Device and method for automatically detecting through-hole rate of honeycomb sandwich composite-based acoustic liner

A method for automatically detecting a through-hole rate of a honeycomb sandwich composite-based acoustic liner using a device for automatically detecting a through-hole rate of a honeycomb sandwich composite-based acoustic liner, the device comprising:

• a customized tooling;
• a data acquisition system;
• a motion mechanism;
• and
• a data processing system;
• wherein the data acquisition system is configured to acquire a three-dimensional (3D) point cloud data of a surface of an acoustic liner;
• the data acquisition system comprises a two-dimensional (2D) laser profile sensor and a controller;
• the 2D laser profile sensor is configured to collect the 3D point cloud data of the surface of the acoustic liner by parallel movement shooting;
• and the controller is configured to be connected with a graphics workstation to input an acquired 3D point cloud data for subsequent processing;
• the motion mechanism is configured to support the data acquisition system to perform translational scanning;
• the motion mechanism comprises an industrial robot with six degrees of freedom;
• an end of the industrial robot is capable of moving according to a path designed by an offline programming software;
• and the 2D laser profile sensor is fixed at the end of the industrial robot;
• the data processing system comprises the graphics workstation;
• the data processing system is configured to play a role of path planning and data storage in a data acquisition process, and output a detection result through an improved algorithm;
• and
• the customized tooling is designed according to a mounting hole of the 2D laser profile sensor and a mounting hole of the end of the industrial robot to enable a position and angle of the 2D laser profile sensor relative to the end of the industrial robot to be fixed;
• the method comprising:
• (S1) acquiring, by the device, a 3D point cloud data of the acoustic liner;
• (S2) detecting a spatial distribution of the 3D point cloud data, and preprocessing the 3D point cloud data to partially filter noise point clouds, and remove a hole bottom data;
• (S3) detecting a main plane where a micro-hole is located;
• identifying a boundary point cloud of the micro-hole based on a neighborhood calculation method by analyzing a pattern of the micro-hole, and extracting a relevant point;
• and
• (S4) subjecting the boundary point cloud to clustering segmentation;
• calculating a diameter of an actual hole and a hole center coordinate of the actual hole through a circle fitting algorithm based on geometric fitting, and searching the hole bottom data in an original data along a direction of the main plane to get an accurate quality detection result.