Automatic assembly of aero engine low pressure turbine shaft based on 3D vision measurement
Jiaxiang Wang, Kunyong Chen

TL;DR
This paper presents a high-precision, non-contact 3D vision measurement method for aero-engine turbine shaft assembly, enabling automatic alignment, docking, and inspection in tight spaces, improving automation and accuracy.
Contribution
It introduces a structured light binocular measurement system with advanced point cloud processing and feature matching for precise turbine shaft assembly.
Findings
Measurement accuracy of mounting surface matching < 0.05mm
Measurement accuracy of mounting hole matching < 0.1 degree
Successful implementation on an automatic assembly test-bed
Abstract
In order to solve the problem of low automation of Aero-engine Turbine shaft assembly and the difficulty of non-contact high-precision measurement, a structured light binocular measurement technology for key components of aero-engine is proposed in this paper. Combined with three-dimensional point cloud data processing and assembly position matching algorithm, the high-precision measurement of shaft hole assembly posture in the process of turbine shaft docking is realized. Firstly, the screw thread curve on the bolt surface is segmented based on PCA projection and edge point cloud clustering, and Hough transform is used to model fit the three-dimensional thread curve. Then the preprocessed two-dimensional convex hull is constructed to segment the key hole location features, and the mounting surface and hole location obtained by segmentation are fitted based on RANSAC method. Finally,…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsImage and Object Detection Techniques · Advanced Numerical Analysis Techniques · Robotics and Sensor-Based Localization
MethodsPrincipal Components Analysis
