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Machine Vision-Based Detection of Surface Defects of 3D-Printed Objects

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2021 ASEE Virtual Annual Conference Content Access


Virtual Conference

Publication Date

July 26, 2021

Start Date

July 26, 2021

End Date

July 19, 2022

Conference Session

Mechanical Engineering Division Poster Session

Tagged Division

Mechanical Engineering

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Paper Authors


Ma Muktadir North Carolina A&T State University

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M A Muktadir is a Graduated Student of Mechanical Engineering at North Carolina A&T State University. His research interests include; Finite Element Analysis, Mechanical Design, Machine Learning, Image Processing, Material Science, Additive Manufacturing, and Robotics. M A Muktadir received a B.S. in Mechanical Engineering from Bangladesh University of Professionals in 2011.

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Sun Yi North Carolina A&T State University

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Dr. Sun Yi is an associate professor of Mechanical Engineering at North Carolina A&T State University. He has developed new and novel methods for sensing and control algorithms for dynamic systems, which are adaptive and robust. The methods have also been applied to networked robots and UAVs/UGVs using AI, neural networks, sensor fusion, machine visions, and adaptive control. He has managed research projects supported by DoD, NASA, Dept. Energy, and Dept. Transportation.

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Due to advances in 3D printing technologies, 3D object manufacturing has attracted great attention nowadays. The market size of 3D printing is increasing exponentially ranging from tiny toys to nuclear reactors. The major advantage of this 3D print manufacturing over conventional manufacturing tools is that it can produce a complex object within a short period of time with a flexible but precise design. On the other hand, this tool has shown disadvantages as well. One of the main disadvantages is forming irregularities and defects within the 3D objects, which cost a significant amount of time and resources. Now the main challenge is to detect the problem in a timely manner and find a good solution, which might save a lot of time and money. In this study, a machine learning (ML) technique with a 3D vision camera is employed to detect and classify the defects of 3D objects. First, images collected from a depth (3D) camera are utilized to train the model. Then, the trained model is tested on a large set of real objects to detect defects. With the application of this technique, it is possible to detect the defects while printing. As surface defects are not only a serious issue for 3D printed products but also for many other types of manufacturing methodologies, we hope the research outcome can be applied in different manufacturing areas for maintenance of the pavement to the advanced inspection in a timely manner with high accuracy.

Muktadir, M., & Yi, S. (2021, July), Machine Vision-Based Detection of Surface Defects of 3D-Printed Objects Paper presented at 2021 ASEE Virtual Annual Conference Content Access, Virtual Conference. 10.18260/1-2--37471

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