Abstract:

In order to solve the difficulty in quantitatively predicting the evolution of hidden damage for welded joints by applying metal magnetic memory (MMM) technology, a quantitative MMM prediction model is presented based on unbiased gray theory and fuzzy weighted Markov theory. Steel Q235 welded plate specimens are tested by TSC-4M-8 MMM Instrument. By contrasting the result of X ray synchronous detection, the distribution of MMM tangential and normal signals are obtained. When the macroscopic defects come into being, the signals meet the conventional MMM criterion which the tangential signalHp(x) occurs peak value and the normal signalHp(y) has zero value. However, at the stage of hidden damage, the signals don’t conform to the traditional MMM criteria, which means thatHp(x) andHp(y) can’t accurately judge the appearance and degree of hidden damage. For this reason, the ratio of orthogonal gradient vector sum (K_r) is introduced, which can more sensitively reflect the existence of the hidden damage. Based on unbiased gray fuzzy weighted Markov chain, the quantitative MMM prediction model is established. The results show the maximum relative error of the model is 5.046 4% decreased from 38.492 5%, which provides a novel way for the quantitative prediction of the future development of the weld hidden damage and the maintenance strategy selection in practical engineering.

Key words: fuzzy membership, Markov chain, MMM, unbiased gray, hidden damage