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Journal article(2025)
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Harikrishnan Vijayakumaran, Jonathan B. Russ, Glaucio H. Paulino, Miguel A. Bessa
Additive manufacturing methods together with topology optimization have enabled the creation of multiscale structures with controlled spatially-varying material microstructure. However, topology optimization or inverse design of such structures in the presence of nonlinearities remains a challenge due to the expense of computational homogenization methods and the complexity of differentiably parameterizing the microstructural response. A solution to this challenge lies in machine learning techniques that offer efficient, differentiable mappings between the material response and its microstructural descriptors. This work presents a framework for designing multiscale heterogeneous structures with spatially varying microstructures by merging a homogenization-based topology optimization strategy with a consistent machine learning approach grounded in hyperelasticity theory. We leverage neural architectures that adhere to critical physical principles such as polyconvexity, objectivity, material symmetry, and thermodynamic consistency to supply the framework with a reliable constitutive model that is dependent on material microstructural descriptors. Our findings highlight the potential of integrating consistent machine learning models with density-based topology optimization for enhancing design optimization of heterogeneous hyperelastic structures under finite deformations.
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Additive manufacturing methods together with topology optimization have enabled the creation of multiscale structures with controlled spatially-varying material microstructure. However, topology optimization or inverse design of such structures in the presence of nonlinearities remains a challenge due to the expense of computational homogenization methods and the complexity of differentiably parameterizing the microstructural response. A solution to this challenge lies in machine learning techniques that offer efficient, differentiable mappings between the material response and its microstructural descriptors. This work presents a framework for designing multiscale heterogeneous structures with spatially varying microstructures by merging a homogenization-based topology optimization strategy with a consistent machine learning approach grounded in hyperelasticity theory. We leverage neural architectures that adhere to critical physical principles such as polyconvexity, objectivity, material symmetry, and thermodynamic consistency to supply the framework with a reliable constitutive model that is dependent on material microstructural descriptors. Our findings highlight the potential of integrating consistent machine learning models with density-based topology optimization for enhancing design optimization of heterogeneous hyperelastic structures under finite deformations.
Book chapter(2024)
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Alessandro Comitti, Harikrishnan Vijayakumaran, Mohammad Hosein Nejabatmeimandi, Luis Seixas, Adrian Cabello, Diego Misseroni, Massimo Penasa, Christoph Paech, Miguel Bessa, More authors...
The building construction industry is the largest anthropogenic source of pollution, with massive energy consumption and substantial CO2 emissions. Lightweight tension structures allow the simultaneous implementation of several sustainable strategies by using recyclable low-carbon structural membranes offering a greener alternative to glass and other cladding materials. Their efficient structural load-bearing mechanisms result in significant weight savings in buildings and a drastic reduction of the environmental impact associated with material production, transportation, use, and disposal. A subgroup of lightweight materials, structural fabrics, and foils has been gaining popularity among designers and architects in recent years because of their desirable features such as high stiffness, strength, ductility, durability, and functional properties. While these structural membranes open new crucial perspectives for the clean energy transition and have been recently employed worldwide, their full potential is still limited by the lack of construction codes, advanced optimization tools, and comprehensive viscous-thermo-mechanical constitutive models. This chapter aims to foster the design of membrane structures by presenting their basic principles and recent advancements in the field. It covers the design approaches, employed materials and efforts in their characterization and modeling, implications on the sustainability of the built environment, current challenges, and future pathways from both academic research and engineering design viewpoints.
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The building construction industry is the largest anthropogenic source of pollution, with massive energy consumption and substantial CO2 emissions. Lightweight tension structures allow the simultaneous implementation of several sustainable strategies by using recyclable low-carbon structural membranes offering a greener alternative to glass and other cladding materials. Their efficient structural load-bearing mechanisms result in significant weight savings in buildings and a drastic reduction of the environmental impact associated with material production, transportation, use, and disposal. A subgroup of lightweight materials, structural fabrics, and foils has been gaining popularity among designers and architects in recent years because of their desirable features such as high stiffness, strength, ductility, durability, and functional properties. While these structural membranes open new crucial perspectives for the clean energy transition and have been recently employed worldwide, their full potential is still limited by the lack of construction codes, advanced optimization tools, and comprehensive viscous-thermo-mechanical constitutive models. This chapter aims to foster the design of membrane structures by presenting their basic principles and recent advancements in the field. It covers the design approaches, employed materials and efforts in their characterization and modeling, implications on the sustainability of the built environment, current challenges, and future pathways from both academic research and engineering design viewpoints.
