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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.

Background: Now-a-days, newer hardened steel materials are coming rapidly into the market due to its wide applications in various fields of engineering. So the machinability investigation of these steel materials is one of the prime concern for practicing engineers, prior to actual machining. Methods: The present study addresses surface roughness, flank wear and chip morphology during dry hard turning of AISI 4340 steel (49 HRC) using CVD (TiN/TiCN/Al 2 O 3 /TiN) multilayer coated carbide tool. Three factors (cutting speed, feed and depth of cut) and three-level factorial experiment designs with Taguchi's L 9 Orthogonal array (OA) and statistical analysis of variance (ANOVA) were performed to investigate the consequent effect of these cutting parameters on the tool and workpiece in terms of flank wear and surface roughness. For better understanding of the cutting process, surface topography of machined workpieces, wear mechanisms of worn coated carbide tool and chip morphology of generated chips were observed by scanning electron microscope (SEM). Consequently, multiple regression analysis was adopted to develop mathematical model for each response, along with various diagnostic tests were performed to check the validity and efficacy of the proposed model. Finally, to justify the economical feasibility of coated carbide tool in hard turning application, a cost analysis was performed based on Gilbert's approach by evaluating the tool life under optimized cutting condition (suggested by response optimization technique).