Use of visual scanning to automatically generate an accurate tool path is presented. Emphasis is on combining low resolution vision with visual control of a precision machine tool to attain the accuracy required for shear spinning. A simplified edge detection method is used to obtain the required sub-pixel accuracy of the mandrel profile. Two diflerent tool paths are generated wing two different methods. First, a part program is generated assuming all data points extmcted during scanning are joined by straight lines. Second, assuming that all segments of the mandrel profile consists of circular sections and straight line segments, a curvature detection algorithm is wed to identih segments. Then the segments are put together in a part program. Dimensional accuracy of components spun using these two methods are compared with the actual profile. While both methods gave acceptable results, the segment extracted tool path produced a component with superior dimensional accumcy.

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A new method to identify spatial gradients of edges using grey level variations is presented. This method is especially useful when camera resolution is low. A high accuracy edge profile can be obtained by blending the extracted edge gradients and edge positions. This paper experimentally proves that the grey level gradient is a function of the spatial gradient of a profile that transits across a pixel. A calibration equation is established relating the grey level gradients and edge gradients. This equation can be used to extract the gradients of an unknown profile. As positions along the profile are readily available, by blending in the profile gradients, an object profile with increased accuracy can be obtained. Experiments were conducted on a specially built rig to verify the validity of the theoretical formulations. An interpolation algorithm is presented to demonstrate how improved accuracy can be obtained using gradient blending.

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