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In production engineering, highest surface qualities and low tolerances are produced by grinding processes. Defects of the grinding wheel caused by unbalance, wrong dressing cycles, cavities, or waviness result in vibrations and damage of workpiece or spindle. Therefore, monitoring of the tool is important in practical use. In order to avoid scrap, small faults of the grinding wheel and very small amplitudes of the resulting vibrations are of major interest. This paper presents a new monitoring method, which estimates the defects recursively during the grinding process. Different sensors are analyzed using this method. Among sensors for academic purposes, typical sensors in grinding machines such as the displacement, acceleration, motor current, and acoustic emission are in use for monitoring. Compared to monitoring in the frequency domain and signal feature extraction by filtering, the recursive estimation can reduce noise. In cylindrical plunge grinding, wavy tool defects may result in unequal wear and self-exiting waviness of the wheel over a long period of grinding and start at very low amplitudes. This regenerative chatter is subject of monitoring of highest productivity of grinding processes and is therefore analyzed in this paper as well as the strongly depending selection of dressing cycles initiated by the monitoring algorithm.

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A simulation method for the continuous path controlled grinding process is described in this paper. The process model allows a virtual analysis of the process parameters, different input conditions and their influence concerning workpiece geometry. The machine structure is represented by a structural dynamic system. The dynamic influence of the machine axes is considered by its control system parameters. In this machine model, the positioning error of the axes can be calculated in relation to the reference position. This is used to compute the local engagement numerically. The process force is estimated by means of an empirical grinding force model. Hence, the overall process force can be calculated and fed back into the dynamic model. Closed-loop modeling allows to rate the influence of different parameters with respect to process errors. Finally, a parameter study and the model verification under real conditions are presented on the basis of the wheel head oscillating pin grinding process of crankshafts.