Coordinate measuring machines (CMMs) have been recognized as a powerful tool for inspection and measurement purposes. Maximum utilization of CMMs requires the development of an automated inspection planning system. A computeraided inspection planning (CAIP) system leads to minimization of the total time needed for inspection process and hence the overall cost of the final product. This work introduces a computer aided inspection system that reads a B-rep solid model in SAT format as an input and produces the final CMM program in DMIS format. The system includes the following: rule-based feature recognition module that identifies and extracts the necessary inspection features from the solid model, sampling strategy module to determine the number and the location of the needed measuring points on each inspection feature, accessibility analysis module to determine the number of probe orientations that can reach the measured points without collision, finally, a clustering module to minimize the total number of probe orientations need to fully inspect the entire part. All algorithms are developed using ACIS geometric kernel and object oriented programming using C++. The results are verified on CMM.
All remote sensing imagery, from satellites, is inherently subjected to geometric distortions. Therefore geometric corrections, as preprocessing operations, are normally required prior to imagery analysis and extraction of information.This paper conducts geometric correction procedure of sample of raw satellite images using georeferenced images (image-to-image registration) of the same area. In this procedure, many well-distributed ground control points (GCPs) pairs (from both images) are identified. Then a proper transformation polynomial is applied to map the original image GCPs coordinates into the new georeferenced image GCPs coordinates. A resampling process is carried out to recalculate the gray level values for pixels in the transformed output image (new pixels locations) based on pixel values in the input image. Also, this paper presents an analysis study of the effect of variation of the number of GCPs and the order of the mapping polynomials on the accuracy of geometric correction process. The Root Mean Square Error (RMS), at the selected GCPs, are calculated and used as a measure of accuracy of the obtained results.
Automated feature recognition has recognized as the front end of fully automated computer aided process planning (CAPP) systems. Automated feature recognition is considered to be the link between computer aided design (CAD) and CAPP systems. Feature recognition converts the geometrical and topological data contained in CAD file into application orientated features for planning purposes. This work introduces an automated rule based feature recognition algorithm to extract prismatic features from a boundary representation (Brep) solid model. The automatic feature recognition algorithm is developed using ACIS geometric kernel and C++ object oriented programming. The recognition system input is a Brep solid model in sat format. The developed algorithm is a part of a complete computer aided inspection planning system.
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