Process Monitoring and Inspection for Industrial Applications

Course Provider

Centre for Professional and Continuing Education ([email protected])


Continuing Education and Training Certificate


Despite rapid development of additive manufacturing (AM), lack of quality assurance and repeatability in AM printed parts remains a major obstacle hindering widespread adoption of AM technologies. Process monitoring and inspections are identified as an effective approach to improve the part quality and repeatability.

This course provides an overview of key techniques for process monitoring and quality control of AM processes. First, relevant signals, sensors, and systems in process monitoring and inspection techniques are discussed. Subsequently, monitoring signatures of printing process and possible defects interpreted from the monitoring signatures are reviewed. Major methods for quality control and feedback control are then presented, followed by a summary of associated standards and toolkits. Finally, future outlook on the process monitoring is outlined.

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At the end of this course, the learners will learn:

1. Recognize the role and significance of process monitoring in AM process.
2. Understand the working principles of commonly used process monitoring methods, and related signals, signals, sensors, systems, advantages and disadvantages, and general applications cases.
3. Know how to implement in-principal quality and feedback control in process monitoring.
4. Be aware of relevant industry standards and tools, and the future development trends.

Day 1

1. Backgrounds
    a) Additive manufacturing
    b) Process monitoring and inspection

2. Signals, sensors and techniques for process monitoring
    a) Optical signal
    b) Thermal signal
    c) X-ray signal
    d) Acoustic signal
    e) Other signals

    For each signal, we introduce its corresponding sensors, the practice of use, advantages and disadvantages, and general applications in AM process monitoring.

3. Applications in AM processes
    a) PBF process
    b) DED process
    c) Material extrusion process
    d) Other AM processes

    For each process, we introduce the typical monitoring system, signatures, and detected defects.

4. Quality / Feedback control
    a) Process parameters
    b) Signal processing and feedback control
        i. Convectional method
        ii. Machine learning (ML) approach
    c) ML Applications in AM process and process monitoring

Day 2

5. Lab tour/demo of plastic printing
Lab tour/demo of power bed fusion printing
Lab tour/demo of process monitoring in AM process

6. Standards and toolkits 
    a) Relevant ASTM and ISO standards
    b) Some codes or tools for process monitoring
7. Insights and future outlook
8. Summary 
    Discussion and Assessment

This course is suitable for
1. Those operating the process monitoring machines in AM and other related fields.
2. Engineers working on the standardization of process monitoring in AM process and the development of monitoring machines, softwares, tools, etc.
3. Graduate students/researchers interested in the process monitoring of AM process.

Standard Course Fee: S$1,728.00

SSG Funding Support

 Course fee

Course fee payable after SSG funding, if eligible under various schemes


BEFORE funding & GST

AFTER funding & 7% GST

AFTER funding & 8% GST

Singapore Citizens (SCs) and Permanent Residents (PRs) (Up to 70% funding)




Enhanced Training Support for SMEs (ETSS)



SCs aged ≥ 40 years old
SkillsFuture Mid-career Enhanced Subsidy (MCES)
(Up to 90% funding)

• NTU/NIE alumni may utilise their $1,600 Alumni Course Credits. Click here for more information.

Note: Course fee payment made before 1 Jan 2023 will be subject to GST at 7%, and payment made on or after 1 Jan 2023 will be subject to GST at 8%.

Read more about funding


Dr. Nguyen obtained his PhD in Mechanical Engineering from Interdisciplinary Graduate School, NTU in 2017. He has bachelor’s degree in Mechanical Engineering from Ho Chi Minh University of Technology in 2013. His research focuses on the role of microstructure in guiding mechanical behaviors of materials (e.g., failure mechanism, tensile and fatigue performance). He is currently working on in-situ quality control of Additive Manufacturing processes.


Dr. Zhang obtained his PhD in Power Engineering and Engineering Thermophysics from Tsinghua University (Beijing) in 2019. He has bachelor’s degree in Thermal Energy and Power Engineering from Beijing Institute of Technology in 2014. His research focuses on the role of droplet impact dynamics, freezing/icing, and process monitoring and quality control of Additive Manufacturing processes.

Dr Tuan Tran

Dr Tuan TRAN

Dr Tran is currently an Associate Professor of the School of Mechanical & Aerospace Engineering, Nanyang Technological University (NTU), Singapore. He received his BSc degree in Engineering Mechanics from the Hanoi University of Science, Vietnam in 2004. He then pursued his graduate study which focused on thin-film flows and turbulent frictional drag at the University of Illinois at Urbana-Champaign, USA. Upon completion of his PhD degree in 2010, he worked at the Physics of Fluids Group at the University of Twente, The Netherlands, as a post-doctoral researcher. He also acts as the Deputy Director of the NTU hub at National Additive Manufacturing Innovation Cluster (NAMIC) with the mission to both provide financial support for translational research in 3D printing and promote adoption of 3D printing in Singapore’s industrial ecosystem. His research in 3D printing encompasses a wide range of activities, from fundamental works in development of droplet- and powder-based 3D printing technologies to translational works such as standards and qualification in 3D printing and 3D printing of conventional and wearable electronics.

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