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The aim of this study is to determine the effect of the machining parameters and tool geometry. The turning operation is carried out as per the Design of Experiments (DoE) of Response Surface Methodology (RSM) to predict the temperature rise of aluminium-6061 as a cutting material and Al2O3 coated carbide tool is used as a cutting tool for turning operation. The ANOVA analysis is used to measure the performance quality and mathematical model is developed. The values of probability >(F) is less than 0.05 indicates, the model conditions are significant. The cutting speed is the most influencing parameters compared to other parameters. For the optimum machining parameters leading to temperature rise, the Artificial Neural Network (ANN) model is trained and tested using MAT Lab software. The ANN recommends best minimum predicted value of temperature rise. The confirmatory analysis results, the predicted values were found to be in commendable agreement with the experimental values.

The Effect of Machining Parameters and Optimization of Temperature Rise in Turning Operation of Aluminium-6061 Using RSM and Artificial Neural Network