{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T00:35:53Z","timestamp":1785890153133,"version":"3.56.0"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2022,1,7]],"date-time":"2022-01-07T00:00:00Z","timestamp":1641513600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,7]],"date-time":"2022-01-07T00:00:00Z","timestamp":1641513600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"vingroup innovation foundation","award":["VINIF.2020.DA15"],"award-info":[{"award-number":["VINIF.2020.DA15"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Intell Manuf"],"published-print":{"date-parts":[[2023,4]]},"DOI":"10.1007\/s10845-021-01896-8","type":"journal-article","created":{"date-parts":[[2022,1,7]],"date-time":"2022-01-07T18:03:02Z","timestamp":1641578582000},"page":"1701-1719","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":49,"title":["Fast and accurate prediction of temperature evolutions in additive manufacturing process using deep learning"],"prefix":"10.1007","volume":"34","author":[{"given":"Thinh Quy Duc","family":"Pham","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Truong Vinh","family":"Hoang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9672-4254","authenticated-orcid":false,"given":"Xuan","family":"Van Tran","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Quoc Tuan","family":"Pham","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seifallah","family":"Fetni","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laurent","family":"Duch\u00eane","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hoang Son","family":"Tran","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anne-Marie","family":"Habraken","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,1,7]]},"reference":[{"key":"1896_CR1","unstructured":"Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, & Isard, M. (2016). Tensorflow: A system for large-scale machine learning. In 12th {USENIX} symposium on operating systems design and implementation ({OSDI} 16) (pp. 265\u2013283)."},{"issue":"4\u20135","key":"1896_CR2","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/0925-2312(93)90006-O","volume":"5","author":"S-I Amari","year":"1993","unstructured":"Amari, S.-I. (1993). Backpropagation and stochastic gradient descent method Backpropagation and stochastic gradient descent method. Neurocomputing, 5(4\u20135), 185\u2013196.","journal-title":"Neurocomputing"},{"key":"1896_CR3","doi-asserted-by":"crossref","unstructured":"Arnst, M., & Ponthot, J.-P. (2014). An overview of nonintrusive characterization, propagation, and sensitivity analysis of uncertainties in computational mechanics: An overview of nonintrusive characterization, propagation, and sensitivity analysis of uncertainties in computational mechanics. International Journal for Uncertainty Quantification 4(5).","DOI":"10.1615\/Int.J.UncertaintyQuantification.2014006990"},{"key":"1896_CR4","doi-asserted-by":"crossref","unstructured":"Baykaso\u011flu, C., Akyildiz, O., Tunay, M., & To, A. C. (2020). A process-microstructure finite element simulation framework for predicting phase transformations and microhardness for directed energy deposition of Ti6Al4V. Additive Manufacturing 101252.","DOI":"10.1016\/j.addma.2020.101252"},{"key":"1896_CR5","doi-asserted-by":"publisher","first-page":"278","DOI":"10.1016\/j.addma.2019.02.002","volume":"27","author":"PM Bhatt","year":"2019","unstructured":"Bhatt, P. M., Kabir, A. M., Peralta, M., Bruck, H. A., & Gupta, S. K. (2019). A robotic cell for performing sheet lamination-based additive manufacturing. Additive Manufacturing, 27, 278\u2013289.","journal-title":"Additive Manufacturing"},{"issue":"4","key":"1896_CR6","doi-asserted-by":"publisher","first-page":"1009","DOI":"10.1007\/s10845-020-01599-6","volume":"32","author":"Z Cheng","year":"2021","unstructured":"Cheng, Z., Wang, H., & Liu, G.-R. (2021). Deep convolutional neural network aided optimization for cold spray 3D simulation based on molecular dynamics. Journal of Intelligent Manufacturing, 32(4), 1009\u20131023.","journal-title":"Journal of Intelligent Manufacturing"},{"key":"1896_CR7","doi-asserted-by":"publisher","first-page":"461","DOI":"10.1016\/j.addma.2019.03.015","volume":"27","author":"C Culmone","year":"2019","unstructured":"Culmone, C., Smit, G., & Breedveld, P. (2019). Additive manufacturing of medical instruments: A state-of-the-art review. Additive Manufacturing, 27, 461\u2013473.","journal-title":"Additive Manufacturing"},{"key":"1896_CR8","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1016\/j.promfg.2020.08.016","volume":"50","author":"S Fetni","year":"2020","unstructured":"Fetni, S., Enrici, T. M., Niccolini, T., Tran, H.-S., Dedry, O., Jardin, R., & Habraken, A. M. (2020). 2D thermal finite element analysis of laser cladding of 316L+ WC composite coatings. Procedia Manufacturing, 50, 86\u201392.","journal-title":"Procedia Manufacturing"},{"key":"1896_CR9","doi-asserted-by":"crossref","unstructured":"Garland, A. P., White, B. C., Jared, B. H., Heiden, M., Donahue, E., & Boyce, B. L. (2020). Deep convolutional neural networks as a rapid screening tool for complex additively manufactured structures. Additive Manufacturing 101217.","DOI":"10.1016\/j.addma.2020.101217"},{"key":"1896_CR10","unstructured":"Gockel, J., & Beuth, J. (2013). Understanding Ti-6Al-4V microstructure control in additive manufacturing via process maps. In: Solid freeform fabrication proceedings (pp. 666\u2013674)."},{"key":"1896_CR11","volume-title":"Deep learning with Keras Deep learning with keras","author":"A Gulli","year":"2017","unstructured":"Gulli, A., & Pal, S. (2017). Deep learning with Keras Deep learning with keras. Birmingham: Packt Publishing Ltd."},{"issue":"2","key":"1896_CR12","doi-asserted-by":"publisher","first-page":"347","DOI":"10.1007\/s10845-020-01575-0","volume":"32","author":"Y Guo","year":"2021","unstructured":"Guo, Y., Lu, W. F., & Fuh, J. Y. H. (2021). Semi-supervised deep learning based framework for assessing manufacturability of cellular structures in direct metal laser sintering process. Journal of Intelligent Manufacturing, 32(2), 347\u2013359.","journal-title":"Journal of Intelligent Manufacturing"},{"key":"1896_CR13","doi-asserted-by":"crossref","unstructured":"Haghighi, A., & Li, L. (2020). A hybrid physics-based and data-driven approach for characterizing porosity variation and filament bonding in extrusion-based additive manufacturing. 36, 101399.","DOI":"10.1016\/j.addma.2020.101399"},{"key":"1896_CR14","doi-asserted-by":"crossref","unstructured":"Hann, S. Y., Cui, H., Nowicki, M., & Zhang, L. G. (2020). 4D printing soft robotics for biomedical applications. Additive Manufacturing 101567.","DOI":"10.1016\/j.addma.2020.101567"},{"key":"1896_CR15","doi-asserted-by":"publisher","first-page":"519","DOI":"10.1016\/j.surfcoat.2017.02.071","volume":"315","author":"N Hashemi","year":"2017","unstructured":"Hashemi, N., Mertens, A., Montrieux, H.-M., Tchuindjang, J. T., Dedry, O., Carrus, R., & Lecomte-Beckers, J. (2017). Oxidative wear behaviour of laser clad high speed steel thick deposits: Influence of sliding speed, carbide type and morphology. Surface and Coatings Technology, 315, 519\u2013529.","journal-title":"Surface and Coatings Technology"},{"key":"1896_CR16","doi-asserted-by":"crossref","unstructured":"Hoang, T.-V., & Matthies, H. G. (2021). An efficient computational method for parameter identification in the context of random set theory via bayesian inversion. International Journal for Uncertainty Quantification, 11(4).","DOI":"10.1615\/Int.J.UncertaintyQuantification.2020031869"},{"key":"1896_CR17","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1016\/j.triboint.2016.10.007","volume":"110","author":"T-V Hoang","year":"2017","unstructured":"Hoang, T.-V., Wu, L., Paquay, S., Golinval, J.-C., Arnst, M., & Noels, L. (2017). A computational stochastic multiscale methodology for mems structures involving adhesive contact. Tribology International, 110, 401\u2013425.","journal-title":"Tribology International"},{"key":"1896_CR18","doi-asserted-by":"publisher","first-page":"5357","DOI":"10.1038\/srep05357","volume":"4","author":"DC Hofmann","year":"2014","unstructured":"Hofmann, D. C., Roberts, S., Otis, R., Kolodziejska, J., Dillon, R. P., Suh, J.-O., & Borgonia, J.-P. (2014). Developing gradient metal alloys through radial deposition additive manufacturing. Scientific Reports, 4, 5357.","journal-title":"Scientific Reports"},{"key":"1896_CR19","doi-asserted-by":"publisher","unstructured":"Jardin, R. T., Tchuindjang, J. T., Duch\u00eane, L., Tran, H.-S., Hashemi, N., Carrus, R., & Habraken, A. M. (2019). Thermal histories and microstructures in direct energy deposition of a high speed steel thick deposit. Materials Letters, 236, 42\u201345. https:\/\/doi.org\/10.1016\/j.matlet.2018.09.157","DOI":"10.1016\/j.matlet.2018.09.157"},{"issue":"11","key":"1896_CR20","doi-asserted-by":"publisher","first-page":"1554","DOI":"10.3390\/met10111554","volume":"10","author":"RT Jardin","year":"2020","unstructured":"Jardin, R. T., Tuninetti, V., Tchuindjang, J. T., Hashemi, N., Carrus, R., Mertens, A., & Habraken, A. M. (2020). Sensitivity analysis in the modeling of a high-speed, steel, thin wall produced by directed energy deposition. Metals, 10(11), 1554.","journal-title":"Metals"},{"issue":"4","key":"1896_CR21","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1016\/j.ajme.2017.09.003","volume":"54","author":"M Javaid","year":"2018","unstructured":"Javaid, M., & Haleem, A. (2018). Additive manufacturing applications in medical cases: A literature based review. Alexandria Journal of Medicine, 54(4), 411\u2013422.","journal-title":"Alexandria Journal of Medicine"},{"issue":"5\u20138","key":"1896_CR22","doi-asserted-by":"publisher","first-page":"1659","DOI":"10.1007\/s00170-015-8289-2","volume":"86","author":"C Kamath","year":"2016","unstructured":"Kamath, C. (2016). Data mining and statistical inference in selective laser melting. The International Journal of Advanced Manufacturing Technology, 86(5\u20138), 1659\u20131677.","journal-title":"The International Journal of Advanced Manufacturing Technology"},{"issue":"2","key":"1896_CR23","doi-asserted-by":"publisher","first-page":"475","DOI":"10.1007\/s10115-018-1174-1","volume":"57","author":"C Kamath","year":"2018","unstructured":"Kamath, C., & Fan, Y. J. (2018). Regression with small data sets: a case study using code surrogates in additive manufacturing. Knowledge and Information Systems, 57(2), 475\u2013493.","journal-title":"Knowledge and Information Systems"},{"key":"1896_CR24","doi-asserted-by":"crossref","unstructured":"Kempen, K., Vrancken, B., Buls, S., Thijs, L., Van Humbeeck, J., & Kruth, J.-P. (2014). Selective laser melting of crack-free high density M2 high speed steel parts by baseplate preheating. The Journal of Manufacturing Science and Engineering, 136(6).","DOI":"10.1115\/1.4028513"},{"key":"1896_CR25","unstructured":"Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980."},{"issue":"2\u20133","key":"1896_CR26","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1016\/j.ijmachtools.2003.10.019","volume":"44","author":"S Kolossov","year":"2004","unstructured":"Kolossov, S., Boillat, E., Glardon, R., Fischer, P., & Locher, M. (2004). 3D FE simulation for temperature evolution in the selective laser sintering process. International Journal of Machine Tools and Manufacture, 44(2\u20133), 117\u2013123.","journal-title":"International Journal of Machine Tools and Manufacture"},{"issue":"3","key":"1896_CR27","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1016\/j.optlastec.2011.08.026","volume":"44","author":"A Kumar","year":"2012","unstructured":"Kumar, A., Paul, C., Pathak, A., Bhargava, P., & Kukreja, L. (2012). A finer modeling approach for numerically predicting single track geometry in two dimensions during laser rapid manufacturing. Optics and Laser Technology, 44(3), 555\u2013565.","journal-title":"Optics and Laser Technology"},{"key":"1896_CR28","doi-asserted-by":"crossref","unstructured":"Kumar, L. J., & Nair, C. K. (2017). Current trends of additive manufacturing in the aerospace industry. In Advances in 3d printing & additive manufacturing technologies (pp. 39\u201354). Springer.","DOI":"10.1007\/978-981-10-0812-2_4"},{"key":"1896_CR29","doi-asserted-by":"publisher","first-page":"101444","DOI":"10.1016\/j.addma.2020.101444","volume":"36","author":"XY Lee","year":"2020","unstructured":"Lee, X. Y., Saha, S. K., Sarkar, S., & Giera, B. (2020). Automated detection of part quality during two-photon lithography via deep learning. Additive Manufacturing, 36, 101444.","journal-title":"Additive Manufacturing"},{"issue":"4\u20135","key":"1896_CR30","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1177\/0278364917710318","volume":"37","author":"S Levine","year":"2018","unstructured":"Levine, S., Pastor, P., Krizhevsky, A., Ibarz, J., & Quillen, D. (2018). Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection. The International Journal of Robotics Research, 37(4\u20135), 421\u2013436.","journal-title":"The International Journal of Robotics Research"},{"key":"1896_CR31","doi-asserted-by":"publisher","first-page":"473","DOI":"10.1016\/j.matdes.2017.11.028","volume":"139","author":"J Li","year":"2018","unstructured":"Li, J., Jin, R., & Hang, Z. Y. (2018). Integration of physically-based and data-driven approaches for thermal field prediction in additive manufacturing. Materials and Design, 139, 473\u2013485.","journal-title":"Materials and Design"},{"issue":"5","key":"1896_CR32","doi-asserted-by":"publisher","first-page":"1387","DOI":"10.1007\/s00170-020-06113-z","volume":"111","author":"P-Y Lin","year":"2020","unstructured":"Lin, P.-Y., Shen, F.-C., Wu, K.-T., Hwang, S.-J., & Lee, H.-H. (2020). Process optimization for directed energy deposition of ss316l components. The International Journal of Advanced Manufacturing Technology, 111(5), 1387\u20131400.","journal-title":"The International Journal of Advanced Manufacturing Technology"},{"key":"1896_CR33","unstructured":"Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. In: Advances in Neural Information Processing Systems (pp. 4765\u20134774)."},{"key":"1896_CR34","unstructured":"Lyons, B. (2014). Additive manufacturing in aerospace: Examples and research outlook. The Bridge, 44(3)."},{"key":"1896_CR35","first-page":"1108","volume":"26","author":"B Manjunath","year":"2020","unstructured":"Manjunath, B., Vinod, A., Abhinav, K., Verma, S., & Sankar, M. R. (2020). Optimisation of process parameters for deposition of colmonoy using directed energy deposition process. Materials Today: Proceedings, 26, 1108\u20131112.","journal-title":"Materials Today: Proceedings"},{"key":"1896_CR36","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/j.mfglet.2018.10.002","volume":"18","author":"M Mozaffar","year":"2018","unstructured":"Mozaffar, M., Paul, A., Al-Bahrani, R., Wolff, S., Choudhary, A., Agrawal, A., & Cao, J. (2018). Data-driven prediction of the high-dimensional thermal history in directed energy deposition processes via recurrent neural networks. Manufacturing Letters, 18, 35\u201339.","journal-title":"Manufacturing Letters"},{"issue":"3","key":"1896_CR37","doi-asserted-by":"publisher","first-page":"691","DOI":"10.1093\/biomet\/78.3.691","volume":"78","author":"NJ Nagelkerke","year":"1991","unstructured":"Nagelkerke, N. J. (1991). A note on a general definition of the coefficient of determination. Biometrika, 78(3), 691\u2013692.","journal-title":"Biometrika"},{"key":"1896_CR38","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1016\/j.compositesb.2018.02.012","volume":"143","author":"TD Ngo","year":"2018","unstructured":"Ngo, T. D., Kashani, A., Imbalzano, G., Nguyen, K. T., & Hui, D. (2018). Additive manufacturing (3D printing): A review of materials, methods, applications and challenges. Composites Part B: Engineering, 143, 172\u2013196.","journal-title":"Composites Part B: Engineering"},{"key":"1896_CR39","doi-asserted-by":"crossref","unstructured":"O\u2019Malley, F. L., Millward, H., Eggbeer, D., Williams, R., & Cooper, R. (2016). The use of adenosine triphosphate bioluminescence for assessing the cleanliness of additive-manufacturing materials used in medical applications. Additive Manufacturing, 9, 25\u201329.","DOI":"10.1016\/j.addma.2015.12.002"},{"key":"1896_CR40","doi-asserted-by":"crossref","unstructured":"Park, H. S., Nguyen, D. S., Le-Hong, T., & Van Tran, X. (2021). Machine learning-based optimization of process parameters in selective laser melting for biomedical applications. Journal of Intelligent Manufacturing, 1\u201316.","DOI":"10.1007\/s10845-021-01773-4"},{"key":"1896_CR41","doi-asserted-by":"publisher","first-page":"112734","DOI":"10.1016\/j.cma.2019.112734","volume":"362","author":"K Ren","year":"2020","unstructured":"Ren, K., Chew, Y., Zhang, Y., Fuh, J., & Bi, G. (2020). Thermal field prediction for laser scanning paths in laser aided additive manufacturing by physics-based machine learning. Computer Methods in Applied Mechanics and Engineering, 362, 112734.","journal-title":"Computer Methods in Applied Mechanics and Engineering"},{"key":"1896_CR42","doi-asserted-by":"crossref","unstructured":"Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). \u201cWhy should I trust you?\u201d explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 1135\u20131144).","DOI":"10.1145\/2939672.2939778"},{"key":"1896_CR43","doi-asserted-by":"publisher","first-page":"101017","DOI":"10.1016\/j.addma.2019.101017","volume":"32","author":"M Roy","year":"2020","unstructured":"Roy, M., & Wodo, O. (2020). Data-driven modeling of thermal history in additive manufacturing. Additive Manufacturing, 32, 101017.","journal-title":"Additive Manufacturing"},{"issue":"28","key":"1896_CR44","first-page":"307","volume":"2","author":"LS Shapley","year":"1953","unstructured":"Shapley, L. S. (1953). A value for n-person games. Contributions to the Theory of Games, 2(28), 307\u2013317.","journal-title":"Contributions to the Theory of Games"},{"key":"1896_CR45","doi-asserted-by":"publisher","first-page":"100906","DOI":"10.1016\/j.addma.2019.100906","volume":"30","author":"H Shen","year":"2019","unstructured":"Shen, H., Pan, L., & Qian, J. (2019). Research on large-scale additive manufacturing based on multi-robot collaboration technology. Additive Manufacturing, 30, 100906.","journal-title":"Additive Manufacturing"},{"issue":"8","key":"1896_CR46","doi-asserted-by":"publisher","first-page":"2133","DOI":"10.1080\/01431160802549278","volume":"30","author":"D Stathakis","year":"2009","unstructured":"Stathakis, D. (2009). How many hidden layers and nodes? International Journal of Remote Sensing, 30(8), 2133\u20132147.","journal-title":"International Journal of Remote Sensing"},{"issue":"10","key":"1896_CR47","doi-asserted-by":"publisher","first-page":"1429","DOI":"10.1016\/S0893-6080(03)00138-2","volume":"16","author":"DR Wilson","year":"2003","unstructured":"Wilson, D. R., & Martinez, T. R. (2003). The general inefficiency of batch training for gradient descent learning. Neural Networks, 16(10), 1429\u20131451.","journal-title":"Neural Networks"},{"issue":"1\u20132","key":"1896_CR48","doi-asserted-by":"publisher","first-page":"1600118","DOI":"10.1002\/minf.201600118","volume":"36","author":"DA Winkler","year":"2017","unstructured":"Winkler, D. A., & Le, T. C. (2017). Performance of deep and shallow neural networks, the universal approximation theorem, activity cliffs, and qsar. Molecular Informatics, 36(1\u20132), 1600118.","journal-title":"Molecular Informatics"},{"key":"1896_CR49","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1016\/j.addma.2016.06.012","volume":"12","author":"Q Yang","year":"2016","unstructured":"Yang, Q., Zhang, P., Cheng, L., Min, Z., Chyu, M., & To, A. C. (2016). Finite element modeling and validation of thermomechanical behavior of Ti-6Al-4V in directed energy deposition additive manufacturing. Additive Manufacturing, 12, 169\u2013177.","journal-title":"Additive Manufacturing"},{"key":"1896_CR50","doi-asserted-by":"publisher","first-page":"686","DOI":"10.1016\/j.matdes.2016.07.114","volume":"109","author":"Z Zhang","year":"2016","unstructured":"Zhang, Z., Farahmand, P., & Kovacevic, R. (2016). Laser cladding of 420 stainless steel with molybdenum on mild steel A36 by a high power direct diode laser. Materials Design, 109, 686\u2013699.","journal-title":"Materials Design"},{"key":"1896_CR51","doi-asserted-by":"crossref","unstructured":"Zhu, Q., Liu, Z., & Yan, J. (2020). Machine learning for metal additive manufacturing: Predicting temperature and melt pool fluid dynamics using physics-informed neural networks. arXiv preprint arXiv:2008.13547","DOI":"10.1007\/s00466-020-01952-9"}],"container-title":["Journal of Intelligent Manufacturing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-021-01896-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10845-021-01896-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-021-01896-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,15]],"date-time":"2023-03-15T17:13:00Z","timestamp":1678900380000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10845-021-01896-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,7]]},"references-count":51,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023,4]]}},"alternative-id":["1896"],"URL":"https:\/\/doi.org\/10.1007\/s10845-021-01896-8","relation":{},"ISSN":["0956-5515","1572-8145"],"issn-type":[{"value":"0956-5515","type":"print"},{"value":"1572-8145","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,7]]},"assertion":[{"value":"21 July 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 December 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 January 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}