基于粒子群优化模糊C焊缝等级磁记忆定量识别模型  

Quantitative metal magnetic memory classification model of weld grades based on particle swarm optimization fuzzy C-means

作　　者：邢海燕[1] 刘超 徐成[1] 陈玉环 王松弘泽 XING Hai-yan;LIU Chao;XU Cheng;CHEN Yu-huan;WANG Song-hong-ze(School of Mechanical Science and Engineering,Northeast Petroleum University,Daqing 163318,China)

机构地区：[1]东北石油大学机械科学与工程学院,黑龙江大庆163318

出　　处：《吉林大学学报（工学版）》2022年第3期525-532,共8页Journal of Jilin University:Engineering and Technology Edition

基　　金：黑龙江省自然科学基金联合引导项目(LH2019A004).

摘　　要：针对不同焊缝等级之间磁记忆特征参数的模糊性导致的定量等级识别困题的问题,提出了基于粒子群优化模糊C均值聚类算法(FCM)的焊缝等级定量识别模型。以Q235钢预制未焊透焊缝试件为试验材料,进行疲劳拉伸试验,采用TSC-5M-32型金属磁记忆检测仪进行磁记忆信号检测,提取三维合成特征参数向量作为实验数据,同时将磁记忆检测结果与X射线检测结果进行对比以提供对照依据。考虑到FCM初始聚类中心随机确定,易陷入局部最优,同时人为设置权值m易导致聚类精度不高,引入具有全局搜索和高效运算能力的粒子群算法,对FCM的初始聚类中心及权值m进行优化。将FCM目标函数倒数的修正公式作为粒子群算法的适应函数,样本个体及权值m作为粒子进行编码,通过更新粒子速度和位置,获得全局最优聚类中心并将m收敛到最优解,建立了基于粒子群优化FCM的焊缝等级定量识别模型。模型验证结果表明,模型分类正确率达97.93%,可为实际工程焊缝临界状态识别和设备安全定量评价提供新思路。Aiming at the difficulty of grade quantitative classification caused by the fuzziness of metal magnetic memory(MMM) characteristic parameters among different weld grades, a quantitative classification model based on particle swarm optimization fuzzy c-means clustering(FCM)is proposed.The fatigue tensile test was carried out on the Q235 steel weld specimen prefabricated by an incomplete penetration defect. The MMM signal was detected by the TSC-5 M-32 MMM instrument. The threedimensional composite characteristic parameter vector was extracted from the experimental data. At the same time,the MMM test results were compared with the X-ray test results to provide a reference.Considering that the initial clustering center of the FCM algorithm is determined randomly,it is easy to fall into the local optimum,and the artificial setting of weight m leads to low clustering accuracy. Particle swarm optimization(PSO)algorithm with global search and high efficiency is introduced to optimize the initial clustering center and weight m of the FCM algorithm. The modified reciprocal formula of the FCM objective function is used as the fitness function of the PSO algorithm,and the sample individuals and weight m are encoded as particles. The speed and position of the particle are updated to obtain the global optimal cluster center,and m is converged to the optimal solution. The quantitative MMM classification model based on the FCM clustering center and m optimized by PSO is established for different weld defect grades. The results show that the classification accuracy of the model is 97.93%,which provides a new idea for the quantitative identification of weld defect levels and evaluation of equipment safety.

关 键 词：磁记忆 焊缝等级 模糊C均值 粒子群算法 聚类中心 

分 类 号：TH13[机械工程—机械制造及自动化] TG44[金属学及工艺—焊接]