Abstract
This work aims to model and investigate the effect of cutting speed, feed rate, depth of cut and the workpiece temperature on surface roughness and flank wear (responses) of Monel-400 during turning operation. It also aims to optimize the machining parameters of the above operation. A power-law model is developed for this purpose and is corroborated by comparing the results with the artificial neural network (ANN) model. Based on the coefficient of determination (R2), mean square error (MSE), and mean absolute percentage error (MAPE) the results of the power-law model are found to be in close agreement with that of ANN. Also, the proposed power law and ANN models for surface roughness and flank wear are in close agreement with the experiment results. For the power-law model R2, MSE, and MAPE were found to be 99.83%, 9.9×10-4, and 3.32×10-2, and that of ANN were found to be 99.91%, 5.4×10-4, and 5.96×10-2, respectively for surface roughness and flank wear. An error of 0.0642% (minimum) and 8.7346% (maximum) for surface roughness and 0.0261% (minimum) and 4.6073% (maximum) for flank wear were recorded between the observed and experimental results, respectively. In order to optimize the objective functions obtained from power-law models of the surface roughness and flank wear, GA (genetic algorithm) was used to determine the optimal values of the operating parameters and objective functions thereof. The optimal value of 2.1973 µm and 0.256 mm were found for surface roughness and flank wear, respectively.
Highlights
Hard turning is a turning of material with a hardness range from 45 to 68 HRC (Fig. 1)
Power law and artificial neural network (ANN) model have been used for modeling surface roughness and flank wear during hot turning of Monel-400
The same data set was used for training and validation of the ANN model to carry out the comparison between the results of the two models
Summary
Hard turning is a turning of material with a hardness range from 45 to 68 HRC (Fig. 1). Hard turning has many advantages in addition to the cost of operation, such as faster metal removal rate, reduced cycle time, good surface finish and environmentally friendly, over grinding operation [1]. The material is strain-hardened due to the presence of retained austenite. The new machining industries aim to produce components at low product cost with good quality in minimum time. To achieve a good cutting performance in turning, the selection of optimum cutting parameters is important. Machinability of hardened materials was evaluated by cutting force for better surface roughness and tool wear by several researchers
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