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8月27日密西根大学Dearborn分校陈磊博士学术报告

发布于:2019/08/26

报告题目:Promoting metallic additive manufacturing - a multiscale computational framework integrating experimental validation and data-driven approaches

主讲人:Dr. Lei Chen

时间:2019年8月27日下午15:00 - 16:00

地点:机械学院316报告厅

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主讲人简介:Dr. Lei Chen is currently an assistant professor at University of Michigan-Dearborn (previously at Mississippi State University). Dr. Chen received his BS and MS degrees from Huazhong University of Science & Technology, China in 2005 and 2007 respectively, and PhD degree from the National University of Singapore in 2012. Chen’s research interest is in the broad area of advanced manufacturing and materials design, with a focus on additive manufacturing of metals and piezoelectric composites. Chen has published over 70 authored and co-authored papers in top international journals includingNature,Nature Communications,Advanced Materials,Acta Materialia,ASME Journal of Manufacturing Science and Engineering,Additive Manufacturing,Journal of Power Sources, etc. Chen has received 1921 citations to date. He has received a number of awards from universities and organizations worldwide. Recent awards include the prestigious ASEE Southeastern Section New Researcher Award (2018), ORAU Ralph E. Powe Junior Faculty Enhancement Award (2017), Southeastern Conference Visiting Faculty Travel Award (2016), Y. Z. Hsu Scientific Paper Award (2015), Chinese Excellent Self-financed Student Abroad Award (2012), and President Graduate Fellowship Award at National University of Singapore (2009).

报告摘要:Additive manufacturing (AM), or 3D printing, offers the ability to fabricate customized, complex parts traditionally unobtainable for a variety of applications However, the metallic AM introduces a melt pool with rapid thermal cycles that result in microstructures featured with anisotropy and porosity, thus deteriorating the mechanical properties of AM metallic builds. This presentation will discuss our research on how to manipulate and promote the microstructures and properties of AM fabricated metallic components, by a multiscale computational framework integrating experimental validation and data-driven approaches. In particular, I will present an “in-house” multiscale computational framework including macroscopic finite-element based manufacturing process modelling, mesoscopic phase-field modelling of microstructure evolution and design, and crystal plasticity calculation of material properties. The talk will also present our recent research on the integration of data-driven approaches (e.g., surrogate models) with physics-based computational and experimental methods to achieve the uncertainty quantification and robust design of metallic AM.

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