Hierarchical Error Assessment of CAD Models for Aircraft Manufacturing-and-Measurement
Jin Huang, Honghua Chen, Mingqiang Wei

TL;DR
This paper introduces HEA-MM, a hierarchical error assessment framework for aircraft CAD models that uses structured light scanning and multi-level analysis to improve manufacturing quality control.
Contribution
The paper presents a novel hierarchical error assessment framework with a primitive refinement method and feature-level analysis for aircraft CAD models.
Findings
Effective error detection at global, part, and feature levels
Improved accuracy in circular hole detection and analysis
Demonstrated robustness on various aircraft CAD models
Abstract
The most essential feature of aviation equipment is high quality, including high performance, high stability and high reliability. In this paper, we propose a novel hierarchical error assessment framework for aircraft CAD models within a manufacturing-and-measurement platform, termed HEA-MM. HEA-MM employs structured light scanners to obtain comprehensive 3D measurements of manufactured workpieces. The measured point cloud is registered with the reference CAD model, followed by an error analysis conducted at three hierarchical levels: global, part, and feature. At the global level, the error analysis evaluates the overall deviation of the scanned point cloud from the reference CAD model. At the part level, error analysis is performed on these patches underlying the point clouds. We propose a novel optimization-based primitive refinement method to obtain a set of meaningful patches of…
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Taxonomy
TopicsManufacturing Process and Optimization · Advanced Measurement and Metrology Techniques · Advanced Numerical Analysis Techniques
MethodsSparse Evolutionary Training
