Characterizing residual stress on pipework weld repairs using machine learning

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Nuclear power plant systems are constructed by joining pressure vessels and pipingcomponents using modern welding processes. Welded structures may also be subjected to weld repair eitherduring fabrication to mitigate manufacturing defects, or during service to maintain the original design life, orto provide life extension. The PhD project will review the latest developments in data mining and machine learningmethods, identify baseline parameters controlling residual stresses at weld repairs, collect high qualitytraining data using data mining methods, develop/optimise a suitable machine learning tool, train the toolusing the mined data, validate the outputs against independent measurements, and create residual stresscharacterisation tool with a user-friendly interface for engineers in industry.A major limitation in using ANN for prediction of residual stress in weldments is the shortage of high qualitymeasurement data for training. The innovation and challenge of the proposed PhD project will be to exploitthe opportunity of combining measurement results and synthetic data from validated simulations in orderto predict/estimate residual stress profiles in families of repair weldments (that lie within the training dataparameter envelope).