generative design to enable the fabrication of complex structures with functional materials.

Our research in battery manufacturing focuses on developing advanced manufacturing processes for solid-state batteries and lithium-ion batteries. We develop probabilistic frameworks that ensure robust battery performance while maintaining system reliability and safety throughout the manufacturing process. Our work includes reliability-based design optimization and process control for enhanced battery performance.

We leverage AI and machine learning to optimize semiconductor manufacturing processes, including process control, quality assurance, and yield optimization. Our research focuses on developing intelligent manufacturing systems that improve production efficiency, reduce defects, and enhance the reliability of semiconductor devices to mitigate electromigration.
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