Title: Quantitative testing of micro-cracks by the MFL technique based on GA-BP neural network

Authors: Zhongchao Qiu; Ruilei Zhang; Weimin Zhang

Addresses: School of Mechanical and Electrical Engineering, Shandong Management University, Jinan, 250100, China ' School of Mechanical and Electrical Engineering, Shandong Management University, Jinan, 250100, China ' School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China

Abstract: Magnetic flux leakage (MFL) testing is one of the traditional electromagnetic non-destructive test (NDT) techniques, and the focus of the MFL technique is to predict the sizes of defects, particularly micro-cracks. In this paper, parameters identification of artificial rectangular micro-cracks ranging between 0.1-0.3 mm by the MFL technique is investigated with a BP neural network improved by a genetic algorithm (GA-BP neural network). In order to predict the sizes of artificial rectangular micro-cracks ranging between 0.1-0.3 mm, a MFL system based on anisotropic magneto-resistive (AMR) sensors is developed, and parameters identification is implemented with a GABP neural network. The results show that parameters identification of artificial rectangular micro-cracks can be implemented effectively with the developed MFL system and a GA-BP neural network, which provides a basis for predicting the sizes of the natural cracks. [Received 19 July 2016; Accepted 16 November 2016]

Keywords: MFL technique; parameters identification; GA-BP neural network; artificial rectangular micro-crack.

DOI: 10.1504/IJMR.2017.085417

International Journal of Manufacturing Research, 2017 Vol.12 No.2, pp.165 - 176

Received: 19 Jul 2016
Accepted: 20 Nov 2016
Published online: 25 Jul 2017 *

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