  This paper aims to propose an online two-stage thermal history prediction
method, which could be integrated into a metal AM process for performance
control. Based on the similarity of temperature curves (curve segments of a
temperature profile of one point) between any two successive layers, the first
stage of the proposed method designs a layer-to-layer prediction model to
estimate the temperature curves of the yet-to-print layer from measured
temperatures of certain points on the previously printed layer. With
measured/predicted temperature profiles of several points on the same layer,
the second stage proposes a reduced order model (ROM) (intra-layer prediction
model) to decompose and construct the temperature profiles of all points on the
same layer, which could be used to build the temperature field of the entire
layer. The training of ROM is performed with an extreme learning machine (ELM)
for computational efficiency. Fifteen wire arc AM experiments and nine
simulations are designed for thin walls with a fixed length and unidirectional
printing of each layer. The test results indicate that the proposed prediction
method could construct the thermal history of a yet-to-print layer within 0.1
seconds on a low-cost desktop computer. Meanwhile, the method has acceptable
generalization capability in most cases from lower layers to higher layers in
the same simulation, as well as from one simulation to a new simulation on
different AM process parameters. More importantly, after fine-tuning the
proposed method with limited experimental data, the relative errors of all
predicted temperature profiles on a new experiment are smaller than 0.09, which
demonstrates the applicability and generalization of the proposed two-stage
thermal history prediction method in online applications for metal AM.
