{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T00:57:42Z","timestamp":1778893062068,"version":"3.51.4"},"reference-count":43,"publisher":"Walter de Gruyter GmbH","issue":"9","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,9,25]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>In the circular factory, uncertain attributes of object instances and process steps are found at diverse occasions. Even if uncertainty can also be found to some extent in linear production, the high variation of product attributes of used objects causes the process steps in the circular factory to generate a much higher variability of the properties of the objects handled in circular processes. In consequence, a methodology is needed to model, handle and manage uncertainties at all relevant situations within the circular factory. In contrast to linear production, the uncertainty of attributes cannot be extended to an object class (with the same production history), but must be assigned to each object instance (with its own history) individually. In this contribution, the basic concepts for managing uncertainty in the circular factory are introduced. As a common basis, probabilities are used to express uncertainty, thus being compatible with the traditional and proven concepts of measurement science and stochastics. To describe the individual information state of object instances, it is complemented with a joint probability distribution describing all relevant object attributes. Some examples for processes within the circular factory demonstrate how uncertainty is considered to manage the uncertainty related challenges of used objects.<\/jats:p>","DOI":"10.1515\/auto-2024-0009","type":"journal-article","created":{"date-parts":[[2024,9,10]],"date-time":"2024-09-10T13:34:52Z","timestamp":1725975292000},"page":"829-843","source":"Crossref","is-referenced-by-count":4,"title":["Managing uncertainty in product and process design for the circular factory"],"prefix":"10.1515","volume":"72","author":[{"given":"Michael","family":"Heizmann","sequence":"first","affiliation":[{"name":"KIT, Institute of Industrial Information Technology (IIIT) , Karlsruhe , Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"J\u00fcrgen","family":"Beyerer","sequence":"additional","affiliation":[{"name":"KIT, Institute for Anthropomatics and Robotics (IAR), and Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB , Karlsruhe , Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stefan","family":"Dietrich","sequence":"additional","affiliation":[{"name":"KIT, Institute for Applied Materials (IAM) , Karlsruhe , Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luisa","family":"Hoffmann","sequence":"additional","affiliation":[{"name":"KIT, Institute of Industrial Information Technology (IIIT) , Karlsruhe , Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jan-Philipp","family":"Kaiser","sequence":"additional","affiliation":[{"name":"KIT, wbk Institute of Production Science , Karlsruhe , Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gisela","family":"Lanza","sequence":"additional","affiliation":[{"name":"KIT, wbk Institute of Production Science , Karlsruhe , Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alina","family":"Roitberg","sequence":"additional","affiliation":[{"name":"University of Stuttgart, Institute for Artificial Intelligence , Stuttgart , Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rainer","family":"Stiefelhagen","sequence":"additional","affiliation":[{"name":"KIT, Computer Vision for Human-Computer Interaction Lab (cv:hci) , Karlsruhe , Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nicole","family":"Stricker","sequence":"additional","affiliation":[{"name":"Aalen University , Aalen , Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Helena","family":"Wexel","sequence":"additional","affiliation":[{"name":"KIT, wbk Institute of Production Science , Karlsruhe , Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Frederik","family":"Zanger","sequence":"additional","affiliation":[{"name":"KIT, wbk Institute of Production Science , Karlsruhe , Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2024,9,10]]},"reference":[{"key":"2025031705022696157_j_auto-2024-0009_ref_001","unstructured":"ISO\/IEC Guide 98-3:2008-09, Uncertainty of Measurement \u2013 Part 3: Guide to the Expression of Uncertainty in Measurement, Geneva, Switzerland, ISO\/IEC, 2008."},{"key":"2025031705022696157_j_auto-2024-0009_ref_002","unstructured":"M. Sewell, \u201cWhy probability?\u201d 2023. Available at: http:\/\/www.stats.org.uk\/why-probability\/."},{"key":"2025031705022696157_j_auto-2024-0009_ref_003","doi-asserted-by":"crossref","unstructured":"D. V. Lindley, \u201cScoring rules and the inevitability of probability,\u201d Int. Stat. Rev., vol. 50, no. 1, pp. 1\u201326, 1982. https:\/\/doi.org\/10.2307\/1402448.","DOI":"10.2307\/1402448"},{"key":"2025031705022696157_j_auto-2024-0009_ref_004","doi-asserted-by":"crossref","unstructured":"L. V. Lindley, \u201cThe probability approach to the treatment of uncertainty in artificial intelligence and expert systems,\u201d Stat. Sci., vol.\u00a02, no.\u00a01, pp.\u00a017\u201324, 1987. https:\/\/doi.org\/10.1214\/ss\/1177013427.","DOI":"10.1214\/ss\/1177013427"},{"key":"2025031705022696157_j_auto-2024-0009_ref_005","doi-asserted-by":"crossref","unstructured":"T. Tolio, et al.., \u201cDesign, management and control of demanufacturing and remanufacturing systems,\u201d CIRP Ann., vol.\u00a066, no.\u00a02, pp.\u00a0585\u2013609, 2017. https:\/\/doi.org\/10.1016\/j.cirp.2017.05.001.","DOI":"10.1016\/j.cirp.2017.05.001"},{"key":"2025031705022696157_j_auto-2024-0009_ref_006","doi-asserted-by":"crossref","unstructured":"S. Mete, Z. A. \u00c7il, E. \u00d6zceylan, K. A\u011fpak, and O. Batta\u00efa, \u201cAn optimisation support for the design of hybrid production lines including assembly and disassembly tasks,\u201d Int. J. Prod. Res., vol.\u00a056, no.\u00a024, pp.\u00a07375\u20137389, 2018. https:\/\/doi.org\/10.1080\/00207543.2018.1428774.","DOI":"10.1080\/00207543.2018.1428774"},{"key":"2025031705022696157_j_auto-2024-0009_ref_007","doi-asserted-by":"crossref","unstructured":"E. \u00d6zceylan, C. B. Kalayci, A. G\u00fcng\u00f6r, and S. M. Gupta, \u201cDisassembly line balancing problem: a review of the state of the art and future directions,\u201d Int. J. Prod. Res., vol.\u00a057, nos. 15\u201316, pp.\u00a04805\u20134827, 2019. https:\/\/doi.org\/10.1080\/00207543.2018.1428775.","DOI":"10.1080\/00207543.2018.1428775"},{"key":"2025031705022696157_j_auto-2024-0009_ref_008","doi-asserted-by":"crossref","unstructured":"F. T. Altekin and C. Akkan, \u201cTask-failure-driven rebalancing of disassembly lines,\u201d Int. J. Prod. Res., vol.\u00a050, no.\u00a018, pp.\u00a04955\u20134976, 2012. https:\/\/doi.org\/10.1080\/00207543.2011.616915.","DOI":"10.1080\/00207543.2011.616915"},{"key":"2025031705022696157_j_auto-2024-0009_ref_009","doi-asserted-by":"crossref","unstructured":"J. Pfrommer, D. Stogl, K. Aleksandrov, S. Escaida Navarro, B. Hein, and J. Beyerer, \u201cPlug & produce by modelling skills and service-oriented orchestration of reconfigurable manufacturing systems,\u201d at \u2013 Automatisierungstechnik, vol.\u00a063, no.\u00a010, pp.\u00a0790\u2013800, 2015. https:\/\/doi.org\/10.1515\/auto-2014-1157.","DOI":"10.1515\/auto-2014-1157"},{"key":"2025031705022696157_j_auto-2024-0009_ref_010","doi-asserted-by":"crossref","unstructured":"M. C. May, S. Schmidt, A. Kuhnle, N. Stricker, and G. Lanza, \u201cProduct generation module: automated production planning for optimized workload and increased efficiency in matrix production systems,\u201d Procedia CIRP, vol.\u00a096, pp.\u00a045\u201350, 2021. https:\/\/doi.org\/10.1016\/j.procir.2021.01.050.","DOI":"10.1016\/j.procir.2021.01.050"},{"key":"2025031705022696157_j_auto-2024-0009_ref_011","unstructured":"S. Schindler, Strategische Planung von Technologieketten f\u00fcr die Produktion, Dissertation, Munich, 2014."},{"key":"2025031705022696157_j_auto-2024-0009_ref_012","doi-asserted-by":"crossref","unstructured":"N. Stricker, A. Pfeiffer, E. Moser, B. K\u00e1d\u00e1r, G. Lanza, and L. Monostori, \u201cSupporting multi-level and robust production planning and execution,\u201d CIRP Ann., vol.\u00a064, no.\u00a01, pp.\u00a0415\u2013418, 2015. https:\/\/doi.org\/10.1016\/j.cirp.2015.04.115.","DOI":"10.1016\/j.cirp.2015.04.115"},{"key":"2025031705022696157_j_auto-2024-0009_ref_013","doi-asserted-by":"crossref","unstructured":"B. V. Dasarathy, \u201cInformation Fusion \u2013 what, where, why, when, and how?\u201d Fusion, vol.\u00a02, no.\u00a02, pp.\u00a075\u201376, 2001. https:\/\/doi.org\/10.1016\/s1566-2535(01)00032-x.","DOI":"10.1016\/S1566-2535(01)00032-X"},{"key":"2025031705022696157_j_auto-2024-0009_ref_014","doi-asserted-by":"crossref","unstructured":"G. Koliander, Y. El-Laham, P. M. Djuri\u0107, and F. Hlawatsch, \u201cFusion of probability density functions,\u201d Proc. IEEE, vol.\u00a0110, no.\u00a04, pp.\u00a0404\u2013453, 2022. https:\/\/doi.org\/10.1109\/jproc.2022.3154399.","DOI":"10.1109\/JPROC.2022.3154399"},{"key":"2025031705022696157_j_auto-2024-0009_ref_015","doi-asserted-by":"crossref","unstructured":"N. Wu, The Maximum Entropy Method, Berlin, Springer, 1997.","DOI":"10.1007\/978-3-642-60629-8"},{"key":"2025031705022696157_j_auto-2024-0009_ref_016","unstructured":"M. Huber, Nonlinear Gaussian Filtering Theory, Algorithms, and Applications, Karlsruhe, KIT Scientific Publishing, 2015."},{"key":"2025031705022696157_j_auto-2024-0009_ref_017","unstructured":"W3C Provenance Working Group, \u201cThe PROV data model,\u201d 2013. Available at: http:\/\/www.w3.org\/TR\/2013\/prov-overview."},{"key":"2025031705022696157_j_auto-2024-0009_ref_018","doi-asserted-by":"crossref","unstructured":"O. C. Schrempf, O. Feiermann, and U. D. Hanebeck, \u201cOptimal mixture approximation of the product of mixtures,\u201d in 2005 7th International Conference on Information Fusion, Philadelphia, PA, USA, 2005, pp.\u00a085\u201392.","DOI":"10.1109\/ICIF.2005.1591840"},{"key":"2025031705022696157_j_auto-2024-0009_ref_019","unstructured":"J. Pearl, \u201cBayesian networks: a model of self-activated memory for evidential reasoning,\u201d in Proceedings of the 7th Conference of the Cognitive Science Society, 1985, pp.\u00a0329\u2013334."},{"key":"2025031705022696157_j_auto-2024-0009_ref_020","doi-asserted-by":"crossref","unstructured":"M. Errington and S. J. Childe, \u201cA business process model of inspection in remanufacturing,\u201d J. Remanufacturing, vol. 3, no. 7, pp. 1\u201322, 2013. https:\/\/doi.org\/10.1186\/2210-4690-3-7.","DOI":"10.1186\/2210-4690-3-7"},{"key":"2025031705022696157_j_auto-2024-0009_ref_021","doi-asserted-by":"crossref","unstructured":"G. Cai and S. Mahadevan, \u201cUncertainty quantification of manufacturing process effects on macroscale material properties,\u201d Int. J. Multiscale Comput. Eng., vol.\u00a014, no.\u00a03, pp.\u00a0191\u2013213, 2016. https:\/\/doi.org\/10.1615\/intjmultcompeng.2016015552.","DOI":"10.1615\/IntJMultCompEng.2016015552"},{"key":"2025031705022696157_j_auto-2024-0009_ref_022","doi-asserted-by":"crossref","unstructured":"Z. Hu and S. Mahadevan, \u201cUncertainty quantification in prediction of material properties during additive manufacturing,\u201d Scr. Mater., vol.\u00a0135, pp.\u00a0135\u2013140, 2017. https:\/\/doi.org\/10.1016\/j.scriptamat.2016.10.014.","DOI":"10.1016\/j.scriptamat.2016.10.014"},{"key":"2025031705022696157_j_auto-2024-0009_ref_023","doi-asserted-by":"crossref","unstructured":"H. Zhang, S. Liu, H. Lu, Y. Zhang, and Y. Hu, \u201cRemanufacturing and remaining useful life assessment,\u201d in Handbook of Manufacturing Engineering and Technology, 2015, pp.\u00a03137\u20133193.","DOI":"10.1007\/978-1-4471-4670-4_112"},{"key":"2025031705022696157_j_auto-2024-0009_ref_024","doi-asserted-by":"crossref","unstructured":"X. Zhang, W. Cui, W. Li, and F. Liou, \u201cA hybrid process integrating reverse engineering, pre-repair processing, additive manufacturing, and material testing for component remanufacturing,\u201d Materials, vol.\u00a012, no.\u00a012, p.\u00a01961, 2019. https:\/\/doi.org\/10.3390\/ma12121961.","DOI":"10.3390\/ma12121961"},{"key":"2025031705022696157_j_auto-2024-0009_ref_025","doi-asserted-by":"crossref","unstructured":"J.-P. Wu, R. Kang, and X.-Y. Li, \u201cUncertain accelerated degradation modeling and analysis considering epistemic uncertainties in time and unit dimension,\u201d Reliab. Eng. Syst. Saf., vol. 201, no. C, 2020, Art. no. 106967. https:\/\/doi.org\/10.1016\/j.ress.2020.106967.","DOI":"10.1016\/j.ress.2020.106967"},{"key":"2025031705022696157_j_auto-2024-0009_ref_026","unstructured":"ANSYS, Inc. Ansys GRANTA EduPack Software, Cambridge, UK, ANSYS, Inc., 2023. Available at: www.ansys.com\/materials."},{"key":"2025031705022696157_j_auto-2024-0009_ref_027","doi-asserted-by":"crossref","unstructured":"Z. Wang, et al.., \u201cUncertainty quantification and reduction in metal additive manufacturing,\u201d NPJ Comput. Mater., vol.\u00a06, no.\u00a01, p.\u00a0175, 2020. https:\/\/doi.org\/10.1038\/s41524-020-00444-x.","DOI":"10.1038\/s41524-020-00444-x"},{"key":"2025031705022696157_j_auto-2024-0009_ref_028","doi-asserted-by":"crossref","unstructured":"S. Mahadevan, P. Nath, and Z. Hu, \u201cUncertainty quantification for additive manufacturing process improvement: recent advances,\u201d ASCE ASME J. Risk Uncertain. Eng. Syst. B Mech. Eng., vol. 8, no. 1, 2022, Art. no. 010801. https:\/\/doi.org\/10.1115\/1.4053184.","DOI":"10.1115\/1.4053184"},{"key":"2025031705022696157_j_auto-2024-0009_ref_029","doi-asserted-by":"crossref","unstructured":"V. Lubkowitz, P. Fischmann, V. Schulze, and F. Zanger, \u201cInfluence of initial powder layer thickness and focus deviation on the properties of hybrid manufactured parts by Laser Powder Bed Fusion,\u201d Procedia CIRP, vol.\u00a0111, pp.\u00a087\u201391, 2022. https:\/\/doi.org\/10.1016\/j.procir.2022.08.136.","DOI":"10.1016\/j.procir.2022.08.136"},{"key":"2025031705022696157_j_auto-2024-0009_ref_030","doi-asserted-by":"crossref","unstructured":"E. Segebade, M. Gerstenmeyer, S. Dietrich, F. Zanger, and V. Schulze, \u201cInfluence of anisotropy of additively manufactured AlSi10Mg parts on chip formation during orthogonal cutting,\u201d Procedia CIRP, vol.\u00a082, pp.\u00a0113\u2013118, 2019. https:\/\/doi.org\/10.1016\/j.procir.2019.04.043.","DOI":"10.1016\/j.procir.2019.04.043"},{"key":"2025031705022696157_j_auto-2024-0009_ref_031","doi-asserted-by":"crossref","unstructured":"Z. Hu and S. Mahadevan, \u201cUncertainty quantification and management in additive manufacturing: current status, needs, and opportunities,\u201d Int. J. Adv. Des. Manuf. Technol., vol.\u00a093, pp.\u00a02855\u20132874, 2017. https:\/\/doi.org\/10.1007\/s00170-017-0703-5.","DOI":"10.1007\/s00170-017-0703-5"},{"key":"2025031705022696157_j_auto-2024-0009_ref_032","doi-asserted-by":"crossref","unstructured":"A. Panda, A. K. Sahoo, R. Kumar, and D. Das, \u201cA concise review of uncertainty analysis in metal machining,\u201d Mater. Today: Proc., vol. 26, no. 2, pp. 1734\u20131739, 2020. https:\/\/doi.org\/10.1016\/j.matpr.2020.02.365.","DOI":"10.1016\/j.matpr.2020.02.365"},{"key":"2025031705022696157_j_auto-2024-0009_ref_033","doi-asserted-by":"crossref","unstructured":"Q. Ren, L. Baron, and M. Balazinski, \u201cApplication of type-2 fuzzy estimation on uncertainty in machining: an approach on acoustic emission during turning process,\u201d in NAFIPS 2009-2009 Annual Meeting of the North American Fuzzy Information Processing Society, IEEE, 2019, pp.\u00a01\u20136.","DOI":"10.1109\/NAFIPS.2009.5156421"},{"key":"2025031705022696157_j_auto-2024-0009_ref_034","doi-asserted-by":"crossref","unstructured":"Q. Liu, M. Janardhana, B. Hinton, M. Brandt, and K. Sharp, \u201cLaser cladding as a potential repair technology for damaged aircraft components,\u201d Int. J. Struct. Integr., vol.\u00a02, no.\u00a03, pp.\u00a0314\u2013331, 2011. https:\/\/doi.org\/10.1108\/17579861111162914.","DOI":"10.1108\/17579861111162914"},{"key":"2025031705022696157_j_auto-2024-0009_ref_035","unstructured":"S. Kramer, K. Drechsel, M. Jarwitz, V. Schulze, and F. Zanger, \u201cPotential of contactless support structures for improving the part quality of AlSi10Mg PBF-LB parts,\u201d in Fraunhofer Direct Digital Manufacturing Conference, 2023."},{"key":"2025031705022696157_j_auto-2024-0009_ref_036","doi-asserted-by":"crossref","unstructured":"D. Gauder, M. Biehler, J. Goelz, V. Schulze, and G. Lanza, \u201cIn-process acoustic pore detection in milling using deep learning,\u201d CIRP J. Manuf. Sci. Technol., vol.\u00a037, pp.\u00a0125\u2013133, 2022. https:\/\/doi.org\/10.1016\/j.cirpj.2022.01.008.","DOI":"10.1016\/j.cirpj.2022.01.008"},{"key":"2025031705022696157_j_auto-2024-0009_ref_037","unstructured":"D. Hendrycks and K. Gimpel, \u201cA baseline for detecting misclassified and out-of-distribution examples in neural networks,\u201d in Proceedings of International Conference on Learning Representations (ICLR), 2016."},{"key":"2025031705022696157_j_auto-2024-0009_ref_038","doi-asserted-by":"crossref","unstructured":"A. Nguyen, J. Yosinski, and J. Clune, \u201cDeep neural networks are easily fooled: high confidence predictions for unrecognizable images,\u201d in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp.\u00a0427\u2013436.","DOI":"10.1109\/CVPR.2015.7298640"},{"key":"2025031705022696157_j_auto-2024-0009_ref_039","unstructured":"C. Guo, G. Pleiss, Y. Sun, and K. Q. Weinberger, \u201cOn calibration of modern neural networks,\u201d in Proceedings of the 34th International Conference on Machine Learning, vol.\u00a070, 2017, pp.\u00a01321\u20131330."},{"key":"2025031705022696157_j_auto-2024-0009_ref_040","doi-asserted-by":"crossref","unstructured":"A. Roitberg, et al.., \u201cIs my driver observation model overconfident? Input-guided calibration networks for reliable and interpretable confidence estimates,\u201d IEEE Trans. Intell. Transp. Syst., vol.\u00a023, no.\u00a012, pp.\u00a025271\u201325286, 2022. https:\/\/doi.org\/10.1109\/tits.2022.3196410.","DOI":"10.1109\/TITS.2022.3196410"},{"key":"2025031705022696157_j_auto-2024-0009_ref_041","unstructured":"A. Kendall and Y. Gal, \u201cWhat uncertainties do we need in Bayesian deep learning for computer vision?\u201d in Advances in Neural Information Processing Systems, 2017, pp.\u00a05580\u20135590."},{"key":"2025031705022696157_j_auto-2024-0009_ref_042","unstructured":"Y. Gal and Z. Ghahramani, \u201cDropout as a Bayesian approximation: representing model uncertainty in deep learning,\u201d in International Conference on Machine Learning, 2016, pp.\u00a01050\u20131059."},{"key":"2025031705022696157_j_auto-2024-0009_ref_043","unstructured":"B. Lakshminarayanan, A. Pritzel, and C. Blundell, \u201cSimple and scalable predictive uncertainty estimation using deep ensembles,\u201d in Advances in Neural Information Processing Systems, 2017, pp.\u00a06402\u20136413."}],"container-title":["at - Automatisierungstechnik"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/auto-2024-0009\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/auto-2024-0009\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,17]],"date-time":"2025-03-17T05:03:25Z","timestamp":1742187805000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/auto-2024-0009\/html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,1]]},"references-count":43,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2024,9,10]]},"published-print":{"date-parts":[[2024,9,25]]}},"alternative-id":["10.1515\/auto-2024-0009"],"URL":"https:\/\/doi.org\/10.1515\/auto-2024-0009","relation":{},"ISSN":["0178-2312","2196-677X"],"issn-type":[{"value":"0178-2312","type":"print"},{"value":"2196-677X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,1]]}}}