Department of Mechanical Engineering, National University of Singapore, Singapore, SingaporeAdditive manufacturing; 3D printing; 3D bioprinting; Artificial Intelligence; Machine learning
Dr Sing Swee Leong joins the Department of Mechanical Engineering, NUS, as an Assistant Professor in August 2021. Prior to joining NUS, he was a Presidential Postdoctoral Fellow at the School of Mechanical and Aerospace Engineering and Singapore Centre for 3D Printing, Nanyang Technological University, Singapore, after receiving the prestigious fellowship in 2020. Swee Leong was named a Highly Cited Researcher by Clarivate in 2024, 2023, and 2022. In 2022, he was also awarded the Young Professional Award by ASTM International for his work in additive manufacturing and contribution in standard development for the field. As a scientist and innovator, Swee Leong’s interest is enabling material development and creating strategic and sustainable values for Industry 4.0 and beyond through the use and integration of advanced manufacturing. He is actively involved in inter-disciplinary research.
Artificial intelligence (AI) is often linked to machine learning, neural networks, machine vision or even automation. The premise of AI is that the machine can solve a given problem by itself, with minimal human intervention, based on data and previous experiences. Additive manufacturing (AM), or 3D printing, as defined by the ISO/ASTM standards, is a group of manufacturing techniques that fabricate parts from 3D model data, usually layer upon layer as opposed to subtractive and formative manufacturing methodologies.
Due to the digital thread in AM, they are inherently suited to be integrated with Al, from the pre-process to the actual 3D printing and finally, the post-processing. In this Special Issue, state-of-the-art reviews and current research results which focus on using AI in AM will be reported. These include, but not limited to, computer vision for in-process monitoring systems, feedback control in 3D printing and process optimization for AM and related post-processing. Submissions related to novel applications, designs and processes in 3D printing and AI are also welcomed.
Contributions focused on AI in AM in any of the following topics are of particular interest:
- Design of 3D printable materials;
- In-process monitoring and quality control for AM;
- New AI applications in AM.
Machine learning applications for quality improvement in laser powder bed fusion: A state-of-the-art review
Artificial intelligence-driven material development for additive manufacturing: A critical review
Advancing intelligent additive manufacturing: Machine learning approaches for process optimization and quality control