AN INNOVATIVE APPROACH TO INCREASE THE ACCURACY OF MULTIAXIS MACHINESFOR PROCESS-INTERMITTENT INSPECTION

A method for enhancing the effectiveness and robustness of process-int ermittent inspection is presented. The method is developed first using mathematical models and measurements closely related to the real part s, thus reducing the uncertainty in both the error estimation and comp ensation for multi-axis machines. A predictive search algorithm is the n developed to identify the minimum number of appropriate measuring po ints (with respect to the designated tolerance)for arbitrarily shaped manufacturing parts. The identified measuring points are robust in tha t they least sensitive to uncertainties in measurement and modeling. T he search algorithm uses computer simulation with information obtained from previous measurements. Consequently, the measurement effort invo lved in error modeling has been greatly reduced The error model derive d from the method can be used to correct the process-intermittent prob ing data for a more accurate assessment of workpiece dimensions. Altho ugh the method is designed for general application in multi-axis machi nes (i.e., machine tools, robots, and coordinate measuring machines), this paper focuses on the specific application of a machining center E xperimental results demonstrate the effectiveness of the error modelin g method for accuracy improvement of the machining center for process- intermittent inspection.