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In this work, the Artificial Neural Network (ANN) was used to model ferroelectric hysteresis using data measured from soft lead zirconate titanate [Pb (Zr1−xTix)O3 or PZT] ceramics as an application. Data from experiments were split into training, testing and validation dataset. Four ANN models were developed separately to predict output of the hysteresis area, remnant, coercivity and squareness. Each model has two neurons in the input layer, which represent field amplitude and field frequency. The ANNs were trained with varying number of hidden layer and number of neurons in each layer to find the best network architecture with highest accuracy. After the networks have been trained, they were used to predict hysteresis properties of the unseen testing patterns of input. The predicted and the testing data were found to match very well which suggests the ANN success in modeling ferroelectric hysteresis properties obtained from experiments.

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Abstract. Coordinate measuring machine (CMM) is a device used to measure physical geometry of object. It has been used widely in industry for quality control and monitoring of production process due to its high accuracy and precision. Measurement accuracy is very crucial in quality inspection as measurement result is used to decide if the product should become defect. CMM accuracy depends on several factors such as probe size, number of touching point and measurement speed. This research applied design of experiment technique to optimize setting of those parameters to obtain high measurement accuracy. A case study from swage hole measurement of hard disk drive actuator arm was use to demonstrate the proposed technique. The result showed that CMM accuracy of the case study company was significantly increased after CMM has been set with parameters optimized with design of experiment.

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