System for recognizing tensile damage state of 16Mn steel force-bearing part by adopting neural netw
The invention discloses a system for recognizing the tensile damage state of a 16Mn steel force-bearing part by adopting a neural network and coupling iteration, and the system comprises a plurality of acoustic emission transducers (4), a multipath preamplifier (3), an acoustic emission device (2) and a 16Mn steel force-bearing part nondestructive testing unit (1). The 16Mn steel force-bearing part nondestructive testing unit (1) comprises a filtering module (11) and a data fusion module (12) and is embedded in the memory of the acoustic emission device (2). The tensile damage state of the 16Mn steel is recognized by the acoustic emission neural network and the data fusion model, and the system for recognizing and diagnosing the tensile damage state of the 16Mn steel is established: firstly, a damage degree mark model is established by the neural network, and then the model is used for diagnosing the information of each acoustic emission transducer partially; then an elementary probability value of the data fusion module is constructed by the output result of the neural network; and at last, the tensile damage state is diagnosed by adopting a combinational formula of data fusion. By adopting the model, the damage state of the 16Mn steel in the tensile process is recognized and diagnosed, thus providing basis for reliable running.
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