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Through-Thickness Residual Stress Profiles in Austenitic Stainless Steel Welds: A Combined Experimental and Prediction Study

机译:奥氏体不锈钢焊缝中的贯穿厚度残余应力型材:一种综合实验和预测研究

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摘要

Economic and safe management of nuclear plant components relies on accurate prediction of welding-induced residual stresses. In this study, the distribution of residual stress through the thickness of austenitic stainless steel welds has been measured using neutron diffraction and the contour method. The measured data are used to validate residual stress profiles predicted by an artificial neural network approach (ANN) as a function of welding heat input and geometry. Maximum tensile stresses with magnitude close to the yield strength of the material were observed near the weld cap in both axial and hoop direction of the welds. Significant scatter of more than 200 MPa was found within the residual stress measurements at the weld center line and are associated with the geometry and welding conditions of individual weld passes. The ANN prediction is developed in an attempt to effectively quantify this phenomenon of ‘innate scatter’ and to learn the non-linear patterns in the weld residual stress profiles. Furthermore, the efficacy of the ANN method for defining through-thickness residual stress profiles in welds for application in structural integrity assessments is evaluated.
机译:核电站组件的经济和安全管理依赖于焊接诱导的残余应力的准确预测。在该研究中,使用中子衍射和轮廓法测量了通过奥氏体不锈钢焊缝厚度的残余应力分布。测量数据用于验证由人工神经网络方法(ANN)预测的残余应力分布,作为焊接热输入和几何形状的函数。在焊缝的轴向和箍方向的焊接帽附近观察到靠近材料的屈服强度的幅度靠近屈服强度的最大拉伸应力。在焊接中心线的残余应力测量内发现了大于200mPa的大于200mP的显着散射,并且与单个焊接通行证的几何形状和焊接条件相关联。在试图有效地量化了“先天散射”的尝试并学习焊接残余应力分布中的非线性图案来实现ANN预测。此外,评估用于在结构完整性评估中焊接焊缝中焊缝中的通孔残留应力分布的ANN方法的功效。

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