A pre-assembly analysis technology of aircraft components based on measured data

Peng, Yun; Hao, Long; Huang, Xiang; Li, Shuanggao

doi:10.1088/1361-6501/ac60f7

Cited by 5 publications

(2 citation statements)

References 29 publications

(25 reference statements)

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“…Particularly in the military equipment maintenance sector, disassembly and reassembly of components constitute a significant portion of the entire maintenance duration [4] . Consequently, assembly sequence planning emerges as a critical and foundational research task within the domains of digital pre-assembly [5], [6] , CIMS [7], [8] , and maintainability analysis of military equipment.…”

Section: Introductionmentioning

confidence: 99%

Research on assembly sequence planning method based on artificial neural networks

Zhang,

Feng,

Chai

2024

International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023)

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Assembly sequence planning is acknowledged as a pivotal aspect of the assembly process, playing a vital role in the realization of production automation and the assembly and disassembly of equipment. It is a current research focal point within both national and international realms of Computer Integrated Manufacturing Systems (CIMS) and equipment maintenance domains. This paper introduces and executes a neural network-based methodology for automated assembly sequence planning. The proposed method initially employs assembly interconnection matrices, assembly graphs, and penalty matrices to delineate the preferential relationships and assembly costs among components. Thereafter, it utilizes a Backpropagation (BP) neural network to resolve the optimal product assembly sequence adhering to these constraints. The efficacy of this method provides a substantive contribution to the automation of assembly sequences and is potentially beneficial for optimizing complex assembly tasks in industrial applications. Future work may extend towards refining this approach and testing its adaptability across various manufacturing scenarios.

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Section: Introductionmentioning

confidence: 99%

Research on assembly sequence planning method based on artificial neural networks

Zhang,

Feng,

Chai

2024

International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023)

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“…Currently, large gear structures are typically assembled manually. Because manual assembly is prone to bumps, it is difficult to assemble, and its efficiency is low [4,5]. In recent years, sensors, such as pressure sensors [6], displacement sensors [7], laser sensors [8,9] and vision sensors [10][11][12] have been used for mechanical part pose detection.…”

Section: Introductionmentioning

confidence: 99%

Online measurement method for assembly pose of gear structure based on monocular vision

Kong

Zhou

Huang

2023

Meas. Sci. Technol.

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The gear structure is an important part of the transmission device. The majority of manual methods are currently used to complete the assembly of the large internal gear. Manual assembly is difficult and inefficient. Therefore, an online measurement method for the assembly pose of the gear structure based on monocular vision is proposed. After the critical features of the gear structure have been detected, a duality elimination method based on traversal mapping dots is proposed to obtain the correct solution for the spatial circle pose. Concurrently, the circle pose optimization model is established to enhance pose precision. Then, a new calibration board was designed to finish the hand-eye calibration of the parallel mechanism and camera. Finally, the correction amount required to complete the internal and external teeth assembly is calculated based on the iterative update of the pose measurement method. Experimental results show that the comprehensive accuracy of the pose measurement method exceeds 0.2 mm, the average assembly time is approximately 14 minutes, and the assembly success rate is approximately 97%. It has been realized that simulation gear structure parts can be assembled automatically.

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Improving pose estimation accuracy for large hole shaft structure assembly based on super-resolution

Zhou

Huang

et al. 2023

Review of Scientific Instruments

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Image resolution is crucial to visual measurement accuracy, but on the one hand, the cost of increasing the resolution of the acquisition device is prohibitive, and on the other hand, the resolution of the image inevitably decreases when photographing objects at a distance, which is particularly common in the assembly of large hole shaft structures for pose measurement. In this study, a deep learning-based method for super-resolution of large hole shaft images is proposed, including a super-resolution dataset for hole shaft images and a new deep learning super-resolution network structure, which is designed to enhance the perception of edge information in images through the core structure and improve efficiency while improving the effect of image super-resolution. A series of experiments have proven that the method is highly accurate and efficient and can be applied to the automatic assembly of large hole shaft structures.

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A pre-assembly analysis technology of aircraft components based on measured data

Cited by 5 publications

References 29 publications

Research on assembly sequence planning method based on artificial neural networks

Research on assembly sequence planning method based on artificial neural networks

Online measurement method for assembly pose of gear structure based on monocular vision

Improving pose estimation accuracy for large hole shaft structure assembly based on super-resolution

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