Effect of cutting speed on surface quality by bandsawing
The surface profile behavior of workpieces sawn has been investigated. Six species were sawn under the condition of four different kinds of cutting speed by the band saw. The real surface profile of a sawn workpiece is composed of two sections, which are the additivity of the waviness of filtered wave and roughness curve. The roughness of center line average as well as amplitude decrease with the increasing of cutting speed for different kinds of species used in the experiment.
Description of roughness profiles by separating the random and periodic components
Abstract The probability that it is not possible to obtain a significant description of roughness profiles composed of random and periodic components by using statistical methods is discussed. It is shown that the random and periodic components can be separated by using the frequency spectrum and the autocorrelation function of the profile. The physical significance of the separate components is discussed and it is shown that it is possible to describe a long crested surface by a single profile using these components The method of separating the random and periodic components allows a parametrical description of the roughness profile to be given. It is then possible to use these parameters to calculate a synthetic signal sign with the same basic properties as the measured profile.
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.