Additive Manufacturing Design and Application Lab
Research Field
Dr. Yen-Ting Li is an Assistant Professor in the Department of Mechanical and Electro-Mechanical Engineering at Tamkang University. His research and teaching focus on process–material–structure integration in advanced manufacturing, with a long-term emphasis on binder jetting (BJ) for 3D-printed sand molds/cores and its downstream casting applications.
Dr. Li’s work aims to establish engineering-ready, data-verifiable methodologies for controlling BJ process outcomes—particularly binder spreading/penetration behavior in porous powder beds—and translating those mechanisms into practical design rules for improving strength, dimensional accuracy, and permeability in printed sand parts.
The PI’s research group (Additive Manufacturing & Binder Jetting–oriented research team) targets the controllability and reproducibility of powder-bed processes. The lab develops a unified framework combining:
Measurable quality metrics (e.g., penetration depth, spreading area, dimensional fidelity, strength-related proxies)
Physics-informed simplified models (porous-media transport / diffusion-penetration abstractions to reduce computational overhead)
Experiment–model calibration loops that turn lab data into manufacturable process windows.
This “measurable → modelable → reproducible” workflow is designed to accelerate parameter tuning and enable scalable translation from lab prototypes to production-oriented BJ sand tooling.
Theme A — Binder–Powder Interaction and Quality Modeling in Binder Jetting
Developing interpretable models and experimental protocols to quantify binder spreading/penetration/migration in granular powder beds and link them to macroscopic part quality (strength, permeability, geometry).
Theme B — Greyscale Printing Strategies for High-Precision Sand Tooling
Exploring greyscale (dose/level) control as an engineering knob for local property tuning (e.g., strength–permeability balance, surface quality, and accuracy), and building parameterizable toolchains for data generation and optimization.
Theme C — Model-Accelerated Design: Porous-Media & Reduced-Order Approaches
Constructing reduced-order / simplified models that can be calibrated by experiments and used for rapid prediction of spreading diameter and penetration depth to support process optimization and robust design.
Theme D — Sustainable Manufacturing for 3D-Printed Sand Molds
Investigating recycled sand utilization in 3D-printed sand mold processes, linking sustainability targets with measurable manufacturability and performance outcomes.
Chinese Institute of Engineers: Student Paper Competition – Honorable Mention (2025)
Ph.D., Department of Mechanical Engineering, National Taiwan University of Science and Technology (NTUST)
B.S., Department of Mechatronic Engineering, National Taiwan Normal University (NTNU)
Postdoctoral Researcher, Empower Vocational Education Research Center, NTUST
Assistant Professor, Department of Mechanical and Electro-Mechanical Engineering, Tamkang University
Job Description
What you will gain:
Research training: experimental design, measurement, data analysis, and reproducible workflows
Engineering skills: practical image processing and programming (Python or MATLAB is sufficient)
Research outputs: co-authorship, technical reports, posters/conference submissions (subject to progress/results)
Team collaboration: weekly progress updates, milestone tracking, and research documentation
Typical tasks (flexible by semester):
Sample fabrication, measurement, and experiment execution
Data and image processing (print images, slice/grayscale files, parameter tables)
Literature review and presentation of results (Chinese/English acceptable)
Preferred Intern Educational Level
Senior undergraduate (3rd/4th year) or Master’s students in Mechanical/Mechatronics/Materials/Manufacturing/Industrial Engineering/CS-AI related fields
Commitment of at least 60 days
Skill sets or Qualities
Must-have qualities:
Strong curiosity and research discipline; proactive progress reporting
Careful documentation mindset (traceable experiments, parameters, and measurement workflows)
Nice-to-have (any one is sufficient):
Basics of image processing / computer vision (e.g., OpenCV, similarity metrics such as SSIM)
Python or MATLAB for data processing and plotting
Statistics / DOE fundamentals (regression, ANOVA, sensitivity analysis)
Experience in 3D printing, casting, powders, or porous materials
Solid technical reading in English (journal papers and concise summaries)