PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Fractal Dimension as Robust Estimate of Low Carbon Steels Hardness

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Application of computational methods in engineering and science constantly increases, which is also visible in sector of material science, often with promising results. In following paper, authors would like to propose fractal dimension, a mathematical method of quantifying self-similarity and complexity of spatial patterns, as robust method of hardness estimation of low carbon steels. A dataset of microstructure images and corresponding Vickers hardness measurements of S235JR steel under different delivery conditions was created. Then, three different computational methods for evaluation of materials hardness based on microstructure image were tested. In this paper those methods are called: (i) Otsu-based index, (ii) fractal dimension index and (iii) vision transformer index. The results were compared with method used in literature for similar problems. Comparison showed that fractal dimension performs better than other evaluated methods, in terms of median absolute error, which value was equal to 4.12 HV1, which is significantly lower than results achieved by Otsu-based index and vision transformer index, which were 4.49 HV1 and 5.07 HV1 respectively. Those results can be attributed to the relative robustness of fractal dimension index, when compared to other methods. Robust estimation is preferable, due to the high amount of noise in the dataset, which is a consequence of the nature of used material.
Twórcy
  • Faculty of Microsystem Electronics and Photonics, Wrocław University of Science and Technology, ul. Janiszewskiego 11, 50-372 Wrocław, Poland
  • Faculty of Mechanical Engineering, Wrocław University of Science and Technology, ul. Łukasiewicza 5, 50-371 Wrocław, Poland
  • Faculty of Information and Communication Technology, Wrocław University of Science and Technology, ul. Janiszewskiego 11, 50-372 Wrocław, Poland
  • Faculty of Mechanical Engineering, Wrocław University of Science and Technology, ul. Łukasiewicza 5, 50-371 Wrocław, Poland
Bibliografia
  • 1. M. Szala, M. Szafran, W. Macek, S. Marchenko and T. Hejwowski, Abrasion Resistance of S235, S355, C45, AISI 304 and Hardox 500 Steels with Usage of Garnet, Corundum and Carborundum Abrasives, Advances in Science and Technology Research Journal, vol. 13, no. 4, pp. 151–161, 2019, https://doi.org/10.12913/22998624/113244.
  • 2. M. Szala, M. Walczak and A. Świetlicki, Effect of microstructure and hardness on cavitation erosion and dry sliding wear of HVOF deposited CoNiCrAlY, NiCoCrAlY and NiCrMoNbTa coatings, Materials, vol. 15, no. 1, 2022, https://doi.org/10.3390/ma15010093.
  • 3. P. Zhang, S. X. Li and Z. F. Zhang, General relationship between strength and hardness, Materials Science and Engineering A, vol. 529, no. 1, 2011, https://doi.org/10.1016/j.msea.2011.08.061.
  • 4. F. Khodabakhshi and A. P. Gerlich, On the correlation between indentation hardness and tensile strength in friction stir processed materials, Materials Science and Engineering A, vol. 789, 2020, https://doi.org/10.1016/j.msea.2020.139682.
  • 5. “ISO 6507–1 – Metallic materials – Vickers hardness test – Part 1: Test method,” 2018.
  • 6. H. Tsybenko, F. Farzam, G. Dehm and S. Brinckmann, Scratch hardness at a small scale: Experimental methods and correlation to nanoindentation hardness, Tribology International, vol. 163, 2021, https://doi.org/10.1016/j.triboint.2021.107168.
  • 7. R. Thompson Martínez, G. Alvarez Bestard, A. Martins Almeida Silva and S.C. Absi Alfaro, Analysis of GMAW process with deep learning and machine learning techniques, Journal of Manufacturing Processes, vol. 62, 2021, https://doi.org/10.1016/j.jmapro.2020.12.052.
  • 8. S. Guessasma, G. Montavon and C. Coddet, Modeling of the APS plasma spray process using artificial neural networks: basis, requirements and an example, Computational Materials Science, vol. 29, no. 3, pp. 315–333, 2004, https://doi.org/10.1016/j.commatsci.2003.10.007.
  • 9. S. Krajewski and J. Nowacki, Dual-phase steels microstructure and properties consideration based on artificial intelligence techniques, Archives of Civil and Mechanical Engineering, vol. 14, no. 2, 2014, https://doi.org/10.1016/j.acme.2013.10.002.
  • 10. C. Herriott and A. D. Spear, Predicting microstructure-dependent mechanical properties in additively manufactured metals with machine- and deep-learning methods, Computational Materials Science, vol. 175, p. 109599, 2020, https://doi.org/10.1016/j.commatsci.2020.109599.
  • 11. T.M. Nunes, V. H.C. De Albuquerque, J.P. Papa, C. C. Silva, P. G. Normando, E.P. Moura and J.M.R. Tavares, Automatic microstructural characterization and classification using artificial intelligence techniques on ultrasound signals, Expert Systems with Applications, vol. 40, no. 8, pp. 3096–3105, 2013, https://doi.org/10.1016/j.eswa.2012.12.025.
  • 12. J. Jung, J.I. Yoon, H.K. Park, J.Y. Kim and H.S. Kim, An efficient machine learning approach to establish structure-property linkages, Computational Materials Science, vol. 156, pp. 17–25, 2019, https://doi.org/10.1016/j.commatsci.2018.09.034.
  • 13. J. Jung, J.I. Yoon, H.K. Park, J.Y. Kim and H.S. Kim, Bayesian approach in predicting mechanical properties of materials: Application to dual phase steels, Materials Science and Engineering A, vol. 743, 2019, https://doi.org/10.1016/j.msea.2018.11.106.
  • 14. C. Payares-Asprino, Prediction of Mechanical Properties as a Function of Welding Variables in Robotic Gas Metal Arc Welding of Duplex Stainless Steels SAF 2205 Welds Through Artificial Neural Networks, Advances in Materials Science, vol. 21, no. 3, 2021, https://doi.org/10.2478/adms2021–0019.
  • 15. T. Trzepieciński, H.G. Lemu, Ł. Chodoła, D. Ficek and I. Szczęsny, Modelling Anisotropic Phenomena of Friction of Deep-Drawing Quality Steel Sheets Using Artificial Neural Networks, Advances in Materials Science, vol. 21, no. 3, 2021, https://doi.org/10.2478/adms-2021–0016.
  • 16. N. Khatavkar, S. Swetlana and A. K. Singh, Accelerated prediction of Vickers hardness of Coand Ni-based superalloys from microstructure and composition using advanced image processing techniques and machine learning, Acta Materialia, vol. 196, pp. 295–303, 2020, https://doi.org/10.1016/j.actamat.2020.06.042.
  • 17. X. Hu, J. Li, Z. Wang and J. Wang, A microstructure-informatic strategy for Vickers hardness forecast of austenitic steels from experimental data, Materials & Design, vol. 201, p. 109497, 2021, https://doi.org/10.1016/j.matdes.2021.109497.
  • 18. R. Tibshirani, Regression Shrinkage and Selection via the Lasso, Journal of the Royal Statistical Society. Series B (Methodological), vol. 58, pp. 267–288, 1996, https://doi.org/10.1111/j.2517–6161.1996.tb02080.x.
  • 19. P.J. Huber, Robust Regression: Asymptotics, Conjectures and Monte Carlo, The Annals of Statistics, vol. 1, pp. 799–821, 1973, https://doi.org/10.1214/aos/1176342503.
  • 20. B.B. Mandelbrot, How long is the coast of Britain? Statistical self-similarity and fractional dimension, Science, vol. 156, no. 3775, pp. 636–638, 1967, http://dx.doi.org/10.1126/science.156.3775.636.
  • 21. A. Kouzani, F. He and K. Sammut, Face image matching using fractal dimension, Conference: Image Processing, vol. 3, pp. 642–646, 1999, https://doi.org/10.1109/ICIP.1999.817194.
  • 22. H. Xu, J. Yan, N. Persson, W. Lin and H. Zha, Fractal Dimension Invariant Filtering and Its CNN-Based Implementation, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3825–3833, 2017, https://doi.org/10.1109/CVPR.2017.407.
  • 23. Q. Duan, J. An, H. Mao, D. Liang, H. Li, S. Wang, C. Huang, Review about the Application of Fractal Theory in the Research of Packaging Materials, Materials, 2021; 14(4):860. https://doi.org/10.3390/ma14040860.
  • 24. J.P. Hyslip and L.E. Vallejo, “Fractal analysis of the roughness and size distribution of granular materials,” Engineering Geology, vol. 48, no. 3–4, 1997, https://doi.org/10.1016/S0013–7952(97)00046-X.
  • 25. J.Z. Wang, J. Ma, Q.B. Ao, H. Zhi and H.P. Tang, “Review on Fractal Analysis of Porous Metal Materials,” Journal of Chemistry, vol. 2015, 2015, http://dx.doi.org/10.1155/2015/427297.
  • 26. A. Akrami, N. Nasiri and V. Kulish, Fractal dimension analysis of Mg2Si particles of Al–15%Mg2Si composite and its relationships to mechanical properties, Results in Materials, vol. 7, p. 100118, 2020, https://doi.org/10.1016/j.rinma.2020.100118.
  • 27. W. Macek, R. Branco, M. Korpyś and T. Łagoda, Fractal dimension for bending–torsion fatigue fracture characterisation, Measurement: Journal of the International Measurement Confederation, vol. 184, 2021, https://doi.org/10.1016/j.measurement.2021.109910.
  • 28. ISO 4948–1: Steels – Classification – Part 1: Classification of steels into unalloyed and alloy steels based on chemical composition, 1982.
  • 29. EN 10027–1: Designation systems for steels – Part 1: Steel names, 2016.
  • 30. A.K. Krella, D.E. Zakrzewska and A. Marchewicz, The resistance of S235JR steel to cavitation erosion, Wear, Vols. 452–453, 2020, https://doi.org/10.3390/ma14061456.
  • 31. EN 10025 – European standards for structural steel. Hot rolled products of structural steels., 2019.
  • 32. ASTM E407–07 – Standard Practice for Microetching Metals and Alloys, 2015.
  • 33. F. Zhuang, Z. Qi, K. Duan, D. Xi, Y. Zhu, H. Zhu, H. Xiong and Q. He, A Comprehensive Survey on Transfer Learning, Proceedings of the IEEE, vol. 109, no. 1, 2021, https://doi.org/10.48550/arXiv.1911.02685.
  • 34. Nobuyuki Otsu, A Threshold Selection Method from Gray-Level Histograms, IEEE Trans. Syst. Man Cybern, vol. 9, no. 1, 1979, https://doi.org/10.1109/TSMC.1979.4310076.
  • 35. M. Tan and Q. Le, EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks, Proceedings of the 36th International Conference on Machine Learning, vol. 97, pp. 6105–6114, 2019, https://doi.org/10.48550/arXiv.1905.11946.
  • 36. A. Kolesnikov, L. Beyer, X. Zhai, J. Puigcerver, J. Yung, S. Gelly and N. Houlsby, Big Transfer (BiT): General Visual Representation Learning, European conference on computer vision, 2019, https://doi.org/10.48550/arXiv.1912.11370.
  • 37. J. Devlin, M.-W. Chang, K. Lee, K. T. Google and A. I. Language, BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), Minneapolis, Minnesota, Association for Computational Linguistics, 2019, pp. 4171–4186.https://doi.org/10.48550/arXiv.1810.04805.
  • 38. A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit and N. Houlsby, An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale, arXiv, 2020, https://doi.org/10.48550/arXiv.2010.11929.
  • 39. A. Vaswani, G. Brain, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser and I.Polosukhin, Attention Is All You Need, Conference on Neural Information Processing Systems, 2017 https://doi.org/10.48550/arXiv.1706.03762.
  • 40. K. He, X. Zhang, S. Ren and J. Sun, Deep Residual Learning for Image Recognition, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, https://doi.org/10.48550/arXiv.1512.03385.
  • 41. T. Ridnik, E. Ben-Baruch, A. Noy and L. Zelnik-Manor, ImageNet-21K Pretraining for the Masses, NeurIPS 2021 Datasets and Benchmarks, 2021, https://doi.org/10.48550/arXiv.2104.10972.
  • 42. J. Canny, A Computational Approach to Edge Detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 6, pp. 679–698, 1986, https://doi.org/10.1109/TPAMI.1986.4767851.
  • 43. E.W. Weisstein, Minkowski-Bouligand Dimension. From MathWorld–A Wolfram Web Resource., [Online]. Available: https://mathworld.wolfram.com/Minkowski-BouligandDimension.html.
  • 44. C.E. Rasmussen and C.K. I. Williams, Chapter 2: Regression, in Gaussian Processes for Machine Learning, MIT University Press Group Ltd, 2006, pp. 25–51.
  • 45. J.C. Platt, Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods, in Advances in Large Margin Classifiers, MIT Press, 1999, pp. 61–74.
  • 46. C–C. Chang and C–J. Lin, LIBSVM: A library for support vector machines, ACM Transactions on Intelligent Systems and Technology, vol. 3, no. 2, 2007, https://doi.org/10.1145/1961189.1961199.
  • 47. K. He, X. Zhang, S. Ren and J. Sun, Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification, 2015 IEEE International Conference on Computer Vision (ICCV), 2015, pp. 1026–1034, https://doi.org/10.48550/arXiv.1502.01852.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-0bedb8bd-6b8b-4450-abfe-a10502691350
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.