Abstract
Pulsed Nd:YAG laser has high intensity and high quality beam characteristics, which can be used to produce micro-grooves and micro-turning surface on advanced engineering ceramics. The present research attempts to develop mathematical models by using response surface methodology approach for correlating the machining process parameters and the process responses during laser micro-turning of aluminum oxide (Al2 O3 ) ceramics. The process parameters such as laser average power, pulse frequency, workpiece rotating speed, assist air pressure and Y feed rate were varied during experimentation. The rotatable central composite design experimental planning has been used to design the experimentation. The performance measures considered are surface roughness (Ra ) and micro-turning depth deviation. Multi-objective optimization has been carried out for achieving the desired surface roughness as well as minimum depth deviation during laser micro-turning operation. Further, an artificial neural network (ANN) model has been developed to predict the process criteria. Levenberg-Marquadt training algorithm is used for multilayer feed forward backpropagation neural network. The developed ANN model has 5-10-2 feed forward network. There are 5 neurons in the input layer, 10 neurons in the hidden layer and 2 neurons in the output layers corresponding to two output responses, respectively. The developed ANN model has been validated using data obtained by conducting additional set of experiments. It was found that the developed ANN model can predict the process criteria more accurately than response surface methodology (RSM) based developed models.
Highlights
Aluminium oxide (Al2O3) finds potential use in probably the broadest number of engineering applications such as automobile, aerospace, biomedical, etc. due to its extreme hardness, strength, stability in high temperature and high degree of resistance to wear and corrosion
The comparative plots of artificial neural network (ANN) model predicted results with 32 settings of experimental results for surface roughness (Ra) and micro-turning depth deviation are shown in Figures 5 and 6, respectively
In the present research study, laser micro-turning of cylindrical alumina ceramic material has been carried out based on response surface methodology (RSM) design of experiments (DoE)
Summary
Aluminium oxide (Al2O3) finds potential use in probably the broadest number of engineering applications such as automobile, aerospace, biomedical, etc. due to its extreme hardness, strength, stability in high temperature and high degree of resistance to wear and corrosion. The various process parameters involved in laser micro-turning operation are laser beam parameters (pulse energy, pulse frequency, pulse duration), workpiece motion parameters (axial feed rate, rotating speed), assist gas parameters (assist-gas pressure, types of gas, types of nozzle), etc. These process parameters play dynamic role during laser micro-turning operation of engineering ceramic materials. Multiresponse optimization process parametric setting must be searched out to get desired accurate dimensional features in different components With these intentions, in the present research, an attempt has been made to design and develop a model using a combined approach of response surface methodology (RSM). The ANN model has been used to predict the multi-optimization parametric setting to achieve desired responses during laser micro-turning operation with pulsed Nd:YAG laser
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