{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T19:27:49Z","timestamp":1768073269238,"version":"3.49.0"},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,11,21]],"date-time":"2020-11-21T00:00:00Z","timestamp":1605916800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,11,21]],"date-time":"2020-11-21T00:00:00Z","timestamp":1605916800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100013348","name":"Innosuisse - Schweizerische Agentur f\u00fcr Innovationsf\u00f6rderung","doi-asserted-by":"publisher","award":["(27359.1 PFIW-IW)"],"award-info":[{"award-number":["(27359.1 PFIW-IW)"]}],"id":[{"id":"10.13039\/501100013348","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Machine Vision and Applications"],"published-print":{"date-parts":[[2021,1]]},"DOI":"10.1007\/s00138-020-01142-w","type":"journal-article","created":{"date-parts":[[2020,11,21]],"date-time":"2020-11-21T10:03:42Z","timestamp":1605953022000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Measurement and inspection of electrical discharge machined steel surfaces using deep neural networks"],"prefix":"10.1007","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3143-8107","authenticated-orcid":false,"given":"Jamal","family":"Saeedi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Matteo","family":"Dotta","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrea","family":"Galli","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Adriano","family":"Nasciuti","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Umang","family":"Maradia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marco","family":"Boccadoro","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luca Maria","family":"Gambardella","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alessandro","family":"Giusti","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,11,21]]},"reference":[{"key":"1142_CR1","volume-title":"Electrical Discharge Machining","author":"EC Jameson","year":"2001","unstructured":"Jameson, E.C.: Electrical Discharge Machining. SME, Dearborn, Michigan (2001)"},{"issue":"381","key":"1142_CR2","first-page":"1","volume":"8","author":"W Sun","year":"2018","unstructured":"Sun, W., Yao, B., Chen, B., He, Y., Cao, X., Zhou, T., Liu, H.: Noncontact surface roughness estimation using 2D complex wavelet enhanced ResNet for intelligent evaluation of milled metal surface quality. Appl. Sci. 8(381), 1\u201324 (2018)","journal-title":"Appl. Sci."},{"issue":"90","key":"1142_CR3","first-page":"1","volume":"9","author":"J Wang","year":"2018","unstructured":"Wang, J., Sanchez, J., Iturrioz, J., Ayesta, I.: Geometrical defect detection in the wire electrical discharge machining of fir-tree slots using deep learning techniques. Appl. Sci. 9(90), 1\u20138 (2018)","journal-title":"Appl. Sci."},{"issue":"2195","key":"1142_CR4","first-page":"1","volume":"8","author":"X Sun","year":"2018","unstructured":"Sun, X., Gu, J., Tang, S., Li, J.: Research progress of visual inspection technology of steel products\u2014a review. Appl. Sci. 8(2195), 1\u201325 (2018)","journal-title":"Appl. Sci."},{"key":"1142_CR5","unstructured":"Luk, F., Huynh, V.: A vision system for in-process surface quality assessment. In: Proceedings of the Vision, SME Conference, Detroit, Michigan, pp. 12\u201343 (1987)"},{"key":"1142_CR6","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1115\/1.2830135","volume":"120","author":"C Bradley","year":"1998","unstructured":"Bradley, C., Bohlmann, J., Kurada, S.: A fiber optic sensor for surface roughness measurement. J. Manuf. Sci. Eng. 120, 359\u2013367 (1998)","journal-title":"J. Manuf. Sci. Eng."},{"key":"1142_CR7","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1016\/S0007-8506(07)63347-2","volume":"31","author":"S Hisyoshi","year":"1982","unstructured":"Hisyoshi, S., Masanori, O.: Surface roughness measurement by scanning electron microscope. Ann. CIRP 31, 457\u2013462 (1982)","journal-title":"Ann. CIRP"},{"key":"1142_CR8","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/S0141-6359(97)00001-9","volume":"20","author":"M Bjuggren","year":"1997","unstructured":"Bjuggren, M., Krummenacher, L., Mattsson, L.: Non contact surface roughness measurement of engineering surface by total integrated infrared scattering. Precis. Eng. 20, 33\u201345 (1997)","journal-title":"Precis. Eng."},{"key":"1142_CR9","doi-asserted-by":"publisher","first-page":"631","DOI":"10.1016\/j.proeng.2014.11.783","volume":"90","author":"MAR Khan","year":"2014","unstructured":"Khan, M.A.R., Rahman, M.M., Kadirgama, K.: Neural network modeling and analysis for surface characteristics in electrical discharge machining. Procedia Eng. 90, 631\u2013636 (2014)","journal-title":"Procedia Eng."},{"key":"1142_CR10","doi-asserted-by":"publisher","first-page":"2603","DOI":"10.1007\/s00170-018-2070-2","volume":"97","author":"M Pour","year":"2018","unstructured":"Pour, M.: Determining surface roughness of machining process types using a hybrid algorithm based on time series analysis and wavelet transform. Int. J. Adv. Manuf. Technol. 97, 2603\u20132619 (2018)","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"1142_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00170-014-5828-1","volume":"73","author":"G Samta\u015f","year":"2014","unstructured":"Samta\u015f, G.: Measurement and evaluation of surface roughness based on optic system using image processing and artificial neural network. Int. J. Adv. Manuf. Technol. 73, 1\u20134 (2014)","journal-title":"Int. J. Adv. Manuf. Technol."},{"issue":"5","key":"1142_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/ma11050771","volume":"11","author":"AM Atieh","year":"2018","unstructured":"Atieh, A.M., Rawashdeh, N.A., AlHazaa, A.N.: Evaluation of surface roughness by image processing of a shot-peened, TIG-welded aluminum 6061\u2013T6 alloy: an experimental case study. Materials 11(5), 1\u201318 (2018)","journal-title":"Materials"},{"issue":"1\u20132","key":"1142_CR13","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1016\/0924-0136(91)90225-4","volume":"28","author":"DEP Hoy","year":"1991","unstructured":"Hoy, D.E.P., Yu, F.: Surface quality assessment using computer vision methods. J. Mater. Process. Technol. 28(1\u20132), 265\u2013274 (1991)","journal-title":"J. Mater. Process. Technol."},{"issue":"2","key":"1142_CR14","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1007\/s12596-018-0457-y","volume":"47","author":"RSU Raju","year":"2018","unstructured":"Raju, R.S.U., Ramesh, R., Raju, V.R., Mohammad, S.: Curvelet transforms and flower pollination algorithm based machine vision system for roughness estimation. J. Opt. 47(2), 243\u2013250 (2018)","journal-title":"J. Opt."},{"key":"1142_CR15","doi-asserted-by":"crossref","unstructured":"Zhou, L. et al: Study on brittle graphite surface roughness detection based on gray-level co-occurrence matrix. In: Proceedings of the 3rd International Conference on Mechanical, Control and Computer Engineering (2018)","DOI":"10.1109\/ICMCCE.2018.00062"},{"key":"1142_CR16","unstructured":"Ghodrati, S., Kandi, S.G., Mohseni, M.: A histogram-based image processing method for visual and actual roughness prediction of sandpaper. In: Proceedings of the 6th International Congress on Color and Coatings (2015)"},{"key":"1142_CR17","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1115\/1.2920243","volume":"112","author":"A Majumdar","year":"1990","unstructured":"Majumdar, A., Bhushan, B.: Role of fractal geometry in roughness characterisation and contact mechanics of surface. J. Tri. ASME 112, 205\u2013216 (1990)","journal-title":"J. Tri. ASME"},{"issue":"8","key":"1142_CR18","doi-asserted-by":"publisher","first-page":"2583","DOI":"10.1007\/s00034-014-9764-y","volume":"33","author":"J Saeedi","year":"2014","unstructured":"Saeedi, J., Faez, K., Moradi, M.H.: Hybrid fractal-wavelet method for multi-channel EEG signal compression. Circuits Syst. Signal Process. 33(8), 2583\u20132604 (2014)","journal-title":"Circuits Syst. Signal Process."},{"issue":"5","key":"1142_CR19","first-page":"30","volume":"4","author":"DA Fadare","year":"2009","unstructured":"Fadare, D.A., Oni, A.O.: Development and application of a machine vision system for measurement of surface roughness. J. Eng. Appl. Sci. 4(5), 30\u201337 (2009)","journal-title":"J. Eng. Appl. Sci."},{"key":"1142_CR20","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1007\/s00170-011-3480-6","volume":"59","author":"P Morala-Arg\u00fcello","year":"2012","unstructured":"Morala-Arg\u00fcello, P., Barreiro, J., Alegre, E.: A evaluation of surface roughness classes by computer vision using wavelet transform in the frequency domain. Int. J. Adv. Manuf. Technol. 59, 213\u2013220 (2012)","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"1142_CR21","doi-asserted-by":"publisher","first-page":"412","DOI":"10.1007\/BF01304620","volume":"14","author":"DM Tsai","year":"1998","unstructured":"Tsai, D.M., Chen, J.J., Chen, J.F.: A vision system for surface roughness assessment using neural networks. Int. J. Adv. Manufac. Technol. 14, 412\u2013422 (1998)","journal-title":"Int. J. Adv. Manufac. Technol."},{"key":"1142_CR22","first-page":"1","volume":"3978410","author":"S Luo","year":"2017","unstructured":"Luo, S., Yang, J., Gao, Q., Zhou, S., Zhan, C.A.: The edge detectors suitable for retinal OCT image segmentation. J. Healthcare Eng. 3978410, 1\u201313 (2017)","journal-title":"J. Healthcare Eng."},{"issue":"1","key":"1142_CR23","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1117\/1.1631315","volume":"13","author":"M Sezgin","year":"2004","unstructured":"Sezgin, M., Sankur, B.: Survey over image thresholding techniques and quantitative performance evaluation. J. Electron. Imaging 13(1), 146\u2013165 (2004)","journal-title":"J. Electron. Imaging"},{"issue":"11","key":"1142_CR24","first-page":"27","volume":"29","author":"B Sathya","year":"2011","unstructured":"Sathya, B., Manavalan, R.: Image segmentation by clustering methods: performance analysis. Int. J. Comput. Appl. 29(11), 27\u201332 (2011)","journal-title":"Int. J. Comput. Appl."},{"key":"1142_CR25","first-page":"176","volume":"88","author":"L Yi","year":"2016","unstructured":"Yi, L., Li, G., Jiang, M.: An end-to-end steel strip surface defects recognition system based on convolutional neural networks. Steel Res. Int. 88, 176\u2013187 (2016)","journal-title":"Steel Res. Int."},{"key":"1142_CR26","doi-asserted-by":"crossref","unstructured":"Masci, J., Meier, U., Ciresan, D., Schmidhuber, J.: Steel defect classification with max-pooling convolutional neural networks. In: International Joint Conference on Neural Networks, Brisbane, QLD, Australia. IEEE, Piscataway, NJ, USA, vol. 20, pp. 1\u20136 (2012)","DOI":"10.1109\/IJCNN.2012.6252468"},{"issue":"3","key":"1142_CR27","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1007\/s10044-011-0235-9","volume":"16","author":"J Saeedi","year":"2013","unstructured":"Saeedi, J., Faez, K.: A classification and fuzzy-based approach for digital multi-focus image fusion. Pattern Anal. Appl. 16(3), 365\u2013379 (2013)","journal-title":"Pattern Anal. Appl."},{"key":"1142_CR28","unstructured":"Saeedi, J.: Image fusion in the multi-scale transforms domain using fuzzy logic and particle swarm optimization. Master dissertation, Amirkabir University of technology (2010)"},{"key":"1142_CR29","unstructured":"ISO 4287 Geometrical Product Specifications (GPS)\u2014Surface texture: Profile method\u2014Terms, definitions and surface texture parameters (1997)"},{"key":"1142_CR30","unstructured":"Boccadoro, M., Giusti, A., Gambardella, L.M.: Method for machining and inspecting of workpieces. European Patent No. EP3326749 B1 (2016)"},{"issue":"12","key":"1142_CR31","doi-asserted-by":"publisher","first-page":"2751","DOI":"10.1109\/TMM.2017.2710804","volume":"19","author":"Q You","year":"2017","unstructured":"You, Q., Pang, R., Cao, L., Luo, J.: Image-based appraisal of real estate properties. IEEE Trans. Multimedia 19(12), 2751\u20132759 (2017)","journal-title":"IEEE Trans. Multimedia"},{"issue":"11","key":"1142_CR32","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y Lecun","year":"1998","unstructured":"Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278\u20132324 (1998)","journal-title":"Proc. IEEE"},{"issue":"6","key":"1142_CR33","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.: Imagenet classification with deep convolutional neural networks. Commun. ACM 60(6), 84\u201390 (2012)","journal-title":"Commun. ACM"},{"key":"1142_CR34","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556 (2015)"},{"key":"1142_CR35","unstructured":"Andrew, G.H. et al.: MobileNets: efficient convolutional neural networks for mobile vision applications. arXiv:1704.04861 (2017)"},{"key":"1142_CR36","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S. Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"1142_CR37","doi-asserted-by":"crossref","unstructured":"Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1800\u20131807 (2017)","DOI":"10.1109\/CVPR.2017.195"},{"key":"1142_CR38","volume-title":"Deep Learning with Python","author":"F Chollet","year":"2017","unstructured":"Chollet, F.: Deep Learning with Python. Manning Publications Co, Shelter Island (2017)"},{"key":"1142_CR39","doi-asserted-by":"crossref","unstructured":"Maradia, U., Scuderi, M., Knaak, R., Boccadoro, M., Beltrami, I., Stirnimann, J., Wegener, K.: Super-finished surfaces using Meso-micro EDM. In: Proceedings of the 17th CIRP Conference on Electro Physical and Chemical Machining (ISEM), pp. 157\u2013162 (2013)","DOI":"10.1016\/j.procir.2013.03.076"},{"issue":"2","key":"1142_CR40","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1109\/83.217222","volume":"2","author":"L Vincent","year":"1993","unstructured":"Vincent, L.: Morphological grayscale reconstruction in image analysis: applications and efficient algorithms. IEEE Trans. Image Process. 2(2), 176\u2013201 (1993)","journal-title":"IEEE Trans. Image Process."},{"issue":"5","key":"1142_CR41","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1109\/34.1000236","volume":"24","author":"C Dorin","year":"2002","unstructured":"Dorin, C., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 603\u2013619 (2002)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"9","key":"1142_CR42","doi-asserted-by":"publisher","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","volume":"37","author":"K He","year":"2015","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1904\u20131916 (2015)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1142_CR43","doi-asserted-by":"crossref","unstructured":"Grauman, K., Darrell, T.: The pyramid match kernel: discriminative classification with sets of image features. In: 10th IEEE International Conference on Computer Vision, vol. 1, pp. 1458\u20131465 (2005)","DOI":"10.1109\/ICCV.2005.239"},{"key":"1142_CR44","doi-asserted-by":"crossref","unstructured":"Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2169\u20132178 (2006)","DOI":"10.1109\/CVPR.2006.68"},{"issue":"1","key":"1142_CR45","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/0031-3203(95)00067-4","volume":"29","author":"T Ojala","year":"1996","unstructured":"Ojala, T., Pietikinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recogn. 29(1), 51\u201359 (1996)","journal-title":"Pattern Recogn."},{"key":"1142_CR46","first-page":"886","volume":"1","author":"N Dalal","year":"2005","unstructured":"Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. Int. Conf. Comput. Vis. Pattern Recogn. 1, 886\u2013893 (2005)","journal-title":"Int. Conf. Comput. Vis. Pattern Recogn."},{"key":"1142_CR47","doi-asserted-by":"crossref","unstructured":"Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1\u20139 (2001)","DOI":"10.1109\/CVPR.2001.990517"},{"issue":"3","key":"1142_CR48","first-page":"273","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes, C., Vapnik, V.N.: Support-vector networks. Mach. Learn. 20(3), 273\u2013297 (1995)","journal-title":"Mach. Learn."},{"key":"1142_CR49","doi-asserted-by":"crossref","unstructured":"Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. ArXiv e-prints (2016)","DOI":"10.1145\/2939672.2939785"},{"key":"1142_CR50","unstructured":"Ho, T.K.: Random decision forests. In: Proceedings of the 3rd International Conference on Document Analysis and Recognition, Montreal, QC, pp. 278\u2013282 (1995)"},{"key":"1142_CR51","first-page":"1","volume":"50","author":"N Neogi","year":"2014","unstructured":"Neogi, N., Mohanta, D.K., Dutta, P.K.: Review of vision-based steel surface inspection systems. J. Image Video Proc. 50, 1\u201319 (2014)","journal-title":"J. Image Video Proc."},{"key":"1142_CR52","doi-asserted-by":"crossref","unstructured":"Sun, Y., Wang, X., Tang, X.: Deeply learned face representations are sparse, selective, and robust. arXiv preprint arXiv:1412.1265 (2014)","DOI":"10.1109\/CVPR.2015.7298907"},{"key":"1142_CR53","volume-title":"Pattern Recognition and Machine Learning","author":"CM Bishop","year":"2006","unstructured":"Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Berlin (2006)"}],"container-title":["Machine Vision and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00138-020-01142-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00138-020-01142-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00138-020-01142-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,17]],"date-time":"2024-08-17T21:00:39Z","timestamp":1723928439000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00138-020-01142-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,21]]},"references-count":53,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,1]]}},"alternative-id":["1142"],"URL":"https:\/\/doi.org\/10.1007\/s00138-020-01142-w","relation":{},"ISSN":["0932-8092","1432-1769"],"issn-type":[{"value":"0932-8092","type":"print"},{"value":"1432-1769","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,11,21]]},"assertion":[{"value":"20 April 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 August 2020","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 October 2020","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 November 2020","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"21"}}