Prediction of residual stresses in girth welded pipes using an artificial neural network approach

Management of operating nuclear power plants greatly relies on structural integrity assessments for safety critical pressure vessels and piping components. In the present work, residual stress profiles of girth welded austenitic stainless steel pipes are characterised using an artificial neural network approach. The network has been trained using residual stress data acquired from experimental measurements found in literature. The neural network predictions are validated using experimental measurements undertaken using neutron diffraction and the contour method. The approach can be used to predict through-wall distribution of residual stresses over a wide range of pipe geometries and welding parameters thereby finding potential applications in structural integrity assessment of austenitic stainless steel girth welds

Creep Crack Growth Modeling of Low Alloy Steel using Artificial Neural Network

Abstract: Prediction of crack growth under creep condition is prime requirement in order to avoid costly and timeconsuming creep crack growth tests. To predict, in a reliable way, the growth of a major crack in a structural components operating at high temperatures, requires a fracture mechanics based approach. In this Study a novel technique, which uses Finite Element Method (FEM) together with Artificial Neural Networks (ANN) has been developed to predict the fracture mechanics parameter (C*) in a 1%Cr1%MoV low alloy rotor steel under wide range of loading and temperatures. After confirming the validity of the FEM model with experimental data, a collection of numerical and experimental data has been used for training the various neural networks models. Three networks have been used to simulate the process, the perceptron multilayer network with tangent transfer function that uses 9 neurons in the hidden layer, gives the best results. Finally, for validation three case studies at 538°C, 550°C and 594°C temperatures are employed. The proposed model has proved that a combinations of ANN and FEM simulation performs well in estimation of C* and it is a powerful designing tool for creep crack growth characterization

Weld Residual Stress Profiles for Structural Integrity Assessment

Economic and safe management of operating nuclear power plants can be highly dependent on the structural integrity assessments for safety critical pressure vessels and piping components. In engineering fracture assessment procedures the full (3-D) residual stress field at a welded joint is usually simplified by considering a representative onedimensional profile through the wall-thickness of the stress tensor component acting normal to the crack face. The stress intensity factor, calculated from this estimated through-thickness stress profile, is used directly in the fracture assessment. Therefore, assessments of defects in welds can be highly sensitive to the through-thickness residual stress profiles assumed in the calculations. There is a need for reliable characterisation of residual stresses in welded structures such as in stainless steel girth welded pipes as there are a lot of discrepancies in the current methodologies used. For example, bounding residual profiles found in fitness for service assessment procedures have been based on examination of residual stress measurements, finite element weld simulation and expert judgment. This approach suffers from the drawback that the upper bound curve can increase as more measurements and data scatter are obtained. The consequence of this is that structural integrity assessments of defective plant can be over-conservative by a large margin, and may lead to unnecessary and costly repair or inspection. This thesis illustrates how a neural network model, can be developed and applied to predict through-thickness residual stress profiles in austenitic stainless steel pipe girth welds for simplified fracture assessments. The model is validated by comparing predictions with new experimental measurements made using neutron diffraction and contour method. The new measurements were undertaken by fabricating six pipe girth welds with a range of wall-thickness, weld heat input and weld groove geometries. The robustness of the developed artificial neural network (ANN) approach is demonstrated by sensitivity studies in input variables and training data. The performance and suitability of the ANN approach is discussed by comparison with stress profiles recommended in defect assessment procedures. This is followed by an evaluation of whether the use of neural network bounding profiles can lead to non-conservative estimates of stress intensity factor in fracture assessments. The neural network approach shows sufficient potential to be developed into an alternative prediction tool for use in fracture assessment of welded components

NASA Tech Briefs, December 1991

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