Research on defect detection algorithm of additive manufacturing powder spreading based on improved Faster R CNN

Yaoming Shang; Chengxiang Xiao; Kanghua Pan; Liang Xue

doi:10.1117/12.2645471

30 August 2022 Research on defect detection algorithm of additive manufacturing powder spreading based on improved Faster R CNN

Yaoming Shang, Chengxiang Xiao, Kanghua Pan, Liang Xue

Author Affiliations +

Proceedings Volume 12309, International Conference on Advanced Manufacturing Technology and Manufacturing Systems (ICAMTMS 2022); 123092H (2022) https://doi.org/10.1117/12.2645471
Event: International Conference on Advanced Manufacturing Technology and Manufacturing System (ICAMTMS 2022), 2022, Shijiazhuang, China

Abstract

In order to promote the industrialization of SLM process, this paper puts forward a new and efficient detection method, analyzes the defects in SLM process, and puts forward a nonlinear detection method for silk laying process in SLM process. Based on the Faster R CNN framework, the algorithm is optimized to increase the refinement and fusion of the deep information in the image, and a multi-scale feature fusion algorithm based on the original network is constructed. The algorithm model is trained, the performance is discussed, and the application suggestions are given.

Citation Download Citation

Yaoming Shang, Chengxiang Xiao, Kanghua Pan, and Liang Xue "Research on defect detection algorithm of additive manufacturing powder spreading based on improved Faster R CNN", Proc. SPIE 12309, International Conference on Advanced Manufacturing Technology and Manufacturing Systems (ICAMTMS 2022), 123092H (30 August 2022); https://doi.org/10.1117/12.2645471

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KEYWORDS

Additive manufacturing

Data modeling

Defect detection

Detection and tracking algorithms

Feature extraction

Spatial light modulators

Data fusion

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