Quantitative study on weak magnetic detection defects of metal structure based on IWOA-BP algorithm

Metal structures are widely used in industry. Metal structures in service are prone to crack defects under tensile and compressive fatigue load.In order to realize quantitative detection of metal structures’ crack defects, a quantitative analysis method of metal structures’ weak magnetic detection based on back propagation (BP) neural network was studied. In view of the poor effect and low efficiency of BP neural network in parameter adjustment, the improved whale optimization algorithm (IWOA) based on Sine chaotic mapping was adopted to optimize the BP neural network parameter adjustment mode, giving consideration to global optimization while improving the local optimization ability, and then the optimal parameters searched by IWOA were assigned to BP neural network, improving the quality of initial network parameters.The length, width and depth of the artificial rectangular slot were quantified by inversion. The results show that the average prediction accuracy of IWOA-BP neural network is above 80%, and the prediction accuracy of depth, length and width is improved respectively by 106.72%, 9.68% and 6.86%.