Rapid surface defect identification for additive manufacturing with in-situ point cloud processing and machine learning

ABSTRACT

Surface monitoring is an essential part of quality assurance for additive manufacturing (AM). Surface defects need to be identified early in the AM process to avoid further deterioration of the part quality. In this paper, a rapid surface defect identification method for directed energy deposition (DED) is proposed. The main contribution of this work is the development of an in-situ point cloud processing with machine learning methods that enable automatic surface monitoring without sensor intermittence. An in-house software platform with a multi-nodal architecture is developed. In-situ point cloud processing steps, including filtering, segmentation, surface-to-point distance calculation, point clustering, and machine learning feature extraction, are performed by multiple subprocesses running simultaneously. The combined unsupervised and supervised machine learning techniques are applied to detect and classify surface defects. The proposed method is experimentally validated, and a surface defect identification accuracy of 93.15% is achieved.

KEYWORDS:

• Additive manufacturing
• directed energy deposition
• surface monitoring
• defect identification
• machine learning

Acknowledgements

This work was supported by A*ccelerate under the GAP grant for Machine Learning Platform for Manufacturing Equipment and Solutions (ACCL/19-GAP025-R20A). We also wish to acknowledge the funding support for this project from Nanyang Technological University under the Undergraduate Research Experience on Campus (URECA) programme.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by A*ccelerate [grant number ACCL/19-GAP025-R20A].

Notes on contributors

Lequn Chen

Lequn Chen is a student of Mechanical Engineering at Nanyang Technological University, Singapore.

Xiling Yao

Xiling Yao is a research scientist at Singapore Institute of Manufacturing Technology.

Peng Xu

Peng Xu is a research scientist at Singapore Institute of Manufacturing Technology.

Seung Ki Moon

Seung Ki Moon is an associate professor at Nanyang Technological University, Singapore.

Guijun Bi

Guijun Bi is a senior research scientist at Singapore Institute of Manufacturing Technology.