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The present paper introduces an innovative strain energy function (SEF) for incompressible anisotropic fiber-reinforced materials. This SEF is specifically designed to understand the mechanical behavior of carbon fiber-woven fabric. The considered model combines polyconvex invariants forming an integrity basisin polynomial form, which is inspired by the application of Noether’s theorem. A single solution can be obtained during the identification because of the relationship between the SEF we have constructed and the material parameters, which are linearly dependent. The six material parameters were precisely determined through a comparison between the closed-form solutions from our model and the corresponding tensile experimental data with different stretching ratios, with determination coefficients consistently reaching a remarkable value of 0.99. When considering only uniaxial tensile tests, our model can be simplified from a quadratic polynomial to a linear polynomial, thereby reducing the number of material parameters required from six to four, while the fidelity of the model’s predictive accuracy remains unaltered. The comparison between the results of numerical calculations and experiments proves the efficiency and accuracy of the method.

Semi-solid powder rolling (SSPR) is widely used to produce alloy strips with fine grains and excellent performances in the automotive, aerospace and shipbuilding industries. During SSPR, powder temperature, as a very important parameter, greatly affects strips’ microstructures and mechanical properties, which have been investigated by many researchers, but its effect on the forming process and mechanism has rarely been studied. Therefore, based on online experimental detection and transient simulation, the microstructures, strip temperatures, relative densities and rolling forces at different conditions were, respectively, measured, calculated, compared and analyzed in order to study the deformation process and mechanism during SSPR. The result shows that with the increase in powder temperature, the strip temperature and relative density increase, while the rolling force decreases. The grains of the strips are refined after SSPR, and fine and dense microstructures are obtained at 600 °C, which is the optimum powder temperature. In the main deformation sections (II and III), when the contact normal force exists and reaches a maximum, the relative density and rolling force increase rapidly. At these sections, the strips rolled at 600 °C are mainly in a porous solid state, and powder crushing dominates the strip deformation. Therefore, SSPR at 600 °C and below can be considered porous or powder hot rolling, integrating powder crushing, solidification, deformation, densification and grain coarsening. Moreover, as the simulated values are basically consistent with experimental values, the thermomechanical coupling model based on the Fourier equation and its parameters are confirmed to be reasonable.

High-velocity oxygen fuel (HVOF) spraying is a promising technique for depositing protective coatings. The performances of HVOF-sprayed coatings are affected by in-flight particle properties, such as temperature and velocity, that are controlled by the spraying parameters. However, obtaining the desired coatings through experimental methods alone is challenging, owing to the complex physical and chemical processes involved in the HVOF approach. Compared with traditional experimental methods, a novel method for optimizing and predicting coating performance is presented herein; this method involves combining machine learning techniques with thermal spray technology. Herein, we firstly introduce physics-informed neural networks (PINNs) and convolutional neural networks (CNNs) to address the overfitting problem in small-sample algorithms and then apply the algorithms to HVOF processes and HVOF-sprayed coatings. We proposed the PINN and CNN hierarchical neural network to establish prediction models for the in-flight particle properties and performances of NiCr–Cr3C2 coatings (e.g., porosity, microhardness, and wear rate). Additionally, a random forest model is used to evaluate the relative importance of the effect of the spraying parameters on the properties of in-flight particles and coating performance. We find that the particle temperature and velocity as well as the coating performances (porosity, wear resistance, and microhardness) can be predicted with up to 99% accuracy and that the spraying distance and velocity of in-flight particles exert the most substantial effects on the in-flight particle properties and coating performance, respectively. This study can serve as a theoretical reference for the development of intelligent HVOF systems in the future.