The novel non-isothermal Hot Forming and cold die Quenching (HFQ) process can
enable the cost-effective production of complex shaped, high strength aluminium
alloy panel components. However, the unfamiliarity of designing for the new
process prevents its widescale adoption in industrial settings. Recent research
efforts focus on the development of advanced material models for finite element
simulations, used to assess the feasibility of new component designs for the
HFQ process. However, FE simulations take place late in design processes,
require forming process expertise and are unsuitable for early-stage design
explorations. To address these limitations, this study presents a novel
application of a Convolutional Neural Network (CNN) based surrogate as a means
of rapid manufacturing feasibility assessment for components to be formed using
the HFQ process. A diverse dataset containing variations in component geometry,
blank shapes, and processing parameters, together with corresponding physical
fields is generated and used to train the model. The results show that near
indistinguishable full field predictions are obtained in real time from the
model when compared with HFQ simulations. This technique provides an invaluable
tool to aid component design and decision making at the onset of a design
process for complex-shaped components formed under HFQ conditions.