基于多层级LSTM的铝板缺陷检测

ALUMINUM PLATE DEFECT DETECTION BASED ON MULTILEVEL LSTM

铝板因其优良的抗疲劳性和延展性广泛应用于航空航天和建筑等领域.然而在生产过程中,由于外部环境、操作工艺等的限制往往会产生各种各样的缺陷,从而影响其力学性能,例如会降低铝材料的强度、延展性和韧性等,导致其使用寿命的缩短.在单次发射和接收超声波的情况下,论文提出了多层级长短期记忆(long short-term memory, LSTM)神经网络的方法,用于辅助检测铝板的夹杂(气泡)缺陷.利用有限元软件COMSOL Multiphysics模拟含有夹杂缺陷的铝板中超声波的传播过程,导出含有缺陷信息的波形数据,通过训练波形数据,得到可以反映波形数据与夹杂缺陷大小和位置关系的网络模型.此外,该模型采用硬投票的方法以缓解网络训练过程中繁杂的参数调整问题,提高了检测结果的可靠性.结果表明:夹杂缺陷半径检测的准确率超过了98%,夹杂缺陷深度检测的准确率达到1,夹杂缺陷横坐标位置检测的准确率超过95%.为LSTM神经网络应用于超声无损检测提供了借鉴.

Aluminum plates are widely used in fields such as aviation,aerospace,and construction due to their excellent fatigue resistance and ductility.However,during the production process,various defects can occur due to external environmental factors,operating processes and so on.These defects can affect the mechanical properties of aluminum plates,such as reducing their strength,ductility,and toughness,which leads to a shortened service life.This article proposes a method of using a multilevel long short-term memory(LSTM)neural network for assisting the detection of inclusions(void defects)in aluminum plates under the condition of single transmission and reception of ultrasonic waves.The propagation of ultrasonic waves in an aluminum plate containing inclusion defects is simulated using the finite element software COMSOL Multiphysics.Waveform data containing defect information is then derived.By training the waveform data,a neural network model reflecting the relationship between waveform data and the size and location of inclusions is obtained.In addition,we adopt a hard voting method to alleviate the problem of complex parameter adjustment during network training and improve the reliability of the detection results.The results show that the accuracy of radius detection for inclusion defects exceeds 98%,the accuracy of depth detection for inclusion defects reaches 1,and the accuracy of horizontal position detection for inclusion defects exceeds 95%.It provides reference for the application of LSTM neural network in ultrasonic non-destructive testing.