Abstract
Nowadays in highly competitive precision industries, the micromachining of advanced engineering materials is extremely demand as it has extensive application in the fields of automobile, electronic, biomedical and aerospace engineering. The present work addresses the modeling and optimization study on dimensional deviations of square-shaped microgroove in laser micromachining of aluminum oxide (Al2O3) ceramic material with pulsed Nd:YAG laser by considering the air pressure, lamp current, pulse frequency, pulse width and cutting speed as process parameters. Thirty-two sets of laser microgrooving trials based on central composite design (CCD) design of experiments (DOEs) are performed, and response surface method (RSM), artificial neural network (ANN) and genetic algorithm (GA) are subsequently applied for mathematical modeling and multi-response optimization. The performance of the predictive ANN model based on 5-8-8-3 architecture gave the minimum error (MSE = 0.000099) and presented highly promising to confidence with percentage error less than 3% in comparison with experimental result data set. The ANN model combined with GA leads to minimum deviation of upper width, lower width and depth value of − 0.0278 mm, 0.0102 mm and − 0.0308 mm, respectively, corresponding to optimum laser microgrooving process parameters such as 1.2 kgf/cm2 of air pressure, 19.5 Amp of lamp current, 4 kHz of pulse frequency, 6% of pulse width and 24 mm/s of cutting speed. Finally, the results have been verified by performing a confirmatory test.
Highlights
Advanced engineering ceramics have been widely used in the development of critical components, because of their superior characteristics such as electrical insulation, high hardness, low thermal expansion coefficient, corrosion resistance, high temperature resistance and low weightto-strength ratio (Doloi et al 2007; Hafezalkotob and Hafezalkotob 2016), and these are extremely hard-to-cut materials due to extreme brittleness
The results showed that there is a close agreement between the genetic algorithm (GA) parameter settings with particle swarm optimization (PSO) results
In response surface methodology (RSM), the second-order quadratic equation is the most common response model, and the approximation of the response function is obtained in the form of predictive variables by establishing the relationship between input parameters and DOF Sequential SS Adjusted SS Adjusted MS F
Summary
Advanced engineering ceramics have been widely used in the development of critical components, because of their superior characteristics such as electrical insulation, high hardness, low thermal expansion coefficient, corrosion resistance, high temperature resistance and low weightto-strength ratio (Doloi et al 2007; Hafezalkotob and Hafezalkotob 2016), and these are extremely hard-to-cut materials due to extreme brittleness. Owing to these complexities, the task to machine a component with deterministic precision becomes challenging. The model development by RSM and ANN is a convenient method for the product as well as process improvement and has received a considerable attention by the researchers in the last two decades
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