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Machines
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Design, Manufacturing, Measurement, and Quality Control Technologies for Assembly
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Design, Manufacturing, Measurement, and Quality Control Technologies for Assembly

A Special Issue of Machines (ISSN 2075-1702) belonging to the section "Advanced Manufacturing".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 2337

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
Special Issues, Collections and Topics in MDPI journals
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 Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the accelerated evolution of global manufacturing toward smart, efficient, and low-carbon paradigms, design, manufacturing, measurement, and quality control technologies tailored for assembly processes have become indispensable cornerstones of product development and lifecycle management. These interrelated technologies directly influence assembly efficiency, product reliability, cost-effectiveness, and market competitiveness across industries such as aerospace, automotive, precision machinery, and electronic manufacturing. To showcase cutting-edge theoretical advancements, technological innovations, and industrial application practices in this interdisciplinary field, facilitate global academic exchange and knowledge translation, and drive the sustainable development of assembly-oriented technologies, this Special Issue “Design, Manufacturing, Measurement, and Quality Control Technologies for Assembly” is officially launched. We invite researchers, engineers, and postgraduates worldwide to contribute original research findings and comprehensive reviews.

Considering the division according to professional technical fields, research topics of interest include, but are not limited to, the following:

(1) Assembly-oriented design technologies:

  • Intelligent assembly process planning and optimization;
  • Tolerance analysis, synthesis, and allocation for assembly systems;
  • Modular, platform-based, and disassembly-friendly design;
  • Integration of assembly design with CAD/CAPP/CAE systems;
  • Digital twin-driven assembly design and validation;
  • Part design for assembly compatibility (e.g., geometry optimization, mating feature design, ease of handling and insertion);
  • Design for part standardization, interchangeability, and assembly adaptability.

(2) Assembly-centric manufacturing technologies:

  • Intelligent assembly process planning and optimization;
  • Tolerance analysis, synthesis, and allocation for assembly systems;
  • Modular, platform-based, and disassembly-friendly design;
  • Integration of assembly design with CAD/CAPP/CAE systems;
  • Digital twin-driven assembly design and validation.

(3) Assembly process measurement and inspection technologies:

  • In situ/online measurement for assembly quality monitoring;
  • Three-dimensional optical measurement, laser scanning, and coordinate measuring technologies;
  • Micro/nano-scale measurement for precision assembly;
  • Multi-sensor fusion and data fusion in assembly measurement;
  • Measurement data analytics and visualization for assembly optimization.

(4) Assembly quality control and assurance technologies:

  • Statistical Process Control (SPC) and quality prediction models for assembly;
  • Fault diagnosis, prognosis, and fault-tolerant assembly technologies;
  • Intelligent quality inspection systems (machine vision, AI-based detection);
  • Assembly quality traceability and closed-loop control;
  • Robust design for assembly quality improvement.

(5) Cross-disciplinary applications and innovations:

  • Smart assembly technologies in Industry 4.0 environments;
  • Digital twin and virtual assembly simulation;
  • Industrial Internet of Things (IIoT)-enabled assembly quality management;
  • Assembly technologies for emerging industries (e.g., new energy vehicles, aerospace);
  • Human–robot collaboration in assembly processes.

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 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-anonymized 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

  • assembly
  • process and product design
  • manufacturing
  • measurement
  • quality control

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Published Papers (4 papers)

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18 pages, 16707 KB  
Article
Simulation-Based Design of Process Parameters for Human–Machine Collaborative Aircraft Assembly Riveting
by Ji Li, Junjie Dan, Yaling Tian, Min Ling, Heng Zhao, Weiqiang Mo, Yi Luo and Yaoming Zhou
Machines 2026, 14(8), 904; https://doi.org/10.3390/machines14080904 - 7 Aug 2026
Viewed by 346
Abstract
In aircraft assembly, riveting is a critical joining method that directly determines structural integrity, fatigue life, and overall airframe reliability. With the increasing adoption of human–machine collaborative systems for complex assembly tasks, the rational design of riveting process parameters has become essential for [...] Read more.
In aircraft assembly, riveting is a critical joining method that directly determines structural integrity, fatigue life, and overall airframe reliability. With the increasing adoption of human–machine collaborative systems for complex assembly tasks, the rational design of riveting process parameters has become essential for ensuring consistent assembly quality. However, traditional experimental parameter optimization is time-consuming and costly, and lacks generalizability across varying working conditions. To address this challenge, this paper proposes a simulation-based design method for rapidly constructing process parameter schemes in human–machine collaborative aircraft assembly riveting. A theoretical dynamic model of the pneumatic reciprocating riveting gun is established to derive the relationship between input air pressure and piston impact velocity, providing physically grounded loading conditions for numerical simulation. A sequentially coupled numerical simulation method is developed using Ansys LS-DYNA and its Restart function to accurately model the entire multiple reciprocating impact forming process, which incorporating preloading analysis to reflect actual clamping conditions and reset analysis with applied damping to eliminate post-impact oscillations. Taking the riveting assembly of Aluminum (AL) 2024T351 rivets and AL 7039 aluminum sheets as a case study, the simulation successfully reproduces the rivet forming evolution over twelve consecutive impacts, revealing a two-stage deformation mechanism consisting of elastic springback and superimposed elastic-plastic deformation. Experimental verification on a self-built human–machine collaborative riveting platform demonstrates excellent agreement with simulation results in impact counts and upset head height. The proposed method provides a reliable, efficient, and low-cost approach for assembly process parameter calibration, offering direct theoretical support for assembly quality control, process robustness, and reliability assurance in aircraft manufacturing. Full article
(This article belongs to the Special Issue Design, Manufacturing, Measurement, and Quality Control Technologies for Assembly)
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28 pages, 5892 KB  
Article
An Empirical Complexity-Based Approach to Assembly Line Balancing in Manual Assembly Systems
by Amanda Aljinović Meštrović, Nikola Gjeldum, Boženko Bilić and Marko Mladineo
Machines 2026, 14(7), 722; https://doi.org/10.3390/machines14070722 - 26 Jun 2026
Viewed by 435
Abstract
Due to the increasing heterogeneity of consumer needs and preferences, manufacturing companies are forced to expand their product range to maintain market share while avoiding cost increases. However, increasing product variety increases the complexity of assembly systems and complicates planning, design, and production [...] Read more.
Due to the increasing heterogeneity of consumer needs and preferences, manufacturing companies are forced to expand their product range to maintain market share while avoiding cost increases. However, increasing product variety increases the complexity of assembly systems and complicates planning, design, and production management. The quantification of manufacturing complexity and its impact on key performance indicators remains a subject of debate. To examine the relationship between assembly complexity, assembly line balance, and productivity from an operator-oriented perspective, an empirical complexity indicator for mixed-model assembly workstations is proposed. This indicator is based on experimentally collected data and analysis of working time variability. The proposed indicator is evaluated through controlled experimental case studies conducted in a learning factory environment. The results indicate that the relationship between complexity and productivity is not linear. Instead, within the investigated experimental boundaries, the observed trend suggests a turning point beyond which further increases in complexity are associated with decreased productivity, while line balance continues to improve. This finding suggests that integrating the proposed complexity indicator into production planning and management may support decision-making related to assembly line balancing and complexity management in manual assembly systems. Full article
(This article belongs to the Special Issue Design, Manufacturing, Measurement, and Quality Control Technologies for Assembly)
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20 pages, 7845 KB  
Article
Modeling of Part Surface Topography Based on Adaptive Composite Kernel Functions
by Wenbin Tang, Xingchen Jiang and Jingzhe Wang
Machines 2026, 14(6), 588; https://doi.org/10.3390/machines14060588 - 25 May 2026
Viewed by 498
Abstract
Part surface topography is characterized by complex multi-scale and multi-feature coupling, and accurate topography modeling is essential for predicting assembly precision in high-performance mechanical systems. Gaussian Process Regression (GPR) offers a principled, probabilistic framework for surface modeling from sparse measurements, but its performance [...] Read more.
Part surface topography is characterized by complex multi-scale and multi-feature coupling, and accurate topography modeling is essential for predicting assembly precision in high-performance mechanical systems. Gaussian Process Regression (GPR) offers a principled, probabilistic framework for surface modeling from sparse measurements, but its performance depends critically on kernel function selection. A fixed single kernel lacks the flexibility to represent surfaces that simultaneously exhibit smooth trends, periodic textures, and linear drift. To address this limitation, an adaptive composite kernel method is proposed. Initial GPR residuals are analyzed through statistical hypothesis tests and spectral decomposition to identify which geometric features are present; matching base kernels—Squared Exponential (SE), Periodic (PER), and Linear (LIN)—are then selected and combined additively or multiplicatively. Experiments on three representative synthetic surfaces show that the composite kernels reduce RMSE by up to 95.09% relative to the single SE kernel. Validation on a machined part confirms that the method successfully transfers to real measured data, achieving a 30.65% RMSE reduction and raising R2 from 0.9536 to 0.9777. The results demonstrate that residual-analysis-driven kernel selection yields physically interpretable models with substantially improved reconstruction accuracy. Full article
(This article belongs to the Special Issue Design, Manufacturing, Measurement, and Quality Control Technologies for Assembly)
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Other

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54 pages, 1833 KB  
Systematic Review
Ergonomics-Aware Task Allocation for Human-Centric Collaborative Assembly: A Systematic Review
by Qiangwei Bao, Xi Zhang, Shuo Su and Feiyan Guo
Machines 2026, 14(8), 894; https://doi.org/10.3390/machines14080894 - 5 Aug 2026
Viewed by 539
Abstract
Human-centric manufacturing is reshaping collaborative production systems by repositioning human capabilities, safety, experience, and well-being as central concerns in the design and operation of intelligent manufacturing. As manufacturing moves toward Industry 5.0, task allocation in assembly-oriented collaborative systems is no longer only a [...] Read more.
Human-centric manufacturing is reshaping collaborative production systems by repositioning human capabilities, safety, experience, and well-being as central concerns in the design and operation of intelligent manufacturing. As manufacturing moves toward Industry 5.0, task allocation in assembly-oriented collaborative systems is no longer only a matter of productivity, cycle time, or resource utilization, but also a key mechanism for protecting worker safety, workload balance, ergonomic compatibility, and long-term well-being. Although existing reviews have addressed various aspects of collaborative manufacturing and ergonomics, a systematic synthesis of how ergonomics is embedded into task allocation for collaborative assembly remains limited. To address this gap, this paper systematically reviews 95 studies identified from WoS and Scopus using an expanded keyword-based search strategy, with April 2026 retained as the publication eligibility cutoff. Studies were included when ergonomics or related human-factor considerations materially influenced task allocation, task assignment, planning, scheduling, or line-balancing decisions in AI-enabled and robot-assisted collaborative manufacturing, with emphasis on assembly-related settings such as workstations, workcells, and assembly lines. The literature is analyzed from four perspectives: ergonomic objectives, allocation scenarios, temporal responsiveness, and computational approaches. Given the heterogeneity of modeling, optimization, simulation, and design studies, a narrative synthesis rather than meta-analysis was conducted. The results show that physiological ergonomics remains the dominant dimension, accounting for 70 of the 95 studies. Recent studies increasingly incorporate multidimensional ergonomic risks, fatigue progression, worker trust, human preference, and real-time human-state information into allocation decisions. The reviewed studies also indicate a transition from static and assessment-informed allocation toward adaptive, state-aware, and cyber-physical allocation. Finally, the review identifies future directions concerning multidimensional ergonomic modeling, assessment-to-decision transformation, real-time adaptive allocation, human-centric interaction, and transferable industrial validation for collaborative assembly systems. No review registration was undertaken. Full article
(This article belongs to the Special Issue Design, Manufacturing, Measurement, and Quality Control Technologies for Assembly)
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