Geometric Computing for Multi-Axis Additive Manufacturing

报告时间 2018年12月21日

Title: Geometric Computing for Multi-Axis Additive Manufacturing

 

Speaker: Charlie C.L. Wang

Time10:00-11:00, Dec. 21

PlaceN3-332

Abstract

Called 3D printing, the process of Additive manufacturing (AM) in most commercial systems is however taken in a 2.5D manner – materials are accumulated layer upon layer in planes along a fixed printing direction. Overhanging regions are generally fabricated by inserting supporting structures, which are difficult to remove. This talk covers the techniques recently developed for overcoming this challenge by adding rotation into 3D printing. First of all, motivated by a work of orientation-driven shape optimizer attempting to slim down the need of support, rotations have been introduced to handle overhangs. Secondly, the method for determining an optimal printing direction is introduced. Our framework for computing an optimized 3D printing direction is formulated as a combination of metrics including area of support, visual saliency, preferred viewpoint and smoothness preservation. A training-and-learning methodology is developed to obtain a closed-form solution for our perceptual model. A solid decomposition based approach is applied to segment a model into sub-regions that are printed along different (but fixed) directions in a support-free way.

Lastly, a more advanced hardware with continuous multi-axis motions (e.g., a robotic arm) can be utilized for 3D printing along more complicated tool-paths – i.e., a real 3D printing process. Automatic tool-path planning for multi-axis3D printing is based on two successive decompositions, first volume-to-surfaces and then surfaces-to-curves. Details of this technique and its potential in a variety of applications will be presented at the end of this talk.

 

Bio:

Prof. Charlie C. L. Wang is a Fellow of American Society of Mechanical Engineers (ASME) with expertise in geometric computing, design and manufacturing. Before being re-appointed back to the Chinese University of Hong Kong (CUHK) in July 2018, he worked as Professor and Chair of Advanced Manufacturing at Delft University of Technology, The Netherlands (2016-2018) and Professor (2015-2016) / Associate Professor (2009-2015) / Assistant Professor (2003-2009) of Mechanical and Automation Engineering at CUHK. He also holds a non-paid position as Professor of Advanced Manufacturing at TU Delft (2018-2023), and he was a visiting professor at University of Southern California (2011). Prof. Wang received a few awards from professional societies including the ASME CIE Excellence in Research Award (2016), the ASME CIE Young Engineer Award (2009), the Best Paper Awards of ASME CIE Conferences (twice in 2008 and 2001 respectively), the Prakash Krishnaswami CAPPD Best Paper Award of ASME CIE Conference (2011), and the NAMRI/SME Outstanding Paper Award (2013). He received his B.Eng. degree (1998) in mechatronics engineering from Huazhong University of Science and Technology and his M.Phil (2000) and Ph.D. (2002) degrees in mechanical engineering from Hong Kong University of Science and Technology (HKUST). His research interests include geometric computing, computational design, advanced manufacturing, and robotics.