Anomaly Detection in the Production Process of Stamping Progressive Dies Using the Shape- and Size-Adaptive Descriptors Article Swipe
In the production process of progressive die stamping, anomaly detection is essential for ensuring the safety of expensive dies and the continuous stability of the production process. Early monitoring processes involve manually inspecting the quality of post-production products to infer whether there are anomalies in the production process, or using some sensors to monitor some state signals during the production process. However, the former is an extremely tedious and time-consuming task, and the latter cannot provide warnings before anomalies occur. Both methods can only detect anomalies after they have occurred, which usually means that damage to the die has already been caused. In this paper, we propose a machine-vision-based method for real-time anomaly detection in the production of progressive die stamping. This method can detect anomalies before they cause actual damage to the mold, thereby stopping the machine and protecting the mold and machine. In the proposed method, a whole continuous motion scene cycle is decomposed into a standard background template library, and the potential anomaly regions in the image to be detected are determined according to the difference from the background template library. Finally, the shape- and size-adaptive descriptors of these regions and corresponding reference regions are extracted and compared to determine the actual anomaly regions. The experimental results indicate that this method can achieve reasonable accuracy in the detection of anomalies in the production process of stamping progressive dies. The experimental results demonstrate that this method not only achieves satisfactory accuracy in anomaly detection during the production of progressive die stamping, but also attains competitive performance levels when compared with methods based on deep learning. Furthermore, it requires simpler preliminary preparations and does not necessitate the adoption of the deep learning paradigm.
Related Topics
Concepts
Anomaly detection
Process (computing)
Stamping
Anomaly (physics)
Die (integrated circuit)
Computer science
Production (economics)
Artificial intelligence
Machine vision
Pattern recognition (psychology)
Engineering
Mechanical engineering
Condensed matter physics
Physics
Macroeconomics
Economics
Operating system
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s23218904
- https://www.mdpi.com/1424-8220/23/21/8904/pdf?version=1698851795
- OA Status
- gold
- Cited By
- 2
- References
- 62
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4388228579
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4388228579Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/s23218904Digital Object Identifier
- Title
-
Anomaly Detection in the Production Process of Stamping Progressive Dies Using the Shape- and Size-Adaptive DescriptorsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-11-01Full publication date if available
- Authors
-
Liang Ma, Fanwu MengList of authors in order
- Landing page
-
https://doi.org/10.3390/s23218904Publisher landing page
- PDF URL
-
https://www.mdpi.com/1424-8220/23/21/8904/pdf?version=1698851795Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/1424-8220/23/21/8904/pdf?version=1698851795Direct OA link when available
- Concepts
-
Anomaly detection, Process (computing), Stamping, Anomaly (physics), Die (integrated circuit), Computer science, Production (economics), Artificial intelligence, Machine vision, Pattern recognition (psychology), Engineering, Mechanical engineering, Condensed matter physics, Physics, Macroeconomics, Economics, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2Per-year citation counts (last 5 years)
- References (count)
-
62Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| publication_date | 2023-11-01 |
| publication_year | 2023 |
| referenced_works | https://openalex.org/W3197549428, https://openalex.org/W4361216867, https://openalex.org/W4311881773, https://openalex.org/W2321806326, https://openalex.org/W2213558756, https://openalex.org/W4298145934, https://openalex.org/W2298614539, https://openalex.org/W1980455871, https://openalex.org/W2025485681, https://openalex.org/W2987840306, https://openalex.org/W2019427480, https://openalex.org/W2078087367, https://openalex.org/W1985204311, https://openalex.org/W2736973763, https://openalex.org/W2734715312, https://openalex.org/W2901931129, https://openalex.org/W6674756089, https://openalex.org/W2310112415, https://openalex.org/W2165221377, https://openalex.org/W2754563389, https://openalex.org/W3003842158, https://openalex.org/W2962449556, https://openalex.org/W2998291476, https://openalex.org/W1686810756, https://openalex.org/W2097117768, https://openalex.org/W2194775991, https://openalex.org/W2794865285, https://openalex.org/W2912272978, https://openalex.org/W2922017402, https://openalex.org/W2980326480, https://openalex.org/W2612454721, https://openalex.org/W2793387000, https://openalex.org/W2982512126, https://openalex.org/W3016245448, https://openalex.org/W2973760928, https://openalex.org/W4318567011, https://openalex.org/W3043075211, https://openalex.org/W3175308890, https://openalex.org/W3167259153, https://openalex.org/W4220901260, https://openalex.org/W3135077060, https://openalex.org/W3022506809, https://openalex.org/W4224213953, https://openalex.org/W3209793239, https://openalex.org/W4382998620, https://openalex.org/W1974991749, https://openalex.org/W2046852638, https://openalex.org/W2314444742, https://openalex.org/W2911912534, https://openalex.org/W2143023146, https://openalex.org/W2147555557, https://openalex.org/W2119605622, https://openalex.org/W204002496, https://openalex.org/W1965930075, https://openalex.org/W2151103935, https://openalex.org/W2963881378, https://openalex.org/W2963163009, https://openalex.org/W2994615081, https://openalex.org/W2389154218, https://openalex.org/W2097145484, https://openalex.org/W3048794851, https://openalex.org/W2884436604 |
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