Mini-symposium "Physics-Based and Data-Driven Solutions for Additive Manufacturing" for USNCCM 17

Dear Colleagues,

We are organizing a symposium on Physics-Based and Data-Driven Solutions for Additive Manufacturing within the 17th U.S. National Congress on Computational Mechanics (USNCCM) at Albuquerque, New Mexico, July 23-27, 2023 (https://17.usnccm.org/).

We will be delighted if we can count on your participation or the participation of a member of your group. We believe your talk will be significant to the symposium's success. If you can accept our invitation, we would ask you to submit an abstract for 605. Physics-Based and Data-Driven Solutions for Additive Manufacturing: (https://17.usnccm.org/605) before Jan 31st , 2023. 

More information about the symposium can be found below.

 605. Physics-Based and Data-Driven Solutions for Additive Manufacturing

 Description:

Additive manufacturing is promised to be the next generation of manufacturing techniques due to its unique capability of producing complex structures and multi-material components. However, applying conventional numerical methods for a reliable process-structure-properties-performance (PSPP) model is challenging because of the multiscale and multi-field physics nature of the manufacturing process. Recent advances in monitoring and testing techniques (e.g., co-axial melting pool monitoring, X-ray for defects detection, etc.) allows the collection of both in- and ex-situ PSPP data, representing the tremendous potential for the fundamental knowledge of AM process and materials. Physics-based machine learning (ML) for AM has drawn significant interest since it can integrate data from various resources, such as sensors, cameras, high-fidelity simulations, etc., with physical model for the predictive PSPP relationship. These new and exciting developments are pushing the transformation from conventional modeling and design for AM to an interpretable data-driven paradigm. This mini-symposium aims to provide a platform for experts working on physics-based ML for AM to share recent progress and identify the research needs to advance the methods for engineering applications. 

 The topics include, but are not limited to:

·        In/ex-situ data integration with multi-scale and multi-field physics modeling for AM

·        Combination of part-scale and powder-scale simulations

·        In-situ monitor and data analysis for physical model calibration

·        Uncertainty quantification in AM process and materials properties prediction

·        Combined simulation and in-situ processing monitor for rapid build qualification 

·        Effects of AM microstructure and defects on mechanical properties 

·        Feedback control for manipulating microstructures in as built structures 

·        Computational and data-driven design of functional structures, bio-inspired composites, etc.

·        Scaling law identification and discovery for AM process and materials

Thank you very much, and we look forward to hearing from you. 

Lin Cheng, Worcester Polytechnic Institute, lcheng@wpi.edu

Jinhui Yan, University of Illinois at Urbana-Champaign, yjh@illinois.edu

Miguel Bessa, Brown University, miguel_bessa@brown.edu

Qizhi He, University of Minnesota Twin Cities, qzhe@umn.edu