Ascertaining Optimal Process Parameters for Laser Cutting and Drilling of Basaltglass Hybrid Composite

Abstract

Materials with excellent mechanical properties is the key requirement of modern industries. Researchers have reported that the hybrid basalt-glass composite has excellent mechanical properties and are widely used in marine applications. However, precise machining of this hybrid composite is still a matter of concern. In the present work, fabrication, testing and laser machining of hybrid basalt-glass composite has been done. It is a desirable need to evaluate the machining parameters for effective utilization of a process and the material. Selection of an appropriate optimal range of process parameters is quite essential for achieving high-quality machining and is a challenging task within this domain of study. The aim of this research is to develop a robust prediction model which can suggest the desired range of process parameters for accomplishing better machining quality, precision, and geometrical accuracy. Experiments have been performed on a 300W (CNC-PCT 300) pulsed Nd: YAG laser cutting system at various levels of input machining parameters. Thereafter, response surface methodology (RSM) and artificial neural network (ANN) techniques have been adopted to develop prediction models in terms of input process parameters for geometrical quality characteristics: Top and Bottom Kerf Deviations for cutting, Hole Circularity and Heat Affected Zone for drilling. These developed models have been validated by comparing the predicted values with the experimental ones. Finally, these models have been optimized using multi-objective genetic algorithm technique, in order to ascertain an optimal range of machining parameters pertaining to better quality machining with high precision and geometrical accuracy. newline