Name
Abstract | ||
|---|---|---|
Technology can give industries the ability to create products/materials/services that meet customer needs and comply with applicable regulatory obligations. In this context, an automatic damage detection system is proposed for sandwich panels. Instead of relying on manual inspection, the system is based on artificial vision and operates with high accuracy in an industrial environment, ensuring traceability in product quality, reducing the percentage of returns caused by imperfections. The adaptive thresholding method seeks to identify the pixel intensities found on the surface of the sandwich panel. Unlike existing methods, the proposed algorithm is based on an adaptive threshold that uses the local characteristics of an image to segment and classify damage on the surfaces of sandwich panels, seeking to reject or accept a product according to the quality levels defined by the standard. The experimental results propose to generate a comparison with a sandwich panel damage detection method based on a convolutional neural network. The results of the experiment show that the proposed thresholding-based method has better accuracy and F1Score than deep learning methods. Moreover, this system is able to improve the industrial standards of sandwich panel manufacturing according to the standard, which limits the allowable imperfections, pointing out only the maximum admissible value of manufacturing imperfections to obtain a quality product. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1007/978-3-031-15471-3_25 | Hybrid Artificial Intelligent Systems |
Keywords | DocType | ISSN |
Damage identification, Deep learning, Feature extraction, Computer vision | Conference | 0302-9743 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Florez Sebastian Lopez | 1 | 0 | 0.34 |
| Silva Marcos Severt | 2 | 0 | 0.34 |
| González-Briones Alfonso | 3 | 0 | 0.34 |
| Chamoso Pablo | 4 | 0 | 0.34 |