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Team member 


Dr. GOH Guo Dong

Research Fellow

Nanyang Technological University

Guo Dong’s interest is in additive manufacturing, or 3D printing, particularly in the extrusion-based technique for composites materials. He is right now learning computer vision and machine learning.

He completed his Ph.D. research in Mechanical Engineering, in 2020 from Nanyang Technological University, Singapore. His research was focused on Mode I interlaminar fracture toughness of the 3D printed fiber reinforced thermoplastics. His research involves the fabrication of aerospace-related structures such as UAVs.

In 2015, he graduated with a Bachelor's degree in Aerospace Engineering (Honours), also from Nanyang Technological University, Singapore, specializing in Aircraft Reliability & Maintainability and Autonomous Unmanned Aerial Vehicle (UAV).

Additive Manufacturing of Fiber Reinforced Polymer Composite


Additive  Manufacturing  (AM)  has  brought  about  a  revolution  in  manufacturing  complex products  with customized  features.  It is finding  application  in  everything  from  aerospace, automotive, consumer to biomedical as it evolves. AM of composites is especially attractive as it  holds  promise  to  improve,  modify and  diversify  the  properties  of  generic  materials  by introducing reinforcements. AM techniques focusing on continuous fibers have been evaluated in-depth  to  cover  all  aspects; as  these  hold the promise of becoming the  next-generation  composite  fabrication  methodology. Potential future  works and  challenges in printing fiber reinforced polymer composites (FRPC) have also been identified. These findings ascertained the potential of extrusion-based AM technique towards the fabrication of continuous fiber reinforced composites in a dimensionally defined construct.

Machine learning in 3D printing

Additive manufacturing (AM) or 3D printing is growing rapidly in the manufacturing industry and has gained a lot of attention from various fields owing to its ability to fabricate parts with complex features. The reliability of the 3D printed parts has been the focus of the researchers to realize AM as an end-part production tool. Machine learning (ML) has been applied in various aspects of AM to improve the whole design and manufacturing workflow especially in the era of industry 4.0. In this review article, various types of ML techniques are first introduced. It is then followed by the discussion on their use in various aspects of AM such as design for 3D printing, material tuning, process optimization, in situ monitoring, cloud service, and cybersecurity. Potential applications in the biomedical, tissue engineering and building and construction will be highlighted. The challenges faced by ML in AM such as computational cost, standards for qualification and data acquisition techniques will also be discussed. In the authors’ perspective, in situ monitoring of AM processes will significantly benefit from the object detection ability of ML. As a large data set is crucial for ML, data sharing of AM would enable faster adoption of ML in AM. Standards for the shared data are needed to facilitate easy sharing of data. The use of ML in AM will become more mature and widely adopted as better data acquisition techniques and more powerful computer chips for ML are developed.

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