Recognizing steel elements with BRDF and k-nearest neighbors
The paper deals with analysis of recognition of surface quality with reflective structures. Such surfaces are common in metallic materials cut using a saw or polished. There are no easy methods to identify such elements after machining. This issue is crucial in the industry for quality control as re...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Polish Academy of Sciences
2023-11-01
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| Series: | Metrology and Measurement Systems |
| Subjects: | |
| Online Access: | https://journals.pan.pl/Content/130335/art08_int_cor.pdf |
| _version_ | 1797299299790356480 |
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| author | Adam Ciszkiewicz Janusz Jaglarz Tadeusz Uhl |
| author_facet | Adam Ciszkiewicz Janusz Jaglarz Tadeusz Uhl |
| author_sort | Adam Ciszkiewicz |
| collection | DOAJ |
| description | The paper deals with analysis of recognition of surface quality with reflective structures. Such surfaces are common in metallic materials cut using a saw or polished. There are no easy methods to identify such elements after machining. This issue is crucial in the industry for quality control as recognition of the elements, for instance after failure, allows for a detailed study of their manufacturing process. Firstly, six cuboid steel elements were obtained from a larger beam with a circular saw. Then, the bidirectional reflection distribution function (BRDF) was obtained for each element 3 times. The BRDF profiles were used in custom recognition software based on the K-nearest neighbors algorithm. In total, 140 variants of the classifier were tested and analyzed. Additionally, each variant was solved 200 times with different splits of the dataset. The results showed a high multiclass accuracy in all considered variants of the algorithm, with multiple variants achieving 100% accuracy. This level of performance was attained with only 1 to 2 training samples per class. Its low numerical complexity, easy experimental procedure, and “one-shot” nature allow for fast recognition, which is crucial in industrial applications. |
| first_indexed | 2024-03-07T22:47:38Z |
| format | Article |
| id | doaj.art-73b75b94197644118c45f8d0519a426f |
| institution | Directory Open Access Journal |
| issn | 2300-1941 |
| language | English |
| last_indexed | 2024-03-07T22:47:38Z |
| publishDate | 2023-11-01 |
| publisher | Polish Academy of Sciences |
| record_format | Article |
| series | Metrology and Measurement Systems |
| spelling | doaj.art-73b75b94197644118c45f8d0519a426f2024-02-23T14:16:25ZengPolish Academy of SciencesMetrology and Measurement Systems2300-19412023-11-01vol. 30No 4721736https://doi.org/10.24425/mms.2023.147958Recognizing steel elements with BRDF and k-nearest neighborsAdam Ciszkiewicz0Janusz Jaglarz1Tadeusz Uhl2Faculty of Mechanical Engineering, Cracow University of Technology, Al. Jana Pawła II 37, 31-864 Cracow, PolandFaculty of Material Engineering, Cracow University of Technology, Al. Jana Pawła II 37, 31-864 Cracow, PolandDepartment of Robotics and Mechatronics, Faculty of Mechanical Engineering and Robotics, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059, Cracow, PolandThe paper deals with analysis of recognition of surface quality with reflective structures. Such surfaces are common in metallic materials cut using a saw or polished. There are no easy methods to identify such elements after machining. This issue is crucial in the industry for quality control as recognition of the elements, for instance after failure, allows for a detailed study of their manufacturing process. Firstly, six cuboid steel elements were obtained from a larger beam with a circular saw. Then, the bidirectional reflection distribution function (BRDF) was obtained for each element 3 times. The BRDF profiles were used in custom recognition software based on the K-nearest neighbors algorithm. In total, 140 variants of the classifier were tested and analyzed. Additionally, each variant was solved 200 times with different splits of the dataset. The results showed a high multiclass accuracy in all considered variants of the algorithm, with multiple variants achieving 100% accuracy. This level of performance was attained with only 1 to 2 training samples per class. Its low numerical complexity, easy experimental procedure, and “one-shot” nature allow for fast recognition, which is crucial in industrial applications.https://journals.pan.pl/Content/130335/art08_int_cor.pdfmetallic surfacereflective surfacebidirectional reflection distribution functionclassification |
| spellingShingle | Adam Ciszkiewicz Janusz Jaglarz Tadeusz Uhl Recognizing steel elements with BRDF and k-nearest neighbors Metrology and Measurement Systems metallic surface reflective surface bidirectional reflection distribution function classification |
| title | Recognizing steel elements with BRDF and k-nearest neighbors |
| title_full | Recognizing steel elements with BRDF and k-nearest neighbors |
| title_fullStr | Recognizing steel elements with BRDF and k-nearest neighbors |
| title_full_unstemmed | Recognizing steel elements with BRDF and k-nearest neighbors |
| title_short | Recognizing steel elements with BRDF and k-nearest neighbors |
| title_sort | recognizing steel elements with brdf and k nearest neighbors |
| topic | metallic surface reflective surface bidirectional reflection distribution function classification |
| url | https://journals.pan.pl/Content/130335/art08_int_cor.pdf |
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