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

Journal article

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.

Authors: Uzun, F; Papadaki, C; Wang, Z; Korsunsky, AM

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Elsevier
• Journal: Mechanics of Materials
• Volume: 143
• Article number: 103316
• Publication date: 2020-01-07
• Acceptance date: 2020-01-06
• DOI: 10.1016/j.mechmat.2020.103316
• EISSN: 1872-7743
• ISSN: 0167-6636
• Language: English
• Keywords: residual strain; neutron strain scanning; Inconel alloys; artificial intelligence; reconstruction; eigenstrain contour method; FFR