Smart Remanufacturing

Dear Colleagues,

Remanufacturing is the process of returning a product that has reached the end of its service life to a condition at least as good as that of the original product. Remanufacturing is part of a circular economy aimed at minimising waste and conserving raw materials and energy, while also cutting greenhouse gas emissions and landfill space requirements. By saving input costs, remanufacturing can yield more affordable products and wider profit margins at the same time. Thus, remanufacturing is good for consumers and producers as well as for the environment; in this sense, remanufacturing is intrinsically smart manufacturing. However, from the technology point of view, the current state of much of the remanufacturing industry can be said to be relatively backward. While many original equipment manufacturers have embraced modern solutions such as digital twins, cyber–physical systems, artificial intelligence, smart sensors, big data and autonomous collaborative robots, remanufacturers tend to utilise tools and techniques from the last century.

The International Workshop on Autonomous Remanufacturing (IWAR) is an interdisciplinary forum for researchers, engineers, scientists, scholars, and industrial leaders to present their latest research results, ideas, developments, and applications in the field of sustainable remanufacturing, enabling interactive exchanges of state-of-the-art knowledge.

IWAR 2024 will be held in a hybrid mode, with the physical conference being held at Wayne State University (https://wayne.edu/). Wayne State University is located in the Midtown area of Detroit, Michigan, U.S.A.

IWAR 2024 aims to bring together the leading innovators in autonomous remanufacturing in an effort to strengthen the body of knowledge on the design, modelling, and control of remanufacturing processes and systems. Contributions are accepted on topics related to many remanufacturing fields, including, but not limited to, the topics listed below.

Topics

• Operations Management in Remanufacturing;
• Design for Remanufacturing;
• Quality Assurance in Remanufacturing;
• Design for Disassembly and Disassembly Planning;
• Virtual, Augmented, and Cross Reality in Remanufacturing and Disassembly;
• Cloud Remanufacturing;
• Autonomous Remanufacturing;
• Robotics for Remanufacturing;
• Human Robot Cooperation in Remanufacturing;
• Reverse Logistics for Remanufacturing Supply Chains;
• Life Cycle Assessment and Life Cycle Costing for Technology Selection in Remanufacturing;
• Industry 4.0 and/or 5.0 in Remanufacturing;
• Machine Learning and Analytics Approaches in Remanufacturing;
• Cybersecurity in Remanufacturing.

This Special Issue will look at how smart manufacturing technologies or any other advanced technologies can be directly employed or adapted to make remanufacturing technologically smarter.  

Prof. Dr. Zude Zhou
Prof. Dr. Quan Liu
Prof. Dr. Wenjun Xu
Prof. Dr. F. Javier Ramírez
Dr. Marcello Fera
Dr. Mario Caterino
Prof. Dr. Duc Truong Pham
Dr. Jeremy Rickli
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Automation is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• human–robot collaborative disassembly of cores
• smart sorting and inspection of components
• automated product re-assembly
• flexible tooling for assembly and disassembly
• intelligent disassembly planning and autonomous re-planning
• product condition monitoring and remaining useful life prediction
• digital twin modelling and control of remanufacturing operations
• case studies in smart remanufacturing

Title: Robotic Disassembly Platform for Disassembly of a Plug-In Hybrid Electric Vehicle Battery: a Case Study
Authors: Mo Qu, D. T. Pham, Faraj Altumi, Adeyemisi Gbadebo, Natalia Hartono, Kaiwen Jiang, Mairi Kerin, Feiying Lan, Marcel Micheli, Shuihao Xu, and Yongjing Wang
Affiliation: University of Birmingham
Abstract: Efficient processing of end-of-life lithium-ion batteries of electric vehicles is important and a pressing challenge for a circular economy. Regardless of whether the processing strategy is recycling, repurposing or remanufacturing, the first processing step would usually involve disassembly. As battery disassembly is a dangerous task, efforts have been made to robotise it. In this paper, a robotic disassembly platform using four industrial robots is proposed to automate the non-destructive disassembly of a plug-in hybrid electric vehicle battery pack into modules. The work was conducted as a case study to demonstrate the concept of autonomous disassembly of an electric vehicle battery pack. A two-step object localisation method based on visual information is used to overcome positional uncertainties from different sources and is validated by experiments. Also, the unscrewing system is highlighted, and its functions, such as handling untightened fasteners, loosening jammed screws, and changing the nutrunner adapters with square drives, are detailed. Furthermore, the time required for each operation is compared with that taken by human operators. Finally, the limitations of the platform are reported and future research directions are suggested.