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In-Process Digital Monitoring of Additive Manufacturing: Proposed Machine Learning Approach and Potential Implications on Sustainability

Charles, Amal; Salem, Mahmoud; Moshiri, Mandaná; Elkaseer, Ahmed; Scholz, Steffen G.

Abstract:
Additive Manufacturing (AM) technologies have recently gained significance amongst industries as well as everyday consumers. This is largely due to the benefits that they offer in terms of design freedom, lead-time reduction, mass-customization as well as potential sustainability improvements due to efficiency in resource usage. However, conventional manufacturing industries are reluctant to integrate AM within their established process chains due to the unpredictability of the process and the quality of the final parts that are printed. Conventional manufacturing process have the advantage of decades of research in developing process knowledge and optimization, which culminates in accurate process predictability. This gap in process understanding is one that AM will need to cover in a short time. AM does have the benefit of being a digital manufacturing process and with the adoption of advanced Artificial Intelligence (AI) and Machine Learning (ML) techniques in production lines, there may not have been a better industrial age for its implementation. This paper presents a case for actively developing AM processes using ML. Then a method for in-process monitoring of the printing process is presented and discussed. ... mehr



Originalveröffentlichung
DOI: 10.1007/978-981-15-8131-1_27
Zugehörige Institution(en) am KIT Karlsruhe Nano Micro Facility (KNMF)
Institut für Automation und angewandte Informatik (IAI)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2020
Sprache Englisch
Identifikator ISBN: 978-981-15-8130-4
ISSN: 2190-3018, 2190-3026
KITopen-ID: 1000123923
HGF-Programm 43.22.03 (POF III, LK 01)
Printed Materials and Systems
Erschienen in Sustainable Design and Manufacturing 2020 – Proceedings of the 7th International Conference on Sustainable Design and Manufacturing (KES-SDM 2020), 9-11 September 2020. Ed.: S. Scholz
Verlag Springer, Singapore
Seiten 297–306
Serie Smart Innovation, Systems and Technologies ; 200
Bemerkung zur Veröffentlichung Die Veranstaltung fand wegen der Corona-Pandemie als Online-Event statt.
Vorab online veröffentlicht am 11.09.2020
Schlagwörter Additive manufacturing; Machine learning; Sustainability
Nachgewiesen in Scopus
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