Key Takeaways & Executive Findings
- •• Digital twin technology enables real-time visualization and prediction of assembly processes, enhancing inversion analysis accuracy and expanding parameter ranges for precision optimization. • Traditional assembly precision methods rely on dimensional chain design and stepwise tolerance checks, which are insufficient for dynamic multi-factor analysis and problem localization. • Key enabling technologies for DT-driven assembly precision inversion include multi-attribute sensing, high-fidelity virtual modeling, twin synchronization, multi-physical field simulation, and performance twin construction. • Future integration with human-cyber-physical systems, augmented reality, and generative intelligence will further advance DT-driven assembly precision inversion in industrial applications.
Abstract
Assembly precision greatly influences the performance of complex high-end equipment. The traditional industrial assembly process and deviation transfer are implicit and uncertain, causing problems like poor component fit and hard-to-trace assembly stress concentration. Assemblers can only check whether the dimensional tolerance of the component design is exceeded step by step in combination with prior knowledge. Inversion in industrial assembly optimizes assembly and design by comparing real and theoretical results and doing inversion analysis to reduce assembly deviation. The digital twin (DT) technology visualizes and predicts the assembly process by mapping real and virtual model parameters and states simultaneously, expanding parameter range for inversion analysis and improving inversion result accuracy. Problems in improving industrial assembly precision and the significance and research status of DT-driven parametric inversion of assembly tools, processes and object precision are summarized. It analyzes vital technologies for assembly precision inversion such as multi-attribute assembly process parameter sensing, virtual modeling of high-fidelity assembly systems, twin synchronization of assembly process data models, multi-physical field simulation, and performance twin model construction of the assembly process. Combined with human-cyber-physical system, augmented reality, and generative intelligence, the outlook of DT-driven assembly precision inversion is proposed, providing support for DT’s use in industrial assembly and precision improvement.
1. Introduction
The application of intelligent industrial assembly in high-end manufacturing (aerospace, automotive, shipbuilding, etc.) boosts production efficiency, guarantees product quality, and cuts labor costs [1]. Assembly precision directly influences the performance of high-end equipment, like rotor concentricity of aero-engines [2] and unfolding flatness of satellite antennas [3, 4]. By improving assembly precision, assembly deviation can be reduced, assembly performance can be enhanced, and high-performance service of high-end equipment can be ensured. Traditional industrial assembly processes and deviation transfer are implicit and uncertain [5, 6], leading to hard-to-trace and optimize problems like poor component fit, excessive tolerances, concentrated stress, inconsistent product quality, and unpredictable assembly issues.
The traditional assembly precision optimization method centers on the design of the dimensional chain. The assembler checks component dimensional tolerance limit exceedance by comparing final and theoretical assembly results, using experts’ experience and knowledge, and stepwise checking component design’s dimensional tolerance [7–9], with a single detection index. The traditional black-box and paper-documented assembly precision design method is unable to dynamically acquire and analyze other factors affecting the assembly precision, causing difficulties in locating problem stages (design /assembly) and curbing deviation trends, as well as accurately locating the problematic links through multi-physical field simulation and virtual modeling under the premise of reducing disassembly. Accurate problem stage location requires inversion technologies based on a mature forward system model to inverse derivation of the system inputs or system model parameters that give rise to these results.
Inversion in the industrial assembly process involves comparing real and theoretical assembly results for optimization and reverse analysis to reduce deviation [10]. However, it still faces challenges of multidisciplinary cross-platform design [11, 12] and off-site collaborative manufacturing [13, 14] due to issues like insufficient dynamic interaction simulation in assembly, delayed real-time data feedback and processing, oversimplified models not matching the physical process, and poor integration with existing manufacturing systems. Meanwhile, lagging in
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Dinghao Cheng, Bingtao Hu, Yixiong Feng, Jiangxin Yang, Ruirui Zhong, Tianyue Wang, Jianrong Tan (2025). Digital Twin-driven Inversion of Assembly Precision for Industrial Equipment: Challenges, Progress and Perspectives. Chinese Journal of Mechanical Engineering. https://doi.org/10.1186/s10033-025-01224-8
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Frequently Asked Questions
What is digital twin-driven assembly precision inversion?
It is a method that uses digital twin technology to visualize and predict assembly processes by mapping real and virtual models, enabling inversion analysis to reduce assembly deviations and improve precision.
What are the main challenges in traditional assembly precision optimization?
Traditional methods rely on dimensional chain design and stepwise tolerance checks, which are implicit, uncertain, and unable to dynamically analyze multiple factors, making it difficult to locate problem stages and curb deviation trends.
What are the key technologies for DT-driven assembly precision inversion?
Key technologies include multi-attribute assembly process parameter sensing, virtual modeling of high-fidelity assembly systems, twin synchronization of assembly process data models, multi-physical field simulation, and performance twin model construction.
How does digital twin improve assembly precision inversion?
Digital twin expands the parameter range for inversion analysis by providing real-time mapping and prediction, improving the accuracy of inversion results and enabling dynamic optimization.
What are the future perspectives of DT-driven assembly precision inversion?
Future perspectives include integration with human-cyber-physical systems, augmented reality, and generative intelligence to further enhance precision and support industrial assembly applications.
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