A method of automatic field of view generation and path planning for automated x‐ray inspection

With the increasing accuracy and stability requirements of SMT equipment, automatic x‐ray inspection (AXI) is used as a new popular type of testing technology. First, an imaging constraint model is proposed to ensure that the detected object is not truncated. Second, an improved iterative self‐organ...

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Published in:	Expert systems Vol. 42; no. 1
Main Authors:	Song, Guiling, Xu, Wei
Format:	Journal Article
Language:	English
Published:	Oxford Blackwell Publishing Ltd 01.01.2025
Subjects:	Algorithms automatic FOV generation Automation Clustering constraint model Field of view Heuristic methods Inspection Machine learning ML4CO Nodes shortest Hamiltonian path Shortest path planning X-rays
ISSN:	0266-4720, 1468-0394
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Abstract	With the increasing accuracy and stability requirements of SMT equipment, automatic x‐ray inspection (AXI) is used as a new popular type of testing technology. First, an imaging constraint model is proposed to ensure that the detected object is not truncated. Second, an improved iterative self‐organizing clustering algorithm was proposed to realize an automatic optimal generation of the field of view and form a Hamiltonian path. Finally, the solution of the shortest Hamiltonian path problem is given by the proposed VTSP heuristic algorithm and VTSP + transformer deep learning algorithm. Experimental results show that, compared with the binary state compression DP method and the local shortest path planning method, the proposed method can support large‐scale nodes, and the performance is improved by 16% on 75 nodes and 31% on 200 nodes. The proposed method achieves a balance between x‐ray imaging efficiency and performance.
AbstractList	With the increasing accuracy and stability requirements of SMT equipment, automatic x‐ray inspection (AXI) is used as a new popular type of testing technology. First, an imaging constraint model is proposed to ensure that the detected object is not truncated. Second, an improved iterative self‐organizing clustering algorithm was proposed to realize an automatic optimal generation of the field of view and form a Hamiltonian path. Finally, the solution of the shortest Hamiltonian path problem is given by the proposed VTSP heuristic algorithm and VTSP + transformer deep learning algorithm. Experimental results show that, compared with the binary state compression DP method and the local shortest path planning method, the proposed method can support large‐scale nodes, and the performance is improved by 16% on 75 nodes and 31% on 200 nodes. The proposed method achieves a balance between x‐ray imaging efficiency and performance.
Author	Xu, Wei Song, Guiling
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Cites_doi	10.1007/978-3-030-78230-6_25 10.3390/min12101296 10.1609/aaai.v33i01.33011443 10.1109/78.165668 10.1109/ICMCECS47690.2020.240847 10.21437/Interspeech.2020-2404 10.1007/978-3-319-99253-2_8 10.1609/aaai.v34i02.5509 10.1007/978-3-031-08011-1_14 10.1609/aaai.v35i8.16916 10.1007/978-3-030-56769-9_9 10.1007/s10601-022-09327-y 10.1016/j.ejor.2020.07.063 10.1002/jgt.22223
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SubjectTerms	Algorithms automatic FOV generation Automation Clustering constraint model Field of view Heuristic methods Inspection Machine learning ML4CO Nodes shortest Hamiltonian path Shortest path planning X-rays
Title	A method of automatic field of view generation and path planning for automated x‐ray inspection
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