Abstract
Silicon nitride ceramics is widely used in aerospace, automotive industry and other fields because it has some advantages, such as high strength, high temperature oxidation resistance and simple to preparation. However, silicon nitride ceramics is one of the typical hard and brittle materials, and its high hardness and brittleness lead to serious subsurface damage in the ordinary grinding process, which seriously reduces the workpiece performance. Ultrasonic assisted grinding is considered as state of the art machining process for brittle and hard to machining materials such as ceramics and optical glasses. The work aims to grasp the influence of the process parameters during longitudinal torsional ultrasonic assisted grinding on the subsurface damage of silicon nitride ceramics. In this study, a theoretical subsurface damage model was proposed with the consideration of the ductile-to-brittle transition removal mechanism and random distribution of wear particles in ultrasonic assisted grinding of ceramics. Firstly, the cutting trajectory and cutting arc length model of a single abrasive particle during longitudinal torsional ultrasonic vibration were established. The unique machining mechanism of longitudinal-torsional ultrasonic assisted grinding was analyzed. Secondly, based on the brittle-plastic transition characteristics of brittle materials and the definition of its critical angle, the probabilistic model of the undeformed chip thickness of a single abrasive particle in longitudinal-torsional ultrasonic assisted grinding was given with considering the stochastic distribution nature of the grits on the surface of the grinding wheel. Then the average normal grinding force model of a single abrasive particle in the process of longitudinal-torsional ultrasonic assisted grinding was established by combination of grinding force in plastic removal stage and brittle removal stage. The parameter k was introduced to represent the influence of overlapping and intersection between different diamond grits. Finally, a model of subsurface damage depth in longitudinal torsional ultrasonic assisted grinding of silicon nitride was established and verified by experiments. The analytical results indicated that longitudinal torsional ultrasonic assisted grinding can reduce the depth of subsurface damage and obtain better surface quality of silicon nitride ceramics by increasing the cutting arc length of a single abrasive particle, reducing the average undeformed chip thickness of a single abrasive particle and reducing the normal grinding force of a single abrasive particle. The subsurface damage of silicon nitride ceramics decreased with the increase of ultrasonic amplitude. When the ultrasonic amplitude was 6 μm, the subsurface damage depth was 5.65 μm. Compared with ordinary grinding, longitudinal-torsional ultrasonic assisted grinding can reduce the subsurface damage depth of silicon nitride by 33.6%. In addition, with the same ultrasonic amplitude, the ability of longitudinal-torsional ultrasonic vibration to reduce the subsurface damage depth increased with the increase of grinding depth and feed speed, and decreased with the increase of rotating speed. The predicted results of the theoretical model were consistent with the experimental results, with the maximum error of 13.38% and the average error of 8.34%. Therefore, it can provide some reference for the prediction of subsurface damage depth in the actual machining of silicon nitride. Longitudinal torsional ultrasonic grinding can effectively reduce the depth of subsurface damage on the machined surface of silicon nitride ceramics, and then improve the service performance of silicon nitride ceramics.
Key words
longitudinal torsional ultrasonic grinding; silicon nitride; subsurface damage; average undeformed chip thickness; grinding; brittle-plastic transition
Cite this article
Download Citations
YAN Yan-yan, MA Qian-li, ZHANG Ya-fei, QIN Fei-yue, ZHAO Bo.
Subsurface Damage and Experiment of Silicon Nitride by Longitudinal Torsional Ultrasonic Assisted Grinding[J]. Surface Technology. 2023, 52(2): 55-66
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}