Modeling of flow and debris ejection in blasting erosion arc machining in end milling mode

Abstract

Blasting erosion arc machining (BEAM) is a typical arc discharge machining technology that was developed around 2012 to improve the machinability of difficult-to-cut materials. End milling BEAM has been successfully developed and preliminarily applied in industry. However, owing to the high complexity of the flow field and the difficulty of observing debris in the discharge gap, studies of the flow and debris in end milling BEAM are limited. In this study, fluid dynamics simulations and particle tracking are used to investigate the flow characteristics and debris ejection processes in end milling BEAM. Firstly, the end milling BEAM mode is introduced. Then the numerical modeling parameters, geometric models, and simulation methods are presented in detail. Next, the flow distribution and debris ejection are described, analyzed, and discussed. The velocity and pressure distributions of the axial feed and radial feed are observed; the rotation speed and milling depth are found to have almost no effect on the flow velocity magnitude. Further, debris is ejected more rapidly in the radial feed than in the axial feed. The particle kinetic energy tends to increase with increasing milling depth, and smaller particles are more easily expelled from the flushing gap. This study attempts to reveal the flow field properties and debris ejection mechanism of end milling BEAM, which will be helpful in gaining a better understanding of BEAM.

Prediction Model of Sound Signal in High-Speed Milling of Wood–Plastic Composites

“…Therefore, establishing a high-precision prediction model of noise in the high-speed milling of WPCs and reducing the noise in high-speed machining by selecting appropriate cutting parameters are of great significance. Sampath et al [ 7 , 8 ] used a finite element model to predict the sound pressure generated during the high-speed end milling of aluminum alloy and found that cutting parameters and tool geometry parameters have important effects on noise. Karakurt et al [ 9 ] used multiple regression analyses to establish a noise model and found that cutting speed has the greatest influence on noise level among the cutting parameters.…”