Magnetically aided AI-Driven Optimization of steel Pipeline manufacturing

This project aims to develop a magnetically aided, AI-driven workflow for optimising steel pipeline manufacturing and predicting structural failure from the earliest production stages. The proposed approach uses magnetic characterisation to identify stress concentrations and early-stage defects before visible damage forms. In ferromagnetic steels, pores, cracks, and highly stressed regions contain increased dislocation densities that hinder magnetic domain movement, leading to measurable changes in magnetic flux behaviour. The project will use micromagnetic simulations to correlate stress distributions, dislocation density, and magnetic response, while fracture mechanics models will link mechanical properties and stress fields with magnetic signatures. Simulation results and experimental magnetic measurements will be integrated into a machine-learning framework capable of predicting pipeline quality, identifying critical stress regions, and forecasting potential failure. The resulting prototype will enable earlier quality assessment and improved optimisation of pipeline manufacturing, thereby enhancing the reliability and resilience of steel pipeline infrastructure.