Analysis of Ni-coating's influence on wetting of Ag solder
Ni-coating's influence on the wetting progress in metal-ceramic bonding was analysed in this paper. Firstly Ag's contact angle was measured through high-temperature experiments, which turned out to be 100° and 25° on non-coated and Ni-coated ceramic substrates, respectively. Then a mathematical model was originally built up to catch the phase interface's motion on different substrates, considering fluid dynamics, melting and solidification, surface tension and liquid-solid heat transfer. Results indicated that Ni-coating affected the angle α directly and could reduce abnormity of joint structure through enhancing Ag solder's wettability on ceramic.
Wettability of SnCuNi-xEu solders and mechanical properties of solder joints
Abstract Wettability balance method was used to investigate the wetting performance of SnCuNi-xEu on Cu substrate, and the mechanical properties and the fracture morphology were studied. The results indicated that the addition of Eu could enhance the properties of solder and solder joints, with the increase of Eu content, tendency of first increase and then decrease could be found in the wetting time, wetting force and the mechanical properties of SnCuNi-xEu, and the optimal content was 0.039%. For SnCuNi-0.039Eu solder joints, the optimum mechanical properties could be found, and the amplitude increased was 20%, with the observation of the fracture morphology, it was found that small dimples could be seen, the toughness fracture for SnCuNi and mixture fracture for SnCuNi-0.039Eu could be demonstrated. And thermal fatigue behavior of SnCuNi solder joints could be enhanced obviously with the 0.039%Eu addition.
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.