Dimension Measurement and Classification of Metallic Materials Using Image Processing and Machine Learning
Yıl 2022,
Cilt: 3 Sayı: 2, 61 - 69, 26.12.2022
Tuba Yener
,
Furkan Hasan Sakacı
,
Şuayb Çağrı Yener
Öz
In industrial processes, dimension measurement and classification of metallic materials at the macroscopic level are performed for various purposes with various methodsIn this study, dimensions of metal materials belonging to three different types such as copper, aluminum and steel have been obtained by using image processing, and their classification has been performed by using machine learning. For the size measurement, over 99.5% accuracy has been achieved based on the quality of the camera module used and the image quality received. The performance of various machine learning methods has been tested for the material classification and the error-free result has been obtained with fine KNN.
Kaynakça
- [1] J. C. Grande, “Principles of Image Analysis,” Metallography, Microstructure, and Analysis, vol. 1, no. 5, pp. 227–243, Oct. 2012.
- [2] A. Picon, O. Ghita, P. F. Whelan, and P. M. Iriondo, “Fuzzy Spectral and Spatial Feature Integration for Classification of Nonferrous Materials in Hyperspectral Data,” IEEE Transactions on Industrial Informatics, vol. 5, no. 4, pp. 483–494, Nov. 2009.
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- [6] B. Jin, W. Hu, and H. Wang, “Image Classification Based on pLSA Fusing Spatial Relationships Between Topics,” IEEE Signal Processing Letters, vol. 19, no. 3, pp. 151–154, Mar. 2012.
- [7] G. Wu et al., “Light Field Image Processing: An Overview,” IEEE Journal of Selected Topics in Signal Processing, vol. 11, no. 7, pp. 926–954, Oct. 2017.
- [8] Bang Zhang, Yang Wang, and Fang Chen, “Multilabel Image Classification Via High-Order Label Correlation Driven Active Learning,” IEEE Transactions on Image Processing, vol. 23, no. 3, pp. 1430–1441, Mar. 2014.
- [9] G. Schwartz and K. Nishino, “Recognizing Material Properties from Images.”
- [10] E. S. Nadimi, J. Herp, M. M. Buijs, and V. Blanes-Vidal, “Texture classification from single uncalibrated images: Random matrix theory approach,” in 2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP), 2017, pp. 1–6.
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[19] A. Lashgari, S. Ghamami, S. Shahbazkhany, G. Salgado-Morán, and D. Glossman-Mitnik, “Fractal Dimension Calculation of a Manganese-Chromium Bimetallic Nanocomposite Using Image Processing,” Journal of Nanomaterials, vol. 2015, pp. 1–9, Apr. 2015.
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- [22] M. J. Mendenhall and E. Merenyi, “Relevance-Based Feature Extraction for Hyperspectral Images,” IEEE Transactions on Neural Networks, vol. 19, no. 4, pp. 658–672, Apr. 2008.
- [23] T. Aujeszky, G. Korres, and M. Eid, “Thermography-based material classification using machine learning,” in 2017 IEEE International Symposium on Haptic, Audio and Visual Environments and Games (HAVE), 2017, pp. 1–6.
- [24] B. L. DeCost and E. A. Holm, “A computer vision approach for automated analysis and classification of microstructural image data,” Computational Materials Science, vol. 110, pp. 126–133, Dec. 2015.
- [25] L. Kunčická, R. Kocich, and T. C. Lowe, “Advances in metals and alloys for joint replacement,” Progress in Materials Science, vol. 88, pp. 232–280, Jul. 2017.
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- [27] Z. Gácsi, “The Application of Digital Image Processing for Materials Science.”
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Yıl 2022,
Cilt: 3 Sayı: 2, 61 - 69, 26.12.2022
Tuba Yener
,
Furkan Hasan Sakacı
,
Şuayb Çağrı Yener
Kaynakça
- [1] J. C. Grande, “Principles of Image Analysis,” Metallography, Microstructure, and Analysis, vol. 1, no. 5, pp. 227–243, Oct. 2012.
- [2] A. Picon, O. Ghita, P. F. Whelan, and P. M. Iriondo, “Fuzzy Spectral and Spatial Feature Integration for Classification of Nonferrous Materials in Hyperspectral Data,” IEEE Transactions on Industrial Informatics, vol. 5, no. 4, pp. 483–494, Nov. 2009.
- [3] N. Salamati and C. Fredembach, “Material Classification Using Color and NIR Images.” https://infoscience.epfl.ch/record/142367/files/SalamatiFS2009.pdf (accessed February 3, 2019).
- [4] S. Su et al., “Material Classification Using Raw Time-of-Flight Measurements.” https://vccimaging.org/Publications/Su2016MCU/Su2016MCU.pdf (accessed February 3, 2019).
- [5] M. Strese, C. Schuwerk, A. Iepure, and E. Steinbach, “Multimodal Feature-Based Surface Material Classification,” IEEE Transactions on Haptics, vol. 10, no. 2, pp. 226–239, Apr. 2017.
- [6] B. Jin, W. Hu, and H. Wang, “Image Classification Based on pLSA Fusing Spatial Relationships Between Topics,” IEEE Signal Processing Letters, vol. 19, no. 3, pp. 151–154, Mar. 2012.
- [7] G. Wu et al., “Light Field Image Processing: An Overview,” IEEE Journal of Selected Topics in Signal Processing, vol. 11, no. 7, pp. 926–954, Oct. 2017.
- [8] Bang Zhang, Yang Wang, and Fang Chen, “Multilabel Image Classification Via High-Order Label Correlation Driven Active Learning,” IEEE Transactions on Image Processing, vol. 23, no. 3, pp. 1430–1441, Mar. 2014.
- [9] G. Schwartz and K. Nishino, “Recognizing Material Properties from Images.”
- [10] E. S. Nadimi, J. Herp, M. M. Buijs, and V. Blanes-Vidal, “Texture classification from single uncalibrated images: Random matrix theory approach,” in 2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP), 2017, pp. 1–6.
- [11] M. Varma and A. Zisserman, “A Statistical Approach to Material Classification Using Image Patch Exemplars,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 11, pp. 2032–2047, Nov. 2009.
- [12] Y. Liu, T. Zhao, W. Ju, and S. Shi, “Materials discovery and design using machine learning,” Journal of Materiomics, vol. 3, no. 3, pp. 159–177, Sep. 2017.
- [13] J. Rejc et al., “The mechanical assembly dimensional measurements with the automated visual inspection system,” Expert Systems with Applications, vol. 38, no. 8, pp. 10665–10675, Aug. 2011.
- [14] E. G. Loewen, “High Speed Optical Scanning Techniques for Dimensional Measurement,” CIRP Annals, vol. 29, no. 2, pp. 513–518, Feb. 1980.
- [15] G. Rosati, G. Boschetti, A. Biondi, and A. Rossi, “On-line dimensional measurement of small components on the eyeglasses assembly line,” Optics and Lasers in Engineering, vol. 47, no. 3–4, pp. 320–328, Mar. 2009.
- [16] P. Klapetek and T. Dziomba, “Dimensional Measurements,” Quantitative Data Processing in Scanning Probe Microscopy, pp. 97–149, Jan. 2018.
- [17] B. Chao, Q. Xinghua, L. Yong, L. Yaping, and L. Jingliang, “Dimensional Measurement of Small Hot Pieces Based on a Monochrome CCD,” Procedia Engineering, vol. 99, pp. 1158–1163, Jan. 2015.
- [18] N. H. Maerz, “Aggregate sizing and shape determination using digital image processing,” 1998, pp. 195–203.
[19] A. Lashgari, S. Ghamami, S. Shahbazkhany, G. Salgado-Morán, and D. Glossman-Mitnik, “Fractal Dimension Calculation of a Manganese-Chromium Bimetallic Nanocomposite Using Image Processing,” Journal of Nanomaterials, vol. 2015, pp. 1–9, Apr. 2015.
- [20] A. Shayei, M. Abbasi, A. Habiban, M. Shabany, and Z. Kavehvash, “A Machine Learning Approach for Material Classification in MMW Imaging Systems based on Frequency Spectra,” in 2018 IEEE International Symposium on Circuits and Systems (ISCAS), 2018, pp. 1–5.
- [21] Quan-De Wang, Zhi-Feng Zhong, and Xian-Pei Wang, “Design and implementation of insulators material hydrophobicity measure system by support vector machine decision tree learning,” in 2005 International Conference on Machine Learning and Cybernetics, 2005, pp. 4328-4334 Vol. 7.
- [22] M. J. Mendenhall and E. Merenyi, “Relevance-Based Feature Extraction for Hyperspectral Images,” IEEE Transactions on Neural Networks, vol. 19, no. 4, pp. 658–672, Apr. 2008.
- [23] T. Aujeszky, G. Korres, and M. Eid, “Thermography-based material classification using machine learning,” in 2017 IEEE International Symposium on Haptic, Audio and Visual Environments and Games (HAVE), 2017, pp. 1–6.
- [24] B. L. DeCost and E. A. Holm, “A computer vision approach for automated analysis and classification of microstructural image data,” Computational Materials Science, vol. 110, pp. 126–133, Dec. 2015.
- [25] L. Kunčická, R. Kocich, and T. C. Lowe, “Advances in metals and alloys for joint replacement,” Progress in Materials Science, vol. 88, pp. 232–280, Jul. 2017.
- [26] A. Pineau, A. A. Benzerga, and T. Pardoen, “Failure of metals I: Brittle and ductile fracture,” Acta Materialia, vol. 107, pp. 424–483, Apr. 2016.
- [27] Z. Gácsi, “The Application of Digital Image Processing for Materials Science.”
- [28] L. Barazzetti and M. Scaioni, “Development and implementation of image-based algorithms for measurement of deformations in material testing.,” Sensors (Basel, Switzerland), vol. 10, no. 8, pp. 7469–95, 2010.