The high-intensity pulsed Nd:YAG laser has the capability to produce both deep grooves and microgrooves on a wide range of engineering materials such as ceramics, composites, and diamond. The micromachining of ceramics is highly demanded in industry because of its wide and potential uses in various fields such as automobile, electronic, aerospace, and biomedical engineering. Engineering ceramic, i.e. aluminium titanate (Al2TiO5), has tremendous application in the automobile and aero-engine industries owing to its excellent thermal properties. The present paper deals with the artificial neural network (ANN) and response surface methodology (RSM) based mathematical modelling and also an optimization analysis of the machining characteristics of the pulsed Nd:YAG laser during the microgrooving operation on Al2TiO5. The experiments were planned and carried out based on design of experiments (DOE). Lamp current, pulse frequency, pulse width, assist air pressure, and cutting speed were considered as machining process parameters during the pulsed Nd:YAG laser microgrooving operation and these parameters were also utilized to develop the ANN predictive model. The response criteria selected for optimization were upper width, lower width, and depth of the trapezoidal microgroove. The optimal process parameter settings were obtained as an assist air pressure of 1.2944 kgf/cm2, lamp current of 19.3070A, pulse frequency of 1.755 kHz, pulse width of 5.7087 per cent of duty cycle, and cutting speed of 10mm/s for achieving the desired upper width, lower width, and depth of the laser microgroove. The output of the RSM optimal data was validated through experimentation and the ANN predictive model. A good agreement is observed between the results based on the ANN predictive model and the actual experimental observations.
Laser surface modified ductile iron by pulsed Nd:YAG laser beam with two-dimensional array distribution
