{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T15:55:18Z","timestamp":1774886118968,"version":"3.50.1"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2022,3,16]],"date-time":"2022-03-16T00:00:00Z","timestamp":1647388800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,3,16]],"date-time":"2022-03-16T00:00:00Z","timestamp":1647388800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100000001","name":"national science foundation","doi-asserted-by":"publisher","award":["CMMI-1903740"],"award-info":[{"award-number":["CMMI-1903740"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Intell Manuf"],"published-print":{"date-parts":[[2023,6]]},"DOI":"10.1007\/s10845-022-01926-z","type":"journal-article","created":{"date-parts":[[2022,3,16]],"date-time":"2022-03-16T18:05:30Z","timestamp":1647453930000},"page":"2307-2319","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Image-based characterization of laser scribing quality using transfer learning"],"prefix":"10.1007","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7791-2468","authenticated-orcid":false,"given":"Mohammad Najjartabar","family":"Bisheh","sequence":"first","affiliation":[]},{"given":"Xinya","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Shing I.","family":"Chang","sequence":"additional","affiliation":[]},{"given":"Shuting","family":"Lei","sequence":"additional","affiliation":[]},{"given":"Jianfeng","family":"Ma","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,3,16]]},"reference":[{"issue":"12","key":"1926_CR1","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","volume":"39","author":"V Badrinarayanan","year":"2017","unstructured":"Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017). SegNet\u202f: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(12), 2481\u20132495. https:\/\/doi.org\/10.1109\/TPAMI.2016.2644615","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"1","key":"1926_CR2","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1023\/a:1007515423169","volume":"36","author":"E Bauer","year":"1999","unstructured":"Bauer, E., & Kohavi, R. (1999). Empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning, 36(1), 105\u2013139. https:\/\/doi.org\/10.1023\/a:1007515423169","journal-title":"Machine Learning"},{"key":"1926_CR3","doi-asserted-by":"publisher","first-page":"1524","DOI":"10.1109\/WACV.2019.00167","volume":"2019","author":"M Bosch","year":"2019","unstructured":"Bosch, M., Foster, K., Christie, G., Wang, S., Hager, G. D., & Brown, M. (2019). Semantic stereo for incidental satellite images. IEEE Winter Conference on Applications of Computer Vision (WACV), 2019, 1524\u20131532. https:\/\/doi.org\/10.1109\/WACV.2019.00167","journal-title":"IEEE Winter Conference on Applications of Computer Vision (WACV)"},{"issue":"2","key":"1926_CR4","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1080\/2151237X.2007.10129236","volume":"12","author":"D Bradley","year":"2007","unstructured":"Bradley, D., & Roth, G. (2007). Adaptive thresholding using the integral image. Journal of Graphics Tools, 12(2), 13\u201321. https:\/\/doi.org\/10.1080\/2151237X.2007.10129236","journal-title":"Journal of Graphics Tools"},{"issue":"2","key":"1926_CR5","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1007\/s40684-017-0029-7","volume":"4","author":"ZY Chua","year":"2017","unstructured":"Chua, Z. Y., Ahn, I. H., & Moon, S. K. (2017). Process monitoring and inspection systems in metal additive manufacturing: Status and applications. International Journal of Precision Engineering and Manufacturing-Green Technology, 4(2), 235\u2013245. https:\/\/doi.org\/10.1007\/s40684-017-0029-7","journal-title":"International Journal of Precision Engineering and Manufacturing-Green Technology"},{"key":"1926_CR6","doi-asserted-by":"publisher","first-page":"865","DOI":"10.1016\/j.promfg.2018.07.111","volume":"26","author":"U Delli","year":"2018","unstructured":"Delli, U., & Chang, S. (2018). Automated process monitoring in 3D printing using supervised machine learning. Procedia Manufacturing, 26, 865\u2013870. https:\/\/doi.org\/10.1016\/j.promfg.2018.07.111","journal-title":"Procedia Manufacturing"},{"issue":"2","key":"1926_CR7","doi-asserted-by":"publisher","first-page":"451","DOI":"10.1109\/TBDATA.2017.2680460","volume":"7","author":"J Ding","year":"2021","unstructured":"Ding, J., Hu, X.-H., & Gudivada, V. (2021). A machine learning based framework for verification and validation of massive scale image data. IEEE Transactions on Big Data, 7(2), 451\u2013467. https:\/\/doi.org\/10.1109\/TBDATA.2017.2680460","journal-title":"IEEE Transactions on Big Data"},{"key":"1926_CR8","doi-asserted-by":"publisher","unstructured":"Ferguson, M., Ak, R., Lee, Y.-T. T., & Law, K. H. (2018). Automatic localization of casting defects with convolutional neural networks. 2017 IEEE International Conference on Big Data (BIGDATA), December, 1726\u20131735. https:\/\/doi.org\/10.1109\/bigdata.2017.8258115","DOI":"10.1109\/bigdata.2017.8258115"},{"key":"1926_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2018\/4920718","volume":"2018","author":"B Fotovvati","year":"2018","unstructured":"Fotovvati, B., Wayne, S. F., Lewis, G., & Asadi, E. (2018). A Review on melt-pool characteristics in laser welding of metals. Advances in Materials Science and Engineering, 2018, 1\u201318. https:\/\/doi.org\/10.1155\/2018\/4920718","journal-title":"Advances in Materials Science and Engineering"},{"key":"1926_CR10","unstructured":"GD&T Straightness. (2014). Geometric dimensioning and tolerancing (GD&T). https:\/\/www.gdandtbasics.com\/straightness\/"},{"issue":"3","key":"1926_CR11","doi-asserted-by":"publisher","first-page":"789","DOI":"10.1007\/s10845-019-01495-8","volume":"31","author":"C Gonzalez-Val","year":"2020","unstructured":"Gonzalez-Val, C., Pallas, A., Panadeiro, V., & Rodriguez, A. (2020). A convolutional approach to quality monitoring for laser manufacturing. Journal of Intelligent Manufacturing, 31(3), 789\u2013795. https:\/\/doi.org\/10.1007\/s10845-019-01495-8","journal-title":"Journal of Intelligent Manufacturing"},{"key":"1926_CR12","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1016\/j.rcim.2017.07.001","volume":"49","author":"M Grasso","year":"2018","unstructured":"Grasso, M., Demir, A. G., Previtali, B., & Colosimo, B. M. (2018). In situ monitoring of selective laser melting of zinc powder via infrared imaging of the process plume. Robotics and Computer-Integrated Manufacturing, 49, 229\u2013239. https:\/\/doi.org\/10.1016\/j.rcim.2017.07.001","journal-title":"Robotics and Computer-Integrated Manufacturing"},{"key":"1926_CR13","doi-asserted-by":"publisher","DOI":"10.1115\/1.4034715","author":"M Grasso","year":"2017","unstructured":"Grasso, M., Laguzza, V., Semeraro, Q., & Colosimo, B. M. (2017). In-Process monitoring of selective laser melting: spatial detection of defects via image data analysis. Journal of Manufacturing Science and Engineering. https:\/\/doi.org\/10.1115\/1.4034715","journal-title":"Journal of Manufacturing Science and Engineering"},{"key":"1926_CR14","doi-asserted-by":"publisher","DOI":"10.1115\/1.4044420","author":"F Imani","year":"2019","unstructured":"Imani, F., Chen, R., Diewald, E., Reutzel, E., & Yang, H. (2019). Deep learning of variant geometry in layerwise imaging profiles for additive manufacturing quality control. Journal of Manufacturing Science and Engineering. https:\/\/doi.org\/10.1115\/1.4044420","journal-title":"Journal of Manufacturing Science and Engineering"},{"key":"1926_CR15","doi-asserted-by":"publisher","DOI":"10.1115\/MSEC2018a-6477","author":"F Imani","year":"2018","unstructured":"Imani, F., Gaikwad, A., Montazeri, M., Rao, P., Yang, H., & Reutzel, E. (2018a). Layerwise in-process quality monitoring in laser powder bed fusion. Additive Manufacturing Bio and Sustainable Manufacturing. https:\/\/doi.org\/10.1115\/MSEC2018a-6477","journal-title":"Additive Manufacturing Bio and Sustainable Manufacturing"},{"key":"1926_CR16","doi-asserted-by":"publisher","DOI":"10.1115\/1.4040615","author":"F Imani","year":"2018","unstructured":"Imani, F., Gaikwad, A., Montazeri, M., Rao, P., Yang, H., & Reutzel, E. (2018b). Process mapping and in-process monitoring of porosity in laser powder bed fusion using layerwise optical imaging. Journal of Manufacturing Science and Engineering, Transactions of the ASME. https:\/\/doi.org\/10.1115\/1.4040615","journal-title":"Journal of Manufacturing Science and Engineering, Transactions of the ASME"},{"issue":"1","key":"1926_CR17","doi-asserted-by":"publisher","first-page":"41","DOI":"10.17977\/um018v2i12019p41-46","volume":"2","author":"IKM Jais","year":"2019","unstructured":"Jais, I. K. M., Ismail, A. R., & Nisa, S. Q. (2019). Adam optimization algorithm for wide and deep neural network. Knowledge Engineering and Data Science, 2(1), 41.","journal-title":"Knowledge Engineering and Data Science"},{"key":"1926_CR18","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1016\/j.solmat.2012.09.017","volume":"108","author":"S Ku","year":"2013","unstructured":"Ku, S., Pieters, B. E., Haas, S., Bauer, A., Ye, Q., & Rau, U. (2013). Electrical characterization of P3 isolation lines patterned with a UV laser incident from the film side on thin-film silicon solar cells. Solar Energy Materials and Solar Cells, 108, 87\u201392. https:\/\/doi.org\/10.1016\/j.solmat.2012.09.017","journal-title":"Solar Energy Materials and Solar Cells"},{"key":"1926_CR19","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1016\/j.phpro.2011.03.128","volume":"12","author":"K-H Leitz","year":"2011","unstructured":"Leitz, K.-H., Redlingsh\u00f6fer, B., Reg, Y., Otto, A., & Schmidt, M. (2011). Metal ablation with short and ultrashort laser pulses. Physics Procedia, 12, 230\u2013238. https:\/\/doi.org\/10.1016\/j.phpro.2011.03.128","journal-title":"Physics Procedia"},{"issue":"145","key":"1926_CR20","first-page":"153","volume":"38","author":"L Li","year":"2014","unstructured":"Li, L., Wu, Y., & Ye, M. (2014). Multi-class image classification based on fast stochastic gradient. Informatica, 38(145), 153.","journal-title":"Informatica"},{"issue":"5","key":"1926_CR21","doi-asserted-by":"publisher","first-page":"2022","DOI":"10.1109\/JSTARS.2016.2646138","volume":"10","author":"X Li","year":"2017","unstructured":"Li, X., Zhang, L., Du, B., Zhang, L., & Shi, Q. (2017). Iterative reweighting heterogeneous transfer learning framework for supervised remote sensing image classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(5), 2022\u20132035. https:\/\/doi.org\/10.1109\/JSTARS.2016.2646138","journal-title":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"},{"key":"1926_CR22","doi-asserted-by":"publisher","unstructured":"Mayr, A., Lutz, B., Weigelt, M., Glabel, T., Kibkalt, D., Masuch, M., Riedel, A., & Franke, J. (2018). Evaluation of machine learning for quality monitoring of laser welding using the example of the contacting of hairpin windings. 2018 8th International Electric Drives Production Conference (EDPC), 1\u20137. https:\/\/doi.org\/10.1109\/EDPC.2018.8658346","DOI":"10.1109\/EDPC.2018.8658346"},{"key":"1926_CR23","doi-asserted-by":"publisher","DOI":"10.1109\/IROS40897.2019.8968129","author":"HK Mousavi","year":"2019","unstructured":"Mousavi, H. K., Nazari, M., Takac, M., & Motee, N. (2019). Multi-agent image classification via reinforcement learning. IEEE International Conference on Intelligent Robots and Systems. https:\/\/doi.org\/10.1109\/IROS40897.2019.8968129","journal-title":"IEEE International Conference on Intelligent Robots and Systems"},{"key":"1926_CR24","unstructured":"NADCA. (2015). NADCA Product Specification Standarts for Die Casting. In North American Die Casting Association. http:\/\/www.caldiecast.com\/docs\/Zinc-and-ZA-Alloy-Data.pdf"},{"key":"1926_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2021.107314","author":"M Najjartabar-Bisheh","year":"2021","unstructured":"Najjartabar-Bisheh, M., Chang, S. I., & Lei, S. (2021). A layer-by-layer quality monitoring framework for 3D printing. Computers & Industrial Engineering. https:\/\/doi.org\/10.1016\/j.cie.2021.107314","journal-title":"Computers & Industrial Engineering"},{"key":"1926_CR26","doi-asserted-by":"publisher","unstructured":"Noh, H., Hong, S., & Han, B. (2015a). Learning deconvolution network for semantic segmentation. Proceedings of the IEEE International Conference on Computer Vision, 2015a Inter, 1520\u20131528. https:\/\/doi.org\/10.1109\/ICCV.2015a.178","DOI":"10.1109\/ICCV.2015a.178"},{"key":"1926_CR27","doi-asserted-by":"publisher","first-page":"1520","DOI":"10.1109\/ICCV.2015.178","volume":"2015","author":"H Noh","year":"2015","unstructured":"Noh, H., Hong, S., & Han, B. (2015b). Learning deconvolution network for semantic segmentation. IEEE International Conference on Computer Vision (ICCV), 2015, 1520\u20131528. https:\/\/doi.org\/10.1109\/ICCV.2015.178","journal-title":"IEEE International Conference on Computer Vision (ICCV)"},{"key":"1926_CR28","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.1979.4310076","author":"N Otsu","year":"1979","unstructured":"Otsu, N. (1979). Threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics. https:\/\/doi.org\/10.1109\/TSMC.1979.4310076","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics"},{"key":"1926_CR29","doi-asserted-by":"publisher","unstructured":"Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: convolutional networks for biomedical image segmentation. 234\u2013241. https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"20","key":"1926_CR30","doi-asserted-by":"publisher","first-page":"1741","DOI":"10.1109\/LPT.2018.2867274","volume":"30","author":"H Roozbahani","year":"2018","unstructured":"Roozbahani, H., Marttinen, P., & Salminen, A. (2018). Real-time monitoring of laser scribing process of cigs solar panels utilizing high-speed camera. IEEE Photonics Technology Letters, 30(20), 1741\u20131744. https:\/\/doi.org\/10.1109\/LPT.2018.2867274","journal-title":"IEEE Photonics Technology Letters"},{"issue":"2","key":"1926_CR31","doi-asserted-by":"publisher","DOI":"10.2351\/1.4983520","volume":"29","author":"H Roozbahani","year":"2017","unstructured":"Roozbahani, H., Salminen, A., & Manninen, M. (2017). Real-time online monitoring of nanosecond pulsed laser scribing process utilizing spectrometer. Journal of Laser Applications, 29(2), 022208. https:\/\/doi.org\/10.2351\/1.4983520","journal-title":"Journal of Laser Applications"},{"key":"1926_CR32","doi-asserted-by":"publisher","unstructured":"Roozbahani, Hamid, Salminen, A., & Manninen, M. (2019). Real-time monitoring of laser scribing process utilizing high-speed camera. 2310, 2310. https:\/\/doi.org\/10.2351\/1.5118570","DOI":"10.2351\/1.5118570"},{"key":"1926_CR33","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/11474.003.0014","author":"TJ Sejnowski","year":"2019","unstructured":"Sejnowski, T. J. (2019). Neural Information Processing Systems. The Deep Learning Revolution. https:\/\/doi.org\/10.7551\/mitpress\/11474.003.0014","journal-title":"The Deep Learning Revolution"},{"issue":"11","key":"1926_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/s19112577","volume":"19","author":"S Sharma","year":"2019","unstructured":"Sharma, S., Ball, J. E., Tang, B., Carruth, D. W., Doude, M., & Islam, M. A. (2019). Semantic segmentation with transfer learning for off-road autonomous driving. Sensors (switzerland), 19(11), 1\u201321. https:\/\/doi.org\/10.3390\/s19112577","journal-title":"Sensors (switzerland)"},{"key":"1926_CR35","doi-asserted-by":"publisher","first-page":"93108","DOI":"10.1109\/ACCESS.2019.2927661","volume":"7","author":"SA Shevchik","year":"2019","unstructured":"Shevchik, S. A., Le-Quang, T., Farahani, F. V., Faivre, N., Meylan, B., Zanoli, S., & Wasmer, K. (2019). Laser welding quality monitoring via graph support vector machine with data adaptive kernel. IEEE Access, 7, 93108\u201393122. https:\/\/doi.org\/10.1109\/ACCESS.2019.2927661","journal-title":"IEEE Access"},{"issue":"1","key":"1926_CR36","doi-asserted-by":"publisher","first-page":"3389","DOI":"10.1038\/s41598-020-60294-x","volume":"10","author":"S Shevchik","year":"2020","unstructured":"Shevchik, S., Le-Quang, T., Meylan, B., Farahani, F. V., Olbinado, M. P., Rack, A., Masinelli, G., Leinenbach, C., & Wasmer, K. (2020). Supervised deep learning for real-time quality monitoring of laser welding with X-ray radiographic guidance. Scientific Reports, 10(1), 3389. https:\/\/doi.org\/10.1038\/s41598-020-60294-x","journal-title":"Scientific Reports"},{"issue":"13","key":"1926_CR37","doi-asserted-by":"publisher","first-page":"18379","DOI":"10.1007\/s11042-019-7179-2","volume":"78","author":"A Unnikrishnan","year":"2019","unstructured":"Unnikrishnan, A., Sowmya, V., & Soman, K. P. (2019). Deep learning architectures for land cover classification using red and near-infrared satellite images. Multimedia Tools and Applications, 78(13), 18379\u201318394. https:\/\/doi.org\/10.1007\/s11042-019-7179-2","journal-title":"Multimedia Tools and Applications"},{"key":"1926_CR38","doi-asserted-by":"publisher","unstructured":"Uzkent, B., Sheehan, E., Meng, C., Tang, Z., Burke, M., Lobell, D., & Ermon, S. (2019). Learning to interpret satellite images using wikipedia. IJCAI International Joint Conference on Artificial Intelligence, 2019-Augus, 3620\u20133626. https:\/\/doi.org\/10.24963\/ijcai.2019\/502","DOI":"10.24963\/ijcai.2019\/502"},{"issue":"10","key":"1926_CR39","doi-asserted-by":"publisher","first-page":"14201","DOI":"10.1364\/OE.419074","volume":"29","author":"X Wang","year":"2021","unstructured":"Wang, X., Yu, X., Berg, M. J., Chen, P., Lacroix, B., Fathpour, S., & Lei, S. (2021). Curved waveguides in silicon written by a shaped laser beam. Optics Express, 29(10), 14201. https:\/\/doi.org\/10.1364\/OE.419074","journal-title":"Optics Express"},{"issue":"2","key":"1926_CR40","doi-asserted-by":"publisher","DOI":"10.2351\/1.5139973","volume":"32","author":"X Wang","year":"2020","unstructured":"Wang, X., Yu, X., Berg, M., DePaola, B., Shi, H., Chen, P., Xue, L., Chang, X., & Lei, S. (2020). Nanosecond laser writing of straight and curved waveguides in silicon with shaped beams. Journal of Laser Applications, 32(2), 022002. https:\/\/doi.org\/10.2351\/1.5139973","journal-title":"Journal of Laser Applications"},{"issue":"2","key":"1926_CR41","doi-asserted-by":"publisher","DOI":"10.2351\/1.5096086","volume":"31","author":"X Wang","year":"2019","unstructured":"Wang, X., Yu, X., Shi, H., Tian, X., Chambonneau, M., Grojo, D., DePaola, B., Berg, M., & Lei, S. (2019). Characterization and control of laser induced modification inside silicon. Journal of Laser Applications, 31(2), 022601. https:\/\/doi.org\/10.2351\/1.5096086","journal-title":"Journal of Laser Applications"},{"issue":"4","key":"1926_CR42","doi-asserted-by":"publisher","first-page":"544","DOI":"10.1016\/j.jss.2010.11.920","volume":"84","author":"X Xie","year":"2011","unstructured":"Xie, X., Ho, J. W. K., Murphy, C., Kaiser, G., Xu, B., & Chen, T. Y. (2011). Testing and validating machine learning classifiers by metamorphic testing. Journal of Systems and Software, 84(4), 544\u2013558. https:\/\/doi.org\/10.1016\/j.jss.2010.11.920","journal-title":"Journal of Systems and Software"},{"issue":"5\u20136","key":"1926_CR43","doi-asserted-by":"publisher","first-page":"598","DOI":"10.1007\/s00170-003-2026-y","volume":"26","author":"YL Yao","year":"2005","unstructured":"Yao, Y. L., Chen, H., & Zhang, W. (2005). Time scale effects in laser material removal: A review. The International Journal of Advanced Manufacturing Technology, 26(5\u20136), 598\u2013608. https:\/\/doi.org\/10.1007\/s00170-003-2026-y","journal-title":"The International Journal of Advanced Manufacturing Technology"},{"key":"1926_CR44","doi-asserted-by":"publisher","first-page":"744","DOI":"10.1109\/WACV.2019.00084","volume":"2019","author":"B Yuan","year":"2019","unstructured":"Yuan, B., Giera, B., Guss, G., Matthews, I., & Mcmains, S. (2019). Semi-supervised convolutional neural networks for in-situ video monitoring of selective laser melting. IEEE Winter Conference on Applications of Computer Vision (WACV), 2019, 744\u2013753. https:\/\/doi.org\/10.1109\/WACV.2019.00084","journal-title":"IEEE Winter Conference on Applications of Computer Vision (WACV)"},{"key":"1926_CR45","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1016\/j.mfglet.2020.01.001","volume":"23","author":"B Zhang","year":"2020","unstructured":"Zhang, B., Hong, K.-M., & Shin, Y. C. (2020). Deep-learning-based porosity monitoring of laser welding process. Manufacturing Letters, 23, 62\u201366. https:\/\/doi.org\/10.1016\/j.mfglet.2020.01.001","journal-title":"Manufacturing Letters"},{"issue":"2","key":"1926_CR46","doi-asserted-by":"publisher","first-page":"671","DOI":"10.1007\/s00339-014-8330-6","volume":"116","author":"X Zhao","year":"2014","unstructured":"Zhao, X., Cao, Y., Nian, Q., Shin, Y. C., & Cheng, G. (2014). Precise selective scribing of thin-film solar cells by a picosecond laser. Applied Physics A, 116(2), 671\u2013681. https:\/\/doi.org\/10.1007\/s00339-014-8330-6","journal-title":"Applied Physics A"},{"key":"1926_CR47","unstructured":"Zoph, B., & Le, Q. V. (2019). Neural architecture search with reinforcement learning. 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings, 1\u201316."}],"container-title":["Journal of Intelligent Manufacturing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-022-01926-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10845-022-01926-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-022-01926-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,26]],"date-time":"2023-04-26T12:10:31Z","timestamp":1682511031000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10845-022-01926-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,16]]},"references-count":47,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2023,6]]}},"alternative-id":["1926"],"URL":"https:\/\/doi.org\/10.1007\/s10845-022-01926-z","relation":{},"ISSN":["0956-5515","1572-8145"],"issn-type":[{"value":"0956-5515","type":"print"},{"value":"1572-8145","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,16]]},"assertion":[{"value":"18 June 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 February 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 March 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}