Remanufacturing - A manufacturing paradigm shift for deep decarbonization in a sustainable economy

再制造——可持续经济中深度脱碳的制造范式转变

• 批准号: 577125-2022
• 负责人: Jahed, HamidHR
• 金额: $ 36.41万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=760357
• 关键词: Remanufacturing; manufacturing; paradigm; shift; deep

Every stage of manufacturing, from raw materials extraction, refinement, and processing to part manufacturing contributes immensely to greenhouse gas (GHG) emissions. A shift toward re-manufacturing, where worn products or component parts are returned to like-new condition, will dramatically reduce anthropogenic GHG emissions caused by material production and distribution, help industries achieve stringent environmental mandates (net zero GHG by 2050), and contribute directly to a circular economy. The proposed project will connect expertise across mechanical and environmental engineering, computer science, supply chain and logistics, and climate change governance and sustainability to drive advances in the technologies needed for part assessment and remanufacturing, and generate data needed to build understanding and predictive models that capture the benefits of a remanufacturing-based decarbonization strategy and how it will impact policy. Specifically, the proposed research includes life assessment methodology for identifying damaged zones of parts at the end of their life, remanufacturing technology (cold spray additive manufacturing) to replace damaged parts, reverse logistics to design the optimum operation for accessing parts at the end of their life cycle and transferring them to local remanufacturers, assessment of GHG savings for one exemplar component, and forecast models that predict savings across different scenarios (e.g., for the automotive industry, we would forecast savings for exemplar components in all new cars manufactured by 2035, and 2050). A Transfer Learning algorithm will be employed to extend proposed methodology to other components. Policy barriers at federal, provincial, and municipal levels will be analyzed and mitigation strategies for sustainable transition to remanufacturing will be developed.The proposed strategy will significantly reduce GHG, provide exceptional economic opportunities for SME, and generate required data for decision makers to propose policies based on remanufacturing.

从原材料提取、精炼和加工到零件制造的每个制造阶段都极大地增加了温室气体（GHG）排放。向再制造转变，将磨损的产品或零部件恢复到新的状态，将大大减少材料生产和分销造成的人为温室气体排放，帮助行业实现严格的环境要求（到2050年实现净零温室气体），并直接促进循环经济。拟议的项目将连接机械和环境工程，计算机科学，供应链和物流以及气候变化治理和可持续性的专业知识，以推动零件评估和再制造所需技术的进步，并生成建立理解和预测模型所需的数据，这些模型将捕获基于再制造的脱碳战略的好处以及它将如何影响政策。具体而言，拟议的研究包括寿命评估方法，以确定在其寿命结束时，零件的损坏区域，再制造技术（冷喷涂增材制造）以更换损坏的零件，逆向物流以设计最佳操作，以便在零件生命周期结束时获取零件并将其转移到当地再制造商，评估一个示例组件的温室气体减排量，以及预测不同情景下的节省的预测模型（例如，对于汽车行业，我们预测到2035年和2050年，所有新汽车制造的示范部件将节省。迁移学习算法将被用来扩展所提出的方法到其他组件。将分析联邦、省和市各级的政策障碍，并制定可持续地向再制造过渡的缓解战略。拟议的战略将显著减少温室气体，为中小企业提供特殊的经济机会，并为决策者提供所需的数据，以提出基于再制造的政策。