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Typical grinding operations in batch production are characterized by multiple data streams sampled at distinct intervals. A unique estimation strategy is proposed for integrating rapidly sampled sensor signals with postprocess inspection data from a series of grinding cycles. After a nonlinear state-space model is derived from existing analytical models, system observability is tested for various combinations of sensors and measurement settings. A multirate simultaneous state and parameter estimation scheme is developed based on extended Kalman filters for real-time estimation of the model parameters and part quality. Results from case studies demonstrate that the proposed scheme enables challenging estimation tasks to be undertaken that cannot be performed using traditional approaches.