SiCp/Cu composites prepared by the dual-directional extrusion process of floating die
SiCp/Cu composites were fabricated by the dual-directional extrusion process of the floating die. The microstructure, wear behavior and conductivity of SiCp/Cu composites were studied by optical microscope, abrasion test and current conductivity instrument. The results show that the SiCp/Cu composites with SiC particle distributed uniformly can be obtained, and the conductivity of composites decreased with the increasing of concentration of SiC particles, the wear resistance behavior of SiCp/Cu composites is evidently better than Cu with the increasing of concentration of SiC particles, the wearing mechanism of composites is a synthetically combined function of micro-cutting wearing and surface peeling off and grain wearing.
Progress on Ceramic Particles Reinforced Copper-matrix Composites
The preparation method of copper-matrix composites was briefly introduced. The status on research progress and properties of copper-matrix composites reinforced by SiC, Al2O3, AlN and Ti3SiC2 particulates were summarized.
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.