Bioinspired hierarchical composite design using machine learning: simulation, additive manufacturing, and experiment

Cited by (557)

Items 301–400 of 557

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4. Bioinspired design of a tissue-engineered ray with machine learning
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5. Prediction of the transverse elastic modulus of the unidirectional composites by an artificial neural network with fiber positions and volume fraction
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6. Customizable anisotropic microlattices for additive manufacturing: Machine learning accelerated design, mechanical properties and structural-property relationships
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9. Development of surrogate predictive models for the nonlinear elasto-plastic response of medium density fibreboard-based sandwich structures
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10. Recent Studies in Mechanical Properties of Selected Hard-Shelled Seeds: A Review
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11. PRESTO: Rapid protein mechanical strength prediction with an end-to-end deep learning model
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12. Methodology for optimizing composite design via biological pattern generation mechanisms
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13. Bioinspired multifunctional antifogging surfaces: Progress, AI design and challenges
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14. Predicting the temperature field of composite materials under a heat source using deep learning
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16. Implementation of Machine Learning for Enhancing Lean Manufacturing Practices for Metal Additive Manufacturing
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18. Coherent nanofiber array buckling-enabled synthesis of hierarchical layered composites with enhanced strength
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19. Study and Application of Machine Learning Methods in Modern Additive Manufacturing Processes
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20. Machine Learning Applications in Composites: Manufacturing, Design, and Characterization
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21. Experimental and numerical investigation on crack propagation in biomimetic nacreous composites with gradient structures
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22. Disorder unlocks the strength-toughness trade-off in metamaterials
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24. Diabolical ironclad beetles inspire tougher joints for engineering applications
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28. Data-driven prediction of future melt pool from built parts during metal additive manufacturing
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29. Influence of the parameters of the convolutional neural network model in predicting the effective compressive modulus of porous structure
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30. Bio-inspired discontinuous composite materials with a machine learning optimized architecture
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34. A deep learning-based approach for the inverse design of the Helmholtz resonators
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37. Modeling Bioinspired Fish Scale Designs via a Geometric and Numerical Approach
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38. Optimization of grid composite configuration to maximize toughness using integrated hierarchical deep neural network and genetic algorithm
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39. A machine learning-based recommendation framework for material extrusion fabricated triply periodic minimal surface lattice structures
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41. Exploring the Fracture Toughness of Tessellated Materials With the Discrete-Element Method
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42. Machine learning-enabled feature classification of evaporation-driven multi-scale 3D printing
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43. Prediction of atomic stress fields using cycle-consistent adversarial neural networks based on unpaired and unmatched sparse datasets
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44. Bioinspired composites: nature’s guidance for advanced materials future
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45. Deep learning-based detection method for analysis of high-pressure hydrogen induced damage in acrylonitrile butadiene rubber for hydrogen mobility
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46. Buckling and postbuckling of architectured materials: A review of methods for lattice structures and metal foams
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47. Machine learning-based multi-objective optimization of thermo-mechanical field of anisotropic plates
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48. Model-based deep learning for additive manufacturing: New frontiers and applications
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49. Wang tiles enable combinatorial design and robot-assisted manufacturing of modular mechanical metamaterials
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50. Thermally and mechanically induced strain gradient fields in architected 2D materials and beam structures
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51. Knee joint vibration signal classification algorithm based on machine learning
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52. Simulation and learning-driven design for architected cement-based materials
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54. Predicting the Destruction of Composite Materials Using Machine Learning Methods
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58. Biomimetic and Bioinspired Materials: Design Strategies, Mechanical Properties, and Engineering Applications—A Review
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59. Prediction of elastic stresses in porous materials using fully convolutional networks
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60. Prediction and optimization of global temperature field of composite materials under multiple heat sources
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63. Biomimetic tough helicoidally structured material through novel electrospinning based additive manufacturing
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64. The Nonlinear Mechanical Constitutive Model and Deep Learning Method to Inverse Design Dual‐Feature‐Integrated Lattice Metamaterial
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65. Powder Bed Additive Manufacturing Using Machine Learning Algorithms for Multidisciplinary Applications: A Review and Outlook
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70. Machine learning for predicting and optimizing energy absorption characteristics of hollow variable cross-section lattice metamaterials
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72. Designing Weakly Coupled Mems Resonators with Machine Learning-Based Method
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73. Addition of collagen type I in agarose created a dose-dependent effect on matrix production in engineered cartilage
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75. Methods to Generate Structurally Hierarchical Architectures in Nanoporous Coinage Metals
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76. A BERT-based model for coupled biological strategies in biomimetic design
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84. Using machine learning and deep learning in additive manufacturing
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86. Combining Finite Element and Machine Learning Methods to Predict Structures of Architectured Interlocking Ceramics
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87. Accelerating Optimization Design of Bio-inspired Interlocking Structures with Machine Learning
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88. A failure criterion for honeycomb structures considering the onset of instability under out-of-plane loads
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89. Opportunities at the frontier of multimaterial additive manufacturing with subvoxel control
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90. Robot-assisted additive manufacturing for aerospace applications: recent trends and its future possibilities
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