  The usage and impact of deep learning for cleaner production and
sustainability purposes remain little explored. This work shows how deep
learning can be harnessed to increase sustainability in production and product
usage. Specifically, we utilize deep learning-based computer vision to
determine the wear states of products. The resulting insights serve as a basis
for novel product-service systems with improved integration and result
orientation. Moreover, these insights are expected to facilitate product usage
improvements and R&D innovations. We demonstrate our approach on two products:
machining tools and rotating X-ray anodes. From a technical standpoint, we show
that it is possible to recognize the wear state of these products using
deep-learning-based computer vision. In particular, we detect wear through
microscopic images of the two products. We utilize a U-Net for semantic
segmentation to detect wear based on pixel granularity. The resulting mean dice
coefficients of 0.631 and 0.603 demonstrate the feasibility of the proposed
approach. Consequently, experts can now make better decisions, for example, to
improve the machining process parameters. To assess the impact of the proposed
approach on environmental sustainability, we perform life cycle assessments
that show gains for both products. The results indicate that the emissions of
CO2 equivalents are reduced by 12% for machining tools and by 44% for rotating
anodes. This work can serve as a guideline and inspire researchers and
practitioners to utilize computer vision in similar scenarios to develop
sustainable smart product-service systems and enable cleaner production.
