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• W4387319099 abstract "It has been widely recognized that structural materials are better designed simultaneously with the target component so that their properties can be tailored to the service loading conditions to meet component performance. This concurrent approach helps avoid over-strengthening the materials and components through unnecessary reinforcements, leading to lightweight, low-cost, yet durable designs. Recent advances in Integrated Computational Materials Engineering (ICME) have enabled researchers and engineers to predict a structure’s performance using its geometry, material (e.g. constituent selection and volume fraction), and processing conditions; however, two challenges arise when existing design optimization algorithms are applied to ICME: (1) The design problem involves mixed-type variables, including continuous, ordinal (e.g. low or high for material processing temperatures), and categorical variables (e.g. steel or plastic for type of materials), and (2) Evaluation of design performance considering manufacturing-induced local material microstructure evolution is computationally expensive. In this chapter, we present a machine-learning-based method to conquer both challenges through constrained Bayesian optimization (BO) with latent-variable Gaussian process (LVGP) modeling. LVGP allows Gaussian process (GP) regression with mixed-variables by mapping all non-continuous variables to a continuous latent space. It is integrated with BO, a method using (LV)GP’s predicted responses and prediction uncertainty to guide the optimal design search, to explore and exploit the disjoint and non-differentiable design spaces spanned by the non-continuous variables. BO is also shown to be highly efficient for optimization of expensive black-box functions. We demonstrate our approach using two design examples, thin-walled hat section components and short-fiber reinforced polymer (SFRP) composites. The former illustrates concurrent structure and material design while the latter focuses on concurrent material and processing optimization. In both examples, we first establish their respective ICME workflows, i.e. the integrated part-scale manufacturing processes and structural performance simulations coupled with material-scale microstructure and behavior models. Then, we show the benefits of using LVGP as a mixed-variable surrogate model to emulate the physics-based simulations, and the performance of constrained BO (CBO) based on LVGP, particularly on how the search is conducted among different levels of categorical variables. Results indicate that the LVGP-CBO framework can effectively design multi-material structural components for ICME through concurrent material, product, and process optimization." @default.
• W4387319099 created "2023-10-04" @default.
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• W4387319099 date "2023-01-01" @default.
• W4387319099 modified "2023-10-05" @default.
• W4387319099 title "Mixed-Variable Concurrent Material, Geometry, and Process Design in Integrated Computational Materials Engineering" @default.
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• W4387319099 doi "https://doi.org/10.1007/978-3-031-36644-4_11" @default.
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