Abstract
The reduction of the lead time in measurement and reverse engineering, and the increased requirements in terms of accuracy and flexibility, have resulted in a great deal of research effort aimed at developing and implementing multi-sensor systems. This paper describes an effective competitive approach for using a tactile probe to compensate the data from a laser line scanner to perform accurate reverse engineering of geometric features. With the data acquired using laser scanning, intelligent feature recognition and segmentation algorithms can be exploited to extract the global surface information of the object. The tactile probe is used to re-measure the geometric features with a small number of sampling points and the obtained information can be subsequently used to compensate the point data patches which are measured by laser scanning system. Then, the compensated point data can be exploited for accurate reverse engineering of a CAD model. The limitations of each measurement system are compensated by the other. Experimental results on three parts validate the rapidity and accuracy of this multi-sensor data fusion approach.
Highlights
Even though tactile and optical sensing technologies are widely used in data acquisition in measurement or reverseF
We propose an effective competitive integration approach for the compensation of a laser line scanner by using a tactile probe to perform the reverse engineering of geometric features
With the coordinate data acquired using the laser scanning, intelligent feature recognition algorithms can be applied to extract the geometric elements of the object
Summary
Multi-sensor systems allow the selection of discrete probing or scanning methods to measure different features. With the coordinate data acquired using the laser scanning, intelligent feature recognition algorithms can be applied to extract the geometric elements of the object These key features of elements can be re-measured by the slower tactile probe with a small number of points, and the geometric elements can be described by mathematical and numerical methods. The presented systems are cooperative integrations where optical sensors acquire the global shape information of objects to guide the touch probes for automatic point sensing. They are, limited to dealing with workpieces with relatively simple features. To the authors’ best knowledge, no relevant research has provided a method to efficiently handle integrated measurement data in RE to use sparse accurate measurement information to improve the overall measurement accuracy for RE applications
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