可靠性(半导体)
管道
无损检测
耐久性
可靠性工程
有限元法
蠕动
对接接头
接头(建筑物)
超声波检测
法律工程学
结构工程
材料科学
机械工程
压力测试(软件)
工程类
压力(语言学)
材料试验
计算机科学
伸缩缝
验收试验
航空航天
故障评估
传感器融合
温度循环
作者
Chuancheng Qin,Zhenyu Gao,Lukuan Qiao,Junming Fan,Jiongming Wen,Y. M. Zhang,Ben Jar
标识
DOI:10.1177/14644207261418470
摘要
This paper comprehensively reviews existing research on the reliability assessment of butt fusion joints in polyethylene (PE) pipes, with a specific focus on defective joints. It examines the types and root causes of defects prevalent in these joints, highlighting the key factors contributing to their formation. Furthermore, the correlations between various defect types and failure mechanisms in butt fusion joints are investigated. The review systematically evaluates the application of mechanical experimental characterization (e.g., tensile, impact, creep testing), finite element analysis (FEA), and non-destructive testing (NDT) methods for assessing the impact of defects on joint performance. Key findings indicate that defects such as cold welds, inclusions, and holes significantly reduce mechanical strength, accelerate crack initiation and failure, and shorten service life. While mechanical testing quantifies defect impacts, the absence of standardized criteria limits its broader applicability. FEA effectively simulates stress distribution and failure modes but depends on high-fidelity material models. Among NDT techniques, ultrasonic phased array detects structural defects (e.g., holes) effectively but struggles with process-induced flaws (e.g., cold welds); microwave technology addresses this gap by exploiting dielectric property variations. This study aims to advance the development of a standardized defect assessment framework for butt fusion joints, thereby enhancing joint reliability and ensuring the long-term safety and durability of PE piping systems across diverse engineering applications. Future research should prioritize establishing standardized defect classification protocols, developing automated NDT analysis techniques, and refining long-term performance prediction models to further improve the safety and longevity of PE pipelines.
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