Fast on-site assembly mating surface status analysis based on Skin Model Shapes

IF 9.1 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Robotics and Computer-integrated Manufacturing Pub Date : 2025-04-16 DOI:10.1016/j.rcim.2025.103024

Li Jinyue , Zhao Gang , Xu Maocheng , Xu Pengpeng , Zhang Pengfei

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引用次数: 0

Abstract

Surface morphology has a considerable impact on the assembly of high-precision mechanical products. To address this issue, an adapted Skin Model Shapes modeling method for on-site assembly is employed. Meanwhile, the computational time would drastically expand as the exponential growth of measurement data hlfaced the increase in workpiece size. Thus, a fast surface mating status analysis method is further introduced for point cloud based Skin Model Shapes. Computational efficiency is improved by reducing the search points through region division. The proposed method is applied to the assembly of rotary components in aero-engines. Test workpieces with detailed morphology and measuring devices are developed to conduct assembly experiments. The experimental results show that the contact areas predicted by the simulation results overlap more than 70% with respect to the actual results, and the computational time is reduced by 82% compared with the existing method.

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基于蒙皮模型形状的快速现场装配配合面状态分析

表面形貌对高精度机械产品的装配有相当大的影响。为了解决这个问题，采用了一种适应现场装配的皮肤模型形状建模方法。同时，随着工件尺寸的增大，测量数据呈指数增长，计算时间将急剧增加。在此基础上，进一步提出了一种基于点云的皮肤模型形状的快速表面匹配状态分析方法。通过区域划分减少了搜索点，提高了计算效率。将该方法应用于航空发动机旋转部件的装配。开发了具有详细形貌和测量装置的测试工件以进行装配实验。实验结果表明，仿真结果预测的接触面积与实际结果重叠超过70%，计算时间比现有方法减少82%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Robotics and Computer-integrated Manufacturing 工程技术-工程：制造

CiteScore

24.10

自引率

13.50%

发文量

160

审稿时长

50 days

期刊介绍： The journal, Robotics and Computer-Integrated Manufacturing, focuses on sharing research applications that contribute to the development of new or enhanced robotics, manufacturing technologies, and innovative manufacturing strategies that are relevant to industry. Papers that combine theory and experimental validation are preferred, while review papers on current robotics and manufacturing issues are also considered. However, papers on traditional machining processes, modeling and simulation, supply chain management, and resource optimization are generally not within the scope of the journal, as there are more appropriate journals for these topics. Similarly, papers that are overly theoretical or mathematical will be directed to other suitable journals. The journal welcomes original papers in areas such as industrial robotics, human-robot collaboration in manufacturing, cloud-based manufacturing, cyber-physical production systems, big data analytics in manufacturing, smart mechatronics, machine learning, adaptive and sustainable manufacturing, and other fields involving unique manufacturing technologies.