Data-Driven Modelling of Complex Current–Voltage Waveform Controlled Gas Metal Arc-Wire DED Processes

다국어 초록 (Multilingual Abstract)

Gas metal arc-based wire arc additive manufacturing (GMA-WAAM) is an attractive process for producing geometricallysimple and large-volume components. In this process, the arc energy and droplet transfer behaviour are primarily controlledby the current–voltage waveforms. Finite element analysis based on 3D transient gas metal arc heat source models has beenextensively used to derive mean process parameters and predict the thermal fields during GMA-WAAM. These models oftensimplify a complex current–voltage waveform by averaging it over a defined time period and use heat source efficiency,heat source parameters, and heat transfer co-efficient as fitting parameters. This simplification leads to inaccuracy in thepredictions of thermal fields. Therefore, a data-driven-based approach is proposed in this work to develop a physically-basedinstantaneous arc heat source model. This model is aimed to effectively describe a complex current–voltage waveform usedfor controlled dip short-circuiting transfer by adapting goldak’s double ellipsoidal arc heat source model. Instantaneousheat source parameters are derived from constant current waveform experiments corresponding to individual instances in ashort-circuiting current–voltage waveform. Arc energies are calculated as a function of instantaneous heat source parameterswhile depositing 1.2 mm diameter Al-Si12 (ER4047) wires on a pure aluminium substrate. Thermal fields are measured usingthese instantaneous parameters and validated with experimental observations. Results show that this data-driven approachpredicts the thermal fields with less than 1% relative error in the peak temperatures using the physically accurate heat sourceefficiency, heat source, and heat transfer parameters for a GMA-WAAM process.