A review on the mechanical metamaterials and their applications in the field of biomedical engineering

Metamaterials are a group of materials/structures which possess novel behaviors not existing in nature. The metamaterials include electromagnetic metamaterials, acoustic metamaterials, mechanical metamaterials, etc. among which the mechanical metamaterials are widely used in the field of biomedical engineering. The mechanical metamaterials are the ones that possess special mechanical behaviors, e.g., lightweight, negative Poisson’s ratio, etc. In this paper, the commonly used mechanical metamaterials are reviewed and their applications in the field of biomedical engineering, especially in bone tissue engineering and vascular stent, are discussed. Finally, the future perspectives of this field are given

Inverse design of anisotropic bone scaffold based on machine learning and regenerative genetic algorithm

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

Design of novel triply periodic minimal surface (TPMS) bone scaffold with multi-functional pores: lower stress shielding and higher mass transport capacity

Background: The bone repair requires the bone scaffolds to meet various mechanical and biological requirements, which makes the design of bone scaffolds a challenging problem. Novel triply periodic minimal surface (TPMS)-based bone scaffolds were designed in this study to improve the mechanical and biological performances simultaneously. Methods: The novel bone scaffolds were designed by adding optimization-guided multi-functional pores to the original scaffolds, and finite element (FE) method was used to evaluate the performances of the novel scaffolds. In addition, the novel scaffolds were fabricated by additive manufacturing (AM) and mechanical experiments were performed to evaluate the performances. Results: The FE results demonstrated the improvement in performance: the elastic modulus reduced from 5.01 GPa (original scaffold) to 2.30 GPa (novel designed scaffold), resulting in lower stress shielding; the permeability increased from 8.58 × 10−9 m2 (original scaffold) to 5.14 × 10−8 m2 (novel designed scaffold), resulting in higher mass transport capacity. Conclusion: In summary, the novel TPMS scaffolds with multi-functional pores simultaneously improve the mechanical and biological performances, making them ideal candidates for bone repair. Furthermore, the novel scaffolds expanded the design domain of TPMS-based bone scaffolds, providing a promising new method for the design of high-performance bone scaffolds

Improving PM2.5 Air Quality Model Forecasts in China Using a Bias-Correction Framework

Chinese cities are experiencing severe air pollution in particular, with extremely high PM2.5 levels observed in cold seasons. Accurate forecasting of occurrence of such air pollution events in advance can help the community to take action to abate emissions and would ultimately benefit the citizens. To improve the PM2.5 air quality model forecasts in China, we proposed a bias-correction framework that utilized the historic relationship between the model biases and forecasted and observational variables to post-process the current forecasts. The framework consists of four components: (1) a feature selector that chooses the variables that are informative to model forecast bias based on historic data; (2) a classifier trained to efficiently determine the forecast analogs (clusters) based on clustering analysis, such as the distance-based method and the classification tree, etc.; (3) an error estimator, such as the Kalman filter, to predict model forecast errors at monitoring sites based on forecast analogs; and (4) a spatial interpolator to estimate the bias correction over the entire modeling domain. One or more methods were tested for each step. We applied five combinations of these methods to PM2.5 forecasts in 2014–2016 over China from the operational AiMa air quality forecasting system using the Community Multiscale Air Quality (CMAQ) model. All five methods were able to improve forecast performance in terms of normalized mean error (NME) and root mean square error (RMSE), though to a relatively limited degree due to the rapid changing of emission rates in China. Among the five methods, the CART-LM-KF-AN (a Classification And Regression Trees-Linear Model-Kalman Filter-Analog combination) method appears to have the best overall performance for varied lead times. While the details of our study are specific to the forecast system, the bias-correction framework is likely applicable to the other air quality model forecast as well

Development and prospect of wisdom mine

For new concept of wisdom mine, its development course, existing status and problems in construction process in China were summarized through its association with automation mine, informatization mine and digital mine. A new definition of wisdom mine was put forward, which emphasized that the wisdom mine was a set of wisdom system and could make the optimal regulation for each part automatically. Vision of the wisdom mine was prospected from five aspects of mine design, safety guarantee, efficient production, economic operation and green environmental protection. Construction planning of the wisdom mine was proposed, which mainly included four aspects: establishing wisdom mine industrial alliance, formulating relevant technical specifications and standards, researching and developing intelligent equipment and developing wisdom decision applications

An Efficient Method for the Inverse Design of Thin-Wall Stiffened Structure Based on the Machine Learning Technique

In this paper, a new method using the backpropagation (BP) neural network combined with the improved genetic algorithm (GA) is proposed for the inverse design of thin-walled reinforced structures. The BP neural network model is used to establish the mapping relationship between the input parameters (reinforcement type, rib height, rib width, skin thickness and rib number) and the output parameters (structural buckling load). A genetic algorithm is added to obtain the inversely designed result of a thin-wall stiffened structure according to the actual demand. In the end, according to the geometric parameters of inverse design, the thin-walled stiffened structure is reconstructed geometrically, and the numerical solutions of finite element calculation are compared with the target values of actual demand. The results show that the maximal inversely designed error is within 5.1%, which implies that the inverse design method of structural geometric parameters based on the machine learning and genetic algorithm is efficient and feasible

A review on the auxetic mechanical metamaterials and their applications in the field of applied engineering

Metamaterials are artificially created materials or structures with properties not found in nature. They encompass electromagnetic, acoustic, and mechanical metamaterials, which are particularly significant in applied engineering. Mechanical metamaterials exhibit unique mechanical properties such as vanishing shear modulus, negative Poisson’s ratio, negative compressibility, etc. This paper reviews the most commonly used mechanical metamaterials and discusses their applications in the field of applied engineering, specifically in vibration isolation, energy absorption, and vibration reduction. The prospects for future developments in this field are also presented

Study on the Energy Absorption Performance of Triply Periodic Minimal Surface (TPMS) Structures at Different Load-Bearing Angles

As engineering demands for structural energy absorption intensify, triply periodic minimal surface (TPMS) structures, known for their light weight and exceptional energy absorption, are increasingly valued in aerospace, automotive, and shipping engineering. In this study, the energy absorption performance of three typical TPMS structures was evaluated (i.e., Gyroid, Diamond, and IWP) using quasi-static compression tests at various load-bearing angles. The results showed that while there is little influence of load-bearing angles on the energy absorption performance of Gyroid structures, its energy absorption is the least of the three structures. In contrast, Diamond structures have notable fluctuation in energy absorption at certain angles. Moreover, IWP (I-graph and Wrapped Package-graph) structures, though highly angle-sensitive, achieve the highest energy absorption. Further analysis of deformation behaviors revealed that structures dominated by bending deformation are stable under multi-directional loads but less efficient in energy absorption. Conversely, structures exhibiting mainly tensile deformation, despite their load direction sensitivity, perform best in energy absorption. By integrating bending and tensile deformations, energy absorption was enhanced through a multi-stage platform response. The data and conclusions revealed in the present study can provide valuable insights for future applications of TPMS structures