{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T21:26:33Z","timestamp":1772832393632,"version":"3.50.1"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,12,23]],"date-time":"2024-12-23T00:00:00Z","timestamp":1734912000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,23]],"date-time":"2024-12-23T00:00:00Z","timestamp":1734912000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Machine Vision and Applications"],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1007\/s00138-024-01650-z","type":"journal-article","created":{"date-parts":[[2024,12,23]],"date-time":"2024-12-23T13:50:42Z","timestamp":1734961842000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Innovative surface roughness detection method based on white light interference images"],"prefix":"10.1007","volume":"36","author":[{"given":"Huguang","family":"Yang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaojing","family":"Su","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Botao","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chenglong","family":"Xia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Han","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingyang","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Taohong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,12,23]]},"reference":[{"key":"1650_CR1","doi-asserted-by":"publisher","first-page":"109677","DOI":"10.1016\/j.measurement.2021.109677","volume":"182","author":"L Zou","year":"2021","unstructured":"Zou, L., Fang, H., Li, Y., Wu, S.: Roughness estimation of high-precision surfaces from line blur functions of reflective images. Measurement. 182, 109677 (2021)","journal-title":"Measurement"},{"key":"1650_CR2","doi-asserted-by":"publisher","first-page":"685","DOI":"10.1016\/S0890-6955(97)00118-1","volume":"38","author":"M Kiran","year":"1998","unstructured":"Kiran, M., Ramamoorthy, B., Radhakrishnan, V.: Evaluation of surface roughness by vision system. Int. J. Mach. Tools Manuf. 38, 685\u2013690 (1998)","journal-title":"Int. J. Mach. Tools Manuf"},{"key":"1650_CR3","doi-asserted-by":"publisher","first-page":"107728","DOI":"10.1016\/j.optlaseng.2023.107728","volume":"169","author":"W Chen","year":"2023","unstructured":"Chen, W., Xiong, Y., Chen, J., Chen, S.: Accurate white light phase-shifting interferometry under PZT scanning error. Opt. Lasers Eng. 169, 107728 (2023)","journal-title":"Opt. Lasers Eng."},{"key":"1650_CR4","unstructured":"Chen, J., Wang, B., Chen, X., et al.: A Micro-Topography Measurement and Compensation Method for the Key Component Surface Based on White-Light Interferometry. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China 23:8307. (2023)"},{"key":"1650_CR5","doi-asserted-by":"publisher","first-page":"11912","DOI":"10.1364\/OE.451746","volume":"30","author":"L Ma","year":"2022","unstructured":"Ma, L., Zhao, Y., Pei, X., et al.: Phase noise estimation based white light scanning interferometry for high-accuracy surface profiling. Opt. Express. 30, 11912\u201311922 (2022)","journal-title":"Opt. Express"},{"key":"1650_CR6","doi-asserted-by":"publisher","first-page":"40540","DOI":"10.1364\/OE.469851","volume":"30","author":"H Liang","year":"2022","unstructured":"Liang, H., Yao, D., Shen, H.: Pseudo Wigner-Ville distribution for 3D white light scanning interferometric measurement. Opt. Express. 30, 40540\u201340556 (2022)","journal-title":"Opt. Express"},{"key":"1650_CR7","doi-asserted-by":"publisher","first-page":"437","DOI":"10.1016\/j.cirp.2022.03.007","volume":"71","author":"A Fischer","year":"2022","unstructured":"Fischer, A., St\u00f6bener, D., Behrends, G.: A lateral-scanning white-light interferometer for topography measurements on rotating objects in process environments. CIRP Ann. 71, 437\u2013440 (2022)","journal-title":"CIRP Ann."},{"key":"1650_CR8","doi-asserted-by":"publisher","first-page":"809","DOI":"10.1364\/OL.413036","volume":"46","author":"S Marbach","year":"2021","unstructured":"Marbach, S., Claveau, R., Wang, F., et al.: Wide-field parallel mapping of local spectral and topographic information with white light interference microscopy. Opt. Lett. 46, 809\u2013812 (2021)","journal-title":"Opt. Lett."},{"key":"1650_CR9","doi-asserted-by":"publisher","first-page":"110199","DOI":"10.1016\/j.measurement.2021.110199","volume":"186","author":"Y Dong","year":"2021","unstructured":"Dong, Y., Li, Z., Zhu, L., Zhang, X.: Topography measurement and reconstruction of inner surfaces based on white light interference. Measurement. 186, 110199 (2021)","journal-title":"Measurement"},{"key":"1650_CR10","doi-asserted-by":"publisher","first-page":"025005","DOI":"10.1088\/1361-6501\/ab4a48","volume":"31","author":"G Zhang","year":"2019","unstructured":"Zhang, G., Yang, S., Fluegge, J., Bosse, H.: Fiber optic white light interferometer for areal surface measurement. Meas. Sci. Technol. 31, 025005 (2019)","journal-title":"Meas. Sci. Technol."},{"key":"1650_CR11","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1016\/j.optcom.2018.11.020","volume":"435","author":"C Tao","year":"2019","unstructured":"Tao, C., Wu, Y., Wang, W., et al.: Experimental investigation of white-light interferometry based on sub-dark-field illumination. Opt. Commun. 435, 108\u2013117 (2019)","journal-title":"Opt. Commun."},{"key":"1650_CR12","doi-asserted-by":"crossref","unstructured":"Ye, L., Qian, J., Haitjema, H., Reynaerts, D.: On-machine chromatic confocal measurement for micro-EDM drilling and milling. Precis Eng 76, 110\u2013123 (2022)","DOI":"10.1016\/j.precisioneng.2022.03.011"},{"key":"1650_CR13","doi-asserted-by":"publisher","first-page":"112144","DOI":"10.1016\/j.measurement.2022.112144","volume":"204","author":"M Niemczewska-Wojcik","year":"2022","unstructured":"Niemczewska-Wojcik, M., Madej, M., Kowalczyk, J., Piotrowska, K.: A comparative study of the surface topography in dry and wet turning using the confocal and interferometric modes. Measurement. 204, 112144 (2022)","journal-title":"Measurement"},{"key":"1650_CR14","doi-asserted-by":"publisher","first-page":"101876","DOI":"10.1016\/j.jobe.2020.101876","volume":"33","author":"A Tsigarida","year":"2021","unstructured":"Tsigarida, A., Tsampali, E., Konstantinidis, A.A., Stefanidou, M.: On the use of confocal microscopy for calculating the surface microroughness and the respective hydrophobic properties of marble specimens. J. Building Eng. 33, 101876 (2021)","journal-title":"J. Building Eng."},{"key":"1650_CR15","doi-asserted-by":"crossref","unstructured":"Yang, H., Zheng, H., Zhang, T.: A review of artificial intelligent methods for machined surface roughness prediction. Tribol. Int. 109935. (2024)","DOI":"10.1016\/j.triboint.2024.109935"},{"key":"1650_CR16","doi-asserted-by":"publisher","first-page":"664","DOI":"10.1016\/j.measurement.2018.07.071","volume":"129","author":"S Chen","year":"2018","unstructured":"Chen, S., Feng, R., Zhang, C., Zhang, Y.: Surface roughness Measurement method based on multi-parameter modeling learning. Measurement. 129, 664\u2013676 (2018)","journal-title":"Measurement"},{"key":"1650_CR17","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/j.measurement.2019.05.079","volume":"145","author":"R Thomazella","year":"2019","unstructured":"Thomazella, R., Lopes, W.N., Aguiar, P.R., et al.: Digital signal processing for self-vibration monitoring in grinding: A new approach based on the time-frequency analysis of vibration signals. Measurement. 145, 71\u201383 (2019)","journal-title":"Measurement"},{"key":"1650_CR18","first-page":"5445","volume":"5","author":"NSK Varma","year":"2018","unstructured":"Varma, N.S.K., Varma, I., Rajesh, S., et al.: Prediction of surface roughness and MRR in grinding process on Inconel 800 alloy using neural networks and ANFIS. Mater. Today: Proc. 5, 5445\u20135451 (2018)","journal-title":"Mater. Today: Proc."},{"key":"1650_CR19","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1016\/j.ifacol.2015.05.029","volume":"48","author":"AA Sarhan","year":"2015","unstructured":"Sarhan, A.A.: Adaptive neuro-fuzzy approach to predict tool wear accurately in turning operations for maximum cutting tool utilization. IFAC-PapersOnLine. 48, 93\u201398 (2015)","journal-title":"IFAC-PapersOnLine"},{"key":"1650_CR20","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1016\/j.jmapro.2022.04.055","volume":"79","author":"S Li","year":"2022","unstructured":"Li, S., Li, S., Liu, Z., Vladimirovich, P.A.: Roughness prediction model of milling noise-vibration-surface texture multi-dimensional feature fusion for N6 nickel metal. J. Manuf. Process. 79, 166\u2013176 (2022)","journal-title":"J. Manuf. Process."},{"key":"1650_CR21","doi-asserted-by":"publisher","first-page":"400","DOI":"10.1016\/j.jmapro.2021.12.046","volume":"74","author":"Y Li","year":"2022","unstructured":"Li, Y., Liu, Y., Tian, Y., et al.: Application of improved fireworks algorithm in grinding surface roughness online monitoring. J. Manuf. Process. 74, 400\u2013412 (2022)","journal-title":"J. Manuf. Process."},{"key":"1650_CR22","first-page":"350","volume":"26","author":"DR Patel","year":"2020","unstructured":"Patel, D.R., Kiran, M.: A non-contact approach for surface roughness prediction in CNC turning using a linear regression model. Mater. Today: Proc. 26, 350\u2013355 (2020)","journal-title":"Mater. Today: Proc."},{"key":"1650_CR23","doi-asserted-by":"publisher","first-page":"353","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, 353\u2013364 (2014)","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"1650_CR24","doi-asserted-by":"publisher","first-page":"3115","DOI":"10.1007\/s00170-021-07733-9","volume":"117","author":"W Chen","year":"2021","unstructured":"Chen, W., Zou, B., Li, Y., Huang, C.: A study of a rapid method for detecting the machined surface roughness. Int. J. Adv. Manuf. Technol. 117, 3115\u20133127 (2021)","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"1650_CR25","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1007\/s00170-020-05691-2","volume":"110","author":"M Too","year":"2020","unstructured":"Too, M., Ratnam, M., Akil, H.: Investigation on the effect of machining parameters on surface roughness during turning of kenaf fiber-reinforced composite using non-contact vision method. Int. J. Adv. Manuf. Technol. 110, 309\u2013325 (2020)","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"1650_CR26","doi-asserted-by":"crossref","unstructured":"Yi, H., Zhao, X., Yang, J.: Evaluation of grinding surface roughness based on color component difference of image. Proceedings of the 4th International Conference on Intelligent Information Processing (2019)","DOI":"10.1145\/3378065.3378098"},{"key":"1650_CR27","doi-asserted-by":"publisher","first-page":"579","DOI":"10.1016\/j.triboint.2018.11.013","volume":"131","author":"H Zhang","year":"2019","unstructured":"Zhang, H., Liu, J., Lu, E., et al.: A novel surface roughness measurement method based on the red and green aliasing effect. Tribol. Int. 131, 579\u2013590 (2019)","journal-title":"Tribol. Int."},{"key":"1650_CR28","doi-asserted-by":"publisher","first-page":"110217","DOI":"10.1016\/j.measurement.2021.110217","volume":"186","author":"Y Chen","year":"2021","unstructured":"Chen, Y., Yi, H., Liao, C., et al.: Visual measurement of milling surface roughness based on Xception model with convolutional neural network. Measurement. 186, 110217 (2021)","journal-title":"Measurement"},{"key":"1650_CR29","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1016\/j.procir.2020.09.166","volume":"94","author":"L Tatzel","year":"2020","unstructured":"Tatzel, L., Le\u00f3n, F.P.: Image-based roughness estimation of laser cut edges with a convolutional neural network. Procedia CIRP. 94, 469\u2013473 (2020)","journal-title":"Procedia CIRP"},{"key":"1650_CR30","doi-asserted-by":"publisher","first-page":"107860","DOI":"10.1016\/j.measurement.2020.107860","volume":"161","author":"AP Rifai","year":"2020","unstructured":"Rifai, A.P., Aoyama, H., Tho, N.H., et al.: Evaluation of turned and milled surfaces roughness using convolutional neural network. Measurement. 161, 107860 (2020)","journal-title":"Measurement"},{"key":"1650_CR31","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1016\/j.procir.2020.02.292","volume":"95","author":"A Giusti","year":"2020","unstructured":"Giusti, A., Dotta, M., Maradia, U., et al.: Image-based measurement of material roughness using machine learning techniques. Procedia CIRP. 95, 377\u2013382 (2020)","journal-title":"Procedia CIRP"},{"key":"1650_CR32","doi-asserted-by":"publisher","first-page":"371","DOI":"10.1016\/j.jmapro.2022.07.009","volume":"81","author":"T Zhang","year":"2022","unstructured":"Zhang, T., Guo, X., Fan, S., et al.: AMS-Net: Attention mechanism based multi-size dual light source network for surface roughness prediction. J. Manuf. Process. 81, 371\u2013385 (2022)","journal-title":"J. Manuf. Process."},{"key":"1650_CR33","doi-asserted-by":"publisher","first-page":"324","DOI":"10.1016\/j.jmapro.2023.09.045","volume":"105","author":"X Guo","year":"2023","unstructured":"Guo, X., Guo, X., Zou, Q., et al.: FE-trans-net: Feature enhancement based single branch deep learning model for surface roughness detection. J. Manuf. Process. 105, 324\u2013337 (2023)","journal-title":"J. Manuf. Process."},{"key":"1650_CR34","doi-asserted-by":"publisher","first-page":"015003","DOI":"10.1088\/2051-672X\/ad1c71","volume":"12","author":"H Yi","year":"2024","unstructured":"Yi, H., Huang, J., Shu, A., Song, K.: Recognition of grinding surface roughness grade based on adversarial domain adaptation under variable illumination. Surf. Topogr. Metrol. Prop. 12, 015003 (2024)","journal-title":"Surf. Topogr. Metrol. Prop."},{"key":"1650_CR35","doi-asserted-by":"publisher","first-page":"045016","DOI":"10.1088\/1361-6501\/ad1d2e","volume":"35","author":"H Yi","year":"2024","unstructured":"Yi, H., Lv, X., Shu, A., et al.: Few-shot detection of surface roughness of workpieces processed by different machining techniques. Meas. Sci. Technol. 35, 045016 (2024)","journal-title":"Meas. Sci. Technol."},{"key":"1650_CR36","first-page":"3523","volume":"44","author":"S Minaee","year":"2021","unstructured":"Minaee, S., Boykov, Y., Porikli, F., et al.: Image segmentation using deep learning: A survey. IEEE Trans. Pattern Anal. Mach. Intell. 44, 3523\u20133542 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1650_CR37","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., Liang, J.: Unet++: A Nested u-net Architecture for Medical Image Segmentation. DLMIA (2018)","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"1650_CR38","doi-asserted-by":"crossref","unstructured":"Cheng, B., Misra, I., Schwing, A.G., et al.: Masked-attention mask transformer for universal image segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2022)","DOI":"10.1109\/CVPR52688.2022.00135"},{"key":"1650_CR39","doi-asserted-by":"crossref","unstructured":"Fu, J., Liu, J., Tian, H., et al.: Dual attention network for scene segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2019)","DOI":"10.1109\/CVPR.2019.00326"},{"key":"1650_CR40","doi-asserted-by":"crossref","unstructured":"Nam, H., Ha, J.-W., Kim, J.: Dual attention networks for multimodal reasoning and matching. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017)","DOI":"10.1109\/CVPR.2017.232"}],"container-title":["Machine Vision and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00138-024-01650-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00138-024-01650-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00138-024-01650-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,18]],"date-time":"2025-01-18T10:29:02Z","timestamp":1737196142000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00138-024-01650-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,23]]},"references-count":40,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["1650"],"URL":"https:\/\/doi.org\/10.1007\/s00138-024-01650-z","relation":{},"ISSN":["0932-8092","1432-1769"],"issn-type":[{"value":"0932-8092","type":"print"},{"value":"1432-1769","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,23]]},"assertion":[{"value":"9 May 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 October 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 December 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 December 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"26"}}