Morphological Box Classification Framework for supporting 3D scanner selection

ABSTRACT

3-Dimensional (3D) scanning systems are becoming more common in the industry nowadays, for inspection and reverse engineering (RE) purposes. Although technical specifications are provided with commercially available scanners, a question could be raised pertaining to the degree of sufficiency of the technical specifications typically provided, with regard to specific application needs such as the scanning of challenging objects. These challenging objects present a less than ideal working condition for some 3D scanners, and the specified accuracy cannot be achieved. This effect varies across different types of 3D scanning technology. A more intuitive specification with regard to the time taken and ease of use will be beneficial to the user, but often not available. Hence, this paper proposes a Morphological Box Classification Framework based on the functional decomposition of the non-contact 3D scanning technology, in order to help users better understand and compare 3D scanners efficiently, and choose a scanner for their application that is able to perform within their desired accuracy, time taken, and ease of use. A case study of 3D scanners evaluation using the proposed framework for a RE application is conducted, and results presented.

KEYWORDS:

• 3D scanner
• morphological box
• digitisation
• reverse engineering
• inspection

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

W. L. K. Nguyen http://orcid.org/0000-0002-2532-754X

A. Aprilia http://orcid.org/0000-0002-3152-4216

G. G. L. Seet http://orcid.org/0000-0002-1505-2167

S. B. Tor http://orcid.org/0000-0001-8872-3032

Additional information

Funding

This research is supported by the National Research Foundation, Prime Minister’s Office, Singapore under its Medium-Sized Centre funding scheme. The authors acknowledge supports from the School of Mechanical and Aerospace Engineering and Singapore Centre for 3D Printing (SC3DP), NTU.

Notes on contributors

W. L. K. Nguyen

W. L. K. Nguyen is currently pursuing his PhD in the Singapore Centre for 3D Printing (SC3DP), School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, under the supervision of Assoc Prof Gerald Seet and Assoc Prof Tor Shu Beng. He received his B.Eng degree from the National University of Singapore. His research interest is in 3D Scanning, Damage Detection, and Geometry Retrieval from Point Cloud, for Repairing via Additive Manufacturing.

A. Aprilia

A. Aprilia is a PhD student in the Singapore Centre for 3D Printing, School of Mechanical and Aerospace Engineering, Nanyang Technological University (NTU), Singapore. Her research interest is in 3D model reconstruction of damaged parts in an automated repair or remanufacturing process. She is pursuing her PhD under the supervision of Assoc Professor Tor Shu Beng and Assoc Professor Seet Gim Lee, Gerald. She received her Bachelor’s degree in Mechanical Engineering from NTU in 2016. Aprilia Aprilia can be contacted at [email protected].

A. Khairyanto

A. Khairyanto received the B.Eng (mechanical & production engineering) degree and M.Eng (mechanical & aerospace engineering) degree from the Nanyang Technological University and was a research officer with the A-Star Institute of Microelectronics. He is currently a Research Associate with the Singapore Center for 3D Printing. His current research interests are in the areas of Reverse Engineering, 3D scanning and Additive Manufacturing.

W. C. Pang

W. C. Pang is a research associate at the Singapore Centre for 3D Printing (SC3DP), which is a national research centre for expanding the 3D printing capabilities in Singapore. Her research work includes point-cloud processing, object recognition, machine learning as well as robot design and human–robot interaction.

G. G. L. Seet

G. G. L. Seet is currently an Associate Professor with the School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore. He lectures in mechatronics, engineering design and real-time systems, at undergraduate and graduate levels. He holds a concurrent appointment as Director of the Robotics Research Centre, and as Head of the Division of Mechatronics and Design.

S. B. Tor

S. B. Tor is currently in the School of Mechanical & Aerospace Engineering since 1990. He obtained his Bachelor of Science from the Department of Mechanical Engineering, University of Westminster, England, the then Polytechnic of Central London. He obtained his PhD from the same University. He joined Hewlett-Packard Asia in 1986, and was subsequently promoted to R&D Productivity Manager. His research interests include micro fabrications, Mold and Tool Design and Manufacturing Informatics. He has done significant research work in his research areas and published over 150 top-quality international journal and conference papers. He has successfully commercialised a productivity CAD tool for plastic injection mould designer: QuickMould TM. The software was merged with the suite of CAD/CAM software from a leading US-based MNC: Unigraphics Solutions. A/P Tor is the faculty fellow of Singapore MIT Alliance (SMA) at Nanyang Technological University (NTU) since 1999. A/P Tor has also been appointed as Lecturer by the MIT department of Mechanical Engineering since 2000 through his involvement in SMA-IMST and SMA-MST programs.