Prediction of Hardness in Friction Stir Processing by Artificial Neural Networks

Abstract

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.

Analysis of tensile strength on friction stir welded Al 6061 composite reinforced with B4C and Cr2O3 using RSM and ANN

“…The selection of appropriate training functions, the number of hidden layers, and some neurons are wholly based on an experiment [35,36]. The ANN model was successfully implemented for microhardness with an R-value of 0.9998 [37]. The ANN model predicted a nearer value to experimental results for wear and coefficient of friction of WC-12Co microwave clads [38].…”

An Overview of Recent Development and Application of Friction Stir Processing Technique

“…Specimen having three number of passes showed best results. Bector et al 56) evaluated the hardness of AA 6061/Al2O3 composite via ANN. ANN results were close to experimental results.…”