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Towards Leveraging End-of-Life Tools as an Asset: Value Co-Creation based on Deep Learning in the Machining Industry

Walk, Jannis; Kühl, Niklas; Schäfer, Jonathan

Sustainability is the key concept in the management of products that reached their end-of-life. We propose that end-of-life products have—besides their value as recyclable assets—additional value for producer and consumer. We argue this is especially true for the machining industry, where we illustrate an automatic characterization of worn cutting tools to foster value co-creation between tool manufacturer and tool user (customer) in the future. In the work at hand, we present a deep-learning-based computer vision system for the automatic classification of worn tools regarding flank wear and chipping. The resulting Matthews Correlation Coefficient of 0.878 and 0.644 confirms the feasibility of our system based on the VGG-16 network and Gradient Boosting. Based on these first results we derive a research agenda which addresses the need for a more holistic tool characterization by semantic segmentation and assesses the perceived business impact and usability by different user groups.

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Preprint §
DOI: 10.5445/IR/1000099219
Veröffentlicht am 27.01.2020
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Informationswirtschaft und Marketing (IISM)
Karlsruhe Service Research Institute (KSRI)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2020
Sprache Englisch
Identifikator KITopen-ID: 1000099219
Erschienen in Proceedings of the 53rd Hawaii International Conference on System Sciences (HICSS-53), Grand Wailea, Maui, HI, January 7-10, 2020
Veranstaltung 53rd Hawaii International Conference on System Sciences (HICSS 2020), Grand Wailea, Maui, Hawaii, 07.01.2020 – 10.01.2020
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