https://popups.uliege.be/esaform21/index.php?id=3885 Index terms fr 0 https://popups.uliege.be/esaform21/index.php?id=3882 Manufacturing process optimisation usually amounts to searching optima in high-dimensional parameter spaces. In industrial practice, this search is most often directed by human-subjective expert judgment and trial-and-error experiments. In contrast, high-fidelity simulation models in combination with general-purpose optimisation algorithms, e.g. finite element models and evolutionary algorithms, enable a methodological, virtual process exploration and optimisation. However, reliable process models generally entail significant computation times, which often renders classical, iterative optimisation impracticable. Thus, efficiency is a key factor in optimisation. One option to increase efficiency is surrogate-based optimisation (SBO): SBO seeks to reduce the overall computational load by constructing a numerically inexpensive, data-driven approximation („surrogate“) of the expensive simulation. Traditionally, classical regression techniques are applied for surrogate construction. However, they typically predict a predefined, scalar performance metric only, which limits the amount of usable information gained from simulations. The advent of machine learning (ML) techniques introduces additional options for surrogates: in this work, a deep neural network (DNN) is trained to predict the full strain field instead of a single scalar during textile forming („draping“). Results reveal an improved predictive accuracy as more process-relevant information from the supplied simulations can be extracted. Application of the DNN in an SBO- framework for blank holder optimisation shows improved convergence compared to classical evolutionary algorithms. Thus, DNNs are a promising option for future surrogates in SBO. Mon, 29 Mar 2021 14:55:15 +0200 Thu, 08 Apr 2021 21:19:38 +0200 https://popups.uliege.be/esaform21/index.php?id=3882 https://popups.uliege.be/esaform21/index.php?id=3654 In addition to thermomechanical treatment, one of the main factors affecting the mechanical properties of steel is the chemical composition. The chemical composition may vary for a special high-strength low-alloy steel to meet certain mechanical property requirements. This work presents an approach, based on the method of physical-chemical modelling developed at the Z.I. Nekrasov Iron and Steel Institute of the National Academy of Sciences of Ukraine, to optimise the chemical composition of high-strength structural steels. The principle of this method is to describe the chemical composition of a melt by a complex of integral model parameters of interatomic interaction, characterising the chemical and structural state of the melt. The experimental data were analysed to obtain the regression model for mechanical properties based on the parameters of interatomic interaction. Finally, a multi-criteria optimisation method was applied to obtain an optimal set of microalloying elements which ensure the required mechanical properties. Mon, 29 Mar 2021 13:57:27 +0200 Thu, 08 Apr 2021 19:06:59 +0200 https://popups.uliege.be/esaform21/index.php?id=3654