Accommodating Casting and Fixturing Errors by Adjusting the Machining Coordinate Frame

Author(s):  
Hua-Wei Ko ◽  
Yujie Chen ◽  
Nien Lee ◽  
Rohit Bhapkar ◽  
Shiv G. Kapoor ◽  
...  

Abstract In order to save valuable machining time expended on machining bad casting, a point-cloud based analysis is proposed to perform a pre-process check on raw casting material conditions. This analysis virtually compares the point cloud data of the raw casting with the nominal CAD model of the final casting and analyzes if the dimensional tolerances on the finished casting can be satisfied by adjusting the coordinate frame in which the casting is machined. The proposed analysis includes the segmentation of the raw point cloud-data followed by extracting the functional features. The material conditions of all planar surfaces are expressed using linear algebraic inequalities. A linear programming-based methodology is developed that helps in aligning the raw casting to the nominal CAD frame so that the conformity is guaranteed. The proposed methodology with the help of slack variables can deal with the casting with unsatisfiable material conditions. An example problem dealing with machining of raw casting clamped on a 4-axis machine-tool is presented to check the validity of proposed method. The virtual gage analysis accurately suggests a solution to compensate part variation caused by fixturing and locating error.

2012 ◽  
Vol 241-244 ◽  
pp. 2129-2132
Author(s):  
Hao Wang ◽  
Dong Yan Wang ◽  
Ting Jian Dong ◽  
Tao Wang

This paper made the point cloud data processing for the aircraft engine’s blade. First, collected rough point cloud data by using visual measuring equipment. Then, noise reduced and smoothed, feature detected the point cloud data, took the reasonable simplification, finished pre-processing the point cloud data. Finally, took the surface fitting for the point cloud data after processed. The result proved that processing the point cloud data reduced modeling and machining time, and improved smoothness of the model.


Author(s):  
Jiayong Yu ◽  
Longchen Ma ◽  
Maoyi Tian, ◽  
Xiushan Lu

The unmanned aerial vehicle (UAV)-mounted mobile LiDAR system (ULS) is widely used for geomatics owing to its efficient data acquisition and convenient operation. However, due to limited carrying capacity of a UAV, sensors integrated in the ULS should be small and lightweight, which results in decrease in the density of the collected scanning points. This affects registration between image data and point cloud data. To address this issue, the authors propose a method for registering and fusing ULS sequence images and laser point clouds, wherein they convert the problem of registering point cloud data and image data into a problem of matching feature points between the two images. First, a point cloud is selected to produce an intensity image. Subsequently, the corresponding feature points of the intensity image and the optical image are matched, and exterior orientation parameters are solved using a collinear equation based on image position and orientation. Finally, the sequence images are fused with the laser point cloud, based on the Global Navigation Satellite System (GNSS) time index of the optical image, to generate a true color point cloud. The experimental results show the higher registration accuracy and fusion speed of the proposed method, thereby demonstrating its accuracy and effectiveness.


Author(s):  
Keisuke YOSHIDA ◽  
Shiro MAENO ◽  
Syuhei OGAWA ◽  
Sadayuki ISEKI ◽  
Ryosuke AKOH

2019 ◽  
Author(s):  
Byeongjun Oh ◽  
Minju Kim ◽  
Chanwoo Lee ◽  
Hunhee Cho ◽  
Kyung-In Kang

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