Online force control of large optical grinding machine for brittle materials assisted by force prediction

Abstract

Trajectory planning of aspherical surfaces with appropriate cutting parameters is always a tedious task, especially on difficult-to-grind materials. Orthogonal experiments are usually designed and conducted first to get a full estimation of forces under different sets of grinding conditions (e.g. depth of cut and feeding velocity). However, all these data will change, as the grinding wheel becomes blunt. To reduce the work on the selection of grinding parameters and keep the grinding process stable, a new force-controlled grinding strategy for large optical grinding machine on brittle material is proposed. The grinding force is controlled by adjusting feeding velocity along the trajectories in real time. The grinding force model is established by analyzing the complex contact area between the arc-shaped wheel and the workpiece. The co-existing of brittle and ductile removal is also considered. For the longtime delay of the system, the controller foresees the grinding force in 0.4 s later based on the model proposed, to prevent the large overshoot of the force (up to 87.5%). The verification of the controller was conducted on silicon carbide ceramics. The force overshoot was reduced to 22.5%, and the motion accuracy was guaranteed by position servo within 5 µm. The subsurface damage along the trajectory was further analyzed and discussed.

Static numerical simulation of radial deformation of ultra-thin dicing blade

“…The grinding force is usually calculated by the sum of all the individual grain forces in grinding area. 26 Here, for the convenience of handling, this discrete grinding force model is simplified as continuous.…”

Tracking changes in grinding wheel condition using reduced features of acoustic emission signals

“…In order to avoid unpredictable catastrophic damages on and beneath the ground surface, consistent grinding wheel condition is necessary. [23][24][25] Fine classification or regression well-trained models are competent for the purpose. But the modeling is hard to be realized, not only for the difficulty of acquiring sufficient training data, but also for the difficulty of precisely labeling.…”