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Wu Z, Pan H, Huang P, Tang J, She W. Biomimetic Mechanical Robust Cement-Resin Composites with Machine Learning-Assisted Gradient Hierarchical Structures. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2405183. [PMID: 38973222 DOI: 10.1002/adma.202405183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Revised: 06/16/2024] [Indexed: 07/09/2024]
Abstract
Biological materials relying on hierarchically ordered architectures inspire the emergence of advanced composites with mutually exclusive mechanical properties, but the efficient topology optimization and large-scale manufacturing remain challenging. Herein, this work proposes a scalable bottom-up approach to fabricate a novel nacre-like cement-resin composite with gradient brick-and-mortar (BM) structure, and demonstrates a machine learning-assisted method to optimize the gradient structure. The fabricated gradient composite exhibits an extraordinary combination of high flexural strength, toughness, and impact resistance. Particularly, the toughness and impact resistance of such composite attractively surpass the cement counterparts by factors of approximately 700 and 600 times, and even outperform natural rocks, fiber-reinforced cement-based materials and even some alloys. The strengthening and toughening mechanisms are clarified as the regional-matrix densifying and crack-tip shielding effects caused by the gradient BM structure. The developed gradient composite not only endows a promising structural material for protective applications in harsh scenarios, but also paves a new way for biomimetic metamaterials designing.
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Affiliation(s)
- Zhangyu Wu
- Jiangsu Key Laboratory of Construction Materials, School of Materials Science and Engineering, Southeast University, Nanjing, 211189, China
| | - Hao Pan
- Institute of Advanced Engineering Structures, Zhejiang University, Hangzhou, 310058, China
| | - Peng Huang
- Jiangsu Key Laboratory of Construction Materials, School of Materials Science and Engineering, Southeast University, Nanjing, 211189, China
| | - Jinhui Tang
- Jiangsu Key Laboratory of Construction Materials, School of Materials Science and Engineering, Southeast University, Nanjing, 211189, China
| | - Wei She
- Jiangsu Key Laboratory of Construction Materials, School of Materials Science and Engineering, Southeast University, Nanjing, 211189, China
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Liu W, Zhang Y, Lyu Y, Bosiakov S, Liu Y. Inverse design of anisotropic bone scaffold based on machine learning and regenerative genetic algorithm. Front Bioeng Biotechnol 2023; 11:1241151. [PMID: 37744255 PMCID: PMC10512832 DOI: 10.3389/fbioe.2023.1241151] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 08/25/2023] [Indexed: 09/26/2023] Open
Abstract
Introduction: Triply periodic minimal surface (TPMS) is widely used in the design of bone scaffolds due to its structural advantages. However, the current approach to designing bone scaffolds using TPMS structures is limited to a forward process from microstructure to mechanical properties. Developing an inverse bone scaffold design method based on the mechanical properties of bone structures is crucial. Methods: Using the machine learning and genetic algorithm, a new inverse design model was proposed in this research. The anisotropy of bone was matched by changing the number of cells in different directions. The finite element (FE) method was used to calculate the TPMS configuration and generate a back propagation neural network (BPNN) data set. Neural networks were used to establish the relationship between microstructural parameters and the elastic matrix of bone. This relationship was then used with regenerative genetic algorithm (RGA) in inverse design. Results: The accuracy of the BPNN-RGA model was confirmed by comparing the elasticity matrix of the inverse-designed structure with that of the actual bone. The results indicated that the average error was below 3.00% for three mechanical performance parameters as design targets, and approximately 5.00% for six design targets. Discussion: The present study demonstrated the potential of combining machine learning with traditional optimization method to inversely design anisotropic TPMS bone scaffolds with target mechanical properties. The BPNN-RGA model achieves higher design efficiency, compared to traditional optimization methods. The entire design process is easily controlled.
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Affiliation(s)
- Wenhang Liu
- Department of Engineering Mechanics, Dalian University of Technology, Dalian, China
| | - Youwei Zhang
- Department of Engineering Mechanics, Dalian University of Technology, Dalian, China
| | - Yongtao Lyu
- Department of Engineering Mechanics, Dalian University of Technology, Dalian, China
- DUT-BSU Joint Institute, Dalian University of Technology, Dalian, China
| | - Sergei Bosiakov
- Faculty of Mechanics and Mathematics, Belarusian State University, Minsk, Belarus
| | - Yadong Liu
- Department of Orthopedics, Dalian Municipal Central Hospital Affiliated of Dalian University of Technology, Dalian, China
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López C. Artificial Intelligence and Advanced Materials. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2208683. [PMID: 36560859 DOI: 10.1002/adma.202208683] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 12/01/2022] [Indexed: 06/09/2023]
Abstract
Artificial intelligence (AI) is gaining strength, and materials science can both contribute to and profit from it. In a simultaneous progress race, new materials, systems, and processes can be devised and optimized thanks to machine learning (ML) techniques, and such progress can be turned into innovative computing platforms. Future materials scientists will profit from understanding how ML can boost the conception of advanced materials. This review covers aspects of computation from the fundamentals to directions taken and repercussions produced by computation to account for the origins, procedures, and applications of AI. ML and its methods are reviewed to provide basic knowledge of its implementation and its potential. The materials and systems used to implement AI with electric charges are finding serious competition from other information-carrying and processing agents. The impact these techniques have on the inception of new advanced materials is so deep that a new paradigm is developing where implicit knowledge is being mined to conceive materials and systems for functions instead of finding applications to found materials. How far this trend can be carried is hard to fathom, as exemplified by the power to discover unheard of materials or physical laws buried in data.
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Affiliation(s)
- Cefe López
- Instituto de Ciencia de Materiales de Madrid (ICMM), Consejo Superior de Investigaciones Científicas (CSIC), Calle Sor Juana Inés de la Cruz 3, Madrid, 28049, Spain
- Donostia International Physics Centre (DIPC), Paseo Manuel de Lardizábal 4, San Sebastián, 20018, España
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Xu C, Ahmed WW, Wu Y. Broad-angle coherent perfect absorption-lasing and super-collimation in two-dimensional non-Hermitian photonic crystals. OPTICS EXPRESS 2023; 31:2112-2119. [PMID: 36785232 DOI: 10.1364/oe.481141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 12/26/2022] [Indexed: 06/18/2023]
Abstract
Coherent perfect absorption-lasing (CPAL) and collimation have been intensively studied for normal and small angle wave incidence. Here, we report a two-dimensional non-Hermitian photonic crystal for broad-angle CPAL and super-collimation. The synergy of a nonsymmorphic glide symmetry of the lattice, gain-loss modulation and an optimization of unit cell induces a parity-time phase transition in the band structure along the Brillouin zone boundary. The transition points, i.e., the exceptional points, form a slab-like contour, with nearly zero dispersion in both real and imaginary parts of the band structure. Such dispersionless band structure significantly enhances the range of incident angle for CPAL and collimation.
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Predicting the Textural Properties of Plant-Based Meat Analogs with Machine Learning. Foods 2023; 12:foods12020344. [PMID: 36673436 PMCID: PMC9858592 DOI: 10.3390/foods12020344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 01/05/2023] [Accepted: 01/09/2023] [Indexed: 01/13/2023] Open
Abstract
Plant-based meat analogs are food products that mimic the appearance, texture, and taste of real meat. The development process requires laborious experimental iterations and expert knowledge to meet consumer expectations. To address these problems, we propose a machine learning (ML)-based framework to predict the textural properties of meat analogs. We introduce the proximate compositions of the raw materials, namely protein, fat, carbohydrate, fibre, ash, and moisture, in percentages and the "targeted moisture contents" of the meat analogs as input features of the ML models, such as Ridge, XGBoost, and MLP, adopting a build-in feature selection mechanism for predicting "Hardness" and "Chewiness". We achieved a mean absolute percentage error (MAPE) of 22.9%, root mean square error (RMSE) of 10.101 for Hardness, MAPE of 14.5%, and RMSE of 6.035 for Chewiness. In addition, carbohydrates, fat and targeted moisture content are found to be the most important factors in determining textural properties. We also investigate multicollinearity among the features, linearity of the designed model, and inconsistent food compositions for validation of the experimental design. Our results have shown that ML is an effective aid in formulating plant-based meat analogs, laying out the groundwork to expediently optimize product development cycles to reduce costs.
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Wang E, Yang F, Shen X, Duan H, Zhang X, Yin Q, Peng W, Yang X, Yang L. Development and Optimization of Broadband Acoustic Metamaterial Absorber Based on Parallel-Connection Square Helmholtz Resonators. MATERIALS 2022; 15:ma15103417. [PMID: 35629445 PMCID: PMC9146988 DOI: 10.3390/ma15103417] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 04/28/2022] [Accepted: 04/29/2022] [Indexed: 11/16/2022]
Abstract
An acoustic metamaterial absorber of parallel-connection square Helmholtz resonators is proposed in this study, and its sound absorption coefficients are optimized to reduce the noise for the given conditions in the factory. A two-dimensional equivalent simulation model is built to obtain the initial value of parameters and a three-dimensional finite element model is constructed to simulate the sound absorption performance of the metamaterial cell, which aims to improve the research efficiency. The optimal parameters of metamaterial cells are obtained through the particle swarm optimization algorithm, and its effectiveness and accuracy are validated through preparing the experimental sample using 3D printing and measuring the sound absorption coefficient by the standing wave tube detection. The consistency between the experimental data and simulation data verifies feasibility of the proposed optimization method and usefulness of the developed acoustic metamaterial absorber, and the desired sound absorption performances for given conditions are achieved. The experimental results prove that parallel-connection square Helmholtz resonators can achieve an adjustable frequency spectrum for the low frequency noise control by parameter optimization, which is propitious to promote its application in reducing the noise in the factory.
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Affiliation(s)
- Enshuai Wang
- College of Field Engineering, Army Engineering University of PLA, Nanjing 210007, China; (E.W.); (F.Y.); (H.D.); (X.Z.); (Q.Y.); (L.Y.)
| | - Fei Yang
- College of Field Engineering, Army Engineering University of PLA, Nanjing 210007, China; (E.W.); (F.Y.); (H.D.); (X.Z.); (Q.Y.); (L.Y.)
| | - Xinmin Shen
- College of Field Engineering, Army Engineering University of PLA, Nanjing 210007, China; (E.W.); (F.Y.); (H.D.); (X.Z.); (Q.Y.); (L.Y.)
- Correspondence: (X.S.); (W.P.); Tel.: +86-025-8082-1451 (X.S.)
| | - Haiqin Duan
- College of Field Engineering, Army Engineering University of PLA, Nanjing 210007, China; (E.W.); (F.Y.); (H.D.); (X.Z.); (Q.Y.); (L.Y.)
| | - Xiaonan Zhang
- College of Field Engineering, Army Engineering University of PLA, Nanjing 210007, China; (E.W.); (F.Y.); (H.D.); (X.Z.); (Q.Y.); (L.Y.)
| | - Qin Yin
- College of Field Engineering, Army Engineering University of PLA, Nanjing 210007, China; (E.W.); (F.Y.); (H.D.); (X.Z.); (Q.Y.); (L.Y.)
| | - Wenqiang Peng
- College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China
- Correspondence: (X.S.); (W.P.); Tel.: +86-025-8082-1451 (X.S.)
| | - Xiaocui Yang
- Engineering Training Center, Nanjing Vocational University of Industry Technology, Nanjing 210023, China;
- MIIT Key Laboratory of Multifunctional Lightweight Materials and Structures (MLMS), Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
| | - Liu Yang
- College of Field Engineering, Army Engineering University of PLA, Nanjing 210007, China; (E.W.); (F.Y.); (H.D.); (X.Z.); (Q.Y.); (L.Y.)
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Ultra-Broadband Bending Beam and Bottle Beam Based on Acoustic Metamaterials. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12063025] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
We report the realization of an ultra-broadband bending beam based on acoustic metamaterials by the theoretical prediction and the numerical validation. The proposed structure is composed of a series of straight tubes with spatially modulated depths. We analytically derive the depth profile required for the generation of an ultra-broadband bending beam, and examine the performance of the metastructure numerically. The design is then extended for the generation of a three-dimensional bottle beam. The transverse trapping behaviours on small rigid objects by the bottle beam are investigated based on the force potential. Our work will help the further study of broadband acoustic meta-structures, and may also find applications in a variety of fields such as ultrasound imaging, health monitoring and particle manipulations.
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Wang J, Wang Y, Chen Y. Inverse Design of Materials by Machine Learning. MATERIALS (BASEL, SWITZERLAND) 2022; 15:1811. [PMID: 35269043 PMCID: PMC8911677 DOI: 10.3390/ma15051811] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 02/13/2022] [Accepted: 02/24/2022] [Indexed: 02/04/2023]
Abstract
It is safe to say that every invention that has changed the world has depended on materials. At present, the demand for the development of materials and the invention or design of new materials is becoming more and more urgent since peoples' current production and lifestyle needs must be changed to help mitigate the climate. Structure-property relationships are a vital paradigm in materials science. However, these relationships are often nonlinear, and the pattern is likely to change with length scales and time scales, posing a huge challenge. With the development of physics, statistics, computer science, etc., machine learning offers the opportunity to systematically find new materials. Especially by inverse design based on machine learning, one can make use of the existing knowledge without attempting mathematical inversion of the relevant integrated differential equation of the electronic structure but by using backpropagation to overcome local minimax traps and perform a fast calculation of the gradient information for a target function concerning the design variable to find the optimizations. The methodologies have been applied to various materials including polymers, photonics, inorganic materials, porous materials, 2-D materials, etc. Different types of design problems require different approaches, for which many algorithms and optimization approaches have been demonstrated in different scenarios. In this mini-review, we will not specifically sum up machine learning methodologies, but will provide a more material perspective and summarize some cut-edging studies.
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Affiliation(s)
- Jia Wang
- School of Space and Environment, Beihang University, Beijing 102206, China;
| | - Yingxue Wang
- National Engineering Laboratory for Risk Perception and Prevention, Beijing 100081, China
| | - Yanan Chen
- School of Materials Science and Engineering, Tianjin University, Tianjin 300072, China;
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Ciaburro G, Iannace G. Modeling acoustic metamaterials based on reused buttons using data fitting with neural network. THE JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA 2021; 150:51. [PMID: 34340477 DOI: 10.1121/10.0005479] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Accepted: 06/07/2021] [Indexed: 06/13/2023]
Abstract
Metamaterials are designed by arranging artificial structural elements according to periodic geometries to obtain advantageous and unusual properties when they are hit by waves. Initially designed to interact with electromagnetic waves, their use naturally extended to sound waves, proving to be particularly useful for the construction of containment and soundproofing systems in buildings. In this work, a new metamaterial has been developed with the use of a polyvinyl chloride membrane on which buttons have been glued. Two types of buttons were used, with different weights, placing them on the membrane according to a radial geometry. Each sample of metamaterial was subjected to sound absorption coefficient measurements using the impedance tube. Measurements were made using the samples by setting three configurations, creating a cavity with different thicknesses. The results of the measurements were subsequently used as input for training a simulation model based on artificial neural networks. The model showed an excellent generalization capacity, returning estimates of the acoustic absorption coefficient of the metamaterial very similar to the measured value. Subsequently, the model was used to perform a sensitivity analysis to evaluate the contribution of the various input variables on the returned output.
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Affiliation(s)
- Giuseppe Ciaburro
- Università della Campania "Luigi Vanvitelli," Via San Lorenzo, 81031 Aversa (Ce), Italy
| | - Gino Iannace
- Università della Campania "Luigi Vanvitelli," Via San Lorenzo, 81031 Aversa (Ce), Italy
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