{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T22:50:03Z","timestamp":1773442203936,"version":"3.50.1"},"reference-count":60,"publisher":"Walter de Gruyter GmbH","issue":"9","funder":[{"DOI":"10.13039\/100007569","name":"Carl Zeiss Foundation","doi-asserted-by":"crossref","award":["project ID: 471687386"],"award-info":[{"award-number":["project ID: 471687386"]}],"id":[{"id":"10.13039\/100007569","id-type":"DOI","asserted-by":"crossref"}]}],"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>The integration of both linear and circular processes in one production system poses significant challenges. In particular, the reprocessing of end-of-life products is associated with uncertainties at all levels of the production system, from the initial planning and control through to the executing production hardware and intralogistics. To address these challenges, this article presents approaches for self-learning and autonomously adapting production equipment for the Circular Factory. Initially, hardware and software solutions are developed to cover the necessary processes. Reprocessing is covered by modular and reconfigurable manufacturing cells, which also include new process chains such as the combination of additive-subtractive processes. The provided capabilities must be applied to ever new products, for example by transferring human procedures for unknown products to the production equipment. Lastly, an overall robust and dynamic production planning and control system is developed that maintains continuous operation even in unforeseen situations. The resulting highly dynamic overall system is connected by an autonomous intralogistics system.<\/jats:p>","DOI":"10.1515\/auto-2024-0005","type":"journal-article","created":{"date-parts":[[2024,9,10]],"date-time":"2024-09-10T13:34:52Z","timestamp":1725975292000},"page":"861-874","source":"Crossref","is-referenced-by-count":5,"title":["Self-learning and autonomously adapting manufacturing equipment for the circular factory"],"prefix":"10.1515","volume":"72","author":[{"given":"J\u00fcrgen","family":"Fleischer","sequence":"first","affiliation":[{"name":"Karlsruhe Institute of Technology , Karlsruhe , Germany"}]},{"given":"Frederik","family":"Zanger","sequence":"additional","affiliation":[{"name":"Karlsruhe Institute of Technology , Karlsruhe , Germany"}]},{"given":"Volker","family":"Schulze","sequence":"additional","affiliation":[{"name":"Karlsruhe Institute of Technology , Karlsruhe , Germany"}]},{"given":"Gerhard","family":"Neumann","sequence":"additional","affiliation":[{"name":"Karlsruhe Institute of Technology , Karlsruhe , Germany"}]},{"given":"Nicole","family":"Stricker","sequence":"additional","affiliation":[{"name":"Hochschule Aalen , Aalen , Germany"}]},{"given":"Kai","family":"Furmans","sequence":"additional","affiliation":[{"name":"Karlsruhe Institute of Technology , Karlsruhe , Germany"}]},{"given":"Julius","family":"Pfrommer","sequence":"additional","affiliation":[{"name":"Fraunhofer IOSB , Karlsruhe , Germany"}]},{"given":"Gisela","family":"Lanza","sequence":"additional","affiliation":[{"name":"Karlsruhe Institute of Technology , Karlsruhe , Germany"}]},{"given":"Malte","family":"Hansjosten","sequence":"additional","affiliation":[{"name":"Karlsruhe Institute of Technology , Karlsruhe , Germany"}]},{"given":"Patrick","family":"Fischmann","sequence":"additional","affiliation":[{"name":"Karlsruhe Institute of Technology , Karlsruhe , Germany"}]},{"given":"Julia","family":"Dvorak","sequence":"additional","affiliation":[{"name":"Karlsruhe Institute of Technology , Karlsruhe , Germany"}]},{"given":"Jan-Felix","family":"Klein","sequence":"additional","affiliation":[{"name":"Karlsruhe Institute of Technology , Karlsruhe , Germany"}]},{"given":"Felix","family":"Rauscher","sequence":"additional","affiliation":[{"name":"Karlsruhe Institute of Technology , Karlsruhe , Germany"}]},{"given":"Andreas","family":"Ebner","sequence":"additional","affiliation":[{"name":"Fraunhofer IOSB , Karlsruhe , Germany"}]},{"given":"Marvin Carl","family":"May","sequence":"additional","affiliation":[{"name":"Karlsruhe Institute of Technology , Karlsruhe , Germany"}]},{"given":"Philipp","family":"G\u00f6nnheimer","sequence":"additional","affiliation":[{"name":"Karlsruhe Institute of Technology , Karlsruhe , Germany"}]}],"member":"374","published-online":{"date-parts":[[2024,9,10]]},"reference":[{"key":"2025031705022700102_j_auto-2024-0005_ref_001","doi-asserted-by":"crossref","unstructured":"M. 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Cerdas, et al.., \u201cDefining circulation factories \u2013 a pathway towards factories of the future,\u201d Proc. CIRP, vol.\u00a029, pp.\u00a0627\u2013632, 2015, https:\/\/doi.org\/10.1016\/j.procir.2015.02.032.","DOI":"10.1016\/j.procir.2015.02.032"},{"key":"2025031705022700102_j_auto-2024-0005_ref_004","doi-asserted-by":"crossref","unstructured":"K. Urano and S. Takata, \u201cModule reconfiguration management for circular factories without discriminating between virgin and reused products,\u201d in Re-Engineering Manufacturing for Sustainability, A. Y. C. Nee, B. Song, and S.-K. Ong, Eds., Singapore, Springer Singapore, 2013, pp.\u00a0603\u2013608.","DOI":"10.1007\/978-981-4451-48-2_98"},{"key":"2025031705022700102_j_auto-2024-0005_ref_005","doi-asserted-by":"crossref","unstructured":"J. Fleischer, et al.., \u201cAgile produktion elektrischer traktionsmotoren als antwort auf volatile m\u00e4rkte und technologien,\u201d Z. f\u00fcr Wirtsch. Fabr., vol.\u00a0116, no.\u00a03, pp.\u00a0128\u2013132, 2021. https:\/\/doi.org\/10.1515\/zwf-2021-0025.","DOI":"10.1515\/zwf-2021-0025"},{"key":"2025031705022700102_j_auto-2024-0005_ref_006","doi-asserted-by":"crossref","unstructured":"J. Fleischer, F. Fraider, F. K\u00f6\u00dfler, D. Mayer, and F. Wirth, \u201cAgile production systems for electric mobility,\u201d Proc. CIRP, vol.\u00a0107, pp.\u00a01251\u20131256, 2022, https:\/\/doi.org\/10.1016\/j.procir.2022.05.140.","DOI":"10.1016\/j.procir.2022.05.140"},{"key":"2025031705022700102_j_auto-2024-0005_ref_007","doi-asserted-by":"crossref","unstructured":"J. Baumg\u00e4rtner, P. G\u00f6nnheimer, and J. Fleischer, \u201cOptimal robot workpiece placement for maximized repeatability,\u201d in Advances in System-Integrated Intelligence, M. 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Levine, \u201cEfficient online reinforcement learning with offline data,\u201d in International Conference on Machine Learning, 2023."},{"key":"2025031705022700102_j_auto-2024-0005_ref_027","unstructured":"O. Eberhard, J. Hollenstein, C. Pinneri, and G. Martius, \u201cPink noise is all you need: colored noise exploration in deep reinforcement learning,\u201d in The Eleventh International Conference on Learning Representations, 2023."},{"key":"2025031705022700102_j_auto-2024-0005_ref_028","unstructured":"A. Raffin, J. Kober, and F. Stulp, \u201cSmooth exploration for robotic reinforcement learning,\u201d in 5th Annual Conference on Robot Learning, 2021."},{"key":"2025031705022700102_j_auto-2024-0005_ref_029","unstructured":"G. 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