基于图像特征融合的粉末床缺陷检测方法

doi:10.7527/S1000-6893.2020.24430

Abstract

Abstract: A visual detection method for powder bed defects in the additive manufacturing process based on feature fusion is proposed to improve the poor detection effect due to unclear expression of single features for powder bed defects. The algorithm firstly extracts the scale-space features, texture features, and geometric features from each powder bed image using methods of SIFT, gray-level co-occurrence matrix and Hu invariant moments; then, these images are translated to the feature matrix by serially fusing the three visual word histograms constructed from the three kinds of features by the bag-of-words model; finally, the feature matrix of each image is reduced by feature selection, and passed to the random forest classifier for model training. Experimental results show that different features have different contributions to the detection of different types of defects; through optimizing the feature fusion parameters, the average accuracy of the proposed algorithm reaches 97.46%, significantly improving the defect detection effect.

Key words: feature fusion, visual detection, powder bed defects, additive manufacturing, quality control

CLC Number:

SHI Binbin, CHEN Zhehan. Defect detection method of powder bed based on image feature fusion[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2021, 42(10): 524430-524430.

References

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Defect detection method of powder bed based on image feature fusion

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