Abstract
Based on experimental data and extensive experience, magnetic coercivity and saturation moment are traditionally used to estimate the microstructure and quality of cemented carbides, especially in the manufacturing industry. This work demonstrates that predictions of the structural and mechanical properties of manufactured WC-Co elements can be derived in principle from magnetic data alone using an artificial neural network (ANN). A collection of WC-Co pellet samples with a wide variety of powder compositions and processing parameters was produced to cover a wide range of characteristic features for ANN training. The total field distribution, extracted from first-order-reversal-curves, serves as input data for the ANN. Microstructural parameters such as mean grain size and mechanical properties such as hardness and fracture toughness can be derived from the purely magnetic measurements with high accuracy, while the transverse rupture strength shows large errors and cannot be predicted.
Published Version
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