Application of Taguchi Method for Optimization of Process Parameters for Wear loss of LM25/Flyash Composite
Metal matrix composites (MMCs) are now gaining their usage in tribological industries because of their inherent properties like high strength to weight ratio, low wear rate etc. The present study deals with investigation relating to the influence of wear parameters like sliding speed, applied load, reinforcement wt% and sliding distance on the dry sliding wear. A pin-on-disc apparatus was used to conduct the dry sliding wear test. The design of experiment approach was employed to acquire data in controlled way using Taguchi method. Multiple regression analysis, Signal-to-Noise ratio and Analysis of Variance were employed to investigate the wear behaviour of aluminium composite. The incorporation of fly ash as reinforcement material in aluminium matrix material improves the tribological characteristics.
IJSRD-International Journal for Scientific Research & Development| Vol. 3, Issue 10, 2015 | ISSN (online): 2321-0613
The use of different kind of Metal-matrix composite materials is in constant growing over the years, because they have better physical, mechanical and tribological properties comparing to matrix materials. In automotive industry they are used for pistons, cylinders, engine blocks, brakes and power transfer system elements. Research attempts have been made in the past toreduce the cost of processing of composites, decrease the weight of the composites, and increase the desired performance characteristics. This paper presents the findings of an experimental investigation on the effects of applied load, RPM, Sliding distance and pin temperature in dry sliding wear studies performed on red mud-based aluminum metal matrix composites (MMC). Red mud is an industrial waste during the production of aluminum from bauxite ore. A pinon-disc apparatus was used to conduct the dry sliding wear test. Taguchi-based L9 orthogonal array has been used to accomplish the objective of the experimental study. Analysis of variance (ANOVA) is employed to find the optimal setting and the effect of each parameter on wear rate.
Fuzzy logic-based forward modeling of Electro Chemical Machining process
Electro Chemical Machining (ECM) is one of the most widely used advanced machining processes to produce complicated shapes from electrically conductive but difficult to machine materials. This paper presents a Fuzzy Logic (FL) - based modeling of ECM process and optimization of its rule base, data base and consequent part utilizing a Genetic Algorithm (GA). A binary coded GA has been used for the said purpose. While modeling with FL, the output parameters, namely Material Removal rate (MRR) and Surface finish (Ra) have been predicted for different combinations of process parameters, such as current, voltage, flow rate and gap between work piece and the tool. A batch mode of training has been provided to the FL with the help of two hundred training data generated artificially using the conventional statistical regression equations. The results of this study on a number of test scenarios show that the proposed genetic-fuzzy approach can predict in a near optimal manner.