PhD Candidate Laxmi Poudel Receives the Excellence in Research Award

The PhD Candidate at SiDi Lab, Laxmi Poudel, has been selected as the recipient of the Excellence in Research Award by the Graduate Professional Student Congress at the University of Arkansas. This award is awarded to one Graduate or Professional student engaged in outstanding academic research, regardless of department, that has caused honor to be bestowed upon said individual and graduate and professional programs at the University of Arkansas within the current academic year through significant contributions to sciences or humanities. Congratulations to Laxmi, and check his recent publications on cooperative and swarm manufacturing.

L. Poudel, W. Zhou, Z. Sha, “Resource-Constrained Scheduling for Multi-Robot Cooperative 3D Printing”, Journal of Mechanical Design, Transactions of the ASME, volume 143, issue 7, pp: 072002 (12), July 2021.

L. Poudel, W. Zhou, Z. Sha, “A Generative Framework for Scheduling Multi-Robot Cooperative 3D Printing“, Journal of Computing and Information Science in Engineering, Transactions of the ASME, volume 20, issue 6, pp: 061011 (12), Dec 2020.

L. Poudel, C. Bair, J. McPerson, Z. Sha, W. Zhou, “A Heuristic Scaling Strategy for Multi-Robot Cooperative 3D Printing”, Journal of Computing and Information Science in Engineering, Transactions of the ASME, volume 20, issue 12, pp: 041002 (12), 2020.

Call For Papers – Intelligence Augmentation for Human Systems Integration

The Systems Engineering Information & Knowledge Management Technical Committee and the Design Theory and Methodology Technical Committee of ASME is organizing a special joint session on the topic of “Intelligence Augmentation for Human Systems Integration” for The ASME 2021 Virtual International Design Engineering Technical Conferences & Computers and Information in Engineering Conference. See the call for papers for details.

Paper Nominated for Best Paper Award

Our paper titled “Part-Aware Product Design Agent Using Deep Generative Network and Local Linear Embedding”, led by PhD student Xingang Li, is recently nominated to receive the Best Paper Award by tracks on The 54th Hawaii International Conference on System Sciences. The work came out to be one of the very best of the 1,449 papers submitted for this year.

In this study, we present a data-driven generative design approach that can augment human creativity in product shape design with the objective of improving system performance. The approach consists of two modules: 1) a 3D mesh generative design module that can generate part-aware 3D objects using variational auto-encoder (VAE), and 2) a low-fidelity evaluation module that can rapidly assess the engineering performance of 3D objects based on locally linear embedding (LLE). This approach has two unique features. First, it generates 3D meshes that can better capture surface details (e.g., smoothness and curvature) given individual parts’ interconnection and constraints (i.e., part-aware), as opposed to generating holistic 3D shapes. Second, the LLE-based solver can assess the engineering performance of the generated 3D shapes to realize real-time evaluation. Our approach is applied to car design to reduce air drag for optimal aerodynamic performance.