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Problem statement:The reinforcements added to an alloy lead to variation in properties. The content and size of the reinforcement influences the properties of composites. Very little research has been carried out in hybrid composites. Work on hybrid LM6 aluminium alloy metal matrix composites (MMC) with flyash and SiC has been initiated here. The effect of the four parameters, size and weight of the reinforcements on the hardness and wear loss has been studied. Approach: Artificial neural networks, from the artificial intelligence family, is a type of information processing system, based on modeling the neural system of human brain. The effect of the parameters was investigated using ANN. Central composite rotatable method of design of experiments was used to arrive at the combination and the number of specimens. The specimens were prepared using the liquid metallurgy route and tested. Pin-on-disc apparatus was used for determining wear. Rockwell hardness on C scale was determined. The data from the experiments were used for training and testing the network. Results: The accuracy in ANN prediction was appreciable with the error estimated for wear loss and hardness being less than 2%. Conclusions/Recommendations: The ANN prediction is quick and economical way of estimating the properties.

In this work the AISI 316L SS was implanted with two different ions namely nitrogen and argon separately at 100 KeV with fluence of 1x1017ions/cm² at room temperature. The crystallographic orientation and surface morphology were studied using X-ray Diffraction (XRD) and Scanning Electron Microscope (SEM). The effects of ion implantation, on the corrosion performance of AISI316L stainless steel were evaluated. Microhardness was also measured by Vickers method by varying the loads. The results of the studies indicated that there was a significant improvement in both corrosion and hardness in the case of implanted samples.

Wear has drastic effects on flank wear during the turning process, which affects tool life and the surface roughness of the finished component. The aim of this study was to evaluate the impact of factors such as cutting speed, feed rate, and depth of cut on flank wear of a highspeed steel (HSS) cutting tool during the turning process of a mild steel component. A mathematical model was developed relating flank wear and the main factors such as cutting speed V, feed rate F, and depth of cut T. Response surface methodology (RSM) was used to develop the mathematical model and the model was checked for adequacy by regression analysis. Main and interaction effects of the control factors on flank wear are presented in graphical form, which helps in selecting quickly the process parameters to achieve the desired quality of machining surface by way of controlling the wear of the cutting tool. It is a very important technique to measure the tool wear on-line, so that the tool can be changed when its profile is lost or it produces a poor quality, which is not acceptable.