Charlie C. L. Wang is a Chair Professor of Smart Manufacturing at the University of Manchester (UoM). Before joining UoM in 2020, he worked as a Professor and Chair of Advanced Manufacturing at Delft University of Technology, The Netherlands (2016) and as a Professor (2015) / Associate Professor (2009) / Assistant Professor (2003) of Mechanical and Automation Engineering at the Chinese University of Hong Kong. He also held a visiting professor position at University of Southern California during his sabbatical leave (2011). He received his B.Eng. degree (1998) in mechatronics engineering from Huazhong University of Science and Technology and his Ph.D. degree (2002) in mechanical engineering from Hong Kong University of Science and Technology (HKUST). Prof. Wang was the recipient of several awards, including the ASME CIE Excellence in Research Award (2016), the ASME CIE Young Engineer Award (2009), seven Best Paper Awards, five project-oriented technology innovation awards and three teaching awards. He was elected to be a Fellow of American Society of Mechanical Engineers (ASME) in 2013, and is currently chairing the executive committee of Solid Modelling Association (SMA).

​​Prof. Wang is well-known for his research in computational design and manufacturing with a focus on geometric computing, optimisation, and modelling. His research can mainly be categorised into the following three aspects:

• Geometric computing for robot-assisted additive manufacturing. His most recent and significant contribution is the process planning approach that he developed to tackle the manufacturing constraints of conventional planar layer-based additive manufacturing (i.e., 3D printing). That is the need of additional supporting structures to prevent the collapse of materials in regions with large overhang. After investigating a whole thread of methods to overcome this challenge by deformation, re-orientation, topology optimisation, hollowing and decomposition, his SIGGRAPH 2018 paper is the first in the world that can automatically generate collision-free and support-free toolpaths in the volumetric space of input freeform models to be 3D printed by multi-axis motions of a robotic system. The continuous exploration of this research further resulted in a first approach that can improve the mechanical strength of a 3D printed model with up to 6.35x when using the same type of materials but dynamically varied toolpath for extrusion. The outcome of Wang’s research in robot-assisted additive manufacturing has attracted a variety of industrial engagements (e.g., Nikon, Airbus, Coriolis Composites, 5AxisWorks, etc.).
• Fundamental technology of highly parallel solid modelling. The market-available solid modellers (e.g., ACIS and Parasolid) use the boundary representation (B-rep) to store and process the shape of an object in computers. Although these approaches based on direct manipulation of B-rep are accurate, the efficiency & robustness problems become very crucial when modelling objects with complex geometry (e.g., porous structures in biomedical applications and 3D printing). To tackle this problem, Charlie’s research group has developed algorithms to perform fast and robust solid modelling operations on complex objects based on Layered Depth-Normal Images (LDNI). This work won the VX Corporation Best Idea Award in the 2009 International CAD Conference and Exhibition and 3 best paper awards from ASME IDETC/CIE & SME NAMRC. He was amongst the first group of researchers that exploited the computational capabilities of many-core commodity graphics processing units (GPUs) to efficiently generate the solid models represented by structured point sets. The developed solid modelling kernel has been used in collaborative projects with industry and research institutes (e.g., MIT, University of Southern California, Dartmouth College, Oxford Engineering Ltd., Belton Tec., etc.).
• Geometric modelling for personalised products. In the past, design automation can only be realized on models with regular shapes such as mechanical parts by the parametric modelling techniques in CAD/CAM. These techniques cannot be applied to ultra-personalized products, which are composed of complex geometry and need to fit the different shapes of human bodies. His research tackled this challenge by enabling the function of automatically deforming the shape of a product, which fits the standard shape of a mannequin, to products with new shapes that fit different individuals. Charlie’s work fundamentally solved the problem of design automation for the products that need to be personalised according to individual body shapes. Based on these contributions to the area of computational design and manufacturing, he was awarded the 2016 CIE Excellence in Research Award by ASME. The developed technology has acquired engagements from a variety of industrial companies (e.g., Adidas, P&G, Shima Seiki, Exuskin, etc.). Along this thread of research, Charlie and his co-investigators at UoM recently received an EPSRC fund to develop deformable mannequins as soft robots for personalised apparel production.

Besides, his recent research has made fundamental contributions in computational design, fabrication, control and motion planning of soft robots.

Prof. Wang has an interdisciplinary engineering background in design, manufacturing, robotics, and computer science. He served as the editorial board members of 9 journals in computational design, digital manufacturing, and robotics, including Computer-Aided Design, IEEE/ASME Transactions on Mechatronics, IEEE Transactions on Automation Science and Engineering, ASME Journal of Computing and Information Science in Engineering, Journal of Engineering Design, Computers & Graphics, International Journal of Precision Engineering and Manufacturing, etc.