Evaluation of machined surface quality of Si3N4 ceramics based on neural network and grey-level co-occurrence matrix

Cutting and extruding processing technology for ceramics based on the edge-chipping effect is a non-traditional rough machining method for engineering ceramics. A set of new methods for evaluating unconventional rough surfaces of such ceramics was developed by using grey-level co-occurrence matrix (GLCM) and a neural network (NN). The influences of three parameters including step size, greyscale quantisation and direction on the GLCM were investigated to measure the morphology of the machined surface of Si3N4 ceramic by using a GLCM with suitable such parameters. Based on a generalised regression network, a prediction model for the textural features of sintered Si3N4 ceramic surfaces was established with multiple processing parameters. Moreover, a competitive layer network was used to sort the roughness grades of the machined surface. The division and cooperation of the generalised regression network and competitive network are able to preferably identify and predict the roughness of the machined surface without contact.