Machine learning-based inverse design of auxetic metamaterial with zero Poisson's ratio

Chang, Yafeng; Wang, Hui; Dong, Qinxi

doi:10.1016/j.mtcomm.2022.103186

Cited by 22 publications

(14 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The predicted metrics can then be used for optimizing the metamaterial design. Chang et al [111], Dong et al [81] and Liu et al [112] developed MLP models and then accelerated the following inverse design using genetic algorithm (GA) optimization for re-entrant structure, cross-chiral metamaterial and auxetic metamaterials with peanut-shaped pores. Other than GA, Vyavahare et al [20] applied the gray relational analysis technique to improve flexural responses and to reduce the weight and fabrication time of their auxetic metamaterials.…”

Section: Neural Network Based Modelsmentioning

confidence: 99%

“…For data availability, due to the cost of real-world experiments, almost all ML-aided design works reviewed in this paper used simulated data, from larger scales like whole system simulations (a vehicle virtual prototype [12]) and metamaterial application simulations (auxetic lattice tubes [29,48], energy absorption blocks [108]) to the unit cell scales (tetrachiral auxetics [82]). To validate the quality of simulated data, some work [19,81,105,111,112,156,157] did real-world experiments to test the consistency between real-world and simulated data. Only the work of Vyavahare et al [20] applied thirty-two real-world specimens to train their MLP due to the lack of a well-established simulation for the fused deposition modeling (FDM) process.…”

Section: Data Collectionmentioning

confidence: 99%

See 1 more Smart Citation

A critical review on the application of machine learning in supporting auxetic metamaterial design

Zhang,

Zhao

2024

J. Phys. Mater.

View full text Add to dashboard Cite

The progress of machine learning (ML) in the past years has opened up new opportunities to the design of auxetic metamaterials. However, successful implementation of ML algorithms remains challenging, particularly for complex problems such as domain performance prediction and inverse design.In this paper, we first reviewed classic auxetic designs and summarized their variants in different applications. The enormous variant design space leads to challenges using traditional design or topology optimization.Therefore, we also investigated how ML techniques can help address design challenges of auxetic metamaterials and when researchers should deploy them. The theories behind the techniques are explained, along with practical application examples from the analysed literature. The advantages and limitations of different ML algorithms are discussed and trends in the field are highlighted. Finally, two practical problems of ML-aided design, design scales and data collection are discussed.

show abstract

Section: Neural Network Based Modelsmentioning

confidence: 99%

Section: Data Collectionmentioning

confidence: 99%

A critical review on the application of machine learning in supporting auxetic metamaterial design

Zhang,

Zhao

2024

J. Phys. Mater.

View full text Add to dashboard Cite

show abstract

“…Huang et al [6] created a ZPR composite structure and developed the mathematical model. Chang et al [7] suggested a machine-learning model. The trained model can have higher computing performance and less reliance on depths of mathematical knowledge.…”

Section: Introductionmentioning

confidence: 99%

In-plane equivalent modulus of a novel zero Poisson’s ratio honeycomb structure

Ling,

Wang,

Yao

et al. 2023

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

This paper proposed a new zero-Possion’s ratio (ZPR) honeycomb structure. The proposed ZPR honeycomb structure improves the traditional semi-reentrant honeycomb structure by introducing vertical cell walls and changing the topological structure between cells to achieve better in-plane deformation capability. The in-plane modulus of the structure was constructed during this research by a mathematical framework and a FEM model. The test specimen of the construction was created using the Stereolithography Apparatus (SLA) technology, and an axial compression experiment was utilized to confirm the model’s accuracy and the zero-ratio Poisson’s property. Geometrical parameters are also investigated to ascertain the relationship between the new zero-Poisson ratio honeycomb structure’s geometrical characteristics and the structure’s flexibility.

show abstract

“…[33] The changes applied to the structure of metamaterials to convert a positive PR to a negative PR sometimes lead to the creation of a structure with zero Poisson's ratio (ZPR). [34][35][36][37][38][39] These structures have high energy absorption and have been the focus of many researchers in recent years. In this work, the main aim is to investigate the sensitivity of this group of metamaterials.…”

Section: Introductionmentioning

confidence: 99%

Novel Linear Piezo‐resistive Auxetic Meta‐Sensors with Low Young's Modulus by a Core–Shell Conceptual Design and Electromechanical Modelling

Taherkhani,

Bodaghi,

Mousavi Nejad Souq

et al. 2023

Macro Materials & Eng

View full text Add to dashboard Cite

Production of piezo‐resistive auxetic sensors is usually carried out through mixing and coating methods. Although these methods are beneficial, Young's modulus of mixed sensors becomes high because of using a high percentage of sensing elements while the durability of coated sensors gets low due to the separation of sensing elements from the sensor surface. This article presents a new core–shell metamaterial model to address the mentioned problems. The shell and the core are produced of polydimethylsiloxane (PDMS) rubber and a mixture of PDMS/graphite powders (73.45 wt% graphite powders), respectively. A finite element model is developed via COMSOL software to predict the electromechanical behaviors of the created sensor and verified by an experimental study. Scanning electron microscope imaging is conducted to detect the separations of the graphite particles. The main important feature of this meta‐sensor is to possess a linear sensitivity due to having zero Poisson's ratio. The advantage of this method is that Young's modulus of the sensor does not decrease (unlike the mixing method), and the sensor‐coated particles do not separate from the sensor after a while (unlike the coating method). The introduced model has advantages that promote potential applications such as using sensory gloves to detect, for instance, human hand movements.

show abstract

Machine learning-based inverse design of auxetic metamaterial with zero Poisson's ratio

Cited by 22 publications

References 32 publications

A critical review on the application of machine learning in supporting auxetic metamaterial design

A critical review on the application of machine learning in supporting auxetic metamaterial design

In-plane equivalent modulus of a novel zero Poisson’s ratio honeycomb structure

Novel Linear Piezo‐resistive Auxetic Meta‐Sensors with Low Young's Modulus by a Core–Shell Conceptual Design and Electromechanical Modelling

Contact Info

Product

Resources

About