Surface prediction in laser-assisted grinding process considering temperature-dependent mechanical properties of zirconia ceramic

Zirconia ceramics are widely applied in many advanced applications in various fields, including biology, aerospace, manufacturing, owing to its outstanding biocompatibility, excellent wear and chemical resistance, and improved toughness over traditional ceramics. Laser-assisted grinding (LAG) is considered as a potential hybrid machining process for low-damage machining of hard and brittle materials (HBMs). In the meanwhile, surface topography and roughness are important indicators to evaluate the surface integrity during the grinding operation. In this study, a grinding wheel model considering stochastic process is established. The thermal distribution of laser irradiation is calculated, and thus the theory for calculation of the critical depth of plastic region (d(c)) under the influence of laser, which also considers the changes of temperature-dependent mechanical properties is described systematically. Based on the aforementioned models, the predictive model of the surface topography for LAG process was proposed. Subsequently, a series of experiments were designed and implemented. The results indicate that the experimental results have a good agreement with that of the simulated surface topography. The error rates of surface roughness Rz and Ra between the simulated and experimental results are both < 8 %. In the meantime, as the laser power increases, plastic mode starts to dominate material removal, and thus the surface integrity is obviously improved. The depth of median crack is also significantly reduced, which can help to reduce the sub-surface damage. Lastly, average power density (I-a(ve)) is defined to evaluate the selection of machining parameters.

Automated summary

Zirconia ceramics are widely used in various fields due to their outstanding biocompatibility, wear resistance, chemical resistance, and improved toughness. Laser-assisted grinding (LAG) is a potential machining process for hard and brittle materials. This study establishes a grinding wheel model considering stochastic process and proposes a predictive model for surface topography in LAG process. Experiments show that increasing laser power improves surface integrity and reduces sub-surface damage. The average power density is defined as an indicator for evaluating machining parameters.