Web process inspection using neural classification of scattering light
Abstract
Web process inspection requires rapid examination of vast amounts of data. The two resulting issues, the sensory system and the computer processing required to detect faults in the sheet material accurately, were examined. It was found that scattered coherent light from the surface of the material being processed could be directly conditioned by a photodetector so as to produce a small set of features which are then examined by a neural network trained to find unsatisfactory surface conditions. A surface inspection system using measurement of the angular distribution over a 25 degrees C cone angle of the scattering was constructed, calibrated, and evaluated for inspection of coated sheet and steel samples. Features, created by a simulated segmented photodetector, were inputs to a neural network which used classification based upon T. Kohonen's (1989) learning vector quantization (LVQ2). The system was evaluated with CrO/sub 2/ coated steel samples. Classification by fault or no-fault categorized 133 samples corrected out of 135, while there were seven errors in one attempt at classification by the various common types of surface fault out of the same number of test samples and nine in another.>
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
SOM was used as an unsupervised classifier in the reviewed works [34,36,43], and LVQ in [62,89].
“While SOM was used in [34,36,43], LVQ was used in [62,89].”
From Review of vision-based steel surface inspection systems (2014) · cites this paper as [89]
Cites
Self-Organization and Associative Memory
Kohonen, T. (1989) · Springer Series in Information Sciences · doi:10.1007/978-3-642-88163-3 · Cited by 8,740 · see citing works
Vector quantization in speech coding
Makhoul, J., Roucos, S. E., & Gish, H. (1985) · Proceedings of the IEEE · doi:10.1109/proc.1985.13340 · Cited by 835 · see citing works
Automated visual inspection: 1981 to 1987
Chin, R. T. (1988) · Computer Vision Graphics and Image Processing · doi:10.1016/0734-189x(88)90108-9 · Cited by 174 · see citing works
An Efficient Nearest Neighbor Search Method
Soleymani, M. R., & Morgera, S. D. (1987) · IRE Transactions on Communications Systems · doi:10.1109/tcom.1987.1096830 · Cited by 66 · see citing works
Measurement of surface roughness parameter using a neural network and laser scattering
Villalobos, L., & Gruber, S. (1991) · Industrial Metrology · doi:10.1016/0921-5956(91)80023-9 · Cited by 4 · see citing works
Self-citationSurface Inspection Based On Stochastic Modelling
Attali, S. F., & Cohen, F. S. (1986) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.938773 · Cited by 2 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Review of vision-based steel surface inspection systems
Neogi, N., Mohanta, D. K., & Dutta, P. K. (2014) · EURASIP Journal on Image and Video Processing · doi:10.1186/1687-5281-2014-50 · Cited by 322 · see citing works
“While SOM was used in [34,36,43], LVQ was used in [62,89].”
BackgroundMethodResearch Progress of Visual Inspection Technology of Steel Products—A Review
Sun, X., Gu, J., Tang, S., & Li, J. (2018) · Applied Sciences · doi:10.3390/app8112195 · Cited by 109 · see citing works
Feature extraction algorithm based on adaptive wavelet packet for surface defect classification
Lee, C. S., Choi, C.-H., Choi, J. Y., Kim, Y. K., & Choi, S. H. (2002) · Proceedings of 3rd IEEE International Conference on Image Processing · doi:10.1109/icip.1996.560968 · Cited by 30 · see citing works
- Authors
- Olsson, L. J., & Gruber, S.
- Affiliations
- Case Western Reserve University (US)
- Venue
- IEEE Transactions on Industrial Electronics
- Published
- 1993-04
- Volume
- 40
- Issue
- 2
- Pages
- 228-234
- ISSN
- 0278-0046 (ISSN-L)
- OpenAlex
- W2121784479
- Cited by
- 40
- References
- 6
- Citations by year
- 2022: 1 · 2020: 1 · 2019: 1 · 2018: 2 · 2015: 2 · 2014: 1 · 2013: 1
- FWCI
- 2.90
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Industrial Electronics (publishedVersion)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Physical Sciences › Engineering › Mechanical Engineering › Non-Destructive Testing Techniques
- Physical Sciences › Engineering › Computational Mechanics › Surface Roughness and Optical Measurements
- Keywords
- surface defect detection, neural network classification, learning vector quantization, coated steel, chromium oxide coatings
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
