Friction stir processing is a technique for the modification and fabrication of surface composites. Surface composites of aluminium have a diverse application in industries like aerospace and automobile. Here, B 4 C nano-particles (< 30 nm) have been employed as the reinforcement on AA7075 based substrate alloy and surface composites have been constructed at varying Tool rotation and Tool traverse speeds. The influence of these varying factors on the ultimate tensile strength (UTS), microhardness (Hv) and residual stress (RS) have been studied. Taguchi L9 orthogonal array was used for the DOE and the changes in the mechanical properties of these nine samples with regards to the tool rotation speeds of 800 rpm, 1000 rpm and 1200 rpm and the tool traverse speeds of 40 mm min −1 , 50 mm min −1 and 60 mm min −1 were investigated. It was found that between the two processing variables, Tool traverse speed was the more significant variable. 800 rpm and 60 mm min −1 were determined to be the optimum parameters for Friction stir processing and the predicted values of microhardness (Hv) through Taguchi analysis were the most accurate.
This research focuses on the use of Artificial Neural Network (ANN) for the prediction of the microhardness of friction stir processed aluminium based metal matrix composite (AA6061+Al2O3). Different specimens were obtained by using rotating speeds of 1100, 1210, 1320 and 1430 rpm and travelling speeds of 36, 48, 60, 72 mm/min. The microhardness value (HV) of the processed surface of each of the samples was measured and the data collected from the specimens was used as learning data for ANN. Higher rotational speed and lower transversal speeds resulted in higher hardness value since processing at higher tool rotational speed causes high material flow and good resistance to the tool pin profile. A uniform increase in microhardness was observed up to 1320 RPM and a subsequent decrease on any further increments of tool rotational speed. Subsequently, the highest values of microhardness were observed with a square mandrel at 1320 RPM and 36 mm/min. The calculated results were satisfactorily compliant with the measured data and the ANN model was successful in predicting the microhardness.
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