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Aerospace
Special Issues
Intelligent Assembly and Measurement Technologies for Next-Generation Aero-Engines
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Intelligent Assembly and Measurement Technologies for Next-Generation Aero-Engines

A special issue of Aerospace (ISSN 2226-4310).

Deadline for manuscript submissions: 31 August 2026 | Viewed by 2274

Special Issue Editors

Prof. Dr. Yongmeng Liu

E-Mail Website
Guest Editor
School of Instrument Science and Engineering, Harbin Institute of Technology, Harbin, China
Interests: ultra-precision measurement and intelligent instrumentation engineering; optoelectronic measurement information engineering; intelligent optical measurement; precision instrumentation and machinery; testing and measurement technology; measurement and control technology and instrumentation; automated detection technology and equipment; aero-engine intelligent assembly measurement technology
Dr. Yingjie Mei

E-Mail Website
Guest Editor
School of Information Science and Engineering, Harbin Institute of Technology, Weihai, China
Interests: ultra-precision measurement and assembly of engine; digital twins in complex systems; mixed-reality complex measurement and assembly environments; multi-source image processing
Dr. Ruirui Li

E-Mail Website
Guest Editor
School of Instrument Science and Engineering, Harbin Institute of Technology, Harbin, China
Interests: precision measurement and intelligent assembly of aero-engine rotors; precision instruments and machinery; tolerance design and allocation technology

Special Issue Information

Dear Colleagues,

As one of the final and most critical processes in aero-engine manufacturing, assembly and measurement accuracy are directly related to breakthroughs in flight safety and performance. With the rapid development of artificial intelligence, digital twins, and flexible sensing technology, intelligent assembly and precision measurement technology are becoming the core driving force for promoting the development and upgrading of the next generation of aero-engines. This Special Issue of Aerospace covers recent developments in aero-engine measurement and assembly methods, including ultra-precision measurement, error accumulation analysis, and intelligent assembly modeling. We are interested in two fields—measurement and assembly. In terms of measurement, the impact of different characteristics such as geometric shape, mass, inertia, gravity, magnetic fields, manufacturing, and performance on engine processing should be considered. Priority should be given to research using intelligent algorithms, new sensors, new modeling methods, and other technologies. In terms of assembly, attention should be paid to the development and application of various assembly methods such as artificial intelligence, augmented reality, virtual reality, and mixed reality. It is recommended to use digital twin technology to achieve unified measurement and assembly of engines. The editor of this Special Issue invites authors to submit papers on methods to address the modeling challenges and technical limitations faced in the measurement and assembly processes of various new engines.

Prof. Dr. Yongmeng Liu
Dr. Yingjie Mei
Dr. Ruirui Li
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 250 words) can be sent to the Editorial Office for assessment.

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. Aerospace 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

  • ultra-precision measurement methods for complex engine components
  • high-precision intelligent assembly process of multi-stage components
  • online measurement and pose control
  • assembly process simulation and closed-loop optimization
  • development of flexible sensors and embedded measurement systems
  • non-destructive testing and deformation monitoring
  • application of big data and AI in assembly deviation diagnosis
  • key technologies for autonomous collaborative assembly of robots
  • in situ measurement methods in extreme environments

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Published Papers (4 papers)

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Research

24 pages, 3705 KB  
Article
DMR-YOLO: A Lightweight Visual Inspection Method for Surface Defect Detection of Aero-Engine Components
by Jinwu Tong, Han Cao, Xinyun Lu, Xin Zhang and Bingbing Gao
Aerospace 2026, 13(4), 360; https://doi.org/10.3390/aerospace13040360 - 13 Apr 2026
Viewed by 357
Abstract
Accurate surface defect detection is essential for ensuring the measurement accuracy and assembly reliability of aero-engine components during manufacturing and assembly processes. Bearings, as critical rotating components in aero-engines, are highly sensitive to surface defects that may lead to stress concentration and premature [...] Read more.
Accurate surface defect detection is essential for ensuring the measurement accuracy and assembly reliability of aero-engine components during manufacturing and assembly processes. Bearings, as critical rotating components in aero-engines, are highly sensitive to surface defects that may lead to stress concentration and premature failure. However, complex defect types, low-contrast textures, and multi-scale characteristics pose significant challenges for existing lightweight visual inspection models. To address these issues, this paper proposes an improved lightweight detection model, termed DMR-YOLO, based on YOLOv8n. A Diverse Branch Block (DBB) is introduced to enhance multi-scale feature extraction and improve the representation of complex defect patterns. A Multi-Level Channel Attention (MLCA) mechanism is embedded to strengthen discriminative feature channels and suppress background interference caused by low-contrast textures. In addition, a ResidualADown module is designed to preserve critical feature information during downsampling, improving the detection of subtle defects. Experimental results on a bearing surface defect dataset show that the proposed model achieves an mAP of 89.3%, representing a 2.8% improvement over YOLOv8n while maintaining real-time inference at 138.6 FPS. Moreover, generalization tests conducted on a steel surface defect dataset demonstrate the robustness and transferability of the proposed method across different datasets. Full article
(This article belongs to the Special Issue Intelligent Assembly and Measurement Technologies for Next-Generation Aero-Engines)
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32 pages, 14091 KB  
Article
A Normalized Objective Function for Multi-Stage Rotor Assembly Optimization Targeting Vibration Suppression Across Critical Speeds
by Yue Chen, Guiyang Liu, Yu Weng and Yuhao Jia
Aerospace 2026, 13(4), 310; https://doi.org/10.3390/aerospace13040310 - 26 Mar 2026
Viewed by 376
Abstract
Excessive vibration during critical speed traversal remains a primary challenge in assembling multi-stage rotors of aero-engines. Conventional assembly optimization methods, which target static geometric and mass eccentricity errors or vibration at a fixed operating speed, are inadequate to ensure smooth passage through multiple [...] Read more.
Excessive vibration during critical speed traversal remains a primary challenge in assembling multi-stage rotors of aero-engines. Conventional assembly optimization methods, which target static geometric and mass eccentricity errors or vibration at a fixed operating speed, are inadequate to ensure smooth passage through multiple critical speeds. To address this gap, we propose a novel, vibration-suppression-oriented assembly optimization model. A normalized objective function is formulated to minimize the overall vibration response across multiple rotor nodes specifically at the first and second critical speeds. This function integrates an assembly error propagation model with a rotor dynamic model that considers flexible dynamic deflection. The optimal assembly angle sequence is solved using a genetic algorithm. Experimental validation on a four-stage rotor demonstrates that the proposed method reduces the maximum vibration displacement amplitude at the first and second critical speeds by 74.7% and 11.9%, respectively, significantly outperforming conventional objectives based on geometric error, unbalanced mass, or single-speed vibration. This work provides a practical and effective strategy to enhance rotor dynamic safety by ensuring low-vibration operation across the critical speeds encountered before reaching the operating speed through optimal assembly. Full article
(This article belongs to the Special Issue Intelligent Assembly and Measurement Technologies for Next-Generation Aero-Engines)
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20 pages, 4743 KB  
Article
A Vibration Response Prediction Model for Multi-Stage Assembled Rotors Based on Synchronous Excitation of Mass Eccentricity Error and Spigot Eccentricity Error
by Yue Chen, Guiyang Liu and Yuhao Jia
Aerospace 2026, 13(3), 218; https://doi.org/10.3390/aerospace13030218 - 27 Feb 2026
Cited by 1 | Viewed by 319
Abstract
The precise prediction of vibration response is crucial for optimizing the assembly quality of multi-stage rotors. Existing models possess two key limitations: they neglect the geometric displacement excitation from spigot eccentricity error and oversimplify rotor behavior by not accounting for the excitation redistribution [...] Read more.
The precise prediction of vibration response is crucial for optimizing the assembly quality of multi-stage rotors. Existing models possess two key limitations: they neglect the geometric displacement excitation from spigot eccentricity error and oversimplify rotor behavior by not accounting for the excitation redistribution caused by significant dynamic deflection at high speeds, particularly near critical speeds. To overcome these shortcomings, this study establishes a novel dynamic model based on the synchronous excitation of both mass and spigot eccentricity errors, which simultaneously incorporates the coupling mechanism of dynamic deflection. System dynamics equations are developed using a finite element approach combined with Timoshenko beam theory and solved via the Newmark-β method. Simulations and experiments on a four-stage rotor demonstrate that the proposed model provides significantly improved accuracy. At sub-critical, first, and second critical speeds, it reduces the maximum prediction error for nodal displacement amplitudes by 6.1%, 9.2%, and 36.4%, respectively, compared to a model neglecting dynamic deflection. Furthermore, analysis confirms that the targeted assembly error excitation exists solely at the fundamental frequency. The developed model, which uniquely integrates dual eccentricity sources with dynamic deflection coupling, is essential for reliable high-speed vibration prediction and assembly optimization, especially for flexible rotors operating near critical speeds. Full article
(This article belongs to the Special Issue Intelligent Assembly and Measurement Technologies for Next-Generation Aero-Engines)
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17 pages, 9736 KB  
Article
An Intelligent Approach for Predicting Unbalance in the Multistage Rotor of an Aero-Engine Based on a Hybrid Neural Network
by Hanwen Cheng, Ruirui Li, Chuanzhi Sun and Yongmeng Liu
Aerospace 2025, 12(12), 1108; https://doi.org/10.3390/aerospace12121108 - 15 Dec 2025
Viewed by 414
Abstract
Aiming to improve the accuracy of the aero-engine’s multi-stage rotor’s mating surface classification and initial unbalance prediction, a new intelligent approach for the unbalance prediction of the aero-engine’s multi-stage rotor is proposed in this paper. Numerical simulations of the proposed scheme were conducted [...] Read more.
Aiming to improve the accuracy of the aero-engine’s multi-stage rotor’s mating surface classification and initial unbalance prediction, a new intelligent approach for the unbalance prediction of the aero-engine’s multi-stage rotor is proposed in this paper. Numerical simulations of the proposed scheme were conducted on actual assembly datasets of actual aero-engine rotors, and assembly experiments implementing actual aero-engine’s multi-stage rotors were carried out to validate the effectiveness of the proposed method. Results of numerical simulation and experimental validation revealed that the proposed hybrid network method was not only capable of efficiently recognizing different types of rotors’ mating surfaces with a satisfactory accuracy of more than 98% in the training process and 93.3% in experiments, but also proved to accurately predict after-assembly initial unbalance with an acceptable error of less than 5% in both simulated and experimental scenarios. Therefore, the method proposed in this paper can not only be used for rotor surface classification, but also can be used to guide the assembly of aero-engine multi-stage rotors. Full article
(This article belongs to the Special Issue Intelligent Assembly and Measurement Technologies for Next-Generation Aero-Engines)
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