Multifunctional Materials Manufacturing Laboratory
Research Field
Dr. Jung-Ting Tsai is currently an assistant professor in the Mechanical Department at the National Taiwan University of Science and Technology (NTUST), Taipei, Taiwan (R.O.C.). He was a post-doc in the Applied Materials Division at Argonne National Laboratory, Lemont, IL, USA, from 2022 to 2023. He received his Ph.D. in Materials Engineering from Purdue University, West Lafayette, IL, USA in 2021. In addition, he has two master's degrees, one at Purdue University, received in 2018, and another at NTUST, which he received in 2012. His research focuses on three major areas: additive manufacturing ceramic materials, cold spray repair and coating, and materials health monitoring. His lead author on the cold spray manuscript has been selected as an Editor's Choice article by the Journal of Thermal Spray Technology in 2021. In addition, he is the recipient of the International Thermal Spray Association Graduate Scholarship. His paper on structural health monitoring was selected as the 2nd best manuscript by the Society of Plastics Engineering in 2018.
A strongly driven consent for sustainable, renewable, and carbon-free manufacturing processes has progressed throughout the upcoming years. This is mainly because drastic climate change has undeniably altered our daily lives, from abnormal temperature fluctuation to limited accessibility of energy resources. Therefore, a continuation of a more cost-effective and scalable manufacturing process has been proposed to combat the swift challenges.
My research area focuses on additive manufacturing (AM), material repair operations (MRO), and structural health monitoring (SHM). The lab will focus on these three main topics and continue seeking resources from industry sponsors and academic financial support. My research focuses on the additive manufacturing of refractory ceramic materials while using cold spray as a tool for repairing/modifying the surface and increasing engineering property performances. In addition, material structural health monitoring via fiber optical sensing is also embedded in the manufactured parts for in-situ onsite monitoring. The combined proposed technology (AM, MRO, and SHM) promotes design flexibility while maximizing engineering performance with time and energy efficiency. This will pave the pathway for the novel "Next Generation" of materials and manufacturing. The technology has many potential applications, such as semiconductor packaging, aerospace components, microwaving heat conservation, (heat) waste collection, recuperators, etc. Furthermore, the proposed technology will have a widespread impact on the decarbonization of minimizing/eliminating the use of fossil fuels.
Additive manufacturing, Cold spray coating, Composite materials, Non-destructive testing (NDT), Structural health monitoring (SHM), CFRP fabrication, Polymer testing
- Establishing a Cold Spray Particle Deposition Window on Polymer Substrate: Editor’s Choice article by the Journal of Thermal Spray Technology, 2021
- Estus H. and Vashti L. Magoon Award for Excellence in Teaching, 2021
- International Thermal Spray Association Graduate Scholarship, 2020
- 2nd best paper award for Integrated Structural Monitoring of Composite Materials via Distributed Optical Sensor in Society of Plastics Engineers- Automotive & Composites Divisions, 2018
- Honorary Member of the Phi Tau Phi Scholastic Society, 2012
Ph.D., Materials Engineering, Purdue University, West Lafayette, IN , 2021
M.S., Materials Engineering, Purdue University, West Lafayette, IN, 2018
M.S., Mechanical Engineering, National Taiwan University of Science and Technology, Taiwan, 2012
B.S., Materials Engineering, Tatung University, Taiwan, 2010
Job Description
This role focuses on the experimental validation and fabrication of advanced MAX phase materials. The intern will develop and validate a hybrid manufacturing workflow combining Digital Light Processing (DLP) additive manufacturing with Powder-based Ultra-fast High-temperature Sintering (P-UHS) to produce dense, phase-pure materials. Responsibilities include process optimization, material fabrication, and collaboration with computational researchers to translate modeling predictions into experimental outcomes. The intern will also assist in evaluating densification, phase purity, and overall material performance.
Preferred Intern Educational Level
Graduate lever or above
Skill sets or Qualities
Required Skill Sets
Materials Science Fundamentals: Understanding of phase diagrams, thermodynamics, and structure–property relationships, especially for ceramics or MAX phases.
Computational Materials Modeling: Familiarity with machine learning or data-driven approaches for materials discovery (experience with ALIGNN or graph neural networks is a plus).
Programming Skills: Proficiency in Python for data analysis, model training, and workflow automation.
Thermodynamic Modeling: Working knowledge of CALPHAD methods and related software/tools.
Data Handling: Ability to manage, preprocess, and analyze large materials datasets.
Experimental Processing Awareness: Basic knowledge of ceramic processing or additive manufacturing techniques (e.g., DLP, sintering processes).
Desirable Technical Skills
Experience with materials informatics platforms (e.g., Materials Project, OQMD).
Familiarity with additive manufacturing and advanced sintering techniques (e.g., P-UHS, SPS).
Knowledge of characterization techniques (XRD, SEM, thermal analysis).
Exposure to high-performance computing (HPC) environments.
Job Description
This role focuses on establishing the computational foundation for advanced MAX phase discovery. The intern will train and deploy a state-of-the-art artificial intelligence model (ALIGNN) using a dataset of over 50,000 compounds to screen for stable MAX phase compositions with enhanced thermal properties. The intern will also conduct CALPHAD-based thermodynamic simulations to identify optimal processing windows and suppress the formation of impurity phases. Responsibilities include data preparation, model validation, thermodynamic analysis, and interpretation of results to guide experimental synthesis.
Preferred Intern Educational Level
Graduate level or above
Skill sets or Qualities
Required Skill Sets
Materials Science Fundamentals: Understanding of phase diagrams, thermodynamics, and structure–property relationships, especially for ceramics or MAX phases.
Computational Materials Modeling: Familiarity with machine learning or data-driven approaches for materials discovery (experience with ALIGNN or graph neural networks is a plus).
Programming Skills: Proficiency in Python for data analysis, model training, and workflow automation.
Thermodynamic Modeling: Working knowledge of CALPHAD methods and related software/tools.
Data Handling: Ability to manage, preprocess, and analyze large materials datasets.
Experimental Processing Awareness: Basic knowledge of ceramic processing or additive manufacturing techniques (e.g., DLP, sintering processes).
Desirable Technical Skills
Experience with materials informatics platforms (e.g., Materials Project, OQMD).
Familiarity with additive manufacturing and advanced sintering techniques (e.g., P-UHS, SPS).
Knowledge of characterization techniques (XRD, SEM, thermal analysis).
Exposure to high-performance computing (HPC) environments.