{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T20:16:00Z","timestamp":1774642560596,"version":"3.50.1"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2021,9,27]],"date-time":"2021-09-27T00:00:00Z","timestamp":1632700800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,9,27]],"date-time":"2021-09-27T00:00:00Z","timestamp":1632700800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100010031","name":"Postdoctoral Research Foundation of China","doi-asserted-by":"publisher","award":["No.2020M682397"],"award-info":[{"award-number":["No.2020M682397"]}],"id":[{"id":"10.13039\/501100010031","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 51805179"],"award-info":[{"award-number":["No. 51805179"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100009000","name":"National Defense Pre-Research Foundation of China","doi-asserted-by":"publisher","award":["No. 18-163-00-TS-004-033-01"],"award-info":[{"award-number":["No. 18-163-00-TS-004-033-01"]}],"id":[{"id":"10.13039\/501100009000","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":[[2023,2]]},"DOI":"10.1007\/s10845-021-01829-5","type":"journal-article","created":{"date-parts":[[2021,9,27]],"date-time":"2021-09-27T08:39:16Z","timestamp":1632731956000},"page":"853-867","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":46,"title":["In situ quality inspection with layer-wise visual images based on deep transfer learning during selective laser melting"],"prefix":"10.1007","volume":"34","author":[{"given":"Jingchang","family":"Li","sequence":"first","affiliation":[]},{"given":"Qi","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Xufeng","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Menglei","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8208-3417","authenticated-orcid":false,"given":"Longchao","family":"Cao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,27]]},"reference":[{"issue":"1","key":"1829_CR1","doi-asserted-by":"publisher","first-page":"12","DOI":"10.3390\/ma10010030","volume":"10","author":"V Alfieri","year":"2017","unstructured":"Alfieri, V., Argenio, P., Caiazzo, F., & Sergi, V. (2017). Reduction of surface roughness by means of laser processing over additive manufacturing metal parts. Materials, 10(1), 12. https:\/\/doi.org\/10.3390\/ma10010030","journal-title":"Materials"},{"issue":"6","key":"1829_CR2","doi-asserted-by":"publisher","first-page":"2505","DOI":"10.1007\/s10845-018-1412-0","volume":"30","author":"M Aminzadeh","year":"2019","unstructured":"Aminzadeh, M., & Kurfess, T. R. (2019). Online quality inspection using Bayesian classification in powder-bed additive manufacturing from high-resolution visual camera images. Journal of Intelligent Manufacturing, 30(6), 2505\u20132523. https:\/\/doi.org\/10.1007\/s10845-018-1412-0","journal-title":"Journal of Intelligent Manufacturing"},{"issue":"1","key":"1829_CR3","doi-asserted-by":"publisher","first-page":"339","DOI":"10.1109\/TII.2019.2917233","volume":"16","author":"Z Chen","year":"2019","unstructured":"Chen, Z., Gryllias, K., & Li, W. (2019). Intelligent fault diagnosis for rotary machinery using transferable convolutional neural network. IEEE Transactions on Industrial Informatics, 16(1), 339\u2013349. https:\/\/doi.org\/10.1109\/TII.2019.2917233","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"1829_CR4","doi-asserted-by":"publisher","first-page":"802","DOI":"10.1016\/j.addma.2019.05.035","volume":"28","author":"T de Terris","year":"2019","unstructured":"de Terris, T., Andreau, O., Peyre, P., Adamski, F., Koutiri, I., Gorny, C., et al. (2019). Optimization and comparison of porosity rate measurement methods of Selective Laser Melted metallic parts. Additive Manufacturing, 28, 802\u2013813. https:\/\/doi.org\/10.1016\/j.addma.2019.05.035","journal-title":"Additive Manufacturing"},{"issue":"8","key":"1829_CR5","doi-asserted-by":"publisher","first-page":"1926","DOI":"10.1109\/TIM.2017.2674738","volume":"66","author":"X Ding","year":"2017","unstructured":"Ding, X., & He, Q. (2017). Energy-fluctuated multiscale feature learning with deep ConvNet for intelligent spindle bearing fault diagnosis. IEEE Transactions on Instrumentation and Measurement, 66(8), 1926\u20131935. https:\/\/doi.org\/10.1109\/TIM.2017.2674738","journal-title":"IEEE Transactions on Instrumentation and Measurement"},{"key":"1829_CR6","unstructured":"Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., et al. Decaf: A deep convolutional activation feature for generic visual recognition. In International conference on machine learning, 2014 (pp. 647\u2013655). arXiv:1310.1531."},{"key":"1829_CR7","doi-asserted-by":"publisher","first-page":"517","DOI":"10.1016\/j.addma.2018.04.005","volume":"21","author":"C Gobert","year":"2018","unstructured":"Gobert, C., Reutzel, E. W., Petrich, J., Nassar, A. R., & Phoha, S. (2018). Application of supervised machine learning for defect detection during metallic powder bed fusion additive manufacturing using high resolution imaging. Additive Manufacturing, 21, 517\u2013528. https:\/\/doi.org\/10.1016\/j.addma.2018.04.005","journal-title":"Additive Manufacturing"},{"key":"1829_CR8","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1016\/j.addma.2014.08.002","volume":"1\u20134","author":"H Gong","year":"2014","unstructured":"Gong, H., Rafi, K., Gu, H., Starr, T., & Stucker, B. (2014). Analysis of defect generation in Ti\u20136Al\u20134V parts made using powder bed fusion additive manufacturing processes. Additive Manufacturing, 1\u20134, 87\u201398. https:\/\/doi.org\/10.1016\/j.addma.2014.08.002","journal-title":"Additive Manufacturing"},{"key":"1829_CR9","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1016\/j.patcog.2017.10.013","volume":"77","author":"J Gu","year":"2018","unstructured":"Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., et al. (2018). Recent advances in convolutional neural networks. Pattern Recognition, 77, 354\u2013377. https:\/\/doi.org\/10.1016\/j.patcog.2017.10.013","journal-title":"Pattern Recognition"},{"key":"1829_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmatprotec.2020.116788","volume":"285","author":"C Guo","year":"2020","unstructured":"Guo, C., Li, S., Shi, S., Li, X., Hu, X., Zhu, Q., et al. (2020). Effect of processing parameters on surface roughness, porosity and cracking of as-built IN738LC parts fabricated by laser powder bed fusion. Journal of Materials Processing Technology, 285, 116788. https:\/\/doi.org\/10.1016\/j.jmatprotec.2020.116788","journal-title":"Journal of Materials Processing Technology"},{"issue":"3","key":"1829_CR11","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1007\/s11465-013-0248-8","volume":"8","author":"N Guo","year":"2013","unstructured":"Guo, N., & Leu, M. (2013). Additive manufacturing: Technology, applications and research needs. Frontiers in Mechanical Engineering, 8(3), 215\u2013243. https:\/\/doi.org\/10.1007\/s11465-013-0248-8","journal-title":"Frontiers in Mechanical Engineering"},{"key":"1829_CR12","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S., & Sun, J. Deep residual learning for image recognition. In 2016 IEEE conference on computer vision and pattern recognition (CVPR), 27\u201330 June 2016 2016 (pp. 770\u2013778). https:\/\/doi.org\/10.1109\/CVPR.2016.90.","DOI":"10.1109\/CVPR.2016.90"},{"key":"1829_CR13","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1109\/MSP.2012.2205597","volume":"29","author":"G Hinton","year":"2012","unstructured":"Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A.-R., Jaitly, N., et al. (2012). Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. Signal Processing Magazine, IEEE, 29, 82\u201397. https:\/\/doi.org\/10.1109\/MSP.2012.2205597","journal-title":"Signal Processing Magazine, IEEE"},{"key":"1829_CR14","unstructured":"Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. R. (2012). Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580."},{"key":"1829_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmachtools.2020.103555","volume":"153","author":"SMH Hojjatzadeh","year":"2020","unstructured":"Hojjatzadeh, S. M. H., Parab, N. D., Guo, Q., Qu, M., Xiong, L., Zhao, C., et al. (2020). Direct observation of pore formation mechanisms during LPBF additive manufacturing process and high energy density laser welding. International Journal of Machine Tools and Manufacture, 153, 103555. https:\/\/doi.org\/10.1016\/j.ijmachtools.2020.103555","journal-title":"International Journal of Machine Tools and Manufacture"},{"issue":"1","key":"1829_CR16","doi-asserted-by":"publisher","first-page":"3088","DOI":"10.1038\/s41467-019-10973-9","volume":"10","author":"SMH Hojjatzadeh","year":"2019","unstructured":"Hojjatzadeh, S. M. H., Parab, N. D., Yan, W., Guo, Q., Xiong, L., Zhao, C., et al. (2019). Pore elimination mechanisms during 3D printing of metals. Nature Communications, 10(1), 3088. https:\/\/doi.org\/10.1038\/s41467-019-10973-9","journal-title":"Nature Communications"},{"key":"1829_CR17","unstructured":"Howard, A., Zhmoginov, A., Chen, L.-C., Sandler, M., & Zhu, M. (2018). Inverted residuals and linear bottlenecks: Mobile networks for classification, detection and segmentation. ArXiv, abs\/1801.04381."},{"key":"1829_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106230","volume":"204","author":"X Huang","year":"2020","unstructured":"Huang, X., Lei, Q., Xie, T., Zhang, Y., Hu, Z., & Zhou, Q. (2020). Deep transfer convolutional neural network and extreme learning machine for lung nodule diagnosis on CT images. Knowledge-Based Systems, 204, 106230. https:\/\/doi.org\/10.1016\/j.knosys.2020.106230","journal-title":"Knowledge-Based Systems"},{"issue":"6","key":"1829_CR19","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84\u201390. https:\/\/doi.org\/10.1145\/3065386","journal-title":"Communications of the ACM"},{"key":"1829_CR20","doi-asserted-by":"publisher","first-page":"300","DOI":"10.1016\/j.ijfatigue.2012.11.011","volume":"48","author":"S Leuders","year":"2013","unstructured":"Leuders, S., Th\u00f6ne, M., Riemer, A., Niendorf, T., Tr\u00f6ster, T., Richard, H. A., et al. (2013). On the mechanical behaviour of titanium alloy TiAl6V4 manufactured by selective laser melting: Fatigue resistance and crack growth performance. International Journal of Fatigue, 48, 300\u2013307. https:\/\/doi.org\/10.1016\/j.ijfatigue.2012.11.011","journal-title":"International Journal of Fatigue"},{"key":"1829_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.addma.2020.101359","volume":"35","author":"CS Lough","year":"2020","unstructured":"Lough, C. S., Wang, X., Smith, C. C., Landers, R. G., Bristow, D. A., Drallmeier, J. A., et al. (2020). Correlation of SWIR imaging with LPBF 304L stainless steel part properties. Additive Manufacturing, 35, 101359. https:\/\/doi.org\/10.1016\/j.addma.2020.101359","journal-title":"Additive Manufacturing"},{"key":"1829_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.addma.2020.101287","volume":"35","author":"QY Lu","year":"2020","unstructured":"Lu, Q. Y., Nguyen, N. V., Hum, A. J. W., Tran, T., & Wong, C. H. (2020). Identification and evaluation of defects in selective laser melted 316L stainless steel parts via in-situ monitoring and micro computed tomography. Additive Manufacturing, 35, 101287. https:\/\/doi.org\/10.1016\/j.addma.2020.101287","journal-title":"Additive Manufacturing"},{"issue":"1","key":"1829_CR23","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1007\/s40684-018-0006-9","volume":"5","author":"T Peng","year":"2018","unstructured":"Peng, T., & Chen, C. (2018). Influence of energy density on energy demand and porosity of 316L stainless steel fabricated by selective laser melting. International Journal of Precision Engineering and Manufacturing-Green Technology, 5(1), 55\u201362. https:\/\/doi.org\/10.1007\/s40684-018-0006-9","journal-title":"International Journal of Precision Engineering and Manufacturing-Green Technology"},{"issue":"6","key":"1829_CR24","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1080\/21663831.2017.1299808","volume":"5","author":"K Prashanth","year":"2017","unstructured":"Prashanth, K., Scudino, S., Maity, T., Das, J., & Eckert, J. (2017). Is the energy density a reliable parameter for materials synthesis by selective laser melting? Materials Research Letters, 5(6), 386\u2013390. https:\/\/doi.org\/10.1080\/21663831.2017.1299808","journal-title":"Materials Research Letters"},{"issue":"4","key":"1829_CR25","doi-asserted-by":"publisher","first-page":"721","DOI":"10.1016\/j.eng.2019.04.012","volume":"5","author":"X Qi","year":"2019","unstructured":"Qi, X., Chen, G., Li, Y., Cheng, X., & Li, C. (2019). Applying neural-network-based machine learning to additive manufacturing: Current applications, challenges, and future perspectives. Engineering, 5(4), 721\u2013729. https:\/\/doi.org\/10.1016\/j.eng.2019.04.012","journal-title":"Engineering"},{"issue":"2","key":"1829_CR26","doi-asserted-by":"publisher","first-page":"561","DOI":"10.1016\/j.cirp.2017.05.011","volume":"66","author":"M Schmidt","year":"2017","unstructured":"Schmidt, M., Merklein, M., Bourell, D. L., Dimitrov, D., Hausotte, T., Wegener, K., et al. (2017). Laser based additive manufacturing in industry and academia. Cirp Annals-Manufacturing Technology, 66(2), 561\u2013583. https:\/\/doi.org\/10.1016\/j.cirp.2017.05.011","journal-title":"Cirp Annals-Manufacturing Technology"},{"key":"1829_CR27","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1016\/j.matdes.2016.10.037","volume":"113","author":"U Scipioni Bertoli","year":"2017","unstructured":"Scipioni Bertoli, U., Wolfer, A. J., Matthews, M. J., Delplanque, J.-P.R., & Schoenung, J. M. (2017). On the limitations of volumetric energy density as a design parameter for selective laser melting. Materials & Design, 113, 331\u2013340. https:\/\/doi.org\/10.1016\/j.matdes.2016.10.037","journal-title":"Materials & Design"},{"issue":"4","key":"1829_CR28","doi-asserted-by":"publisher","first-page":"2446","DOI":"10.1109\/TII.2018.2864759","volume":"15","author":"S Shao","year":"2018","unstructured":"Shao, S., McAleer, S., Yan, R., & Baldi, P. (2018). Highly accurate machine fault diagnosis using deep transfer learning. IEEE Transactions on Industrial Informatics, 15(4), 2446\u20132455. https:\/\/doi.org\/10.1109\/TII.2018.2864759","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"1829_CR29","doi-asserted-by":"publisher","first-page":"598","DOI":"10.1016\/j.addma.2017.11.012","volume":"21","author":"SA Shevchik","year":"2018","unstructured":"Shevchik, S. A., Kenel, C., Leinenbach, C., & Wasmer, K. (2018). Acoustic emission for in situ quality monitoring in additive manufacturing using spectral convolutional neural networks. Additive Manufacturing, 21, 598\u2013604. https:\/\/doi.org\/10.1016\/j.addma.2017.11.012","journal-title":"Additive Manufacturing"},{"key":"1829_CR30","unstructured":"Simonyan, K., & Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. CoRR, abs\/1409.1556."},{"issue":"1","key":"1829_CR31","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1007\/s11837-019-03761-9","volume":"72","author":"R Snell","year":"2020","unstructured":"Snell, R., Tammas-Williams, S., Chechik, L., Lyle, A., Hern\u00e1ndez-Nava, E., Boig, C., et al. (2020). Methods for rapid pore classification in metal additive manufacturing. JOM Journal of the Minerals Metals and Materials Society, 72(1), 101\u2013109. https:\/\/doi.org\/10.1007\/s11837-019-03761-9","journal-title":"JOM Journal of the Minerals Metals and Materials Society"},{"key":"1829_CR32","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.jmsy.2021.01.008","volume":"59","author":"Z Snow","year":"2021","unstructured":"Snow, Z., Diehl, B., Reutzel, E. W., & Nassar, A. (2021). Toward in-situ flaw detection in laser powder bed fusion additive manufacturing through layerwise imagery and machine learning. Journal of Manufacturing Systems, 59, 12\u201326. https:\/\/doi.org\/10.1016\/j.jmsy.2021.01.008","journal-title":"Journal of Manufacturing Systems"},{"key":"1829_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.addma.2020.101457","author":"Z Snow","year":"2020","unstructured":"Snow, Z., Nassar, A., & Reutzel, E. W. (2020). Review of the formation and impact of flaws in powder bed fusion additive manufacturing. Additive Manufacturing. https:\/\/doi.org\/10.1016\/j.addma.2020.101457","journal-title":"Additive Manufacturing"},{"key":"1829_CR34","doi-asserted-by":"publisher","first-page":"371","DOI":"10.1016\/j.powtec.2018.09.090","volume":"342","author":"D Sun","year":"2019","unstructured":"Sun, D., Gu, D., Lin, K., Ma, J., Chen, W., Huang, J., et al. (2019). Selective laser melting of titanium parts: Influence of laser process parameters on macro- and microstructures and tensile property. Powder Technology, 342, 371\u2013379. https:\/\/doi.org\/10.1016\/j.powtec.2018.09.090","journal-title":"Powder Technology"},{"key":"1829_CR35","doi-asserted-by":"publisher","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., et al. Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, 2015 (pp. 1\u20139). https:\/\/doi.org\/10.1109\/CVPR.2015.7298594.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"1829_CR36","doi-asserted-by":"publisher","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition, 2016 (pp. 2818\u20132826). https:\/\/doi.org\/10.1109\/CVPR.2016.308.","DOI":"10.1109\/CVPR.2016.308"},{"key":"1829_CR37","unstructured":"Tan, C., Sun, F., Kong, T., Zhang, W., Yang, C., & Liu, C. A Survey on Deep Transfer Learning. In V. K\u016frkov\u00e1, Y. Manolopoulos, B. Hammer, L. Iliadis, & I. Maglogiannis (Eds.), Artificial neural networks and machine learning\u2013ICANN 2018, Cham, 2018\/\/ 2018 (pp. 270\u2013279): Springer International Publishing."},{"issue":"7","key":"1829_CR38","doi-asserted-by":"publisher","DOI":"10.1088\/0957-0233\/27\/7\/072001","volume":"27","author":"A Thompson","year":"2016","unstructured":"Thompson, A., Maskery, I., & Leach, R. K. (2016). X-ray computed tomography for additive manufacturing: A review. Measurement Science and Technology, 27(7), 072001. https:\/\/doi.org\/10.1088\/0957-0233\/27\/7\/072001","journal-title":"Measurement Science and Technology"},{"key":"1829_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/j.addma.2020.101538","author":"C Wang","year":"2020","unstructured":"Wang, C., Tan, X., Tor, S. B., & Lim, C. (2020). Machine learning in additive manufacturing: State-of-the-art and perspectives. Additive Manufacturing. https:\/\/doi.org\/10.1016\/j.addma.2020.101538","journal-title":"Additive Manufacturing"},{"key":"1829_CR40","doi-asserted-by":"publisher","first-page":"350","DOI":"10.1016\/j.addma.2018.03.019","volume":"21","author":"P Wang","year":"2018","unstructured":"Wang, P., Tan, X., He, C., Nai, M. L. S., Huang, R., Tor, S. B., et al. (2018). Scanning optical microscopy for porosity quantification of additively manufactured components. Additive Manufacturing, 21, 350\u2013358. https:\/\/doi.org\/10.1016\/j.addma.2018.03.019","journal-title":"Additive Manufacturing"},{"issue":"1","key":"1829_CR41","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1109\/TSMC.2017.2754287","volume":"49","author":"L Wen","year":"2017","unstructured":"Wen, L., Gao, L., & Li, X. (2017). A new deep transfer learning based on sparse auto-encoder for fault diagnosis. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 49(1), 136\u2013144. https:\/\/doi.org\/10.1109\/TSMC.2017.2754287","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics: Systems"},{"key":"1829_CR42","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-019-04097-w","author":"L Wen","year":"2019","unstructured":"Wen, L., Li, X., & Gao, L. (2019). A transfer convolutional neural network for fault diagnosis based on ResNet-50. Neural Computing and Applications. https:\/\/doi.org\/10.1007\/s00521-019-04097-w","journal-title":"Neural Computing and Applications"},{"key":"1829_CR43","doi-asserted-by":"publisher","first-page":"112767","DOI":"10.1109\/ACCESS.2019.2930958","volume":"7","author":"G Xu","year":"2019","unstructured":"Xu, G., Shen, X., Chen, S., Zong, Y., Zhang, C., Yue, H., et al. (2019). A deep transfer convolutional neural network framework for EEG signal classification. IEEE Access, 7, 112767\u2013112776. https:\/\/doi.org\/10.1109\/ACCESS.2019.2930958","journal-title":"IEEE Access"},{"key":"1829_CR44","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1016\/j.jmapro.2019.09.010","volume":"46","author":"TY Yu","year":"2019","unstructured":"Yu, T. Y., Li, M., Breaux, A., Atri, M., Obeidat, S., & Ma, C. (2019). Experimental and numerical study on residual stress and geometric distortion in powder bed fusion process. Journal of Manufacturing Processes, 46, 214\u2013224. https:\/\/doi.org\/10.1016\/j.jmapro.2019.09.010","journal-title":"Journal of Manufacturing Processes"},{"key":"1829_CR45","doi-asserted-by":"publisher","first-page":"497","DOI":"10.1016\/j.addma.2019.05.030","volume":"28","author":"B Zhang","year":"2019","unstructured":"Zhang, B., Liu, S., & Shin, Y. C. (2019). In-Process monitoring of porosity during laser additive manufacturing process. Additive Manufacturing, 28, 497\u2013505. https:\/\/doi.org\/10.1016\/j.addma.2019.05.030","journal-title":"Additive Manufacturing"}],"container-title":["Journal of Intelligent Manufacturing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-021-01829-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10845-021-01829-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-021-01829-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,19]],"date-time":"2023-01-19T21:10:20Z","timestamp":1674162620000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10845-021-01829-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,27]]},"references-count":45,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2023,2]]}},"alternative-id":["1829"],"URL":"https:\/\/doi.org\/10.1007\/s10845-021-01829-5","relation":{},"ISSN":["0956-5515","1572-8145"],"issn-type":[{"value":"0956-5515","type":"print"},{"value":"1572-8145","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,27]]},"assertion":[{"value":"4 March 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 August 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 September 2021","order":3,"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 that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}