Research on anti-interference detection of 3D-printed ceramics surface defects based on deep learning.

The inherent brittleness of 3D-printed ceramic parts makes the surface more prone to produce defects. In the 3D-printing environment, the defect regions in the surface are easily covered by interference factors. This brings great difficulties to surface defect detection. Based on this, this paper proposes an anti-interference detection method for surface defects of ceramic parts based on deep learning. This anti-interference detection method is mainly divided into three stages: interference factors identification, interference factors repair, defect detection. Interfering factors in the surface are located and identified through the built Multimodal Feature Layer Fusion-PSP network (MFLF-PSP net) model. MFLF-PSP net's mAP for interference factor identification is up to 95.67%. Then, based on the results of the interference identification, the proposed Parallel Spatial-Channel Attention Mechanism (PSCAM)-RFR net model is used to perform pixel filling and repair in the regions where the interference factor is located. This solves the difficult problem of defect detection caused by interference factors. On this basis, the constructed Inception-SSD network model is used to perform effective defect detection on ceramic surface images. The mAP of the model for the detection of crack defects and bulge defects is 96.34% and 94.92%, respectively. Through the close cooperation of the above three stages, the problem of defect detection caused by interference factors, such as misjudgment and defect region segmentation, has been solved. It provides technical support for automatic detection of ceramic surface quality during 3D-printing. [ABSTRACT FROM AUTHOR]