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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 July 2025 | Viewed by 18048

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

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Published Papers (13 papers)

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Research

Jump to: Review

34 pages, 21083 KiB  
Article
Design and Flow Field Dynamics of a Novel Spiral Die Head for Film Blowing
by Zhihui Wu, Yan Zhao, Baicheng Yang, Yuan Zhou and Zhifeng Wu
Processes 2025, 13(2), 451; https://doi.org/10.3390/pr13020451 - 7 Feb 2025
Viewed by 687
Abstract
Extrusion molding die heads play a critical role in film production, with their structure directly influencing product quality, production efficiency, and die life cycle. This study focused on optimizing the film blowing process by designing a new spiral die head and analyzing the [...] Read more.
Extrusion molding die heads play a critical role in film production, with their structure directly influencing product quality, production efficiency, and die life cycle. This study focused on optimizing the film blowing process by designing a new spiral die head and analyzing the flow field dynamics. The model was constructed via a three-dimensional Boolean operation following fluid mechanics principles to establish a mathematical model based on the die head’s structure and material properties. By varying parameters such as the inlet flow rate, buffer groove length, shaping section length, and non-Newtonian index, the velocity and pressure fields were analyzed using the finite element method. The results show that increasing the inlet velocity and non-Newtonian index significantly impacted the velocity uniformity, inlet and outlet pressure, and pressure drop. A higher inlet velocity led to increased fluctuations in outlet velocity and a higher inlet pressure, while a higher non-Newtonian index resulted in a more uniform outlet velocity and a reduced fluctuation, though with a higher inlet pressure and flow channel pressure drop. The lengthening of the buffer groove and shaping section had minimal effect on the outlet velocity uniformity but increased the inlet and outlet pressures and pressure drop at each stage. This study concluded that a uniform velocity distribution, lower pressure, and reduced energy consumption are crucial for high-quality film production. The optimal parameter values were found to be an inlet flow rate of 0.03 m/s, buffer groove length of 40 mm, shaping section length of 20 mm, and non-Newtonian index of 0.36. These findings provide a theoretical foundation for optimizing die head design and improving thin film quality in practical applications. Full article
(This article belongs to the Special Issue Manufacturing Processes: Enhancements through Smart and Sustainable Approaches)
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35 pages, 844 KiB  
Article
A Rescheduling Strategy for Multipurpose Batch Processes with Processing Time Variation and Demand Uncertainty
by Taicheng Zheng, Dan Li and Jie Li
Processes 2025, 13(2), 312; https://doi.org/10.3390/pr13020312 - 23 Jan 2025
Viewed by 719
Abstract
In this paper, we address the problem of dynamic scheduling of a multipurpose batch process subject to two types of disturbances, namely, processing time variation and demand uncertainty. We propose a rescheduling strategy that combines several ideas. First, when we generate a new [...] Read more.
In this paper, we address the problem of dynamic scheduling of a multipurpose batch process subject to two types of disturbances, namely, processing time variation and demand uncertainty. We propose a rescheduling strategy that combines several ideas. First, when we generate a new schedule, we simultaneously construct a Directed Acyclic Graph (DAG) to represent this new schedule. While each node in the DAG represents an operation, each arc represents the dependency of an operation on another. Based on this DAG, we then use a simple procedure to determine how long an operation is allowed to be delayed without affecting the current makespan. After that, when the new schedule is used for online execution, we trigger a rescheduling procedure only when (1) we infer from the predetermined delayable time information that the current makespan will be extended, or (2) we observe new demands, or (3) the current schedule is not guaranteed to be feasible. In the rescheduling procedure, only the affected operations are allowed to be revised, while those unaffected operations are fixed. By doing this, we can reduce system nervousness and improve computational efficiency. The computational results demonstrate that our method can achieve an order of magnitude of reduction in both the number of operation changes and the computational time with a slightly better long-term makespan, compared to the widely used periodically–completely rescheduling strategy. Full article
(This article belongs to the Special Issue Manufacturing Processes: Enhancements through Smart and Sustainable Approaches)
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13 pages, 5355 KiB  
Article
Real-Time Models for Manufacturing Processes: How to Build Predictive Reduced Models
by Amir M. Horr and Hugo Drexler
Processes 2025, 13(1), 252; https://doi.org/10.3390/pr13010252 - 16 Jan 2025
Viewed by 1304
Abstract
New data science and real-time modeling techniques facilitate better monitoring and control of manufacturing processes. By using real-time data models, industries can improve their processes and identify areas where resources are being wasted. Despite the challenges associated with implementing these data models in [...] Read more.
New data science and real-time modeling techniques facilitate better monitoring and control of manufacturing processes. By using real-time data models, industries can improve their processes and identify areas where resources are being wasted. Despite the challenges associated with implementing these data models in transient and multi-physical processes, they can significantly optimize operations, reduce trial and error, and minimize the overall environmental footprint. Implementing real-time data analytics allows industries to make quicker, informed decisions and immediate corrections to material processes. This ensures that manufacturing sustainability targets are regularly met and product quality is maintained. New concepts such as digital twins and digital shadows have been developed to bridge the gap between physical manufacturing processes and their virtual counterparts. These virtual models can be continuously updated with data from their physical counterparts, enabling real-time monitoring, control, and optimization of manufacturing processes. This paper demonstrates the predictive power of real-time reduced models within the digital twin framework to optimize process parameters using data-driven and hybrid techniques. Various reduced and real-time model-building techniques are investigated, with brief descriptions of their mathematical and analytical foundations. The role of machine learning (ML) and ML-assisted data schemes in enhancing predictions and corrections is also explored. Real-world applications of these reduced techniques for extrusion and additive manufacturing (AM) processes are presented as case studies. Full article
(This article belongs to the Special Issue Manufacturing Processes: Enhancements through Smart and Sustainable Approaches)
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18 pages, 9681 KiB  
Article
Data Digitization in Manufacturing Factory Using Palantir Foundry Solution
by Peter Krajný, Jaroslava Janeková and Jana Fabianová
Processes 2024, 12(12), 2816; https://doi.org/10.3390/pr12122816 - 9 Dec 2024
Viewed by 1840
Abstract
This research describes an online solution for the collection and processing of production data, which are gathered from manufacturing and assembly processes at automotive companies. The solution describes the process for live monitoring of the production health and then evaluation through reports, with [...] Read more.
This research describes an online solution for the collection and processing of production data, which are gathered from manufacturing and assembly processes at automotive companies. The solution describes the process for live monitoring of the production health and then evaluation through reports, with the option to generate reports for up to six months. Since the data are located in multiple sources, it is challenging to monitor them live or generate reports on demand. The solution described in this research outlines applications that simplify users’ tasks and provide immediate insights into the processes and health of production lines. Research will be divided into three applications which are delivered in one package, which is called Cycle Time Deviation (CTD): (i) workshop application for live monitoring; (ii) for evaluating data older than 24 h, the shift report application; and (iii) for comparing and monitoring the impact of process changes on the analysis, the before and after application—the Plant Improvement Tracker (PIT)—will be presented. The aim of the research is to describe the proposed solution that was implemented in a multinational automotive corporation and to outline the benefits gained from the implementation. Full article
(This article belongs to the Special Issue Manufacturing Processes: Enhancements through Smart and Sustainable Approaches)
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12 pages, 5631 KiB  
Article
High Efficiency Producing Technology Applied in Metal Optical Lens by 3D Wax Printing Combined with Investment Casting
by Ken-Chuan Cheng, Chien-Yao Huang, Hsien-Te Lu, Jun-Cheng Chen, Cheng-Fang Ho, A-Cheng Wang and Keng-Yu Chen
Processes 2024, 12(11), 2442; https://doi.org/10.3390/pr12112442 - 5 Nov 2024
Cited by 1 | Viewed by 951
Abstract
3D printing technology can easily and quickly produce small batch models and full-size parts, which has obvious and important benefits in shortening development time. Since metals exhibit excellent mechanical strength and high wear resistance, metal additive manufacturing (MAM) is a popular technology for [...] Read more.
3D printing technology can easily and quickly produce small batch models and full-size parts, which has obvious and important benefits in shortening development time. Since metals exhibit excellent mechanical strength and high wear resistance, metal additive manufacturing (MAM) is a popular technology for making metal parts. However, metal powders and 3D-printing machines are costly, which increases the difficulty of achieving mass production through MAM. In this study, the 3D wax printing and investment casting (WPIC) approach was developed to manufacture high-quality metal optical lenses with high efficiency and low cost. The manufactured lenses had a diameter of 38.1 mm, two radii of curvature (15 and 90 mm), and a cooling channel. These lenses were manufactured through 3D printing by using wax patterns produced through investment casting. The manufacturing efficiency and machining accuracy of the lenses produced using the proposed method were compared with those of lenses produced through MAM and investment casting. The results indicated that the total costs of manufacturing an optical lens through MAM and investment casting were nine and eight times greater, respectively than that of manufacturing an optical lens through WPIC. In addition, the surface roughness of metal lenses manufactured through WPIC was 45% lower than that of lenses manufactured through MAM. Finally, the time required to manufacture 50 metal lenses was only 15 days when WPIC was used; the corresponding time was 25 days and 6 months when MAM and investment casting were used, respectively. According to the above-mentioned results, the WPIC process has excellent advantages in product manufacturing cost and developing schedule over MAM and traditional methods of investment casting. Full article
(This article belongs to the Special Issue Manufacturing Processes: Enhancements through Smart and Sustainable Approaches)
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18 pages, 9234 KiB  
Article
High-Density Polyethylene Pipe Butt-Fusion Joint Detection via Total Focusing Method and Spatiotemporal Singular Value Decomposition
by Haowen Zhang, Qiang Wang, Juan Zhou, Linlin Wu, Weirong Xu and Hong Wang
Processes 2024, 12(6), 1267; https://doi.org/10.3390/pr12061267 - 19 Jun 2024
Viewed by 1365
Abstract
High-density polyethylene (HDPE) pipes are widely used for urban natural gas transportation. Pipes are usually welded using the technique of thermal butt fusion, which is prone to manufacturing defects that are detrimental to safe operation. This paper proposes a spatiotemporal singular value decomposition [...] Read more.
High-density polyethylene (HDPE) pipes are widely used for urban natural gas transportation. Pipes are usually welded using the technique of thermal butt fusion, which is prone to manufacturing defects that are detrimental to safe operation. This paper proposes a spatiotemporal singular value decomposition preprocessing improved total focusing method (STSVD-ITFM) imaging algorithm combined with ultrasonic phased array technology for non-destructive testing. That is, the ultrasonic real-value signal data are first processed using STSVD filtering, enhancing the spatiotemporal singular values corresponding to the defective signal components. The TFM algorithm is then improved by establishing a composite modification factor based on the directivity function and the corrected energy attenuation factor by adding angle variable. Finally, the filtered signal data are utilized for imaging. Experiments are conducted by examining specimen blocks of HDPE materials with through-hole defects. The results show the following: the STSVD-ITFM algorithm proposed in this paper can better suppress static clutter in the near-field region, and the average signal-to-noise ratios are all higher than the TFM algorithm. Moreover, the STSVD-ITFM algorithm has the smallest average error among all defect depth quantification results. Full article
(This article belongs to the Special Issue Manufacturing Processes: Enhancements through Smart and Sustainable Approaches)
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14 pages, 4177 KiB  
Article
Influence of the Material Mechanical Properties on Cutting Surface Quality during Turning
by Il-Seok Kang and Tae-Ho Lee
Processes 2024, 12(6), 1171; https://doi.org/10.3390/pr12061171 - 7 Jun 2024
Cited by 1 | Viewed by 1438
Abstract
In cutting processing, the mechanical properties of the material are very important, and the optimal cutting conditions, depending on strength, hardness, and elongation, affect the quality of the machined surface. Therefore, this study was conducted to obtain optimized cutting conditions such as the [...] Read more.
In cutting processing, the mechanical properties of the material are very important, and the optimal cutting conditions, depending on strength, hardness, and elongation, affect the quality of the machined surface. Therefore, this study was conducted to obtain optimized cutting conditions such as the tool depth of the cut, cutting speed, and feed rate, considering the mechanical properties of the material. AISI 1045 cold-drawn (CD) bars showed an average tensile strength of 695.31 MPa in the tensile test and an average value of 308.6 HV in the Vickers hardness measurement. AISI 1020 CD bars showed a 22.66% lower average tensile strength of 537.74 MPa and an average of 198.77 HV in the hardness measurement. Therefore, AISI 1020 showed a 32.62% higher elongation than AISI 1045. In the measurement results for surface roughness after cutting, different results were observed depending on the strength and elongation at a feed rate of 0.05 mm/rev. AISI 1045 exhibited the highest machining quality, with a surface roughness of approximately 0.374 µm at a cutting speed of 150 m/min, and the cutting depth was 0.4 mm at a feed rate of 0.05 mm/rev. Alternatively, AISI 1020, which had relatively low strength and hardness with high elongation, exhibited the highest machining quality with a roughness of 0.383 µm with similar cutting parameters as AISI 1045. Full article
(This article belongs to the Special Issue Manufacturing Processes: Enhancements through Smart and Sustainable Approaches)
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19 pages, 3365 KiB  
Article
Optimizing Accumulator Performance in Hydraulic Systems through Support Vector Regression and Rotational Factors
by Zilong Xu, Juan Zhou, Hu Chen, Bo Xu and Zhengxiang Shen
Processes 2024, 12(5), 1036; https://doi.org/10.3390/pr12051036 - 20 May 2024
Viewed by 1189
Abstract
The piston-type accumulator is an energy storage device in hydraulic–pneumatic systems, playing a significant role in industries such as petrochemicals, heavy machinery, and steel metallurgy. The displacement parameters of the piston-type accumulator are vitally important for fault diagnosis and early warning in hydraulic [...] Read more.
The piston-type accumulator is an energy storage device in hydraulic–pneumatic systems, playing a significant role in industries such as petrochemicals, heavy machinery, and steel metallurgy. The displacement parameters of the piston-type accumulator are vitally important for fault diagnosis and early warning in hydraulic systems. Traditional displacement measurement methods cannot meet the requirements of the internal testing environment of the accumulator. Therefore, this paper proposes an accumulator piston displacement signal compensation method based on rotational factors and support vector regression. Firstly, empirical mode decomposition is utilized to denoise the signal. Then, rotational factors are used to generate a delay compensation module to compensate for the signal attenuation and time delay caused by metallic reflection and scattering within the cylinder of the radar signal. The support vector regression model is improved based on a hash table to enhance its computational efficiency and achieve radar displacement signal compensation. Finally, a simulation experiment is designed to verify the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Manufacturing Processes: Enhancements through Smart and Sustainable Approaches)
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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
Cited by 3 | Viewed by 1698
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
Cited by 2 | Viewed by 1510
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
Cited by 7 | Viewed by 2395
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 1016
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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Review

Jump to: Research

14 pages, 3823 KiB  
Review
Research Progress on Optimization of Magnetic Pole Devices for Precision Magnetic Grinding of the Inner Surface of Aircraft Engine Bent Pipes
by Chun-Fang Xiao, Jun-Jie Xiao, Bing Han and Cheng Wen
Processes 2025, 13(3), 883; https://doi.org/10.3390/pr13030883 - 17 Mar 2025
Viewed by 285
Abstract
Efficient and high-precision magnetic grinding technology has become a bottleneck technology for the manufacturing and repair of high-performance aircraft engines. Previous studies have mostly focused solely on quality control to determine the effectiveness and feasibility conditions for optimizing the design of magnetic pole [...] Read more.
Efficient and high-precision magnetic grinding technology has become a bottleneck technology for the manufacturing and repair of high-performance aircraft engines. Previous studies have mostly focused solely on quality control to determine the effectiveness and feasibility conditions for optimizing the design of magnetic pole grinding devices. This method is far from meeting the needs of precise and efficient magnetic grinding of the inner surface of aircraft engine bent pipes. This article introduced the method and mechanism of precision magnetic grinding of the inner surface of aircraft engine bent pipes. This article proposed the optimization theory of permanent magnetic pole taper structure based on auxiliary slotted magnetic pole structure and the finite element model of magnetic pole taper of slotted auxiliary magnetic pole structure. This article summarized the influence of the taper of permanent magnetic poles based on auxiliary slotted magnetic poles on magnetic grinding and summarized the evaluation method for the optimization effect of magnetic grinding devices. This article listed the application of magnetic pole device optimization in precision magnetic grinding of the inner surface of aircraft engine bent pipes. This article provided an outlook on the development trend in precision magnetic grinding magnetic pole devices for the inner surface of aircraft engine bent pipes. The conclusion was drawn that establishing a three-dimensional discrete finite element model based on slotted magnetic poles can improve the accuracy and efficiency of magnetic research. Full article
(This article belongs to the Special Issue Manufacturing Processes: Enhancements through Smart and Sustainable Approaches)
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