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

从原材料提取、提炼、加工到零件制造，制造的每个阶段都对温室气体（GHG）排放产生了巨大影响。向再制造的转变，即将磨损的产品或部件退回到类似新的状态，将大大减少材料生产和分销造成的人为温室气体排放，帮助工业实现严格的环境要求（到2050年净零温室气体排放），并直接为循环经济做出贡献。拟议的项目将结合机械和环境工程、计算机科学、供应链和物流、气候变化治理和可持续性等方面的专业知识，推动零部件评估和再制造所需技术的进步，并生成构建理解和预测模型所需的数据，以捕捉基于再制造的脱碳战略的好处及其对政策的影响。具体而言，建议的研究包括用于识别零件寿命结束时损坏区域的寿命评估方法，用于更换损坏零件的再制造技术（冷喷涂增材制造），用于设计在其生命周期结束时获取零件并将其转移到当地再制造商的最佳操作的逆向物流，评估一个范例组件的温室气体节约，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，我们将预测到2035年和2050年制造的所有新汽车的范例部件的节省）。将采用迁移学习算法将提出的方法扩展到其他组件。将分析联邦、省和市各级的政策障碍，并制定向再制造可持续过渡的缓解战略。提出的战略将显著减少温室气体排放，为中小企业提供特殊的经济机会，并为决策者提出基于再制造的政策提供所需的数据。

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.