Digital Twin–Enabled Structural Health Monitoring and Lifecycle Optimization of Welded Heavy Equipment Assemblies Using Finite Element and Manufacturing Data Integration

Abstract

Fatigue analysis and lifecycle management of welded heavy equipment assemblies are complicated by variable-amplitude loading, manufacturing-induced residual stresses, geometric discontinuities, and heterogeneous service environments. A digital twin–based SHM approach is technically appealing because it can integrate the as-built manufacturing data, finite element models, sensor data, inspection history and maintenance plans in an up-to-date engineering continuum. This review evaluates the suitability of digital twin concepts, welding simulation, finite element model updating, structural health monitoring and prognostic decision models for heavy duty welded booms, chassis and frames, lifting structures and other welded components for the assessment of fatigue and fracture. The literature reports methodological advances in isolated sub-problems including modelling of the heat source during the welding process, estimation of welding residual stress, local fatigue assessment, vibration-based monitoring and guided-wave inspection, Bayesian calibration, and remaining life prediction. However, there are few studies that complete the loop from manufacturing data to as-built structural state estimation to operational monitoring and lifecycle optimization. There are still considerable challenges in the model reduction process, the propagation of uncertainty due to variation in the weld process, traceable data architectures, defect-informed updating of meshes, testing of models under field loading and decision policies taking into account downtime, repair quality and inspection value. Future progress will require interoperable digital threads, physics-informed monitoring, probabilistic FE twins, and benchmark datasets representative of heavy-equipment assemblies (HEAs).