RECLAIM: Toward a New Era of Refurbishment and Remanufacturing of Industrial Equipment


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.

Publication DOI:
Divisions: College of Engineering & Physical Sciences
College of Engineering & Physical Sciences > Aston Institute of Urban Technology and the Environment (ASTUTE)
College of Engineering & Physical Sciences > School of Engineering and Technology > Mechanical, Biomedical & Design
Additional Information: © 2021 Zacharaki, Vafeiadis, Kolokas, Vaxevani Xu, Pesch, Ioannidis and Tzovaras. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Uncontrolled Keywords: Artificial Intelligence,refurbishment and remanufacturing,decision support framework,in situ repair,big data analytics,predictive analytics,industry,machine learning
Publication ISSN: 2624-8212
Last Modified: 14 May 2024 07:21
Date Deposited: 05 Mar 2021 13:56
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Related URLs: https://www.fro ... 020.570562/full (Publisher URL)
PURE Output Type: Article
Published Date: 2021-02-15
Accepted Date: 2020-11-16
Authors: Zacharaki, Angeliki
Vafeiadis, Thanasis
Kolokas, Nikolaos
Vaxevani, Aikaterini
Xu, Yuchun (ORCID Profile 0000-0001-6388-813X)
Peschl, Michael
Ioannidis, Dimosthenis
Tzovaras, Dimitrios



Version: Published Version

License: Creative Commons Attribution

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