A mechanistic cutting force model based on ductile and brittle fracture material removal modes for edge surface grinding of CFRP composites using rotary ultrasonic machining. (15th June 2020) - Wang, Hui; Pei, Z.J.; Cong, Weilong
Highlights: A mechanistic cutting force model is developed for RUM edge surface grinding of CFRP. Cutting forces in feeding and depth of cut directions are studied in the model. This model is based on both ductile and brittle fracture material removal modes. The radial grain forces of a single grain under these two modes are obtained. The model prediction agrees well with the experimental results. Abstract: Rotary ultrasonic machining (RUM) has been proven to be an effective method for surface grinding of carbon fiber reinforced plastic (CFRP) composites. Cutting force is considered as the main criterion to evaluate the performance of RUM surface grinding process. The cutting force modeling is essential to better understand such a process. All reported cutting force models for RUM of CFRP are developed based on brittle fracture material removal mode (brittle mode). However, it is recently found that both ductile material removal mode (ductile mode) and brittle mode exist in RUM of CFRP. Among surface grinding processes, edge surface grinding is the mandatory and primary process to remove an amount of composite material from the workpiece edge to achieve the desired workpiece with high precision. In edge surface grinding process, cutting forces in both feeding direction and in depth-of-cut direction (being perpendicular to feeding direction) play important roles in material removal. In addition, the understandings of material removal mechanisms will greatly benefit the modeling development of cutting forces and improve the RUM process. In this study, a mechanistic model based on both ductile mode and brittle mode is developed to predict cutting forces in both feeding and depth-of-cut directions for RUM edge surface grinding of CFRP composites. A series of experiments are conducted to verify this mechanistic model. The model prediction agrees well with the experimental results under different groups of input variables. Graphical abstract: Image, graphical abstract