Purpose The purpose of this paper is to combine the entropy weight method with the cloud model and establish a fire risk assessment method for airborne lithium battery. Design/methodology/approach In this paper, the fire risk assessment index system is established by fully considering the influence of the operation process of airborne lithium battery. Then, the cloud model based on entropy weight improvement is used to analyze the indexes in the system, and the cloud image is output to discuss the risk status of airborne lithium batteries. Finally, the weight, expectation, entropy and hyperentropy are analyzed to provide risk prevention measures. Findings In the risk system, bad contact of charging port, mechanical extrusion and mechanical shock have the greatest impact on the fire risk of airborne lithium battery. The fire risk of natural factors is at a low level, but its instability is 25% higher than that of human risk cases and 150% higher than that of battery risk cases. Practical implications The method of this paper can evaluate any type of airborne lithium battery and provide theoretical support for airborne lithium battery safety management. Originality/value After the fire risk assessment is completed, the risk cases are ranked by entropy weight. By summarizing the rule, the proposed measures for each prevention level are given.

A dynamic textile weaving simulator is established to connect weaving actions to fabric patterns and microstructures. It utilizes the Digital Element Approach (DEA) under the framework of the software package Digital Fabric and Composite Analyser (DFCA). The key components of a Jacquard loom are explicitly modelled utilising the ‘hole/no hole’ principle. Yarn interlacing motion is guided by weaving matrix specified by steps. Shedding, weft insertion, beat-up, and take-up actions are modelled and explained. The inter-fibre contact force, fibre forces (tensile, shear, and bending), and boundary conditions in the weft direction are considered. The weaving process of five cells in the warp direction of a 10-layer 3D orthogonal woven fabric is simulated at the filament level to derive for its microstructure. The results show that the fabric microstructure continues to change after being woven, and the thickness and length of each individual cell decrease with further weaving steps. The microstructures of newly woven cells converge after the weaving of two further cells in the lengthwise direction. The microstructure of the second cell closely matches that of an actual weaved fabric, as evaluated by microscopy images. Penetration occurs between adjacent weft yarns in the same column, which fundamentally changes their microstructure, composition, and material properties. Parametric studies show that the ratio of binder to warp yarn tension determines the fabric thickness. Increases in the take-up length and binder yarn tension lead to a decrease and increase in the reed tension, respectively. This work provides valuable insights into fabric design and manufacture instruction.

Composite plates are widely used in the aircraft manufacturing industry. The projectile damage of composite plates is affected by complex factors such as material, structure, impact velocity, and impact angle. A reliable method is needed for efficient structural health monitoring. In this paper, a composite plate damage prediction and evaluation model based on the cloud model and neural network is proposed; the five types of experimental characteristics are used as input parameters, and the depth and diameter of the damage area are used as output parameters to train the neural network–cloud model. This method transforms the quantitative data of impact damage of the composite plate into qualitative damage by introducing the cloud model, which makes the damage situation more intuitive. The results show that the accuracy of the prediction model is 97.23%, the accuracy of the evaluation model is 92.41%, and the comprehensive accuracy of the model is 89.85%. The composite damage prediction model has a good prediction performance.