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Processes
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Manufacturing Processes: Enhancements through Smart and Sustainable Approaches
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Manufacturing Processes: Enhancements through Smart and Sustainable Approaches

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Manufacturing Processes and Systems".

Deadline for manuscript submissions: 31 May 2024 | Viewed by 2958

Special Issue Editors

Prof. Dr. Diane Mynors

E-Mail Website
Guest Editor
Department of Mechanical and Aerospace Engineering, Brunel University, London UB8 3PH, UK
Interests: manufacturing; sustainability; productivity; knowledge management
Dr. Qingping Yang

E-Mail Website
Guest Editor
Department of Mechanical and Aerospace Engineering, Brunel University, London UB8 3PH, UK
Interests: smart technology; quality engineering; AI; robotics; sustainability

Special Issue Information

Dear Colleagues,

This Special Issue serves as a platform for researchers, scholars, and industry leaders to explore the dynamic synergy between smart technologies and sustainability, making manufacturing smarter, greener, and more efficient.

The last decade has seen a profound transformation of the manufacturing landscape, driven by ever-expanding smart technologies and a growing recognition of the urgent need for sustainability. Smart systems, encompassing Industry 4.0 technologies, the Internet of Things (IoT), artificial intelligence (AI), machine learning, robotics, digital twins, etc., have catalyzed a revolution in how manufacturing processes are conceived, executed, and optimized. These systems have not only introduced unprecedented levels of automation and data-driven decision-making but have also opened up exciting possibilities for enhancing sustainability across the manufacturing spectrum.

Sustainability has emerged as a defining imperative for manufacturing in the 21st century. As global environmental challenges intensify and societal expectations for eco-friendly and ethical practices rise, manufacturers are increasingly called upon to prioritize sustainability in their operations. The concept of sustainability in manufacturing encompasses environmental responsibility, economic viability, and the enhancement of social well-being. It extends beyond reducing carbon footprints to encompass a holistic approach that seeks harmony between economic growth, ecological preservation, and social equity.

Within this dynamic context, the integration of smart systems and sustainability principles has unlocked a new frontier of possibilities. Smart systems offer the tools, precision, and agility needed to implement complex manufacturing operations with unprecedented efficiency and responsiveness. They enable real-time monitoring, predictive maintenance, optimized resource allocation, and seamless collaboration among interconnected devices and systems. The profound benefits can be realized when these smart systems are aligned with sustainability objectives. This alignment transforms manufacturing processes into more than smart factories; it turns them into catalysts for positive environmental, economic, and social impacts.

Scope:

This Special Issue invites original research papers, review articles and case studies that address the applications of smart systems and sustainability within manufacturing processes. We welcome contributions that explore the multifaceted dimensions of smart systems, sustainability principles and their integration, highlighting how smart technologies and sustainability principles collaboratively enhance manufacturing excellence.

Topics of interest include, but are not limited to, the following:

Smart Manufacturing Advancements:

Exploration of the latest developments in Industry 4.0, sensors, robotics, digital twins, IoT, AI, machine learning, and the seamless fusion of smart technologies within manufacturing ecosystems.

Additive Manufacturing:

The role of additive manufacturing, including 3D printing, in sustainable product development and production.

Digital Twins:

In-depth examinations of digital twins' multifaceted applications in manufacturing, including real-time process and quality monitoring, predictive maintenance, and process optimization.

Sustainability as an Integral Component:

Investigations into the incorporation of sustainability principles in manufacturing, including sustainable design, eco-friendly materials, energy-efficient processes, and waste reduction.

Sustainable Supply Chains:

An exploration of sustainable supply chain management, green logistics, and the infusion of smart technologies in supply chain operations.

Energy Efficiency Strategies:

Strategies and technologies aimed at reducing energy consumption and emissions across diverse manufacturing processes.

Environmental Impact Assessment:

Methods and tools for rigorously assessing the environmental footprint of manufacturing systems and products, facilitating informed decision-making that aligns with sustainability goals.

Prof. Dr. Diane Mynors
Dr. Qingping Yang
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. Processes is an international peer-reviewed open access monthly 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 2400 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

  • manufacturing processes
  • smart process enhancements
  • sustainability
  • sustainable manufacturing
  • sustainable supply chains
  • environmental impact assessment
  • additive manufacturing

Published Papers (4 papers)

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Research

19 pages, 6233 KiB  
Article
Fault Diagnosis for Power Batteries Based on a Stacked Sparse Autoencoder and a Convolutional Block Attention Capsule Network
by Juan Zhou, Shun Zhang and Peng Wang
Processes 2024, 12(4), 816; https://doi.org/10.3390/pr12040816 - 18 Apr 2024
Viewed by 313
Abstract
The power battery constitutes the fundamental component of new energy vehicles. Rapid and accurate fault diagnosis of power batteries can effectively improve the safety and power performance of the vehicle. In response to the issues of limited generalization ability and suboptimal diagnostic accuracy [...] Read more.
The power battery constitutes the fundamental component of new energy vehicles. Rapid and accurate fault diagnosis of power batteries can effectively improve the safety and power performance of the vehicle. In response to the issues of limited generalization ability and suboptimal diagnostic accuracy observed in traditional power battery fault diagnosis models, this study proposes a fault diagnosis method utilizing a Convolutional Block Attention Capsule Network (CBAM-CapsNet) based on a stacked sparse autoencoder (SSAE). The reconstructed dataset is initially input into the SSAE model. Layer-by-layer greedy learning using unsupervised learning is employed, combining unsupervised learning methods with parameter updating and local fine-tuning to enhance visualization capabilities. The CBAM is then integrated into the CapsNet, which not only mitigates the effect of noise on the SSAE but also improves the model’s ability to characterize power cell features, completing the fault diagnosis process. The experimental comparison results show that the proposed method can diagnose power battery failure modes with an accuracy of 96.86%, and various evaluation indexes are superior to CNN, CapsNet, CBAM-CapsNet, and other neural networks at accurately identifying fault types with higher diagnostic accuracy and robustness. Full article
(This article belongs to the Special Issue Manufacturing Processes: Enhancements through Smart and Sustainable Approaches)
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16 pages, 11463 KiB  
Article
Defect Detection Algorithm for Battery Cell Casings Based on Dual-Coordinate Attention and Small Object Loss Feedback
by Tianjian Li, Jiale Ren, Qingping Yang, Long Chen and Xizhi Sun
Processes 2024, 12(3), 601; https://doi.org/10.3390/pr12030601 - 18 Mar 2024
Viewed by 594
Abstract
To address the issue of low accuracy in detecting defects of battery cell casings with low space ratio and small object characteristics, the low space ratio feature and small object feature are studied, and an object detection algorithm based on dual-coordinate attention and [...] Read more.
To address the issue of low accuracy in detecting defects of battery cell casings with low space ratio and small object characteristics, the low space ratio feature and small object feature are studied, and an object detection algorithm based on dual-coordinate attention and small object loss feedback is proposed. Firstly, the EfficientNet-B1 backbone network is employed for feature extraction. Secondly, a dual-coordinate attention module is introduced to preserve more positional information through dual branches and embed the positional information into channel attention for precise localization of the low space ratio features. Finally, a small object loss feedback module is incorporated after the bidirectional feature pyramid network (BiFPN) for feature fusion, balancing the contribution of small object loss to the overall loss. Experimental comparisons on a battery cell casing dataset demonstrate that the proposed algorithm outperforms the EfficientDet-D1 object detection algorithm, with an average precision improvement of 4.23%. Specifically, for scratches with low space ratio features, the improvement is 13.21%; for wrinkles with low space ratio features, the improvement is 9.35%; and for holes with small object features, the improvement is 3.81%. Moreover, the detection time of 47.6 ms meets the requirements of practical production. Full article
(This article belongs to the Special Issue Manufacturing Processes: Enhancements through Smart and Sustainable Approaches)
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24 pages, 1710 KiB  
Article
Proposal of Industry 5.0-Enabled Sustainability of Product–Service Systems and Its Quantitative Multi-Criteria Decision-Making Method
by Qichun Jin, Huimin Chen and Fuwen Hu
Processes 2024, 12(3), 473; https://doi.org/10.3390/pr12030473 - 26 Feb 2024
Viewed by 961
Abstract
In the wake of Industry 4.0, the ubiquitous internet of things provides big data to potentially quantify the environmental footprint of green products. Further, as the concept of Industry 5.0 emphasizes, the increasing mass customization production makes the product configurations full of individuation [...] Read more.
In the wake of Industry 4.0, the ubiquitous internet of things provides big data to potentially quantify the environmental footprint of green products. Further, as the concept of Industry 5.0 emphasizes, the increasing mass customization production makes the product configurations full of individuation and diversification. Driven by these fundamental changes, the design for sustainability of a high-mix low-volume product–service system faces the increasingly deep coupling of technology-driven product solutions and value-driven human-centric goals. The multi-criteria decision making of sustainability issues is prone to fall into the complex, contradictory, fragmented, and opaque flood of information. To this end, this work presents a data-driven quantitative method for the sustainability assessment of product–service systems by integrating analytic hierarchy process (AHP) and data envelopment analysis (DEA) methods to measure the sustainability of customized products and promote the Industry 5.0-enabled sustainable product–service system practice. This method translates the sustainability assessment into a multi-criteria decision-making problem, to find the solution that meets the most important criteria while minimizing trade-offs between conflicting criteria, such as individual preferences or needs and the life cycle sustainability of bespoke products. In the future, the presented method can extend to cover more concerns of Industry 5.0, such as digital-twin-driven recyclability and disassembly of customized products, and the overall sustainability and resilience of the supply chain. Full article
(This article belongs to the Special Issue Manufacturing Processes: Enhancements through Smart and Sustainable Approaches)
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17 pages, 5145 KiB  
Article
Enhancing Additive Restoration of Damaged Polymer Curved Surfaces through Compensated Support Beam Utilization
by Dianjin Zhang and Bin Guo
Processes 2024, 12(2), 393; https://doi.org/10.3390/pr12020393 - 16 Feb 2024
Viewed by 424
Abstract
As additive manufacturing advances, it offers a cost-effective avenue for structurally repairing components. However, a challenge arises in the additive repair of suspended damaged surfaces, primarily due to gravitational forces. This can result in excessive deformation during the repair process, rendering the formation [...] Read more.
As additive manufacturing advances, it offers a cost-effective avenue for structurally repairing components. However, a challenge arises in the additive repair of suspended damaged surfaces, primarily due to gravitational forces. This can result in excessive deformation during the repair process, rendering the formation of proper repair impractical and leading to potential failure. In light of this rationale, conventional repair techniques are impractical for extensively damaged surfaces. Thus, this article proposes a novel repair methodology that is tailored to address large-area damage. Moreover, and departing from conventional practices involving the addition and subsequent subtraction of materials for precision machining, the proposed process endeavors to achieve more precise repair outcomes in a single operation. This paper introduces an innovative repair approach employing fused deposition modeling (FDM) to address the complexities associated with the repair of damaged polymer material parts. To mitigate geometric errors in the repaired structural components, beams with minimal deformation are printed using a compensation method. These beams then serve as supports for overlay printing. The paper outlines a methodology by which to determine the distribution of these supporting beams based on the shape of the damaged surface. A beam deformation model is established, and the printing trajectory of the compensated beam is calculated according to this model. Using the deformation model, the calculated deformation trajectories exhibit excellent fitting with the experimentally collected data, with an average difference between the two of less than 0.3 mm, validating the accuracy of the suspended beam deformation model. Based on the statistical findings, the maximum average deformation of the uncompensated sample is approximately 5.20 mm, whereas the maximum deformation of the sampled point after compensation measures around 0.15 mm. Consequently, the maximum deformation of the printed sample post-compensation is mitigated to roughly 3% of its pre-compensation magnitude. The proposed method in this paper was applied to the repair experiment of damaged curved surface components. A comparison was made between the point cloud data of the repaired surface and the ideal model of the component, with the average distance between them serving as the repair error metric. The mean distance between the point clouds of the repaired parts using the proposed repair strategy is 0.197 mm and the intact model surface is noticeably less than the mean distance corresponding to direct repair, at 0.830 mm. The repair error with compensatory support beams was found to be 76% lower than that without compensatory support beams. The surface without compensatory support beams exhibited gaps, while the surface with compensatory support beams appeared dense and complete. Experimental results demonstrate the effectiveness of the proposed method in significantly reducing the geometric errors in the repaired structural parts. The outcomes of the FDM repair method are validated through these experiments, affirming its practical efficacy. It is noteworthy that, although only PLA material was used in this study, the proposed method is general and effective for other polymer materials. This holds the potential to significantly reduce costs for the remanufacturing of widely used polymers. Full article
(This article belongs to the Special Issue Manufacturing Processes: Enhancements through Smart and Sustainable Approaches)
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