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Current methods of machine calibration and software compensation focus on either the joint motion errors (classic machine tool software compensation) or the geometric errors between the joints (robot calibration). However, both types of errors have a significant impact on the volumetric accuracy of a machine tool or robot. We have developed a calibration method that simultaneously identifies joint motion errors and geometric errors in a machine or robot with an arbitrary number and arrangement of links using a laser tracker. The simultaneous identification of all error sources decreases measurement time, with a typical calibration for a moderate sized machine taking about four hours and 200-500 measurements. The model presented is based on a mathematically minimal parametric model of the machine. Parameter identification is done in a statistically significant way, resulting in both the “best-fit” values for the parameters and the statistical confidence in those values.

An automated system drills the outer moldline holes on a military aircraft wing. Currently, the operator manually checks countersink diameter every ten holes as a process quality check. The manual method of countersink inspection (using a countersink gauge with a dial readout) is prone to errors both in measurement and transcription, and is time consuming since the operator must stop the automated equipment before measuring the hole. Machine vision provides a fast, non-contact method for measuring countersink diameter, however, data from machine vision systems is frequently corrupted by non-gaussian noise which causes traditional model fitting methods, such as least squares, to fail miserably. We present a solution for circle measurement using a statistically robust fitting technique that does an exceptional job of identifying the countersink even in the presence of large amounts of structured and non-structured noise such as tear-out, scratches, surface defects, salt-and-pepper, etc.