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Machines
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Advanced Manufacturing and Assembly Technologies for Aerospace Production Systems
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Advanced Manufacturing and Assembly Technologies for Aerospace Production Systems

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Advanced Manufacturing".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 4018

Special Issue Editors

Dr. Feiyan Guo

E-Mail Website
Guest Editor
School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China
Interests: advanced aerospace assembly technologies
Dr. Zhengping Chang

E-Mail Website
Guest Editor
School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an 710072, China
Interests: intelligent manufacturing; advanced composite structure assembly; aerospace digital assembly technology and equipment

Special Issue Information

Dear Colleagues,

Aerospace manufacturing and production technology is an important component of the aviation high-tech industry, involving the entire process of product design, machining and assembly, management, and service support. It plays a decisive role in ensuring the performance of aviation products, shortening development cycles, reducing costs, and improving reliability. With the development of aviation products, the scope of aerospace manufacturing and production technology continues to expand, integrating mechanical, electronic, optical, information, materials, biological sciences, and management. It is a multidisciplinary and technology intensive technology system. Therefore, the technical level of aerospace manufacturing and production plays a leading role in the development of the entire equipment manufacturing industry.

This is a call for papers for a Special Issue on "Advanced Manufacturing and Assembly Technologies for Aerospace Production Systems". This Special Issue will provide a venue for scholars and researchers to share their most recent theoretical and technical successes, as well as to highlight key topics and difficulties for future study in the field. The submitted papers are expected to raise original ideas and potential contributions to theory and practice. Considering the division according to professional technical fields, the following research topics are included, but are not limited to:

  • Manufacturing and assembly technology of composite structures;
  • Mechanical machining technology;
  • Sheet metal forming and assembly technology;
  • Special machining technology;
  • Surface engineering;
  • Welding technique;
  • Mechanical joining technology;
  • Assembly and equipment technology;
  • Digital and intelligent manufacturing and assembly technology;
  • Manufacturing measurement technology;
  • Advanced manufacturing mode and management technology.

Dr. Feiyan Guo
Dr. Zhengping Chang
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. Machines 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

  • aerospace production
  • manufacturing
  • assembly
  • management
  • parameter
  • process
  • equipment

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

Published Papers (5 papers)

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Research

32 pages, 13506 KiB  
Article
VR Co-Lab: A Virtual Reality Platform for Human–Robot Disassembly Training and Synthetic Data Generation
by Yashwanth Maddipatla, Sibo Tian, Xiao Liang, Minghui Zheng and Beiwen Li
Machines 2025, 13(3), 239; https://doi.org/10.3390/machines13030239 - 17 Mar 2025
Viewed by 614
Abstract
This research introduces a virtual reality (VR) training system for improving human–robot collaboration (HRC) in industrial disassembly tasks, particularly for e-waste recycling. Conventional training approaches frequently fail to provide sufficient adaptability, immediate feedback, or scalable solutions for complex industrial workflows. The implementation leverages [...] Read more.
This research introduces a virtual reality (VR) training system for improving human–robot collaboration (HRC) in industrial disassembly tasks, particularly for e-waste recycling. Conventional training approaches frequently fail to provide sufficient adaptability, immediate feedback, or scalable solutions for complex industrial workflows. The implementation leverages Quest Pro’s body-tracking capabilities to enable ergonomic, immersive interactions with planned eye-tracking integration for improved interactivity and accuracy. The Niryo One robot aids users in hands-on disassembly while generating synthetic data to refine robot motion planning models. A Robot Operating System (ROS) bridge enables the seamless simulation and control of various robotic platforms using Unified Robotics Description Format (URDF) files, bridging virtual and physical training environments. A Long Short-Term Memory (LSTM) model predicts user interactions and robotic motions, optimizing trajectory planning and minimizing errors. Monte Carlo dropout-based uncertainty estimation enhances prediction reliability, ensuring adaptability to dynamic user behavior. Initial technical validation demonstrates the platform’s potential, with preliminary testing showing promising results in task execution efficiency and human–robot motion alignment, though comprehensive user studies remain for future work. Limitations include the lack of multi-user scenarios, potential tracking inaccuracies, and the need for further real-world validation. This system establishes a sandbox training framework for HRC in disassembly, leveraging VR and AI-driven feedback to improve skill acquisition, task efficiency, and training scalability across industrial applications. Full article
(This article belongs to the Special Issue Advanced Manufacturing and Assembly Technologies for Aerospace Production Systems)
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18 pages, 4613 KiB  
Article
Virtual and Real Occlusion Processing Method of Monocular Visual Assembly Scene Based on ORB-SLAM3
by Hanzhong Xu, Chunping Chen, Qingqing Yin, Chao Ma and Feiyan Guo
Machines 2025, 13(3), 212; https://doi.org/10.3390/machines13030212 - 6 Mar 2025
Viewed by 431
Abstract
Addressing the challenge of acquiring depth information in aero-engine assembly scenes using monocular vision, which complicates mixed reality (MR) virtual and real occlusion processing, we propose an ORB-SLAM3-based monocular vision assembly scene virtual and real occlusion processing method. The method proposes optimizing ORB-SLAM3 [...] Read more.
Addressing the challenge of acquiring depth information in aero-engine assembly scenes using monocular vision, which complicates mixed reality (MR) virtual and real occlusion processing, we propose an ORB-SLAM3-based monocular vision assembly scene virtual and real occlusion processing method. The method proposes optimizing ORB-SLAM3 for matching and depth point reconstruction using the MNSTF algorithm. MNSTF can solve the problems of feature point extraction and matching in weakly textured and texture-less scenes by expressing the structure and texture information of the local images. It is then proposed to densify the sparse depth map using the double-three interpolation method, and the complete depth map of the real scene is created by combining the 3D model depth information in the process model. Finally, by comparing the depth values of each pixel point in the real and virtual scene depth maps, the virtual occlusion relationship of the assembly scene is correctly displayed. Experimental validation was performed with an aero-engine piping connector assembly scenario and by comparing it with Holynski’s and Kinect’s methods. The results showed that in terms of virtual and real occlusion accuracy, the average improvement was 2.2 and 3.4 pixel points, respectively. In terms of real-time performance, the real-time frame rate of this paper’s method can reach 42.4 FPS, an improvement of 77.4% and 87.6%, respectively. This shows that the method in this paper has good performance in terms of the accuracy and timeliness of virtual and real occlusion. This study further demonstrates that the proposed method can effectively address the challenges of virtual and real occlusion processing in monocular vision within the context of mixed reality-assisted assembly processes. Full article
(This article belongs to the Special Issue Advanced Manufacturing and Assembly Technologies for Aerospace Production Systems)
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23 pages, 21742 KiB  
Article
Modular Design and Layout Planning of Tooling Structures for Aircraft Assembly
by Zhanghu Shi, Chengyu Li, Junshan Hu, Xingtao Su, Hancheng Wang and Wei Tian
Machines 2025, 13(3), 185; https://doi.org/10.3390/machines13030185 - 25 Feb 2025
Viewed by 395
Abstract
Aircraft structures consist of numerous complex components that require a high level of precision to assemble. Tooling plays a crucial role in the assembly of aircraft components, providing the functions of positioning, shape maintenance, and support to guarantee the accuracy of the product. [...] Read more.
Aircraft structures consist of numerous complex components that require a high level of precision to assemble. Tooling plays a crucial role in the assembly of aircraft components, providing the functions of positioning, shape maintenance, and support to guarantee the accuracy of the product. Aiming to obtain reusable assembly tooling that can be rapidly reconfigured, this study focuses on the modular design and layout of tooling structures. The concept of functional elements for the characterization of tooling parts is proposed, and the relationship between each pair of elements is established to clarify the similarities and dependencies among various tooling structures. Based on the analysis of functional elements and their relationships, the tooling structures are divided and recombined into several modules. The detailed module designs are demonstrated by using typical structures such as platforms, columns, and locators as examples. A parametric representation of the multi-source information of tooling modules is proposed, and optimization methods for the layout and configuration of locators and platforms are developed using their parametric information. A reconfigurable tooling process integrated with a monitoring system is designed, realized, and successfully applied to the assembly of a practical type of fuselage. The results from verifying these methods’ efficiencies show that the modular design and reconfiguration planning of tooling only takes about 10 min and a few seconds, respectively, which is far less than the time consumed during traditional tooling design (from several days to weeks). The work in this study provides an engineering paradigm for the serialization and reconfiguration of assembly tooling in aviation manufacturing. Full article
(This article belongs to the Special Issue Advanced Manufacturing and Assembly Technologies for Aerospace Production Systems)
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23 pages, 7417 KiB  
Article
A Fitness Landscape-Based Method for Extreme Point Analysis of Part Surface Morphology
by Jinshan Sun and Wenbin Tang
Machines 2025, 13(2), 136; https://doi.org/10.3390/machines13020136 - 11 Feb 2025
Viewed by 520
Abstract
Advancements in Industry 4.0 and smart manufacturing have increased the demand for precise and intricate part surface geometries, making the analysis of surface morphology essential for ensuring assembly precision and product quality. This study presents an innovative fitness landscape-based methodology for extreme point [...] Read more.
Advancements in Industry 4.0 and smart manufacturing have increased the demand for precise and intricate part surface geometries, making the analysis of surface morphology essential for ensuring assembly precision and product quality. This study presents an innovative fitness landscape-based methodology for extreme point analysis of part surface morphology, effectively addressing the limitations of existing techniques in accurately identifying and analyzing extremum points. The proposed approach integrates adaptive Fitness-Distance Correlation (FDC) with a roughness index to dynamically determine the number and spatial distribution of initial points within the pattern search algorithm, based on variations in surface roughness. By partitioning the feasible domain into subregions according to FDC values, the algorithm significantly reduces optimization complexity. Regions with high ruggedness are further subdivided, facilitating the parallel implementation of the pattern search algorithm within each subregion. This adaptive strategy ensures that areas with intricate surface features are allocated a greater number of initial points, thereby enhancing the probability of locating both regional and global extremum points. To validate the effectiveness and robustness of the proposed method, extensive testing was conducted using five diverse test functions treated as black-box functions. The results demonstrate the method’s capability to accurately locate extremum points across varying surface profiles. Additionally, the proposed method was applied to flatness error evaluation. The results indicate that, compared to using only the raw measurement data, the flatness error increases by approximately 3% when extremum points are taken into account. Full article
(This article belongs to the Special Issue Advanced Manufacturing and Assembly Technologies for Aerospace Production Systems)
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17 pages, 8531 KiB  
Article
Milling-Force Prediction Model for 304 Stainless Steel Considering Tool Wear
by Changxu Wang, Yan Li, Feng Gao, Kejun Wu, Kan Yin, Peng He and Yunjiao Xu
Machines 2025, 13(1), 72; https://doi.org/10.3390/machines13010072 - 20 Jan 2025
Viewed by 814
Abstract
The high-performance alloy, 304 stainless steel, is widely used in various industries. However, its material properties lead to severe tool wear during milling processes, significantly increasing milling force and adversely impacting machining quality and efficiency. Consequently, an accurate milling-force model is crucial for [...] Read more.
The high-performance alloy, 304 stainless steel, is widely used in various industries. However, its material properties lead to severe tool wear during milling processes, significantly increasing milling force and adversely impacting machining quality and efficiency. Consequently, an accurate milling-force model is crucial for guiding the formulation and optimization of machining parameters. This paper presents a milling-force prediction model for 304 stainless steel that incorporates the effect of tool wear, based on the mechanistic modeling approach. Side-milling experiments on 304 stainless steel were conducted to analyze the relationship between milling force and tool wear, identify the model coefficients, and validate the prediction accuracy of the milling-force model. The results demonstrate that the model accurately predicts the milling forces of worn tools while side milling 304 stainless steel under various machining parameters and tool wear conditions. Full article
(This article belongs to the Special Issue Advanced Manufacturing and Assembly Technologies for Aerospace Production Systems)
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