Off-campus WSU users: To download campus access dissertations, please use the following link to log into our proxy server with your WSU access ID and password, then click the "Off-campus Download" button below.
Non-WSU users: Please talk to your librarian about requesting this dissertation through interlibrary loan.
Access Type
WSU Access
Date of Award
January 2025
Degree Type
Dissertation
Degree Name
Ph.D.
Department
Industrial and Manufacturing Engineering
First Advisor
Kyoung-Yun K. Kim
Abstract
4D printing, an emerging innovation in additive manufacturing, builds upon 3D printing by introducing time as an additional dimension. This advancement enables printed structures to transform their shapes, properties, or functionalities in response to external stimuli, such as heat, light, or humidity. Unlike 3D printing, which produces static objects, 4D printing combines smart materials, advanced design methodologies, and process optimization to create dynamic, adaptive products. Applications range from healthcare, where self-adjusting implants and stents can revolutionize patient care, to aerospace, enabling deployable, lightweight structures with minimal mechanical complexity.
Despite its potential, 4D printing faces significant challenges that hinder its development and broader adoption. One major issue is the absence of a unified and reusable definition for 4D printing knowledge, resulting in fragmented frameworks and inconsistent terminology across disciplines. Another challenge lies in the lack of computational representations to model the transformation behaviors of 4D-printed objects, particularly the dynamic changes they undergo in response to stimuli. Without these models, it is difficult to simulate, optimize, or standardize the design processes for 4D printing. Additionally, the diversity of applications and the distributed nature of research complicate efforts to centralize and integrate knowledge resources into a cohesive repository.
This dissertation addresses these challenges through a threefold approach. First, it defines the characteristics of a unified and reusable knowledge model for 4D printing to streamline interdisciplinary communication. Second, it develops computational methods to represent transformation behaviors from static 3D to dynamic 4D systems, enabling simulation and optimization. Third, it proposes strategies for centralizing and integrating diverse knowledge resources, facilitating collaboration and innovation. By leveraging semantic modeling techniques, including knowledge graph development, this study bridges gaps across disciplines and offers a robust framework for understanding and advancing 4D printing.
The contributions of this research are multifaceted. Academically, it provides a systematic approach to 4D printing knowledge modeling, advancing the state of the art. For industry, it enhances the ability to design efficient, functional 4D-printed products. Societally, it fosters innovation in critical areas such as adaptable medical devices and sustainable materials. By addressing these foundational challenges, this dissertation lays the groundwork for the widespread adoption and transformative potential of 4D printing technology.
Recommended Citation
Liu, Shengyu, "Envisioning 4d Futures: Enhancing Design Repositories Through Meta-Ontology Information Modeling And Physical-Mathematical Expression Trees" (2025). Wayne State University Dissertations. 4163.
https://digitalcommons.wayne.edu/oa_dissertations/4163