References
- Polunin AI, Smyshlyaeva LG, Bondarenko TV. Accounting of dynamics of standing waves in a rotating ring with supports. International Journal of Applied Engineering Research. 2015;12:29015-29031.
- Busch K, Kunzmann H, Waldale F. Calibration of coordinate measuring machines. Precision Engineering. 1985;7(3):139-144.
- Weckenmann A, Kenngrossen F. Für die Angabe der Genauigkeit von Koordinatenmessgeräten. Technisches Messen. 1983;50(5):179-184.
- Elshennewy AH, Ham I, Cohen PH. Evaluating the performance of coordinate measuring machines. Quality Progress. 1988;59-65.
- Golubkova MN, Mayorov SA, Ochin EF. Coherent optical processor for detecting surface defects of rotary bodies (in Russian). In: Optical and Radio Wave Methods and Means of Non-Destructive Quality Control of Materials and Products. Part 1. Fergana: FerPI Publ.1981; 262-264.
- Ochin EF. Principles of constructing training automata for detecting surface defects of bodies of rotation (in Russian). Defectoscopy. 1985;7.
- Ochin EF, Chukreev DP. Analysis of algorithms for classification of surface defects of bodies of rotation based on signals of scanning transducers (in Russian). Defectoscopy. 1989;(1).
- Tao X, Zhang D, Ma W, Liu X, Xu D. Automatic metallic surface defect detection and recognition with convolutional neural networks. Applied Sciences. 2018;8(9):1575. https://doi.org/10.3390/app8091575
- Okeke S, Maduh UJ, Sain M. A machine learning method for detection of surface defects on ceramic tiles using convolutional neural networks. Electronics. 2022;11(1):55. https://doi.org/10.3390/electronics11010055
- Lin Z, Lai Y, Pan T, Zhang W, Zheng J, Ge X, Liu Y. A new method for automatic detection of defects in selective laser melting based on machine vision. Materials. 2021;14(15):4175. https://doi.org/10.3390/ma14154175
- Zhou Q, Chen R, Huang B, Liu C, Yu J, Yu X. An automatic surface defect inspection system for automobiles using machine vision methods. Sensors. 2019;19(3):644. https://doi.org/10.3390/s19030644
- Perec A, Pude F, Stirnimann J, Wegener K. Feasibility study on the use of fractal analysis for evaluating the surface quality generated by waterjet. Tehnicki Vjesnik-Technical Gazette. 2015;22:879-883. https://doi.org/10.17559/TV-20140128231244
- Navidi W. Statistics for engineers and scientists. 3rd ed. New York: McGraw-Hill; 2010.
- Koronacki J, Mielniczuk J. Statystyka dla studentów kierunków technicznych i przyrodniczych. Wydawnictwo WNT; 2018.
- Cheng J, Guo B, Liu J, et al. TL-SDD: A transfer learning-based method for surface defect detection with few samples. In: Proceedings of the 2021 7th International Conference on Big Data Computing and Communications (BigCom). Deqing, China. 2021;136-43. https://doi.org/10.1109/BigCom53800.2021.00023
- Khanam R, Hussain M, Hill R, Allen P. A comprehensive review of convolutional neural networks for defect detection in industrial applications. IEEE Access. 2024;12:94250-94295. https://doi.org/10.1109/ACCESS.2024.3425166.
- Bhatt P, Malhan R, Rajendran P, Shah B, Thakar S, Yoon YJ, Gupta S. Image-based surface defect detection using deep learning: a review. J Comput Inf Sci Eng. 2021;21:1-23. https://doi.org/10.1115/1.4049535
- Semitela Â, Pereira M, Completo A, Lau N, Santos JP. Improving industrial quality control: a transfer learning approach to surface defect detection. Sensors (Basel). 2025;25(2):527. doi:10.3390/s25020527.
- Milne A, Xie X. Steel surface roughness parameter prediction from laser reflection data using machine learning models. Int J Adv Manuf Technol. 2024;132:4645-62. https://doi.org/10.1007/s00170-024-13543-6
- Mariniuc AM, Cojocaru D, Abagiu MM. Building surface defect detection using machine learning and 3D scanning techniques in the construction domain. Buildings. 2024;14(3):669. https://doi.org/10.3390/buildings14030669
- Brand AS. Phase uncertainty in digital holographic microscopy measurements in the presence of solution flow conditions. J Res Natl Inst Stand Technol. 2017 Mar 27;122:1-41. https://doi.org/10.6028/jres.122.022
- Božič J, Tabernik D, Skočaj D. Mixed supervision for surface-defect detection: from weakly to fully supervised learning. Comput Ind. 2021;129:103459. https://doi.org/10.1016/j.compind.2021.103459
- Saiwa. Everything about surface defect detection [Internet]. https://saiwa.ai/app/detection/anomaly-detection/
- International Organization for Standardization. ISO 25178-6:2010. Geometrical product specifications (GPS) – Surface texture: Areal. Part 6: Classification of methods for measuring surface texture. 1st ed. Geneva: ISO; 2010. https://www.iso.org/standard/42896.html
- Lemieszewski Ł, Szymczyk J, Ochin E. Architecture of automatic defectoscopy machines on highly reflective rotary surfaces, part II. In: Proceedings of the XXVI International Symposium. Research-Education-Technology. Stralsund, Germany. 2024;26-27:98-103.