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Neutron strain scanning for experimental validation of the artificial intelligence based eigenstrain contour method

机译:基于人工智能的实验验证的中子应变扫描,基于人工智能的特征轮廓方法

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摘要

The demand for energy generation with low carbon emissions evoked the development of ultra-super critical technology that allows operating steam turbines at high temperature and pressure conditions. However, operating at extreme conditions necessitates careful consideration of structural integrity which is affected by residual stresses. Welding is used for joining of components of steam turbines, but this process causes the formation of residual stresses of complex form. Careful investigation is necessary to understand the distribution of potentially detrimental residual stress fields. Eigenstrain theory was previously used for the development of the artificial intelligence based eigenstrain (AI-eig) contour method that allowed advanced modelling of the behaviour of Inconel alloy 740H under thermo-mechanical loading conditions. Models created using this method are capable of evaluating the residual stress fields in the whole specimen or in the parts and slices created using electric discharge machining (EDM). In the previous applications of the AI-eig contour method, the determination of the distribution of eigenstrain in as-welded and heat-treated specimens was followed by the calculation of volumetric residual stresses. In this study, long- and short-transverse components of the residual strains determined by the AI-eig contour method applied to EDM-cut surfaces of the parts of as-welded and heat-treated specimens were validated using the neutron strain scanning method. The results demonstrate the effectiveness of the integrative modelling approach that enables the determination of eigenstrains in the whole specimen and the calculation of residual strains before and after the machining process.
机译:低碳排放对能源产生的需求引发了超超级关键技术的开发,其允许在高温和压力条件下操作汽轮机。然而,在极端条件下操作需要仔细考虑受残留应力影响的结构完整性。焊接用于连接蒸汽轮机的部件,但该过程导致形成复杂形式的残余应力。必须仔细调查,以了解潜在有害的残余应力场的分布。特征主义理论以前用于开发人工智能的基于特征(AI-EIG)轮廓方法,其在热机械负载条件下允许Inconel合金740h的行为进行高级建模。使用该方法创建的模型能够评估整个样本中的残余应力场或使用电气放电加工(EDM)产生的零件和切片。在先前的AI-EIG轮廓方法的应用中,在焊接和热处理的样品中测定特征串的分布,然后计算体积残余应力。在该研究中,使用中子应变扫描方法验证,通过施加到由焊接和热处理标本部件的EDM切割表面的AI-EIG轮廓方法确定的残留菌株的长横向分量。结果证明了整合建模方法的有效性,其能够在加工过程之前和之后测定整个样本中的特征率和计算残留菌株。

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