Biography
David is currently the Senior Principal Scientist at the Institute for High Performance Computing (IHPC) and the Singapore Institute for Manufacturing Technology (SIMTech), both A*STAR institutes located in Singapore. In his current role, he leads the development of generative design and topology optimization capabilities, promotes the use of digital workflows and additive manufacturing (AM) for polymer composites, pioneers advancements in standards for qualification and e-certification, and manages research talent and development.
Before joining IHPC, David was a distinguished Professor and Administrator at the School of Mechanical Engineering at the Georgia Institute of Technology. He also served as a Professor in the Engineering Product Development pillar at the Singapore University of Technology & Design, contributing his expertise to their Digital Manufacturing and Design (DManD) Centre.
David holds a Ph.D. in mechanical engineering from the University of Massachusetts and has a keen interest in computer-aided design, additive manufacturing, and design methodology, with a specialized focus on additive manufacturing design. He has been recognized as a Fellow of ASME and has received the 2013 Solid Freeform Fabrication Symposium International Freeform and Additive Manufacturing Excellence (FAME) Award. He also chairs the ASTM F42 subcommittee on design for additive manufacturing and has been distinguished with the ASTM Award of Merit, which elevated him to the status of Fellow of ASTM.
David has published a leading textbook in the additive manufacturing area, as well as about 150 journal articles, over 180 refereed conference papers, and over 100 other conference presentations. He has an h-index of 69 (Google Scholar) with over 30,000 citations.
Research Interests
David's research interests are primarily in the additive manufacturing (AM) field. His research program can be divided into two parts: 1) Design for AM; and 2) AM processes and materials.
In the design for AM area, his research aims to develop design methods and tools that enable engineering designers to take advantage of the unique capabilities of AM, including geometric complexity, multiple materials, and multi-functionality. He has previously worked on the development of conformal lattice structure design methods, heuristic optimization methods for lattice structures, new computer-aided design (CAD) technologies that integrate material information with part geometric models, and digital workflows for integrating design across the additive manufacturing process chain. David is particularly interested in researching new methods for synthesizing multi-functional, multi-material devices that extend topology optimization and evolutionary programming technologies, particularly for 4D printing, i.e., printing with shape memory materials.
In the AM process and materials areas, his primary focus is on polymer processing with the objective of developing high-precision processes that can scale to production capabilities. His work includes stereolithography, ink-jet printing, and material extrusion (fused deposition modeling) processes. In the stereolithography area, he has developed an interferometry-based real-time sensing and control technology to improve the precision of micro-optic component fabrication. The ink-jet printing project involves the development of a new type of printing head that can deposit high-viscosity fluids at high speed. In the material extrusion area, projects focused on the development of new composite materials that offer improved mechanical properties across a wide range of end-use conditions.
In addition, David has interests in cyber-physical-human system design research (that is, “smart products”) with an emphasis on synthesis methods for product configurations. Underlying much of the design work is an interest in knowledge modelling to capture the rich structure of engineering information, knowledge, and know-how. Extensive knowledge bases are being constructed for AM and designed for AM. Early research is underway in applying knowledge modelling to design for 4D printing and smart product design as well.