Parameter selection and optimization of an intelligent ultrasonic-assisted grinding system for SiC ceramics

Abstract

Ceramic grinding processing technology continues to advance rapidly. Recent studies are increasingly adopting ultrasonic-assisted grinding (UAG) for obtaining improved surface quality and reduced surface roughness. Because different grinding parameters could lead to different machining quality, operators may find it difficult to select grinding parameters on the basis of the expected machining quality alone. In this article, an intelligent UAG system (IUAGS) that provides suitable grinding parameters for the operator is proposed. This IUAGS employs a proposed one-dimensional convolutional neuro-fuzzy network (1DCNFN) to establish a surface roughness prediction model, and then a particle swarm optimization algorithm is used to optimize the grinding parameters. The experimental results demonstrate that our proposed 1DCNFN has a lower mean absolute percentage error (MAPE) in surface roughness prediction than other methods do. Moreover, our IUAGS can provide appropriate UAG parameters on the basis of the specific requirements of the operator.