Effect of pH on electroless nickel deposition on alumina substrates for chip resistor application
Alumina has been used widely in electronics industry specifically for chip resistor application. Currently, the deposition of nickel on alumina surface using electrodeposition method suffered from the insufficient bonding between the metallic coating (nickel) and the ceramic substrate (alumina). Electroless deposition method however can produce strong adhesion between the metallic coating and ceramic substrate. In this research, the effect of pH on the electroless nickel on alumina substrate was investigated. The electroless deposition of nickel on alumina substrate was conducted at pH 4.0, 5.6 and pH 10.0 for 3, 5, 10, 20 and 30 minutes at constant temperature. The crystal structure of the substrate and coating was characterized by using X-ray diffraction (XRD). The surface morphology of the coating was observed by using scanning electron microscopy (SEM) and the coating thickness was measure by X-ray fluorescence (XRF). The coating performance was tested by using Standard ASTM D3359 (Tape-Test). It was found that the optimum pH of plating solution was 10.0 with the maximum thickness was obtained at 30 minutes of deposition with thickness of 1.15 μm. These optimum coating parameters shows homogeneous and uniform nickel deposition on the alumina surface as observed by SEM. Nickel deposition by electroless method at pH 4.0, 5.6, and 10.0 have excellent bonding with alumina substrate as shown by tape test analysis.
The deposition behavior of electroless nickel on alumina substrate
Abstract The electroless deposition of nickel on alumina substrate was investigated with respect to the deposition behavior. The weight gain of the alumina substrate indicated that a three-stage mechanism exists for the deposition process, namely incubation, nucleation plus growth, and growth. The exclusive growth stage starts around 5 min after reaction. The activation energies as well as the effect of the deposition parameters on the rate of the nucleation and growth stages were investigated. The growth stage has a greater activation energy than the nucleation stage.
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.