SBIR Phase I: Machine learning-powered simulation of additive manufacturing for real-time design and process optimization

Exlattice, Inc., Durham NC

Investigators

Abstract

The broader impact of this Small Business Innovation Research (SBIR) Phase I project is to accelerate larger-scale adoption of additive manufacturing (AM) through ultrafast engineering simulation software. The AM industry was worth $12.6 billion in 2020 and holds great potential in providing advanced designs and enabling distributed supply chains for the US aerospace, medical, and automotive industries. However, AM is facing slow adoption due to trial and error processes casued by the lack of an efficient and reliable engineering workflow. The proposed ultrafast simulation technology may provide real-time predictions of possible manufacturing issues for AM parts in the design phase, thereby reducing manufacturing failures and prototyping. The project also seeks to generate systematic knowledge of how machine learning can help overcome some long-lasting fundamental challenges in scientific computing and help advance engineering software used for digital manufacturing. This Small Business Innovation Research (SBIR) Phase I project integrates machine learning with finite element methods (FEM) to develop a proof-of-concept for 3-5 orders of magnitude faster process simulation software for AM used to predict manufacturing failures due to high temperature, residual distortion, and residual stresses. The traditional computation method for part-scale AM simulation takes hours to days and relies on an iterative, layer-wise approach. The proposed project seeks to replace the most time-consuming steps in the traditional simulation method with deep learning and implement a one-step approach. The proposed hybrid data-driven plus physical simulation framework includes the development a feature-driven, deep learning model and a process parameter-based transfer learning model, and coupling these models with the finite element method. The project also aims to apply and benchmark hybrid datasets from AM physical modelling, three-dimentional (3D) scanning of manufactured parts, and in-situ monitoring for training and model scalability. The team seeks to demonstrate technological advantages through pilot testing with streamlined user interfaces and application programming interfaces (APIs) developed in this project. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.