WE Sem & Colloq: "A physics-informed data-driven model for uncertainty quantification and reduction in metal additive manufacturing"

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photo of Lei Chen, University of Michigan - Dearborn
Lei Chen, PhD

Uncertainty quantification (UQ) in metallic additive manufacturing (AM) has attracted tremendous interests in order to dramatically improve product reliability. Model-based UQ, which relies on the validity of a computational model, has been widely explored as a potential substitute for the time-consuming and expensive UQ solely based on experiments. However, its adoption in practical AM process requires the overcoming of two main challenges:

  1. the inaccurate knowledge of uncertainty sources
  2. the intrinsic uncertainty associated with the computational model

Here, we propose a novel data-driven framework to tackle these two challenges by combining high throughput physical simulations and limited experimental data.

We first construct a machine learning (ML) model trained by high throughput physical simulations, for predicting the three-dimensional (3D) melt pool geometry and its uncertainty with respect to AM parameters and uncertainty sources. We then employ a novel sequential Bayesian calibration method to perform parameter calibration and model correction, by using experimental data from AM-Bench of National Institute of Standards and Technology (NIST). The application of the calibrated melt pool model to UQ of the porosity level, an important quality factor, of AM parts, demonstrates its potential use in AM quality control. The proposed UQ framework can be generally applicable to different AM processes, towards physics-based quality control of AM products.

 

Bio

Dr. Lei Chen is currently an assistant professor in the Department of Mechanical Engineering, University of Michigan-Dearborn and affiliated with Michigan Institute for Data Science-Ann Arbor. 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 manufacturing and materials design. The group's research is supported by NSF, ORAU, ARL, ONR-SBIR, MIDAS, and Ford.

Chen has published over 80 authored and co-authored papers in top international journals including:

  • Nature, Nature Communications
  • Nature Partner Journal (npj): Computational Materials
  • Advanced Materials
  • Advanced Energy Materials
  • Acta Materialia
  • International Journal of Plasticity
  • ASME Journal of Manufacturing Science and Engineering 
  • Additive Manufacturing
  • Journal of Power Sources

Chen has received 2768 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).