Abstract
In the present paper, application of data fusion coupled with three electromagnetic nondestructive technologies (magnetic hysteresis loop, Barkhausen noise, and eddy current) has been explored to achieve higher accuracy and reliability of measurements. The ability of the proposed methodology has been evaluated for determining microstructural changes of D2 cold work tool steel during tempering with focus on decomposition of retained austenite and determination of this phase, quantitatively. The proposed data fusion method takes advantage of ordered weighted averaging aggregation operator and fuzzy defined interval to fuse the data produced by different experiments to obtain more accurate and reliable results. The fusion process also uses redundant information produced by applying different mapping functions modeled by neural networks. The results of the experiments revealed that applying data fusion to the outputs obtained from different experiments produces more accurate results compared to the situation that only outputs of one experiment are used.
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