{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T18:26:31Z","timestamp":1774549591853,"version":"3.50.1"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030687984","type":"print"},{"value":"9783030687991","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-68799-1_36","type":"book-chapter","created":{"date-parts":[[2021,3,4]],"date-time":"2021-03-04T08:03:53Z","timestamp":1614845033000},"page":"490-505","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Real-Time Cross-Dataset Quality Production Assessment in Industrial Laser Cutting Machines"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6515-599X","authenticated-orcid":false,"given":"Nicola","family":"Peghini","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1315-5573","authenticated-orcid":false,"given":"Andrea","family":"Zignoli","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5816-1670","authenticated-orcid":false,"given":"Davide","family":"Gandolfi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0663-5659","authenticated-orcid":false,"given":"Paolo","family":"Rota","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3783-5928","authenticated-orcid":false,"given":"Paolo","family":"Bosetti","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,3,5]]},"reference":[{"key":"36_CR1","unstructured":"Alippi, C., Bono, V., Piuri, V., Scotti, F.: Toward real-time quality analysis measurement of metal laser cutting. In: 2002 IEEE International Symposium on Virtual and Intelligent Measurement Systems (IEEE Cat. No. 02EX545), pp. 39\u201344. IEEE (2002)"},{"key":"36_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.optlaseng.2016.07.005","volume":"88","author":"O Anicic","year":"2017","unstructured":"Anicic, O., Jovi\u0107, S., Skrijelj, H., Nedi\u0107, B.: Prediction of laser cutting heat affected zone by extreme learning machine. Opt. Lasers Eng. 88, 1\u20134 (2017)","journal-title":"Opt. Lasers Eng."},{"key":"36_CR3","doi-asserted-by":"publisher","first-page":"64270","DOI":"10.1109\/ACCESS.2018.2877890","volume":"6","author":"S Bianco","year":"2018","unstructured":"Bianco, S., Cadene, R., Celona, L., Napoletano, P.: Benchmark analysis of representative deep neural network architectures. IEEE Access 6, 64270\u201364277 (2018)","journal-title":"IEEE Access"},{"key":"36_CR4","doi-asserted-by":"crossref","unstructured":"Carlucci, F.M., Porzi, L., Caputo, B., Ricci, E., Bulo, S.R.: Autodial: automatic domain alignment layers. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 5077\u20135085. IEEE (2017)","DOI":"10.1109\/ICCV.2017.542"},{"key":"36_CR5","series-title":"Studies in Computational Intelligence","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-50641-4","volume-title":"Machine Learning Algorithms for Industrial Applications","year":"2021","unstructured":"Das, S.K., Das, S.P., Dey, N., Hassanien, A.-E. (eds.): Machine Learning Algorithms for Industrial Applications. SCI, vol. 907. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-50641-4"},{"key":"36_CR6","doi-asserted-by":"crossref","unstructured":"Dong, J., Cong, Y., Sun, G., Zhong, B., Xu, X.: What can be transferred: unsupervised domain adaptation for endoscopic lesions segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4023\u20134032 (2020)","DOI":"10.1109\/CVPR42600.2020.00408"},{"key":"36_CR7","doi-asserted-by":"crossref","unstructured":"Franceschetti, L., Pacher, M., Tanelli, M., Strada, S.C., Previtali, B., Savaresi, S.M.: Dross attachment estimation in the laser-cutting process via convolutional neural networks (CNN). In: 2020 28th Mediterranean Conference on Control and Automation (MED), pp. 850\u2013855. IEEE (2020)","DOI":"10.1109\/MED48518.2020.9183275"},{"key":"36_CR8","unstructured":"Glorot, X., Bordes, A., Bengio, Y.: Domain adaptation for large-scale sentiment classification: a deep learning approach. In: ICML (2011)"},{"key":"36_CR9","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1007\/978-3-030-55180-3_26","volume-title":"Intelligent Systems and Applications","author":"U Halm","year":"2021","unstructured":"Halm, U., Arntz-Schroeder, D., Gillner, A., Schulz, W.: Towards online-prediction of quality features in laser fusion cutting using neural networks. In: Arai, K., Kapoor, S., Bhatia, R. (eds.) IntelliSys 2020. AISC, vol. 1250, pp. 346\u2013359. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-55180-3_26"},{"key":"36_CR10","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"36_CR11","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700\u20134708 (2017)","DOI":"10.1109\/CVPR.2017.243"},{"issue":"8","key":"36_CR12","doi-asserted-by":"publisher","first-page":"1683","DOI":"10.1007\/s10845-016-1206-1","volume":"29","author":"Z Jurkovic","year":"2018","unstructured":"Jurkovic, Z., Cukor, G., Brezocnik, M., Brajkovic, T.: A comparison of machine learning methods for cutting parameters prediction in high speed turning process. J. Intell. Manuf. 29(8), 1683\u20131693 (2018)","journal-title":"J. Intell. Manuf."},{"key":"36_CR13","doi-asserted-by":"publisher","DOI":"10.1201\/9781351128384","volume-title":"Industrial Applications of Machine Learning","author":"P Larra\u00f1aga","year":"2018","unstructured":"Larra\u00f1aga, P., Atienza, D., Diaz-Rozo, J., Ogbechie, A., Puerto-Santana, C.E., Bielza, C.: Industrial Applications of Machine Learning. CRC Press, New York (2018)"},{"key":"36_CR14","doi-asserted-by":"crossref","unstructured":"Li, X., Zhang, W.: Deep learning-based partial domain adaptation method on intelligent machinery fault diagnostics. IEEE Trans. Ind. Electron. (2020)","DOI":"10.1109\/TIE.2020.2984968"},{"key":"36_CR15","doi-asserted-by":"crossref","unstructured":"Munro, J., Damen, D.: Multi-modal domain adaptation for fine-grained action recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 122\u2013132 (2020)","DOI":"10.1109\/CVPR42600.2020.00020"},{"key":"36_CR16","doi-asserted-by":"crossref","unstructured":"Roy, S., Siarohin, A., Sangineto, E., Bulo, S.R., Sebe, N., Ricci, E.: Unsupervised domain adaptation using feature-whitening and consensus loss. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9471\u20139480 (2019)","DOI":"10.1109\/CVPR.2019.00970"},{"key":"36_CR17","unstructured":"Santolini, G.: Deep Learning Models for Cut Interruption Detection in Laser Cutting Machines. Master\u2019s thesis, University of Trento (Department of Industrial Engineering), Trento (2019)"},{"key":"36_CR18","doi-asserted-by":"crossref","unstructured":"Santolini, G., Rota, P., Gandolfi, D., Bosetti, P.: Cut quality estimation in industrial laser cutting machines: a machine learning approach. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (2019)","DOI":"10.1109\/CVPRW.2019.00052"},{"key":"36_CR19","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1016\/j.neucom.2019.08.022","volume":"367","author":"Y Shan","year":"2019","unstructured":"Shan, Y., Lu, W.F., Chew, C.M.: Pixel and feature level based domain adaptation for object detection in autonomous driving. Neurocomputing 367, 31\u201338 (2019)","journal-title":"Neurocomputing"},{"key":"36_CR20","doi-asserted-by":"crossref","unstructured":"Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1\u20139 (2015)","DOI":"10.1109\/CVPR.2015.7298594"},{"issue":"2","key":"36_CR21","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1007\/s11740-017-0718-7","volume":"11","author":"H Tercan","year":"2017","unstructured":"Tercan, H., Al Khawli, T., Eppelt, U., B\u00fcscher, C., Meisen, T., Jeschke, S.: Improving the laser cutting process design by machine learning techniques. Prod. Eng. 11(2), 195\u2013203 (2017)","journal-title":"Prod. Eng."},{"key":"36_CR22","doi-asserted-by":"crossref","unstructured":"Tzeng, E., Hoffman, J., Saenko, K., Darrell, T.: Adversarial discriminative domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7167\u20137176 (2017)","DOI":"10.1109\/CVPR.2017.316"},{"key":"36_CR23","doi-asserted-by":"crossref","unstructured":"Zhang, Y., David, P., Gong, B.: Curriculum domain adaptation for semantic segmentation of urban scenes. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2020\u20132030 (2017)","DOI":"10.1109\/ICCV.2017.223"},{"key":"36_CR24","unstructured":"Zhao, S., et al.: Multi-source domain adaptation for semantic segmentation. In: Advances in Neural Information Processing Systems, pp. 7287\u20137300 (2019)"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition. ICPR International Workshops and Challenges"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-68799-1_36","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,27]],"date-time":"2021-04-27T22:15:31Z","timestamp":1619561731000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-68799-1_36"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030687984","9783030687991"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-68799-1_36","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"5 March 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 January 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 January 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ICPR2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.icpr2020.it\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}