Wire arc additive manufacturing (WAAM) offers significant potential for large-scale metal component production but remains limited by problems such as heat accumulation, residual stresses, and incomplete process automation. This study focuses on the development and validation of a three-level hierarchical WAAM model—bead, layer, and wall—to enable predictive thermo-mechanical simulation and informed experimental fabrication of an aluminium circular form detail. Each hierarchical level is modelled through transient coupled thermal-structural finite element analysis in ABAQUS, incorporating experimentally derived aluminium 6061 material data, heat input implementation through DFLUX, and convection radiation boundary conditions. The methodology includes sequential deposition modelling with 320 beads, thermal monitoring at critical nodes, and structural evaluation of residual stresses. Simulation results show that bead-level heat distribution and residual stresses remain within safe limits, layer-level deposition produces stable thermal oscillations without excessive heat accumulation. While wall-level revealing progressive heat buildup leading to remelting of previously deposited layers. A mitigation strategy is introduced by implementing layer-wise cooling to 60\;^C, which successfully suppresses cumulative thermal loading and restores structural stability. The validated simulations guided an experimental conduction with a robotic GMAW-CMT system, infrared monitoring, and identical process parameters. The manufactured detail closely matches the target geometry, demonstrating the applicability of a three-level hierarchical thermo-mechanical framework for guiding WAAM process planning. This work demonstrates the original contribution of a practical, simulation to experiment workflow based on a three-level hierarchical WAAM model, capable of predicting thermal issues before manufacturing process, informing temperature control strategies and supporting the possibility of the generation of an automated program software for the robot control during the additive manufacturing process.
Anikin et al. (Sun,) studied this question.