Mesh/membrane composite with superior mechanical performance: A deep learning-based design

Zhang, Yunce; Tao, Qiang; Liu, Yuanpeng; Wang, Changguo

doi:10.1016/j.compscitech.2022.109735

Cited by 5 publications

(1 citation statement)

References 41 publications

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“…This not only increases the process cost but also fails to strike a balance between lightweight design and functionality. Inspired by the advanced grid structure [24,25] and variable configuration mesh structure [26][27][28] in terms of structural lightweight, load-bearing, and functionality, several researchers have employed grid structures to achieve improved control over the load-bearing capacity [29,30] and deformation behavior [31] of membrane structures with the advantage of adopting the idea of the discrete actuation of membranes. In a few studies, the shape memory material is applied only at the crease to drive the membrane structure to unfold [32][33][34], which improves the efficiency of structural development, but the configuration of the structure is not stable due to the weak stiffness of the membrane panel.…”

Section: Introductionmentioning

confidence: 99%

Self-Unfolding Properties of Smart Grid-Reinforced Membrane Origami

Hu,

Xia,

Tao

et al. 2024

J. Compos. Sci.

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Origami-based membrane structures have shown great potential to revolutionize the construction of deployable and lightweight space structures in the future. However, the efficient unfolding mechanism puts forward major challenges to the practical realization of space-deployable structures. Here, a smart grid-reinforced membrane origami (SGRMO) is presented. The unfolding action hinges upon the application of forces facilitated by shape memory polymer composites (SMPCs). Subsequent locking action ensues through the restoration of the initial rigidity, accomplished via cooling mechanisms. This novel structure achieves the required lightweight and functionality by employing the grid design concept and effectively reduces the decline in unfolding extent caused by irreversible plastic deformation at the crease. Its recovery properties, including unfolding angle, distance, and surface precision, are experimentally and analytically investigated under different conditions. The results indicate that the structure can be reliably unfolded into the predefined shapes. In the case of Miura-SGRMO, the optimal surface precision is attained when the angle-ψ registers at 30°. The results of this study are expected to serve as the design of ultra-large flexible solar arrays and deployable antenna structures.

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Section: Introductionmentioning

confidence: 99%

Self-Unfolding Properties of Smart Grid-Reinforced Membrane Origami

Hu,

Xia,

Tao

et al. 2024

J. Compos. Sci.

Self Cite

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Image driven deep learning method with FFT solver for predicting the microscale full field stress of stochastic boundary composites

Liao,

Wang,

Wang

et al. 2024

Polymer Composites

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A novel image‐driven deep learning approach embedded with model‐data‐knowledge information can directly predict the full field stress distribution and mechanical properties of composites solely based on initial scanning geometry images. The fiber spatial distribution and morphological features are identified by Scanning Electron Microscopy (SEM) initially. An effective random fiber generation algorithm is further utilized to generate images database comprising equivalent geometric models of various microstructures. Subsequently, the geometry model images database is analyzed by fast Fourier transform (FFT) to produce the database including the elastic modulus parameters and full field stress distributions of composites with distinct microstructures. Finally, an innovative image‐learning comprehensive paradigm uniting convolutional neural networks and convolutional autoencoders is elaborated systematically to learn the inherent laws of geometry images and field images. The results show that the proposed method have capacity to effectively and accurately predict the elastic modulus and full field stress distributions combined with error analysis even for stochastic boundary composites. The proposed methodology is a symbiosis of cutting‐edge image learning and non‐destructive testing techniques for composites, which can directly use the local non‐destructive or scanning images of composite structural components to quickly obtain field information and further predict damage evolution online.Highlights Proposing a novel deep learning approach combined with FFT directly to predict stress distribution of stochastic boundary composites solely based on micro‐images. Adopting equivalent geometric models dispersed by higher pixels mesh density considering extreme thin interface. Incorporate adaptive algorithms for streamlined training optimization. An innovative integration of deep learning and non‐destructive testing technology for the stress distribution and properties prediction online.

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A review on recent applications of machine learning in mechanical properties of composites

Liang,

Wei,

Peng

et al. 2024

Polymer Composites

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Composites are undergoing extensive research and utilization due to their excellent mechanical properties, driven by human needs. Traditionally, the research methods in materials science predominantly rely on empirical theory or experimental trial and error approaches. However, the increased complexity of composite materials results in a greater intricacy in their mechanical behavior. Consequently, the utilization of traditional research methods may not achieve sufficient efficiency. Materials science is rapidly transitioning into a data‐driven era, with machine learning (ML) emerging as a potent tool to expedite materials development and enhance properties prediction. Significant advancements have been achieved in the application of ML to the study of composite mechanics. In this review article, we elucidate various ML methods employed in the construction of constitutive models for isotropic and anisotropic composites, and delve into the research on construction ML models that leverage input data derived from composite processes, structures, and environmental conditions to predict material mechanical properties. Additionally, we summarize recent noteworthy ML applications in composite design and optimization. Finally, possible prospective viewpoints are proposed for future development, with the aim of providing essential scientific guidance for advancing material science and technology through ML.Highlights Machine learning can address complexity in constitutive model of the anisotropic composites. Machine learning predicts mechanical properties of composites well by process and structure. Machine learning enhances efficiency in inverse design to optimize composites. Limitations, challenges, development trends of ML in composites.

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Mesh/membrane composite with superior mechanical performance: A deep learning-based design

Cited by 5 publications

References 41 publications

Self-Unfolding Properties of Smart Grid-Reinforced Membrane Origami

Self-Unfolding Properties of Smart Grid-Reinforced Membrane Origami

Image driven deep learning method with FFT solver for predicting the microscale full field stress of stochastic boundary composites

A review on recent applications of machine learning in mechanical properties of composites

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