Investigation on the surface roughness of glass–ceramic by in-situ laser-assisted machining

Glass–ceramic is a widely used and difficult to machine material with high hardness and brittleness. For this reason, in-situ laser-assisted machining (LAM) of glass–ceramic was carried out with surface roughness as the characteristic value to study the machining quality of glass–ceramic. Orthogonal experiments on in-situ LAM were conducted using the Taguchi method (TM). The range of surface roughness reduction obtained by comparing in-situ LAM with conventional machining is 44.44–61.27%. The optimal combination of machining parameters that can minimize surface roughness obtained through signal-to-noise ratio (S/N) analysis is: spindle speed 450 rpm, feed speed 0.01 mm/rev, cutting depth 14 μm, laser power 70 W. Surface topography analysis confirmed that in-situ LAM can effectively enhance the plastic removal of glass–ceramic. The comparison between pre-heat LAM and in-situ LAM confirms that in-situ LAM machining of glass–ceramic is more reliable. Artificial neural network (ANN) and genetic algorithm (GA) were used to fit and optimize the machining parameters and experimental results in TM orthogonal experiments. The optimal combination of machining parameters obtained after ANN fitting and GA optimization is: spindle speed 400 rpm, feed speed 0.01 mm/rev, cutting depth 16 μm, laser power 75 W. Experiments were conducted using the optimal combination of machining parameters of TM and ANN, the results showed that ANN performs better than TM in predicting minimum surface roughness and optimizing machining parameters. This study provides a reference for in-situ LAM of glass–ceramic and parameter optimization methods for surface roughness.