  As the complexity of modern manufacturing technologies increases, traditional
trial-and-error design, which requires iterative and expensive simulations,
becomes unreliable and time-consuming. This difficulty is especially
significant for the design of hot-stamped safety-critical components, such as
ultra-high-strength-steel (UHSS) B-pillars. To reduce design costs and ensure
manufacturability, scalar-based Artificial-Intelligence-empowered surrogate
modelling (SAISM) has been investigated and implemented, which can allow
real-time manufacturability-constrained structural design optimisation.
However, SAISM suffers from low accuracy and generalisability, and usually
requires a high volume of training samples. To solve this problem, an
image-based Artificial-intelligence-empowered surrogate modelling (IAISM)
approach is developed in this research, in combination with an
auto-decoder-based blank shape generator. The IAISM, which is based on a
Mask-Res-SE-U-Net architecture, is trained to predict the full thinning field
of the as-formed component given an arbitrary blank shape. Excellent prediction
performance of IAISM is achieved with only 256 training samples, which
indicates the small-data learning nature of engineering AI tasks using
structured data representations. The trained auto-decoder, trained
Mask-Res-SE-U-Net, and Adam optimiser are integrated to conduct blank
optimisation by modifying the latent vector. The optimiser can rapidly find
blank shapes that satisfy manufacturability criteria. As a high-accuracy and
generalisable surrogate modelling and optimisation tool, the proposed pipeline
is promising to be integrated into a full-chain digital twin to conduct
real-time, multi-objective design optimisation.
