Perspective: Machine Learning in Design for 3D/4D Printing
Abstract
3D/4D printing offers significant flexibility in manufacturing complex structures with a diverse range of mechanical responses, while also posing critical needs in tackling challenging inverse design problems. The rapidly developing machine learning (ML) approach offers new opportunities and has attracted significant interest in the field. In this perspective paper, we highlight recent advancements in utilizing ML for designing printed structures with desired mechanical responses. First, we provide an overview of common forward and inverse problems, relevant types of structures, and design space and responses in 3D/4D printing. Second, we review recent works that have employed a variety of ML approaches for the inverse design of different mechanical responses, ranging from structural properties to active shape changes. Finally, we briefly discuss the main challenges, summarize existing and potential ML approaches, and extend the discussion to broader design problems in the field of 3D/4D printing. This paper is expected to provide foundational guides and insights into the application of ML for 3D/4D printing design.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Oct 31, 2023
- Source ID
- 10.1115/1.4063684
Entities
People
- Frédéric Demoly
- H. Jerry Qi
- Kun Zhou
- Ruike Renee Zhao
- Xiaohao Sun
Organizations
- Air Force Office of Scientific Research
- Georgia Tech
- Hp
- Institut Universitaire de France
- Nanyang Technological University
- Stanford University