There have been few investigations dealing with the force model on grinding brittle materials. However, the dynamic material removal mechanisms have not yet been sufficiently explicated through the grain–workpiece interaction statuses while considering the brittle material characteristics. This paper proposes an improved grinding force model for Zerodur, which contains ductile removal force, brittle removal force, and frictional force, corresponding to the ductile and brittle material removal phases, as well as the friction process, respectively. The critical uncut chip thickness $a_{gc}$ of brittle–ductile transition and the maximum uncut chip thickness $a_{gmax}$ of a single abrasive grain are calculated to identify the specified material removal mode, while the comparative result between $a_{gmax}$ and $a_{gc}$ can be applied to determine the selection of effective grinding force components. Subsequently, indentation fracture tests are carried out to acquire accurate material mechanical properties of Zerodur in establishing the brittle removal force model. Then, the experiments were conducted to derive the coefficients in the grinding force prediction model. Simulated through this model, correlations between the grinding force and grinding parameters can be predicted. Finally, three groups of grinding experiments are carried out to validate the mathematical grinding force model. The experimental results indicate that the improved model is capable of predicting the realistic grinding force accurately with the relative mean errors of 6.04% to the normal grinding force and 7.22% to the tangential grinding force, respectively.

As a key component of a high-power laser device, fused silica optics needs to bear great laser energy, and laser damage is easily generated on the optical surface. In order to improve the service life and availability of optics, it is necessary to repair the damaged optics. In this work, the repair technique of damaged, fused silica optics was studied. The neural network method was mainly used to establish the correlation between the number of small-scale damage points and the repair depth. The prediction accuracy of the model is better than 90%. Based on the neural network model, the removal depth parameters were optimized with the suppression coefficient of the damage points. The processing effect of the optimized parameters was verified by magnetorheological polishing experiments. In this paper, a repair technique based on a neural network was proposed, which avoids the low efficiency caused by processing iterations in the repair process, and can accurately what was expected. The method proposed in this work has an important reference value in the repair process of fused silica optics.