{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T13:42:28Z","timestamp":1765546948296,"version":"3.37.3"},"reference-count":22,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,9,12]],"date-time":"2022-09-12T00:00:00Z","timestamp":1662940800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,9,12]],"date-time":"2022-09-12T00:00:00Z","timestamp":1662940800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100011736","name":"Ministerium f\u00fcr Wirtschaft, Arbeit und Wohnungsbau Baden-W\u00fcrttemberg","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100011736","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Hochschule Aalen - Technik und Wirtschaft"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Discov Artif Intell"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>In industrial metal forming processes, the generation of datasets for inline and optical quality assessment is expensive and time-consuming. Within the research project <jats:italic>SimKI<\/jats:italic>, conventional metal forming plants were digitalized under the use of perception-based 3D-sensors in combination with a completely redesigned forming tool. The integration of optical quality observation methods connected with a retrofitting approach of the press tool provides the opportunity to generate an information-feedback loop that predicts part defects before their occurrence. Additionally, the <jats:italic>SimKI<\/jats:italic>-method combines conventional statistical measurement methods with AI-based defect detection algorithms that are trained by generic datasets of a finite-element simulation, real component images of a 3D imaging device, and a combination of both. The generated datasets are used to accelerate the training of a DNN-based algorithm to identify the position and deviation from the agreed quality. The high degree of innovation is based on obtaining real-time component quality information under the use of AI-based optical quality assessment, which in turn provides information to the control algorithm of the smart forming tool.<\/jats:p>","DOI":"10.1007\/s44163-022-00034-4","type":"journal-article","created":{"date-parts":[[2022,9,12]],"date-time":"2022-09-12T13:06:53Z","timestamp":1662988013000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Recursive quality optimization of a smart forming tool under the use of perception based hybrid datasets for training of a Deep Neural Network"],"prefix":"10.1007","volume":"2","author":[{"given":"S.","family":"Feldmann","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"M.","family":"Schmiedt","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"J. M.","family":"Schlosser","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"W.","family":"Rimkus","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"T.","family":"Stempfle","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"C.","family":"Rathmann","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,9,12]]},"reference":[{"key":"34_CR1","unstructured":"eurostat: Manufacturing statistics - NACE Rev. 2. Data extracted in March 2020. 2021. https:\/\/ec.europa.eu\/eurostat\/statistics-explained. Accessed 24 June 2021."},{"key":"34_CR2","unstructured":"Federal Ministry for Economic Affairs and Energy (BMWi): 2030 Vision for Industrie 4.0. Shaping Digital Ecosystems Globally (2019)."},{"key":"34_CR3","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-58530-6","volume-title":"Handbuch Industrie 4.0 [Handbook Industry 4.0]","author":"M ten Hompel","year":"2020","unstructured":"ten Hompel M, Vogel-Heuser B, Bauernhansl T. Handbuch Industrie 4.0 [Handbook Industry 4.0]. Berlin: Springer; 2020."},{"key":"34_CR4","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-658-21118-9","volume-title":"Industrie 4.0 f\u00fcr die Praxis [Industy 4.0 in practice]","author":"RM Wagner","year":"2018","unstructured":"Wagner RM. Industrie 4.0 f\u00fcr die Praxis [Industy 4.0 in practice]. Wiesbaden: Springer Fachmedien Wiesbaden; 2018."},{"key":"34_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.procir.2020.05.064","author":"SSH Al-Maeeni","year":"2020","unstructured":"Al-Maeeni SSH, Kuhnhen C, Engel B, Schiller M. Smart retrofitting of machine tools in the context of industry 4.0. Proced CIRP. 2020. https:\/\/doi.org\/10.1016\/j.procir.2020.05.064.","journal-title":"Proced CIRP"},{"key":"34_CR6","unstructured":"Schr\u00f6der C. The challenges of industry 4.0 for small and medium-sized enterprises. A good society - social democracy #2017 plus. Friedrich-Ebert-Stiftung, Division for Economic and Social Policy, Bonn (2016)."},{"key":"34_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.rcim.2008.03.014","author":"E S\u00e1enz de Argando\u00f1a","year":"2008","unstructured":"S\u00e1enz de Argando\u00f1a E, Aztiria A, Garc\u00eda C, Arana N, Izaguirre A, Fillatreau P. Forming processes control by means of artificial intelligence techniques. Robot Comput Integr Manuf. 2008. https:\/\/doi.org\/10.1016\/j.rcim.2008.03.014.","journal-title":"Robot Comput Integr Manuf"},{"key":"34_CR8","doi-asserted-by":"publisher","DOI":"10.17222\/mit.2015.335","author":"S Zhou","year":"2017","unstructured":"Zhou S, Chen Y, Zhang D, Xie J, Zhou Y. Classification of surface defects on steel sheet using convolutional neural networks. Mater Tehnol. 2017. https:\/\/doi.org\/10.17222\/mit.2015.335.","journal-title":"Mater Tehnol."},{"key":"34_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2017.08.002","author":"S Satorres Mart\u00ednez","year":"2017","unstructured":"Satorres Mart\u00ednez S, Ortega V\u00e1zquez C, G\u00e1mez Garc\u00eda J, G\u00f3mez Ortega J. Quality inspection of machined metal parts using an image fusion technique. Measurement. 2017. https:\/\/doi.org\/10.1016\/j.measurement.2017.08.002.","journal-title":"Measurement"},{"key":"34_CR10","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/896\/1\/012088","author":"RW Hall","year":"2017","unstructured":"Hall RW, Foster A, Praturlon AH. Hot forming and quenching pilot process development for low cost and low environmental impact manufacturing. J Phys Conf Ser. 2017. https:\/\/doi.org\/10.1088\/1742-6596\/896\/1\/012088.","journal-title":"J Phys Conf Ser"},{"key":"34_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.proeng.2017.10.1015","author":"S Polak","year":"2017","unstructured":"Polak S, Kaczy\u0144ski P, Gronostajski Z, Jaskiewicz K, Krawczyk J, Skwarski M, Zwierzchowski M, Chorz\u0119pa W. Warm forming of 7075 aluminum alloys. Proced Eng. 2017. https:\/\/doi.org\/10.1016\/j.proeng.2017.10.1015.","journal-title":"Proced Eng"},{"key":"34_CR12","doi-asserted-by":"publisher","DOI":"10.4028\/www.scientific.net\/KEM.651-653.199","author":"E S\u00e1enz de Argando\u00f1a","year":"2015","unstructured":"S\u00e1enz de Argando\u00f1a E, Galdos L, Ortubay R, Mendiguren J, Agirretxe X. Room temperature forming of AA7075 aluminum alloys: W-temper process. Key Eng Mater. 2015. https:\/\/doi.org\/10.4028\/www.scientific.net\/KEM.651-653.199.","journal-title":"Key Eng Mater"},{"key":"34_CR13","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/896\/1\/012091","author":"J Schlosser","year":"2017","unstructured":"Schlosser J, Schneider R, Rimkus W, Kelsch R, Gerstner F, Harrison DK, Grant RJ. Materials and simulation modelling of a crash-beam performance\u2014a comparison study showing the potential for weight saving using warm-formed ultra-high strength aluminium alloys. J Phys Conf Ser. 2017. https:\/\/doi.org\/10.1088\/1742-6596\/896\/1\/012091.","journal-title":"J Phys Conf Ser"},{"key":"34_CR14","volume-title":"Proceedings of the fourteenth international conference on artificial intelligence and statistics","author":"X Glorot","year":"2011","unstructured":"Glorot X, Bordes A, Bengio Y. Deep sparse rectifier neural networks. In: Gordon G, Dunson D, Dud\u00edk M, editors. Proceedings of the fourteenth international conference on artificial intelligence and statistics. Fort Lauderdale: PMLR; 2011."},{"key":"34_CR15","doi-asserted-by":"crossref","unstructured":"Jarrett K, Kavukcuoglu K, Ranzato MA, LeCun Y. What is the best multi-stage architecture for object recognition? In: IEEE 12th International conference 29.09.2009\u201302.10.2009, pp. 2146\u20132153.","DOI":"10.1109\/ICCV.2009.5459469"},{"key":"34_CR16","volume-title":"Advances in neural information processing systems","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. In: Pereira F, Burges CJ, Bottou L, Weinberger KQ, editors. Advances in neural information processing systems. Red Hook: Curran Associates; 2012."},{"key":"34_CR17","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R. Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res. 2014;15:1929\u201358.","journal-title":"J Mach Learn Res"},{"key":"34_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2016.06.008","author":"V Ondra","year":"2017","unstructured":"Ondra V, Sever IA, Schwingshackl CW. A method for detection and characterisation of structural non-linearities using the Hilbert transform and neural networks. Mech Syst Signal Process. 2017. https:\/\/doi.org\/10.1016\/j.ymssp.2016.06.008.","journal-title":"Mech Syst Signal Process"},{"key":"34_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.bea.2022.100048","author":"H Malik","year":"2022","unstructured":"Malik H, Bashir U, Ahmad A. Multi-classification neural network model for detection of abnormal heartbeat audio signals. Biomed Eng Adv. 2022. https:\/\/doi.org\/10.1016\/j.bea.2022.100048.","journal-title":"Biomed Eng Adv"},{"key":"34_CR20","unstructured":"Wang J, Wiens J. AdaSGD: bridging the gap between SGD and Adam. 2020. https:\/\/arxiv.org\/abs\/2006.16541. Accessed 22 Dec 2021."},{"key":"34_CR21","doi-asserted-by":"crossref","unstructured":"Feldmann S, Kempter G, Esslinger R, Tran HT. Support of image-based quality assessment in discrete production scenarios through AI-based decision support. In: Proceedings of the 2020 4th international conference on algorithms, computing and systems. Association for computing machinery, [S.l.] (2020).","DOI":"10.1145\/3423390.3426729"},{"key":"34_CR22","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1602.07360","author":"FN Iandola","year":"2017","unstructured":"Iandola FN, Han S, Moskewicz M, Ashraf K, Dally WJ, Keutzer K. SqueezeNet AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size. ICLR. 2017. https:\/\/doi.org\/10.48550\/arXiv.1602.07360.","journal-title":"ICLR"}],"container-title":["Discover Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-022-00034-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44163-022-00034-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-022-00034-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,5]],"date-time":"2022-10-05T15:12:52Z","timestamp":1664982772000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44163-022-00034-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,12]]},"references-count":22,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["34"],"URL":"https:\/\/doi.org\/10.1007\/s44163-022-00034-4","relation":{},"ISSN":["2731-0809"],"issn-type":[{"type":"electronic","value":"2731-0809"}],"subject":[],"published":{"date-parts":[[2022,9,12]]},"assertion":[{"value":"8 July 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 August 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 September 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable (this article does not contain any studies with human participants or animals performed by any of the authors).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"The authors declare that they have no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"17"}}