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

Prediction of machining induced residual stresses in aluminium alloys using a hierarchical data-driven fuzzy modelling approach

The residual stresses created during shaping and machining play an important role in determining the integrity and durability of metal components. An important aspect of making safety critical components is to determine the machining parameters that create compressive surface stresses, or at least minimise tensile surface stresses. These machining parameters are usually found by trial and error experimentation backed up by limited numerical modelling using Finite Element Methods (FEM) and guided by expert knowledge. The shortcomings of FEM approaches are the length of time needed for the solution of complex models and the inability to learn from data. To solve these problems, a fuzzy modelling approach is presented in this paper and is shown to be successful in modelling machining induced residual stresses