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.

翻译：深度学习在清洁生产和可持续性方面的应用与影响仍鲜有探索。本研究展示了如何利用深度学习提升生产及产品使用过程中的可持续性。具体而言，我们基于深度学习的计算机视觉技术来判定产品的磨损状态。由此获得的洞察为构建具有更高集成度和结果导向性的新型产品服务系统奠定了基础。此外，这些洞察预期将促进产品使用改进与研发创新。我们以两种产品——加工刀具和旋转X射线阳极——为例进行了方法验证。从技术角度看，研究证明通过基于深度学习的计算机视觉识别这些产品的磨损状态是可行的。我们特别利用这两种产品的显微图像检测磨损，并采用U-Net进行语义分割以实现像素级磨损识别。最终获得的平均Dice系数分别为0.631和0.603，验证了所提方法的可行性。据此，专家能够做出更优决策，例如优化加工工艺参数。为评估该方法对环境可持续性的影响，我们进行了生命周期评估，结果显示两种产品均实现了效益提升：加工刀具的二氧化碳当量排放减少12%，旋转阳极减少44%。本研究可作为指导范例，激发研究者和从业者在类似场景中运用计算机视觉技术，开发可持续智能产品服务系统，助力清洁生产。