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As the complexity of modern manufacturing technologies increases, traditionaltrial-and-error design, which requires iterative and expensive simulations,becomes unreliable and time-consuming. This difficulty is especiallysignificant for the design of hot-stamped safety-critical components, such asultra-high-strength-steel (UHSS) B-pillars. To reduce design costs and ensuremanufacturability, scalar-based Artificial-Intelligence-empowered surrogatemodelling (SAISM) has been investigated and implemented, which can allowreal-time manufacturability-constrained structural design optimisation.However, SAISM suffers from low accuracy and generalisability, and usuallyrequires a high volume of training samples. To solve this problem, animage-based Artificial-intelligence-empowered surrogate modelling (IAISM)approach is developed in this research, in combination with anauto-decoder-based blank shape generator. The IAISM, which is based on aMask-Res-SE-U-Net architecture, is trained to predict the full thinning fieldof the as-formed component given an arbitrary blank shape. Excellent predictionperformance of IAISM is achieved with only 256 training samples, whichindicates the small-data learning nature of engineering AI tasks usingstructured data representations. The trained auto-decoder, trainedMask-Res-SE-U-Net, and Adam optimiser are integrated to conduct blankoptimisation by modifying the latent vector. The optimiser can rapidly findblank shapes that satisfy manufacturability criteria. As a high-accuracy andgeneralisable surrogate modelling and optimisation tool, the proposed pipelineis promising to be integrated into a full-chain digital twin to conductreal-time, multi-objective design optimisation.

Image-based Artificial Intelligence empowered surrogate model and shape morpher for real-time blank shape optimisation in the hot stamping process