Zacharaki Angeliki, Vafeiadis Thanasis, Kolokas Nikolaos, Vaxevani Aikaterini, Xu Yuchun, Peschl Michael, Ioannidis Dimosthenis, Tzovaras Dimitrios
Information Technologies Institute, Center for Research and Technology Hellas, Thessaloniki, Greece.
College of Engineering and Physical Sciences, Aston University, Birmingham, United Kingdom.
Front Artif Intell. 2021 Feb 15;3:570562. doi: 10.3389/frai.2020.570562. eCollection 2020.
Refurbishment and remanufacturing are the industrial processes whereby used products or parts that constitute the product are restored. Remanufacturing is the process of restoring the functionality of the product or a part of it to "as-new" quality, whereas refurbishment is the process of restoring the product itself or part of it to "like-new" quality, without being as thorough as remanufacturing. Within this context, the EU-funded project RECLAIM presents a new idea on refurbishment and remanufacturing based on big data analytics, machine learning, predictive analytics, and optimization models using deep learning techniques and digital twin models with the aim of enabling the stakeholders to make informed decisions about whether to remanufacture, upgrade, or repair heavy machinery that is toward its end-of-life. The RECLAIM project additionally provides novel strategies and technologies that enable the reuse of industrial equipment in old, renewed, and new factories, with the goal of saving valuable resources by recycling equipment and using them in a different application, instead of discarding them after use. For instance, RECLAIM provides a simulation engine using digital twin in order to predict maintenance needs and potential faults of large industrial equipment. This simulation engine keeps the virtual twins available to store all available information during the lifetime of a machine, such as maintenance operations, and this information can be used to perform an economic estimation of the machine's refurbishment costs. The RECLAIM project envisages developing new technologies and strategies aligned with the circular economy and in support of a new model for the management of large industrial equipment that approaches the end of its design life. This model aims to reduce substantially the opportunity cost of retaining strategies (both moneywise and resourcewise) by allowing relatively old equipment that faces the prospect of decommissioning to reclaim its functionalities and role in the overall production system.
翻新和再制造是将废旧产品或构成产品的零部件进行修复的工业过程。再制造是将产品或其部分功能恢复到“全新”质量的过程，而翻新则是将产品本身或其部分恢复到“近乎全新”质量的过程，但其彻底程度不如再制造。在此背景下，欧盟资助的RECLAIM项目基于大数据分析、机器学习、预测分析以及使用深度学习技术和数字孪生模型的优化模型，提出了一种关于翻新和再制造的新思路，旨在使利益相关者能够就是否对接近使用寿命末期的重型机械进行再制造、升级或维修做出明智决策。RECLAIM项目还提供了新颖的策略和技术，能够在旧工厂、翻新工厂和新工厂中重新利用工业设备，目标是通过回收设备并将其用于不同的应用来节省宝贵资源，而不是在使用后将其丢弃。例如，RECLAIM提供了一个使用数字孪生的仿真引擎，以预测大型工业设备的维护需求和潜在故障。该仿真引擎使虚拟孪生能够在机器的整个生命周期内存储所有可用信息，如维护操作，这些信息可用于对机器的翻新成本进行经济估算。RECLAIM项目设想开发与循环经济相一致并支持大型工业设备管理新模式的新技术和策略，这种模式接近其设计寿命末期。该模式旨在通过允许面临退役前景的相对老旧设备恢复其功能并在整个生产系统中发挥作用，大幅降低保留策略的机会成本（在资金和资源方面）。
