Error analysis of rib curves based on measured data of airfoil-wainscot
The characteristics of accuracy control of large-scale airfoil-wainscot parts are analyzed. The accuracy of airfoil-wainscot is controlled by its rib curves, and the rib curves are straight lines whether after processing or before on the direction of face projection. The method of compensating the radius of measuring probe is presented also based on measuring data from 1D scanning probe. Aiming at the inspection requirement of the measured part and the characteristics of rib curves, the algorithms of curve matching and error analysis are proposed. The experimental results show that the proposed methods can meet the accuracy requirement of online inspection of airfoil-wainscot parts.
A algorithm for the measuring head's radius compensation
Based on analyses the methods of the measuring head's radius compensation,a new algorithm with enveloping "canker" for measuring head's radius compensation is proposed. The precision of the algorithm is analyses by a example.
Surface roughness monitoring by singular spectrum analysis of vibration signals
Abstract This study assessed two methods for enhanced surface roughness (Ra) monitoring based on the application of singular spectrum analysis (SSA) to vibrations signals generated in workpiece-cutting tool interaction in CNC finish turning operations i.e., the individual analysis of principal components (I-SSA), and the grouping analysis of correlated principal components (G-SSA). Singular spectrum analysis is a non-parametric technique of time series analysis that decomposes a signal into a set of independent additive time series referred to as principal components. A number of experiments with different cutting conditions were performed to assess surface roughness monitoring using both of these methods. The results show that singular spectrum analysis of vibration signal processing discriminated the frequency ranges effective for predicting surface roughness. Grouping analysis of correlated principal components (G-SSA) proved to be the most efficient method for monitoring surface roughness, with optimum prediction and reliability results at a lower analytical-computational cost. Finally, the results show that singular spectrum analysis is an ideal method for analyzing vibration signals applied to the on-line monitoring of surface roughness.