Research project 

1. Advanced Powder Science & Slurry Engineering

We specialize in the science of process-ability, ensuring that raw materials are successfully transformed into high-precision, functional 3D geometries.

• Powder Technology Mapping: Systematically researching how particle morphology, size distribution, and density influence UV-curing kinetics and rheological behavior.

• Photocurable Slurry Development: Synthesis and characterization of high-loading slurries specifically engineered for Digital Light Processing (DLP).

• High-Density Ceramic Sintering: Leveraging specialized sintering aids to achieve near-theoretical density (>97%) in advanced structural ceramics while optimizing energy efficiency.

2. Microstructure Evolution & Interface Science

We treat manufacturing as a metallurgical journey, investigating material evolution at the micron scale to ensure parts meet rigorous industrial standards.

• Surface Functionalization: Pioneering Cold Spray (CS) deposition for the functionalization of substrates and the precision restoration of high-value industrial components.

• Advanced Micro-Characterization: Leveraging Synchrotron Microtomography (X-ray CT) and TEM to visualize pore distribution and interfacial bonding.

3. Thermal Characterization & Performance Reliability

Our lab provides industry-leading tools and statistical validation to accelerate material qualification and ensure mission-critical performance.

• High-Precision Thermal Measurements: Utilizing thermal diffusivity, heat capacity, and material density to analyze thermal conductivity across solid, liquid, and powder forms.

• Mechanical Reliability: Employing advanced statistical analysis to guarantee the characteristic strength and reliability of brittle materials for high-stress applications.

• Performance Engineering: Optimizing thermal and process controls for high-strength alloys (e.g., Ti6Al4V) and refractory ceramics to withstand extreme operating environments.

4. Material Informatics & Predictive Modeling

We integrate computational "Digital Twins" to reduce R&D costs and predict manufacturing outcomes.

• Finite Element Analysis (FEA): Utilizing FEA as a core modeling instrument to simulate complex material behavior and structural responses.

• Machine Learning (ML) Frameworks: Integrating data science with physical experiments to predict material properties and define optimal process windows for additive manufacturing and surface coating.