Semisolid metal alloys, used in thixoforming, are thixotropic. The slurry viscosity is shear-rate-and time dependent provided the microstructure in the semisolid state is nondendritic and consists of solid spheroids surrounded by liquid. Thixoforming takes less than 1 second, during which time the structure of the material breaks down. This breakdown can be studied by viscometry, using rapid data-collection rates, by imposing rapid increases in shear rate on the slurry. An initial rapid (Ͻ 1 second) structural breakdown during a shear-rate jump is followed by a more gradual decrease in viscosity, lasting several minutes. The slurry breaks down more rapidly with a higher final shear rate, but the first breakdown time is independent of the initial shear rate. The reverse is found with the shear-rate drops: recovery times increase with increasing final shear rate. Again, this time is independent of the starting shear rate. The shear stress and viscosity during initial breakdown and initial recovery can be described by single exponential equations. Build-up processes were examined by performing jumps after allowing the slurry to rest at a zero shear rate for different times. The increase in peak stress with rest time reflects the evolving degree of particle agglomeration. Microstructural examination confirms this phenomenon.

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To eliminate the influence of parameter mismatch for fundamental model based sensorless methods, an effective online position error correction method is proposed for permanent magnet synchronous machines in this paper. Based on the derived position error mechanism, i.e. the error varies proportionally to the dq-axis currents, the proposed method injects a sinusoidal current signal with a small amplitude and low frequency into the d-or q-axis current for a short period. During injection, the corresponding sinusoidal response for current injection can be acquired from the estimated speed of the sensorless position observer. It is found that the amplitude of the response in the estimated speed decreases as the parameter mismatch reduces, and eventually reaches a minimum if there is no parameter mismatch. Thus, by applying the least mean square (LMS) algorithm, the amplitude of the response in the estimated speed can be minimised as the parameters are adaptively adjusted to the actual values, and then the position error can be corrected. The proposed method is validated through experiments on a permanent magnet generator drive system.