The Effect of Heat Treatment on Interface Properties of S45C Steel/Copper Compound Casting
ABSTRACT 
 
The bonding of an S45C steel inserted into copper during cast welding and heat treatment was examined. The interface shear strength was made with a push-out test. After cast welding, a cast-welding layer formed between steel and copper. After marquenching, martempering and austempering heat treatment, there was a cast-welding layer near the steel matrix, an irregular layer near the copper matrix and between of them was a middle layer. Through X-ray diffraction analysis was used to determine that the interface layer consisted of carbon and CuFeO2. Through electron probe X-ray microanalysis (EPMA), it was showed that iron atoms and carbons, mostly, diffused into the copper matrix. The interface shear strengths of the compound casting while with marquenching, martempering, and austempering were 6.29 MPa, 8.23 MPa, and 8.33 MPa, respectively; The fractured region was all happened near S45C steel matrix in the cast welding layer.
Interaction between titanium and SiC
Interaction between titanium and SiC particles was studied by electron microprobe, scanning electron microscopy (SEM) and transmission electron microscopy (TEM). The distribution of the reaction products from the SiC to the titanium end is reported. Particular attention is given to the microstructure and phase analysis in areas near to the SiC. The implications of the observed microstructural and structural details are discussed.
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.