TY - JOUR
T1 - CenterNet-based defect detection for additive manufacturing
AU - Wang, Ruoxin
AU - Cheung, Chi Fai
N1 - Funding Information:
The work described in this paper was mainly supported by a grant from the Research Grants Council (Project No. 15202717) and Innovation and Technology Commission (ITC) (Project No.: ITS/076/18FP) of the Government of the Hong Kong Special Administrative Region, China. The authors would also like to express their sincere thanks to the Research Committee of The Hong Kong Polytechnic University for their financial support of the project through a PhD studentship (project account code: RK36).
Publisher Copyright:
© 2021 Elsevier Ltd
PY - 2022/2
Y1 - 2022/2
N2 - Additive manufacturing (AM) has been widely used in the fabrication of optical components. However, surface defects generated during the AM process have an adverse effect on surface quality. Although some studies have explored the defect features based on the processing of information including images, acoustic signals, thermal history, etc., they focus mainly on defect classification or one type of defect detection. Over recent years, convolution neural networks have displayed promising performance in object detection in images in various fields. Therefore, in this paper, to detect and characterize surface defects more comprehensively and accurately, a novel defect detection model based on CenterNet is presented to extract the defect features, including type, location and count simultaneously, in which there are four output heads to predict heatmaps, object size, local offset, and density map, respectively. Moreover, count loss is added in the original objective function to boost the detection performance. To perform the model validation, surface defect dataset is captured through scanning electron microscope on the surfaces of the workpiece made of 316L fabricated by AM. A series of experiments was conducted and the proposed model achieved better detection accuracy on defect dataset compared with other state-of-the-art models.
AB - Additive manufacturing (AM) has been widely used in the fabrication of optical components. However, surface defects generated during the AM process have an adverse effect on surface quality. Although some studies have explored the defect features based on the processing of information including images, acoustic signals, thermal history, etc., they focus mainly on defect classification or one type of defect detection. Over recent years, convolution neural networks have displayed promising performance in object detection in images in various fields. Therefore, in this paper, to detect and characterize surface defects more comprehensively and accurately, a novel defect detection model based on CenterNet is presented to extract the defect features, including type, location and count simultaneously, in which there are four output heads to predict heatmaps, object size, local offset, and density map, respectively. Moreover, count loss is added in the original objective function to boost the detection performance. To perform the model validation, surface defect dataset is captured through scanning electron microscope on the surfaces of the workpiece made of 316L fabricated by AM. A series of experiments was conducted and the proposed model achieved better detection accuracy on defect dataset compared with other state-of-the-art models.
KW - Additive manufacturing
KW - Convolutional neural network (CNN)
KW - Defect detection
KW - Density map estimation
KW - Machine learning
KW - Precision measurement
KW - Selective laser melting
KW - Surface defects
UR - http://www.scopus.com/inward/record.url?scp=85116904338&partnerID=8YFLogxK
U2 - 10.1016/j.eswa.2021.116000
DO - 10.1016/j.eswa.2021.116000
M3 - Journal article
AN - SCOPUS:85116904338
VL - 188
JO - Expert Systems with Applications
JF - Expert Systems with Applications
SN - 0957-4174
M1 - 116000
ER -