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Automation
Special Issues
Smart Remanufacturing
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Smart Remanufacturing

A special issue of Automation (ISSN 2673-4052).

Deadline for manuscript submissions: 31 December 2024 | Viewed by 8716

Special Issue Editors

Prof. Dr. Zude Zhou
*
E-Mail Website1 Website2
Guest Editor
School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, China
Interests: network digital manufacturing; CNC technology; mechatronics
* Lead Guest Editor
Prof. Dr. Quan Liu

E-Mail Website
Guest Editor
School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, China
Interests: nonlinear systems theory; signal processing; industrial/rehabilitation robots
Prof. Dr. Wenjun Xu

E-Mail Website
Guest Editor
School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China
Interests: sustainable manufacturing; man-machine integration and collaborative manufacturing; manufacturing intelligence and manufacturing services; Information physics production system; sensor network; digital twin technology
Prof. Dr. F. Javier Ramírez

E-Mail Website
Guest Editor
School of Industrial Engineering, University of Castilla-La Mancha, Ciudad Real, Spain
Interests: optimization models for industrial processes; optimization of industrial processes for remanufacturing in the circular economy environment; techno-economic analysis of renewable energy generation plants, innovation, and sustainability
Dr. Marcello Fera

E-Mail Website
Guest Editor
Dipartimento di Ingegneria—Università della Campania “Luigi Vanvitelli”, Via Roma, 29, 81031 Aversa, CE, Italy
Interests: industrial manufacturing system design and optimization; industrial production management and optimization
Special Issues, Collections and Topics in MDPI journals
Dr. Mario Caterino

E-Mail Website
Guest Editor
Department of Industrial Engineering, University of Salerno, 84084 Fisciano, Italy
Interests: remanufacturing; industrial system design and optimization; ergonomic
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Duc Truong Pham

E-Mail Website
Guest Editor
Chance Professor of Engineering, School of Engineering, The University of Birmingham, Birmingham B15 2TT, UK
Interests: micro manufacturing; control systems; robotics; intelligent systems
Dr. Jeremy Rickli

E-Mail Website
Guest Editor
Department of Industrial and Systems Engineering, Wayne State Universtiy, 4815 Fourth St., Detroit, MI 48201, USA
Interests: sustainable manufacturing; digital manufacturing enterprise; collaborative robotic automation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

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.

Keywords

  • 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

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

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Research

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19 pages, 7893 KiB  
Article
AI-Driven Crack Detection for Remanufacturing Cylinder Heads Using Deep Learning and Engineering-Informed Data Augmentation
by Mohammad Mohammadzadeh, Gül E. Okudan Kremer, Sigurdur Olafsson and Paul A. Kremer
Automation 2024, 5(4), 578-596; https://doi.org/10.3390/automation5040033 - 20 Nov 2024
Viewed by 528
Abstract
Detecting cracks in cylinder heads traditionally relies on manual inspection, which is time-consuming and susceptible to human error. As an alternative, automated object detection utilizing computer vision and machine learning models has been explored. However, these methods often face challenges due to a [...] Read more.
Detecting cracks in cylinder heads traditionally relies on manual inspection, which is time-consuming and susceptible to human error. As an alternative, automated object detection utilizing computer vision and machine learning models has been explored. However, these methods often face challenges due to a lack of sufficiently annotated training data, limited image diversity, and the inherently small size of cracks. Addressing these constraints, this paper introduces a novel automated crack-detection method that enhances data availability through a synthetic data generation technique. Unlike general data augmentation practices, our method involves copying cracks from one location to another, guided by both random and informed engineering decisions about likely crack formations due to cyclic thermomechanical loads. The innovative aspect of our approach lies in the integration of domain-specific engineering knowledge into the synthetic generation process, which substantially improves detection accuracy. We evaluate our method’s effectiveness using two metrics: the F2 score, which emphasizes recall to prioritize detecting all potential cracks, and mean average precision (MAP), a standard measure in object detection. Experimental results demonstrate that, without engineering insights, our method increases the F2 score from 0.40 to 0.65, while maintaining a stable MAP. Incorporating detailed engineering knowledge further enhances the F2 score to 0.70 and improves MAP to 0.57, representing increases of 63% and 43%, respectively. These results confirm that our approach not only mitigates the limitations of traditional data augmentation but also significantly advances the reliability and precision of crack detection in industrial settings. Full article
(This article belongs to the Special Issue Smart Remanufacturing)
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18 pages, 2463 KiB  
Article
Solving a Stochastic Multi-Objective Sequence Dependence Disassembly Sequence Planning Problem with an Innovative Bees Algorithm
by Xinyue Huang, Xuesong Zhang, Yanlong Gao and Changshu Zhan
Automation 2024, 5(3), 432-449; https://doi.org/10.3390/automation5030025 - 23 Aug 2024
Viewed by 973
Abstract
As the number of end-of-life products multiplies, the issue of their efficient disassembly has become a critical problem that urgently needs addressing. The field of disassembly sequence planning has consequently attracted considerable attention. In the actual disassembly process, the complex structures of end-of-life [...] Read more.
As the number of end-of-life products multiplies, the issue of their efficient disassembly has become a critical problem that urgently needs addressing. The field of disassembly sequence planning has consequently attracted considerable attention. In the actual disassembly process, the complex structures of end-of-life products can lead to significant delays due to the interference between different tasks. Overlooking this can result in inefficiencies and a waste of resources. Therefore, it is particularly important to study the sequence-dependent disassembly sequence planning problem. Additionally, disassembly activities are inherently fraught with uncertainties, and neglecting these can further impact the effectiveness of disassembly. This study is the first to analyze the sequence-dependent disassembly sequence planning problem in an uncertain environment. It utilizes a stochastic programming approach to address these uncertainties. Furthermore, a mixed-integer optimization model is constructed to minimize the disassembly time and energy consumption simultaneously. Recognizing the complexity of the problem, this study introduces an innovative bees algorithm, which has proven its effectiveness by showing a superior performance compared to other state-of-the-art algorithms in various test cases. This research offers innovative solutions for the efficient disassembly of end-of-life products and holds significant implications for advancing sustainable development and the recycling of resources. Full article
(This article belongs to the Special Issue Smart Remanufacturing)
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18 pages, 16454 KiB  
Article
Robotic Disassembly Platform for Disassembly of a Plug-In Hybrid Electric Vehicle Battery: A Case Study
by Mo Qu, D. T. Pham, Faraj Altumi, Adeyemisi Gbadebo, Natalia Hartono, Kaiwen Jiang, Mairi Kerin, Feiying Lan, Marcel Micheli, Shuihao Xu and Yongjing Wang
Automation 2024, 5(2), 50-67; https://doi.org/10.3390/automation5020005 - 1 Apr 2024
Cited by 2 | Viewed by 3008
Abstract
Efficient processing of end-of-life lithium-ion batteries in electric vehicles is an important and pressing challenge in a circular economy. Regardless of whether the processing strategy is recycling, repurposing, or remanufacturing, the first processing step will usually involve disassembly. As battery disassembly is a [...] Read more.
Efficient processing of end-of-life lithium-ion batteries in electric vehicles is an important and pressing challenge in a circular economy. Regardless of whether the processing strategy is recycling, repurposing, or remanufacturing, the first processing step will 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. This work was conducted as a case study to demonstrate the concept of the 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. Full article
(This article belongs to the Special Issue Smart Remanufacturing)
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Review

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24 pages, 2690 KiB  
Review
Artificial Intelligence in Electric Vehicle Battery Disassembly: A Systematic Review
by Zekai Ai, A. Y. C. Nee and S. K. Ong
Automation 2024, 5(4), 484-507; https://doi.org/10.3390/automation5040028 - 24 Sep 2024
Viewed by 2870
Abstract
The rapidly increasing adoption of electric vehicles (EVs) globally underscores the urgent need for effective management strategies for end-of-life (EOL) EV batteries. Efficient EOL management is crucial in reducing the ecological footprint of EVs and promoting a circular economy where battery materials are [...] Read more.
The rapidly increasing adoption of electric vehicles (EVs) globally underscores the urgent need for effective management strategies for end-of-life (EOL) EV batteries. Efficient EOL management is crucial in reducing the ecological footprint of EVs and promoting a circular economy where battery materials are sustainably reused, thereby extending the life cycle of the resources and enhancing overall environmental sustainability. In response to this pressing issue, this review presents a comprehensive analysis of the role of artificial intelligence (AI) in improving the disassembly processes for EV batteries, which is integral to the practical echelon utilization and recycling process. This paper reviews the application of AI techniques in various stages of retired battery disassembly. A significant focus is placed on estimating batteries’ state of health (SOH), which is crucial for determining the availability of retired EV batteries. AI-driven methods for planning battery disassembly sequences are examined, revealing potential efficiency gains and cost reductions. AI-driven disassembly operations are discussed, highlighting how AI can streamline processes, improve safety, and reduce environmental hazards. The review concludes with insights into the future integration of electric vehicle battery (EVB) recycling and disassembly, emphasizing the possibility of battery swapping, design for disassembly, and the optimization of charging to prolong battery life and enhance recycling efficiency. This comprehensive analysis underscores the transformative potential of AI in revolutionizing the management of retired EVBs. Full article
(This article belongs to the Special Issue Smart Remanufacturing)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

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.

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