A group of postgraduate students from SEGi University’s Faculty of Engineering, Built Environment and Information Technology have made significant strides in the field of additive manufacturing with their latest research on Selective Laser Melting (SLM) of Titanium Matrix Composites (TMCs). Under the guidance of leading faculty members, the research explores the intricate relationship between processing parameters, microstructure evolution, and mechanical properties of TMCs, a high-performance material widely used in aerospace, biomedical, and automotive applications.
The study, led by Jun Fang, Yong Chai Tan, Vin Cent Tai, and Chia Ching Kee, provides an in-depth analysis of the challenges and opportunities in fabricating TMCs using SLM technology. Their findings are expected to contribute significantly to the development of stronger, more durable, and lightweight metal composites, paving the way for next-generation manufacturing solutions.
The students’ research focuses on how different SLM processing parameters—such as laser power, scan speed, hatch distance, and layer thickness—affect the final properties of TMCs. Their Master’s thesis delves into the mechanisms that enhance the hardness, tensile strength, and wear resistance of titanium composites while also identifying critical defects such as balling, porosity, and cracking that can compromise material integrity.
One of the key takeaways from their research is that less than 5% reinforcement content by volume can significantly enhance mechanical properties. The study recorded maximum hardness values of approximately 1000 HV and tensile strength close to 1500 MPa, which are significant improvements over conventional titanium alloys. However, these enhancements come with a trade-off—a notable decrease in elongation, meaning reduced material flexibility.
A major challenge in SLM-processed TMCs is the formation of defects, which can severely impact the material’s performance. Through extensive experimentation and computational modeling, the SEGi researchers identified critical factors affecting defect evolution, including:
- Thermal Gradients & Residual Stresses – Leading to cracking and microstructural inconsistencies.
- Non-Uniform Reinforcement Distribution – Causing weak points in the final structure.
- Laser Scanning Strategies – Which influence the degree of porosity and overall densification.
By refining SLM process parameters and optimizing material distribution, the students have proposed new strategies to minimize defects and improve the reliability of TMCs in high-performance industries. Their research also suggests post-processing techniques such as heat treatment and hot isostatic pressing (HIP) as viable solutions for enhancing material properties.
The findings from this study align with global efforts to develop more sustainable and efficient manufacturing technologies. Additive manufacturing techniques like SLM have the potential to reduce material waste, improve energy efficiency, and enable complex designs that were previously impossible with traditional manufacturing methods.
This research also highlights the increasing role of machine learning and artificial intelligence (AI) in optimizing additive manufacturing processes. As the industry moves towards digital-driven solutions, integrating computational modeling with experimental research will be key to further enhancing TMC performance.
This research supports the United Nations Sustainable Development Goals (SDGs) by contributing to:
✅ SDG 9 – Industry, Innovation, and Infrastructure
✅ SDG 12 – Responsible Consumption and Production
✅ SDG 13 – Climate Action