Image analysis-based closed loop quality control for additive manufacturing with fused filament fabrication. (April 2019)

• Record Type: Journal Article
• Authors: Liu, Chenang; Law, Andrew Chung Chee; Roberson, David; Kong, Zhenyu (James)
• Is Part Of: Journal of manufacturing systems. Volume 51(2019)
• Page Start: 75
• Page End: 86
• Publication Date: 2019-04
• Subjects: Image analysis -- Fused filament fabrication (FFF) -- Defect detection/mitigation -- Closed-loop quality control; Manufacturing processes -- Periodicals; Production engineering -- Data processing -- Periodicals; Robots, Industrial -- Periodicals; Production, Technique de la -- Informatique -- Périodiques; Robots industriels -- Périodiques; Electronic journals; 670.42
• DOI: 10.1016/j.jmsy.2019.04.002
• Languages: English
• ISSNs: 0278-6125

Abstract

Highlights: An image-based closed-loop quality control system for fused filament fabrication (FFF) is developed to achieve online defect detection and mitigation. The system consists of two components: (1) an image analysis-based diagnosis; and (2) an automatic adjustment of machine parameters. Real-world case studies demonstrate that the developed system is capable to detect and mitigate the defects in a timely manner. Abstract: Additive manufacturing (AM) is a powerful technology for fabrication of components with complex geometries using a variety of materials. However, one of the major challenges in the AM industry is how to ensure product quality and consistency by detecting and then mitigating the defects, which otherwise can severely deteriorate the quality of AM products and even the sustainability of AM technology. Although optimizing machine parameter settings offline and post-processing of AM products can improve the quality, the effects may be still limited, particularly for the parts with complex geometries. The objective of this study is to develop an image-based closed-loop quality control system for a typical AM process, namely, fused filament fabrication (FFF). This system is implemented by a customized online image acquisition system with a proposed image diagnosis-based feedback quality control method. Based on this novel approach, the typical quality issues can be addressed by efficient and effective defect mitigation via online automatic machine parameter adjustment. The case studies based on an actual FFF platform demonstrate the effectiveness and applicability of the proposed approach.