Peng Li

Due to the anisotropic structure and mechanical properties of composite laminates, internal damage cracks can easily occur. In this study, orthotropic anisotropic glass-fiber-reinforced polymer composites were used as the repair object. Firstly, the anisotropic material was analyzed using the finite element method, the self-healing structural compliance, water head loss, and volume percentage of the microvascular network were taken as the objective functions, and the topology optimization of the microvascular network structure was carried out using non-dominated soring genetic algorithm II. Secondly, the self-healing material with a wall-less microvascular network was prepared via the vacuum-assisted resin transfer molding process and the embedded wire removal method. Finally, the light repair performance was tested using the three-point bending test. The results show that in the case of no intervention for light repair, the average maximum failure load of the self-healing structure after embedding the microvascular network can reach 94.06% of that before embedding; with the introduction of real-time light repair, the average maximum failure load of the self-healing structure with light repair was increased by 4.1% compared with the self-healing structure without light repair. Meanwhile, the second peak load of the light-repaired structure can reach 51.36% of the average maximum failure load, which is 28.56% higher than that of the non-light-repaired structure.

To enhance the mechanics and self-healing properties of the self-healing composite, this study introduces an innovative optimization method for variable-angle fiber-reinforced self-healing composites with microtubule network carriers. The study aims to minimize macroscopic structural compliance and carrier head loss. Firstly, a topological description function (TDF) for the self-healing composite was introduced, taking into account the configuration and geometry of the macroscopic structure and microtubule network carrier as design variables. Secondly, the relationship between the fiber laying angle and component spindle direction was established. An element stiffness matrix for variable-angle fibers was derived to determine the compliance of the self-healing composite. Then, the microtubule network head loss was calculated based on the Hardy Cross method. Finally, by integrating the Moving Morphable Component (MMC) method and the enumeration method, a dual-scale collaborative optimization framework was developed. The set of double-objective Pareto non-inferior solutions of the self-healing composite was obtained by iteration. Numerical examples show that (1) under the same optimization conditions, the non-inferior solution set of variable-angle fiber design is superior to those of fixed-angle fiber designs (0°, 45°, and 90°). (2) Compared with single-objective (compliance) optimization of the carrier-free composite, the Pareto solution set of the variable-angle dual-scale collaborative optimization can provide a better compliance optimization solution, and the maximum compliance solution of the solution set is only 10.64% higher. This paper proposes a method combining variable-angle and dual-scale collaborative optimization, which provides a useful reference for the topology design of a self-healing composite.

Self-healing materials possess the capability to promptly repair minor damages occurring during service, thereby effectively preventing safety accidents. This paper investigates a multi-objective topology optimization method for the macro structure and microtubule network of self-healing materials around pure epoxy resin materials, aiming to enhance the damage healing capability of the microtubule network while meeting the mechanical performance requirements of the macro structure. By introducing the design variables of macro structure and microtubule network, the corresponding topological description functions are established respectively. And study applies logical operations and post-processing techniques to generate an embedded microtubule network structure description. The objective functions include the flexibility of the macro structure, the along-travel head loss, and the total length of the microtubule network, with material volume serving as a constraint. In order to determine the head loss of the three-dimensional microtubule network structure, a Hardy-Cross method based on flow initialization and loop search is proposed. Multi-objective topology optimization is designed based on moving morphable components algorithm, enumeration method and Pareto principle. Develop iterative termination conditions by assessing the disparity between Pareto solution sets in each generation, thereby ensuring algorithm convergence. The numerical example of the Messerschmitt–Bölkow–Blohm (MBB) beamyields a flexibility of 0.059 without a carrier and 0.0728 with a carrier the macrostructural flexibility without a carrier is 81.0% compared to with a carrier, and the macrostructural profiles and the overall flexibility of the MBB beams with/without a carrier are close to each other. This method serves as a reference for optimizing large-scale self-healing structures.

The aim of this article is to provide a sensor distribution optimization method for the effective impact monitoring of composite plates with fewer sensors. In this research, the number of sensors and the minimum difference between categories are used as objective functions I and II, respectively, where the minimum difference is the Euclidean distance between different influence categories. The dual objective functions are defined by means of finite element analysis, the autoregressive (AR) model, and locality−preserving projection (LPP). The sensor distribution is optimized based on Multi−Objective Particle Swarm Optimization (MOPSO). Finally, an impact monitoring method is provided, and an experimental platform is built to verify the method. According to the optimization results, different grid sizes have a certain impact on the identification results, with the smaller the grid size, the smaller the minimum difference between categories. Within a given grid size, the minimum difference between categories increases with the increasing number of sensors. Experiments show that the higher the number of sensors, the higher the recognition rate of the system. Comparing the experimental results with the energy analysis of wavelet bands and PCA methods, it is found that the method used in this study has a higher recognition rate. This research provides an impact monitoring method based on sensor distribution optimization. And the effectiveness of the method is verified by experiments. It provides a useful reference and choice for the structure condition monitoring of composite material plates.

Self-healing is a new strategy for crack defect which is the main reason for the failure of composites. As an extrinsic self-healing system, the microvascular network system is capable of multiple healing cycles and rapid healing of large area damage. However, the embedment of micropipe network will affect the performance of matrix material. In this article, a microvascular network of self-healing material is optimized using non-dominated sorting genetic algorithm II. Two objective functions head loss and void volume fraction are considered. Finite element analysis and Hardy Cross iteration are performed to achieve the quantization of objective functions. One hundred sixty-five optimized solutions were obtained, and the void volume fraction was within the limits of [4.19%, 5.13%], whereas the head loss was within the limits of [9.63×10⁻⁷ m, 6.51×10⁻⁶ m]. According to the optimization results, the network was prepared and tested to validate the design and feasibility. The test result shows that the void volume fraction of the prepared network is 3.77%, lower than the designed value 4.43% which has a little effect on the matrix material. The network is interconnected and the healing agent can flow freely in it. The embedded network does not reduce the performance of epoxy resin. The optimization of microvascular network balances the mechanical properties and self-repairing properties of the matrix material.

Rolling bearings are the most important parts in rotating machinery, and one of the most vulnerable parts to failure. The rolling bearing is a cyclic symmetrical structure that is stable under normal operating conditions. However, when the rolling bearing fails, its symmetry is destroyed, resulting in unstable performance and causing major accidents. If the performance of rolling bearings can be monitored and evaluated in real time, maintenance strategies can be implemented promptly. In this paper, by using wavelet packet energy entropy (WPEE), the early fault-free features of bearing and the failure samples of similar bearings are decomposed firstly, and the energy value is extracted as the original feature, simultaneously. Secondly, a radial basis function (RBF) neural network model is established by using early fault-free features and similar bearing failure characteristics. The bearing full-life data characteristics of the extracted features are added into the RBF model in an iterative manner to obtain performance degradation Indicator. Boxplot was introduced as an adaptive threshold method to determine the failure threshold. Finally, the results are verified by empirical mode decomposition and Hilbert envelope demodulation. A bearing accelerated life experiment is performed to validate the feasibility and validity of the proposed method. The experimental results show that the method can diagnose early fault points in time and evaluate the degree of bearing degradation, which is of great significance for industrial practical applications.

Optimal sensor placement is a significant task for structural health monitoring (SHM). In this paper, an SHM system is designed which can recognize the different impact location and impact degree in the composite plate. Firstly, the finite element method is used to simulate the impact, extracting numerical signals of the structure, and the wavelet decomposition is used to extract the band energy. Meanwhile, principal component analysis (PCA) is used to reduce the dimensions of the vibration signal. Following this, the non-dominated sorting genetic algorithm (NSGA-II) is used to optimize the placement of sensors. Finally, the experimental system is established, and the Product-based Neural Network is used to recognize different impact categories. Three sets of experiments are carried out to verify the optimal results. When three sensors are applied, the average accuracy of the impact recognition is 59.14%; when the number of sensors is four, the average accuracy of impact recognition is 76.95%.

The non-dominated sorting genetic algorithm II is used to design the microvascular networks embedded in self-healing polymeric materials. And the fluid simulation software Fluent is used to validate the optimization result obtained by non-dominated sorting genetic algorithm II. Two objective functions are considered, namely, the void volume fraction and flow efficiency. A total of 222 solutions are obtained, and the head loss is in the range of (3.88 × 10⁻⁷ m, 2.36 × 10⁻⁶ m), whereas the void volume fraction is in the range of (4.39%, 5.12%). The simulation velocities are close to optimization velocities. The average error rate of selected solutions (a), (b), and (c) is 22.6%, 26.4%, and 35.2%, respectively.