PARAMETRIC OPTIMIZATION OF Al-SiC 12 % METAL MATRIX COMPOSITE MACHINING BY ELECTRICAL DISCHARGE MACHINE
In the present study, pure aluminium alloy is mixed with 12% SiC on weight basis to fabricate the metal matrix composite (MMC) material. The objective of this paper is to investigate the effect of process parameters such as pulse on time(Ton), peak current (Ip) and flushing pressure (Fp) on metal removal rate (MRR), tool wear rate (TWR) and surface roughness (SR) during electrical discharge machining (EDM) of Al-SiC12% MMC .The experiment is followed by Central composite design (CCD) method under different combination of process parameters .In this paper Response surface methodology (RSM) is used to develop the mathematical model and to correlate the process parameters with the response. Also Analysis of Variance (ANOVA) technique is used to check the significance of the model and confirmation test is conducted to compare the experimental data with the predicted data to identify the effectiveness of the proposed method. From the main effective plot it is found that peak current is the most significant parameter among the other. Further the morphology study of the composite is carried out by the Field emission Scanning electron microscope (FESEM) of the surface after machining to make a relation with the model.
Multi-Objective Optimization of EDM Process Parameters by Using Passing Vehicle Search (PVS) Algorithm
Electro-Discharge Machining (EDM) is very popular for machining high-strength conductive materials for aerospace and automotive application. These machining involve a range of processing parameters. In order to optimize these for the best performance, a trade-off has to be decided for the responses achieved through machining. Conventional algorithms have long been replaced by advanced optimization algorithms. Performance of meta-heuristic algorithms in relation to traditional deterministic approaches for multi-modal, non-linear engineering problems is very promising in recent days. In this paper, a multi-objective optimization approach is applied using a population-based meta-heuristic algorithm called Passing Vehicle Search (PVS) for optimizing process parameters of various mathematical models formulated by different authors. Different approaches depending on case have been adopted for formulating the multi-objective PVS algorithm and pareto front is obtained for each case to extract the desired results. The performance of multi-objective PVS is compared with different intelligent computing models employed in prior studies and better results are shown in case of former. This approach can be extended to various mathematical models besides those covered in the paper to obtain better optimization results.
Electrochemical detection of copper(II) at a gold electrode modified with a self-assembled monolayer of penicillamine
A penicillamine (PCA) self-assembled monolayer (SAM) was prepared on a gold electrode. It has been found that the modified electrode exhibited a selective response to copper ions. As demonstrated by cyclic voltammetric experiments, the SAM-based electrode showed an attractive ability to preconcentrate efficiently traces of copper(II) from solutions. Under optimum conditions, the anodic peak current was proportional to the concentration of Cu(II) in the range from 8.0 × 10−7 to 1.0 × 10−4 M with a detection limit of 4.0 × 10−7 M. Moreover, this modified gold electrode is also characterized by excellent repeatability, showing a relative standard deviation of 3.2% for nine successive measurements of 1.0 × 10−5 M Cu(II). The PCA/Au SAM gold electrode was used for the determination of Cu(II) in a tap water sample and the results showed a good agreement with the data obtained by atomic emission spectrometry.