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Application of Data Fusion Methods for Nondestructive Assessment of Hardness of D2 Tool Steels

Data fusion methods are widely used in different application areas especially in decision making. In nondestructive detection of hardness which is one of the most important features of cold work tool steels, these methods can effectively be used to increase accuracy of measurements. In this paper, two data fusion methods have been designed to apply on magnetic Barkhausen noise (MBN) method for determining hardness of D2 tool steel nondestructively and compared with other method of decision making. In the proposed fusion methods, different learners are trained to estimate hardness of the objective materials based on the MBN outputs (height, position and width of the peaks). These learners are neural networks with different structures. The decision of the learners for estimating hardness is fused in a fusion process to make final decision about hardness of the material. The experimental results show that data fusion methods applied on MBN outputs have more effectiveness in comparison with other one for assessment of hardness variations of AISI D2 tempered steel.