Digital detection method of large-size skin based on laser tracker

Abstract

The digital detection method of large-scale skin based on laser tracker firstly processes the MBD digital model of skin part with CATIA software, generates detection features, and sets feature measurement points to represent the measurement parameters on the extracted detection features; and then positioning and fixing the skin part on the fixing flat table; measuring the data of skin parts by using the testing system, analyzing software compares the measured data with the theoretical data, and then fits them to establish the functional relationship between the measured data and the theoretical data, and constructs the measuring coordinate system. Then through the detection system, according to the measurement planning document, complete the measurement data collection work of the detection project one by one; The data processing software fits and compares the measured data with the theoretical mathematical model to obtain the analysis results, and displays the product quality characteristics in the form of three-dimensional features and outputs the detection report, which effectively improves the measurement reliability and detection efficiency, and provides data for process optimization.

Description

technical field [0001] The invention relates to the technical field of skin detection, in particular to a large-scale skin digital detection method based on a laser tracker. Background technique [0002] Aircraft skin parts are mainly made of thin plate materials through gate pressing or drawing process to realize the theoretical shape surface forming, and then through chemical milling or mechanical processing technology to realize the skin sinking and the shape of the aircraft contour structure. Aircraft skin parts have large dimensions. , The theoretical shape is complex (hyperboloid shape), and there are many sunken structures, and the skin parts are the surface structure of the aircraft, which directly affects the aerodynamic shape of the aircraft. Therefore, the quality control and inspection of the skin parts are very important. [0003] At present, the inspection of aircraft skin parts adopts the film detection on the mold surface, and judges whether the skin surface ...

AI Technical Summary

Problems solved by technology

[0003] At present, the inspection of aircraft skin parts adopts the film detection on the mold surface, and judges whether the skin surface is qualified by judging the fit between the skin and the mold surface; The chemical milling sample is compared and tested, and the deviation between the skin and the gelatin map or the chemical milling sample is used to judge whether the sunken structure and the position of the through hole are qualified; this traditional detection method is only suitable for a single-curved skin with a simple theoretical shape. Because the chemical milling template and gelatin map for the theoretical shape of the hyperboloid cannot be made, and the shape of the hyperboloid mold is very difficult to process; at the same time, this detection method is a set of qualitative detection methods, which not only has low detection accuracy, but also has many human factors in the measurement. The measurement error is large and the measurement result is unstable

With the development of aviation technology, the aerodynamic surface of aircraft is becoming more and more complex, and there are more and more skins with hyperboloids and even multi-curved surfaces. The existing detection methods can no longer meet the needs of aircraft manufacturing, and aircraft have difficulties in mass production. The cost of equipping a complete set of skin inspection molds for one type of aircraft is relatively high

[0004] With the development of digital manufacturing, most of the skin parts are currently digitally manufactured by vacuum adsorption CNC machining or skin mirror milling flexible clamping. The optimization of digital manufacturing methods must depend on the actual data volume of the actual processed product structure, and qualitative Detection methods cannot provide accurate measured data, and cannot provide data support for digital manufacturing process optimization, which obviously cannot meet the needs of digital manufacturing technology development

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