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Synthesis of the Artificial Intelligence and Model-Based and Statistical Algorithms in the Classification of the Metal Surface Defects

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Title Synthesis of the Artificial Intelligence and Model-Based and Statistical Algorithms in the Classification of the Metal Surface Defects
 
Creator Savic, Suzana Petrovic
Mijailovic, Nikola
Dzunic, Dragan
Kocovic, Vladimir
Devedzic, Goran
 
Subject Convolutional Neural Network
Active Contours
Steel
Defects
Spatial defect shape
 
Description 646-652
Steel has played an indispensable role in numerous industries, particularly in architecture, aerospace, and the automotive
sector, and has been one of the most crucial components in manufacturing. The possibility of defects in the steelmaking
process has had a substantial impact on the quality and service life of the final product. With the objective of ensuring a
timely response in steel production, this paper has presented a model for the classification, detection of defect regions, and
visualization of spatial defects. The model has been founded on the synthesis of convolutional neural network, snake
algorithms, and algorithms for generating spatial defects based on images. The convolutional neural network has been
trained using images from the NEU Surface Defect database, and model evaluation has been carried out on previously
unseen samples that have not been included in the training data. The convolutional neural network has achieved an overall
accuracy of 88.4% with unseen samples from the NEU Surface Defect database, with predictive abilities ranging from
72.7% to 97.7%. Following the classification, a spatial representation of the damage has been generated, and defect
segmentation on the material has been executed. The application of this model in modern industry has the potential to
significantly enhance the performance and quality of high-risk manufacturing processes, mitigate unnecessary losses, and
enable informed decision-making about future steps in a more insightful manner.
 
Date 2023-11-21T04:48:01Z
2023-11-21T04:48:01Z
2023-11
 
Type Article
 
Identifier 0971-4588 (Print); 0975-1017 (Online)
http://nopr.niscpr.res.in/handle/123456789/62911
https://doi.org/10.56042/ijems.v30i4.2105
 
Language en
 
Publisher NIScPR-CSIR,India
 
Source IJEMS Vol.30(4) [August 2023]