Quantitative Metal Magnetic Memory Reliability Modeling for Welded Joints

• Published in: Chinese journal of mechanical engineering Vol. 29; no. 2; pp. 372 - 377
• Main Authors: Xing, Haiyan, Dang, Yongbin, Wang, Ben, Leng, Jiancheng
• Format: Journal Article
• Language: English
• Published: Beijing Chinese Mechanical Engineering Society 01.03.2016; Springer Nature B.V; School of Mechanical Science and Engineering, Northeast Petroleum University, Daqing 163318, China
• Edition: English ed.
• Subjects: Dispersion; Electrical Machines and Networks; Electronics and Microelectronics; Engineering; Engineering Thermodynamics; Fatigue tests; Gaussian distribution; Heat and Mass Transfer; Instrumentation; Killed steels; Machines; Manufacturing; Measurement and Fault Diagnosis; Mechanical Engineering; Normal distribution; Power Electronics; Processes; Quantitative analysis; Reliability analysis; Reliability engineering; Statistical analysis; Theoretical and Applied Mechanics; Welded joints; Welding parameters; X射线检测; 可靠性模型; 定量建模; 拉伸疲劳试验; 损伤程度; 测量噪声; 焊接接头; 金属磁记忆检测; welded joints; quantitative reliability modeling; metal magnetic memory
• ISSN: 1000-9345; 2192-8258
• DOI: 10.3901/CJME.2015.1119.136

Metal magnetic memory（MMM） testing has been widely used to detect welded joints. However, load levels, environmental magnetic field, and measurement noises make the MMM data dispersive and bring difficulty to quantitative evaluation. In order to promote the development of quantitative MMM reliability assessment, a new MMM model is presented for welded joints. Steel Q235 welded specimens are tested along the longitudinal and horizontal lines by TSC-2M-8 instrument in the tensile fatigue experiments. The X-ray testing is carried out synchronously to verify the MMM results. It is found that MMM testing can detect the hidden crack earlier than X-ray testing. Moreover, the MMM gradient vector sum K_（vs） is sensitive to the damage degree, especially at early and hidden damage stages. Considering the dispersion of MMM data, the K_（vs） statistical law is investigated, which shows that K_（vs） obeys Gaussian distribution. So K_（vs） is the suitable MMM parameter to establish reliability model of welded joints. At last, the original quantitative MMM reliability model is first presented based on the improved stress strength interference theory. It is shown that the reliability degree R gradually decreases with the decreasing of the residual life ratio T, and the maximal error between prediction reliability degree R_1 and verification reliability degree R_2 is 9.15%. This presented method provides a novel tool of reliability testing and evaluating in practical engineering for welded joints.