{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T03:27:05Z","timestamp":1767842825298,"version":"3.49.0"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,4,20]],"date-time":"2024-04-20T00:00:00Z","timestamp":1713571200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,4,20]],"date-time":"2024-04-20T00:00:00Z","timestamp":1713571200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100003086","name":"Eusko Jaurlaritza","doi-asserted-by":"publisher","award":["KK-2021\/00048"],"award-info":[{"award-number":["KK-2021\/00048"]}],"id":[{"id":"10.13039\/501100003086","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":[[2025,4]]},"DOI":"10.1007\/s10845-024-02374-7","type":"journal-article","created":{"date-parts":[[2024,4,21]],"date-time":"2024-04-21T02:37:58Z","timestamp":1713667078000},"page":"2583-2599","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Combining physics-based and data-driven methods in metal stamping"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8520-4456","authenticated-orcid":false,"given":"Amaia","family":"Abanda","sequence":"first","affiliation":[]},{"given":"Amaia","family":"Arroyo","sequence":"additional","affiliation":[]},{"given":"Fernando","family":"Boto","sequence":"additional","affiliation":[]},{"given":"Miguel","family":"Esteras","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,4,20]]},"reference":[{"key":"2374_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cirp.2016.06.002","author":"JM Allwood","year":"2016","unstructured":"Allwood, J. M., Duncan, S. R., Cao, J., Groche, P., Hirt, G., Kinsey, B., Kuboki, T., Liewald, M., Sterzing, A., & Tekkaya, A. E. (2016). Closed-loop control of product properties in metal forming. CIRP Annals\u2014Manufacturing Technology. https:\/\/doi.org\/10.1016\/j.cirp.2016.06.002","journal-title":"CIRP Annals\u2014Manufacturing Technology"},{"key":"2374_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compchemeng.2017.09.017","author":"A Bhosekar","year":"2018","unstructured":"Bhosekar, A., & Ierapetritou, M. (2018). Advances in surrogate based modeling, feasibility analysis, and optimization: A review. Computers and Chemical Engineering. https:\/\/doi.org\/10.1016\/j.compchemeng.2017.09.017","journal-title":"Computers and Chemical Engineering"},{"key":"2374_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.compchemeng.2020.106834","author":"T Bikmukhametov","year":"2020","unstructured":"Bikmukhametov, T., & J\u00e4schke, J. (2020). Combining machine learning and process engineering physics towards enhanced accuracy and explainability of data-driven models. Computers and Chemical Engineering. https:\/\/doi.org\/10.1016\/j.compchemeng.2020.106834","journal-title":"Computers and Chemical Engineering"},{"key":"2374_CR4","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324","author":"L Breiman","year":"2001","unstructured":"Breiman, L. (2001). Random forests. Machine Learning. https:\/\/doi.org\/10.1023\/A:1010933404324","journal-title":"Machine Learning"},{"key":"2374_CR5","doi-asserted-by":"publisher","DOI":"10.1201\/9781315139470","volume-title":"Classification and regression trees","author":"L Breiman","year":"1984","unstructured":"Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and regression trees. Chapman and Hall\/CRC. https:\/\/doi.org\/10.1201\/9781315139470"},{"key":"2374_CR6","volume-title":"The evolution of intelligence: The nervous system as a model of its environment","author":"HJ Bremermann","year":"1958","unstructured":"Bremermann, H. J. (1958). The evolution of intelligence: The nervous system as a model of its environment. Department of Mathematics: University of Washington."},{"key":"2374_CR7","doi-asserted-by":"publisher","DOI":"10.1007\/s00170-021-08632-9","author":"H Cai","year":"2022","unstructured":"Cai, H., Xiao, W., & Zheng, K. (2022). The prediction of part thickness using machine learning in aluminum hot stamping process with partition temperature control. The International Journal of Advanced Manufacturing Technology. https:\/\/doi.org\/10.1007\/s00170-021-08632-9","journal-title":"The International Journal of Advanced Manufacturing Technology"},{"key":"2374_CR8","doi-asserted-by":"publisher","unstructured":"Chen, T., & Guestrin, C. (2016). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 785\u2013794). https:\/\/doi.org\/10.1145\/2939672.2939785.","DOI":"10.1145\/2939672.2939785"},{"key":"2374_CR9","doi-asserted-by":"publisher","unstructured":"Cilia, N. D., De\u00a0Stefano, C., Fontanella, F., & Scotto\u00a0di Freca, A. (2019). Variable-length representation for ec-based feature selection in high-dimensional data. In Proceedings of 22nd international conference, EvoApplications (pp. 325\u2013340). Springer. https:\/\/doi.org\/10.1007\/978-3-030-16692-2_22.","DOI":"10.1007\/978-3-030-16692-2_22"},{"key":"2374_CR10","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1109\/TIT.1967.1053964","volume":"1","author":"T Cover","year":"1967","unstructured":"Cover, T., & Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 1, 21\u201327. https:\/\/doi.org\/10.1109\/TIT.1967.1053964","journal-title":"IEEE Transactions on Information Theory"},{"key":"2374_CR11","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-019-04651-6","author":"M Dib","year":"2020","unstructured":"Dib, M., Oliveira, N., Marques, A., Oliveira, M. C., Fernandes, J., Ribeiro, B., & Prates, P. A. (2020). Single and ensemble classifiers for defect prediction in sheet metal forming under variability. Neural Computing and Applications. https:\/\/doi.org\/10.1007\/s00521-019-04651-6","journal-title":"Neural Computing and Applications"},{"key":"2374_CR12","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-84628-480-9","volume-title":"Soft sensors for monitoring and control of industrial processes","author":"L Fortuna","year":"2007","unstructured":"Fortuna, L., Graziani, S., Rizzo, A., & Xibilia, M. G. (2007). Soft sensors for monitoring and control of industrial processes. Springer. https:\/\/doi.org\/10.1007\/978-1-84628-480-9"},{"key":"2374_CR13","doi-asserted-by":"publisher","DOI":"10.1071\/BI9570492","author":"AS Fraser","year":"1957","unstructured":"Fraser, A. S. (1957). Simulation of genetic systems by automatic digital computers ii. Effects of linkage on rates of advance under selection. Australian Journal of Biological Sciences. https:\/\/doi.org\/10.1071\/BI9570492","journal-title":"Australian Journal of Biological Sciences"},{"key":"2374_CR14","doi-asserted-by":"publisher","first-page":"1189","DOI":"10.1214\/aos\/1013203451","volume":"29","author":"JH Friedman","year":"2001","unstructured":"Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29, 1189\u20131232.","journal-title":"Annals of Statistics"},{"key":"2374_CR15","unstructured":"Harsch, D., Heing\u00e4rtner, J., Renkci, Y., & Hora, P. (2017). Influence of scattering material properties on the robustness of deep drawing processes. In 10th forming technology forum. Model based control for smart forming processes"},{"key":"2374_CR16","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1063\/1\/012185","author":"D Harsch","year":"2018","unstructured":"Harsch, D., Heing\u00e4rtner, J., Renkci, Y., & Hora, P. (2018). Metamodel-based methods to verify the feasibility of a process control in deep drawing. Journal of Physics: Conference Series. https:\/\/doi.org\/10.1088\/1742-6596\/1063\/1\/012185","journal-title":"Journal of Physics: Conference Series"},{"key":"2374_CR17","doi-asserted-by":"publisher","DOI":"10.1088\/1757-899X\/418\/1\/012112","volume-title":"Implementation of a tribology-based process control system for deep drawing processes","author":"J Heingartner","year":"2018","unstructured":"Heingartner, J., Bonfanti, D., Harsch, D., Dietrich, F., & Hora, P. (2018). Implementation of a tribology-based process control system for deep drawing processes. Institute of Physics Publishing. https:\/\/doi.org\/10.1088\/1757-899X\/418\/1\/012112"},{"key":"2374_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmecsci.2022.108085","author":"B Hu","year":"2023","unstructured":"Hu, B., Wang, Z., Du, C., Zou, W., Wu, W., Tang, J., Ai, J., Zhou, H., Chen, R., & Shan, B. (2023). Multi-objective Bayesian optimization accelerated design of TPMS structures. International Journal of Mechanical Sciences. https:\/\/doi.org\/10.1016\/j.ijmecsci.2022.108085","journal-title":"International Journal of Mechanical Sciences"},{"key":"2374_CR19","doi-asserted-by":"publisher","unstructured":"Jiang, Y., Yin, S., Dong, J., & Kaynak, O. (2020). A review on soft sensors for monitoring, control, and optimization of industrial processes. IEEE Sensors Journal. https:\/\/doi.org\/10.1109\/JSEN.2020.3033153","DOI":"10.1109\/JSEN.2020.3033153"},{"key":"2374_CR20","doi-asserted-by":"publisher","DOI":"10.1504\/IJCISTUDIES.2020.106532","author":"G Kakandikar","year":"2020","unstructured":"Kakandikar, G., & Nandedkar, V. (2020). Multi-objective optimisation of thickness and strain distribution for automotive component in forming process. International Journal of Computational Intelligence Studies. https:\/\/doi.org\/10.1504\/IJCISTUDIES.2020.106532","journal-title":"International Journal of Computational Intelligence Studies"},{"key":"2374_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcde.2015.08.001","author":"GM Kakandikar","year":"2016","unstructured":"Kakandikar, G. M., & Nandedkar, V. M. (2016). Prediction and optimization of thinning in automotive sealing cover using genetic algorithm. Journal of Computational Design and Engineering. https:\/\/doi.org\/10.1016\/j.jcde.2015.08.001","journal-title":"Journal of Computational Design and Engineering"},{"key":"2374_CR22","doi-asserted-by":"publisher","DOI":"10.1038\/s42254-021-00314-5","author":"GE Karniadakis","year":"2022","unstructured":"Karniadakis, G. E., Kevrekidis, I. G., Lu, L., Perdikaris, P., Wang, S., & Yang, L. (2022). Physics-informed machine learning. Nature Reviews Physics. https:\/\/doi.org\/10.1038\/s42254-021-00314-5","journal-title":"Nature Reviews Physics"},{"key":"2374_CR23","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1038\/s41524-023-01006-7","volume":"1","author":"D Khatamsaz","year":"2023","unstructured":"Khatamsaz, D., Vela, B., Singh, P., Johnson, D. D., Allaire, D., & Arr\u00f3yave, R. (2023). Bayesian optimization with active learning of design constraints using an entropy-based approach. NPJ Computational Materials, 1, 49. https:\/\/doi.org\/10.1038\/s41524-023-01006-7","journal-title":"NPJ Computational Materials"},{"key":"2374_CR24","doi-asserted-by":"publisher","DOI":"10.1007\/s00170-021-08199-5","author":"M Kott","year":"2022","unstructured":"Kott, M., Echler, D., & Groche, P. (2022). Methodological approach for the development of an operator assistance system for the press shop. International Journal of Advanced Manufacturing Technology. https:\/\/doi.org\/10.1007\/s00170-021-08199-5","journal-title":"International Journal of Advanced Manufacturing Technology"},{"key":"2374_CR25","doi-asserted-by":"publisher","DOI":"10.1007\/s10845-021-01789-w","author":"C Kubik","year":"2022","unstructured":"Kubik, C., Knauer, S. M., & Groche, P. (2022). Smart sheet metal forming: Importance of data acquisition, preprocessing and transformation on the performance of a multiclass support vector machine for predicting wear states during blanking. Journal of Intelligent Manufacturing. https:\/\/doi.org\/10.1007\/s10845-021-01789-w","journal-title":"Journal of Intelligent Manufacturing"},{"key":"2374_CR26","doi-asserted-by":"publisher","DOI":"10.1080\/10426914.2020.1729986","author":"S Kumar","year":"2020","unstructured":"Kumar, S., Hariharan, K., & Digavalli, R. (2020). Hybrid optimization of die design in constrained groove pressing. Materials and Manufacturing Processes. https:\/\/doi.org\/10.1080\/10426914.2020.1729986","journal-title":"Materials and Manufacturing Processes"},{"key":"2374_CR27","doi-asserted-by":"publisher","DOI":"10.1088\/1757-899X\/967\/1\/012031","author":"K Lee","year":"2020","unstructured":"Lee, K., Hong, C., Lee, E.-H., & Yang, W. (2020). Comparison of artificial intelligence methods for prediction of mechanical properties. IOP Conference Series: Materials Science and Engineering. https:\/\/doi.org\/10.1088\/1757-899X\/967\/1\/012031","journal-title":"IOP Conference Series: Materials Science and Engineering"},{"key":"2374_CR28","doi-asserted-by":"publisher","DOI":"10.1007\/s10845-022-01932-1","author":"DWW Low","year":"2023","unstructured":"Low, D. W. W., Chaudhari, A., Kumar, D., & Kumar, A. S. (2023). Convolutional neural networks for prediction of geometrical errors in incremental sheet metal forming. Journal of Intelligent Manufacturing. https:\/\/doi.org\/10.1007\/s10845-022-01932-1","journal-title":"Journal of Intelligent Manufacturing"},{"key":"2374_CR29","doi-asserted-by":"publisher","DOI":"10.1063\/5.0038929","author":"S Pawar","year":"2021","unstructured":"Pawar, S., San, O., Aksoylu, B., Rasheed, A., & Kvamsdal, T. (2021). Physics guided machine learning using simplified theories. Physics of Fluids. https:\/\/doi.org\/10.1063\/5.0038929","journal-title":"Physics of Fluids"},{"key":"2374_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.compchemeng.2019.04.003","author":"SJ Qin","year":"2022","unstructured":"Qin, S. J., & Chiang, L. H. (2022). Advances and opportunities in machine learning for process data analytics. Computers and Chemical Engineering. https:\/\/doi.org\/10.1016\/j.compchemeng.2019.04.003","journal-title":"Computers and Chemical Engineering"},{"key":"2374_CR31","doi-asserted-by":"publisher","unstructured":"Rueden, L., Mayer, S., Sifa, R., Bauckhage, C., & Garcke, J. (2020). Combining machine learning and simulation to a hybrid modelling approach: Current and future directions. In Advances in intelligent data analysis: 18th international symposium on intelligent data analysis (pp. 548\u2013560). Springer. https:\/\/doi.org\/10.1007\/978-3-030-44584-3_43.","DOI":"10.1007\/978-3-030-44584-3_43"},{"key":"2374_CR32","doi-asserted-by":"publisher","unstructured":"Ryser, M., Neuhauser, F. M., Hein, C., Hora, P., & Bambach, M. (2021). Surrogate model-based inverse parameter estimation in deep drawing using automatic knowledge acquisition. The International Journal of Advanced Manufacturing Technology. https:\/\/doi.org\/10.1007\/s00170-021-07642-x","DOI":"10.1007\/s00170-021-07642-x"},{"key":"2374_CR33","doi-asserted-by":"publisher","DOI":"10.1007\/s10845-022-01963-8","author":"H Tercan","year":"2022","unstructured":"Tercan, H., & Meisen, T. (2022). Machine learning and deep learning based predictive quality in manufacturing: A systematic review. Journal of Intelligent Manufacturing. https:\/\/doi.org\/10.1007\/s10845-022-01963-8","journal-title":"Journal of Intelligent Manufacturing"},{"key":"2374_CR34","doi-asserted-by":"publisher","DOI":"10.1007\/s10845-023-02129-w","author":"M Unterberg","year":"2023","unstructured":"Unterberg, M., Becker, M., Niemietz, P., & Bergs, T. (2023). Data-driven indirect punch wear monitoring in sheet-metal stamping processes. Journal of Intelligent Manufacturing. https:\/\/doi.org\/10.1007\/s10845-023-02129-w","journal-title":"Journal of Intelligent Manufacturing"},{"key":"2374_CR35","doi-asserted-by":"publisher","DOI":"10.1002\/qre.1924","author":"FA Viana","year":"2016","unstructured":"Viana, F. A. (2016). A tutorial on latin hypercube design of experiments. Quality and Reliability Engineering International. https:\/\/doi.org\/10.1002\/qre.1924","journal-title":"Quality and Reliability Engineering International"},{"key":"2374_CR36","doi-asserted-by":"publisher","unstructured":"Xie, Y., Liu, C., Li, W., Du, M., & Feng, K. (2022). Optimization of stamping process parameters based on an improved particle swarm optimization, genetic algorithm and sparse auto-encoder, back-propagation neural network model. Engineering Optimization. https:\/\/doi.org\/10.1080\/0305215X.2022.2152018","DOI":"10.1080\/0305215X.2022.2152018"}],"container-title":["Journal of Intelligent Manufacturing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-024-02374-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10845-024-02374-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-024-02374-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,10]],"date-time":"2025-04-10T20:19:47Z","timestamp":1744316387000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10845-024-02374-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,20]]},"references-count":36,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,4]]}},"alternative-id":["2374"],"URL":"https:\/\/doi.org\/10.1007\/s10845-024-02374-7","relation":{},"ISSN":["0956-5515","1572-8145"],"issn-type":[{"value":"0956-5515","type":"print"},{"value":"1572-8145","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,20]]},"assertion":[{"value":"11 April 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 March 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 April 2024","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 this manuscript is original, has not been published before, and is not currently being considered for publication elsewhere. This article reflects only the author\u2019s views and the Basque Government is not responsible for any use that may be made of the information contained therein.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}