A novel image feature based self-supervised learning model for effective quality inspection in additive manufacturing

Chun Fai Lui (), Ahmed Maged and Min Xie
Additional contact information
Chun Fai Lui: City University of Hong Kong
Ahmed Maged: Benha University
Min Xie: City University of Hong Kong

Journal of Intelligent Manufacturing, 2024, vol. 35, issue 7, No 28, 3543-3558

Abstract: Abstract With the rapid development of additive manufacturing (AM) technology, quality inspection has become one of the most crucial research topics in additive manufacturing. Although numerous image-based deep learning methods have been successfully developed to monitor and inspect AM product quality effectively, many require substantial labels in order to achieve satisfactory training, which is often impractical in real-life AM processes. In this article, a novel image feature-based self-supervised learning (IFSSL) model is proposed for effective quality inspection in AM. Through a feature-based image fusion approach based on defect-relevant feature extraction, the IFSSL model is able to guide machine vision to focus on highlighted defect-relevant regions in the AM product image. In addition, the defect-relevant features are used to generate pseudo-labels for self-supervised learning. With self-supervision, the IFSSL model leverages the advantages of supervised learning and unsupervised learning by requiring no sample label while retaining defect-relevant information. The effectiveness of the proposed IFSSL method is demonstrated through a real case study of fused deposition modeling product image dataset. Results show that the IFSSL model can guide machine vision to pay more attention to potential defective regions, enabling it to detect and locate faults effectively and automatically for machine vision guided quality inspection.

Keywords: Additive manufacturing; 3D printing; Self-supervised learning; Quality inspection; Process monitoring; Anomaly detection (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10845-023-02232-y Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joinma:v:35:y:2024:i:7:d:10.1007_s10845-023-02232-y

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-023-02232-y

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().