Hardened-depth Forecasting System Based On Artificial Intelligence

Nondestructive testing of metal hardened-depth is an important problem to be solved in machinery industry. Eddy current is an effective method in which the most difficult task is the processing of the signal. Since the penetrative depth and the testing sensitiveness are influenced seriously with the testing frequency. So multi-frequency eddy current is used in place of single-frequency eddy current to involve full information of the objects.Data fusion is a method to combine the redundant and complemented data of the multi-source by some rules. As a result, the consistency interpretation and description is obtained. This paper presents a method to fusion the signal of the eight-frequency eddy current. A fuzzy-genetic data fusion algorithm is utilized. The reasoning is performed by means of fuzzy aggregation functions, the fuzzy aggregation being capable of better set theories. The parameters of the connectives are found by genetic algorithms. The distinctive feature of this algorithm is its capability of fusing data in a near-optimal manner when there exists no information about the reliability of information sources, the degree of redundancy/ complementarities of the information sources, and the structure of the hierarchy.At the end, an example is given to test the reliability of the forecasting system.