  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.
