
handle: 2433/113247 , 2433/146582
Abstract The machining of a cone frustum as specified in National Aerospace Standard (NAS) 979 is widely accepted as a final performance test for five-axis machining centers. Although it gives a good demonstration of the machine’s overall machining performance, it is generally difficult to separately identify each error source in the machine from the measured error profile of the finished workpiece. This paper proposes a set of machining tests for a five-axis machine tool to identify its kinematic errors, one of its most fundamental error sources. In each machining pattern, a simple straight side cutting using a straight end mill is performed. The relationship between geometric errors of the finished workpiece and the machine’s kinematic errors is formulated based on the kinematic model of a five-axis machine. The identification of kinematic errors from geometric errors of finished workpieces is experimentally demonstrated on a commercial five-axis machining center, and the estimates are compared to those estimated based on ball bar measurements.
Measurement, Machining test, Cone frustum, Kinematic errors, Five-axis machine tools
Measurement, Machining test, Cone frustum, Kinematic errors, Five-axis machine tools
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 131 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 1% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 1% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
