Experimental investigation on cutting force and machining parameters optimization in in-situ laser-assisted machining of glass–ceramic

Abstract

Glass-ceramic is a typical hard and brittle material that is difficult to use for ultra precision machining. In order to improve the machining quality of glass–ceramic, orthogonal experiments on in-situ laser-assisted machining (LAM) of glass–ceramic were carried out with cutting force as the characteristic value. The maximum reduction in the resultant force of cutting force in in-situ LAM was 56.13%. The contribution order of each machining parameter to reducing cutting force is: spindle speed > laser power > feed speed > cutting depth. Response surface methodology (RSM) was used to conduct experiments and establish a regression model for predicting cutting force. The influence law of four machining parameters on cutting force was studied through three-dimensional response surface. The optimal combination of machining parameters to minimize cutting force obtained through RSM optimization is: spindle speed 400.51 rpm, feed speed 0.03 mm/rev, cutting depth 18.91 μm, laser power 74.78 W. The RSM experiments were trained, predicted, and optimized using artificial neural network (ANN) and genetic algorithms (GA). The fitting values of the ANN model are highly consistent with the experimental values of RSM. The optimal machining parameter combination obtained through GA optimization is: spindle speed 400 rpm, feed speed 0.01 mm/rev, cutting depth 20 μm, laser power 75 W. Experiments were conducted using the optimal machining parameters of RSM and ANN respectively, and the results showed that ANN is more ideal for optimizing machining parameters and predicting minimum cutting force. This study provides guidance and reference for glass–ceramic in-situ LAM and parameter optimization method for cutting force.