0247 NEUROCOGNITIVE REHABILITATION –A FOUR-CASE PILOT STUDY

Abstract

With the advent of probing systems and their integration with machining centers, coordinate metrology-based datum establishment is eliminating the need for precision hard-contact locators in many machining applications. Unfortunately, the coordinate data collected by these systems are often subject to noise in the form of random and biased errors of the workpiece datum features and measurement system. Consequently it is important to use datum-establishment algorithms that are insensitive to noise and that lead to the least measurable machined-feature error.This paper describes a simulation analysis that was carried out to evaluate the performance of three datum-establishment methods: 3–2–1, sequential least-squares (SQLS) and simultaneous least-squares (SMLS). The analysis involved the simulated drilling of three holes in a two set-up process. Simulated coordinate data were obtained from three planar, nominally orthogonal surfaces subject to perpendicularity errors and random roughness errors. Workpiece datum reference frames were fitted to this data using the three methods, the holes were drilled relative to these reference frames, and their position errors were computed relative to an ANSI-Y14.5M-defined reference frame.This analysis demonstrated that the SMLS method results in significantly less hole-position error [0.0173 mm average, 0.0146 mm standard deviation (SD)] than either the 3–2–1 method (0.0943 mm average, 0.0143 mm SD) or the SQLS method (0.0904 mm average, 0.0245 mm SD). This is despite the fact that the datum reference planes (ANSI Y14.5M) used to define these errors are defined sequentially relative to the extrema of the datum features, whereas the SMLS method defines the datum reference planes simultaneously with the purpose of minimizing fitting error.