会员体验
专利管家(专利管理)
工作空间(专利管理)
风险监控(情报监控)
数据分析(专利分析)
侵权分析(诉讼无效)
联系我们
交流群
官方交流:
QQ群: 891211   
微信请扫码    >>>
现在联系顾问~
热词
    • 21. 发明专利
    • Method and system for classifying type and severity of defect in welded part
    • 焊接部分缺陷类型和严重程度的方法和系统
    • JP2011033627A
    • 2011-02-17
    • JP2010173705
    • 2010-08-02
    • Georgia Tech Research Corpジョージア テク リサーチ コーポレイション
    • UME IFEANYI CHARLESLI RENFUROGGE MATTHEWWU TSUN-YEN
    • G01N29/00B23K31/00G01N29/04G06N3/00
    • G01N29/4445G01N29/11G01N29/4481G01N2291/0258G01N2291/0427G01N2291/2675
    • PROBLEM TO BE SOLVED: To provide a method and system for detecting a defect in a weld to determine the type and seriousness of the detected defect. SOLUTION: In the method for determining the type of a defect in a weld, the ultrasonic response signals gathered from a plurality of the measuring places set along the weld are analyzed to determine a defect position. The defect signal corresponding thereto and a plurality of the defect approach signals corresponding to the defect signal and the ultrasonic response signals from the measuring places on both sides of the defect position, are inputted to an educated artificial neural network. The educated artificial neural network discriminates the type of the defect positioned in the defect position on the basis of the defect signal and a plurality of the defect approach signals to output the type of the defect positioned in the defect position. The educated artificial neural network determines the classification of the degree of the defect on the basis of the defect signal and a plurality of the defect approach signals, and outputs them. COPYRIGHT: (C)2011,JPO&INPIT
    • 要解决的问题:提供一种用于检测焊缝缺陷的方法和系统,以确定检测到的缺陷的类型和严重性。 解决方案:在用于确定焊缝中的缺陷类型的方法中,分析从沿着焊缝设置的多个测量位置收集的超声响应信号,以确定缺陷位置。 与缺陷信号相对应的缺陷信号和与缺陷信号对应的多个缺陷接近信号和来自缺陷位置两侧的测量位置的超声响应信号输入到受过教育的人造神经网络。 受过教育的人造神经网络基于缺陷信号和多个缺陷接近信号来区分位于缺陷位置的缺陷的类型,以输出位于缺陷位置的缺陷的类型。 受过教育的人造神经网络基于缺陷信号和多个缺陷接近信号来确定缺陷程度的分类,并输出它们。 版权所有(C)2011,JPO&INPIT