Search

The manufacturing process of composites is crucial for ensuring structural efficiency and application reliability of their products. Computer-based process simulation plays a significant role in improving the manufacturing quality of composite components and reducing the manufacturing cost. Traditional process simulation relies on physical and chemical mechanisms in manufacturing of composites with the mathematical equations solved by numerical methods such as finite element/finite volume analysis and computer-aided design methods e.g. computer graphics. At present，it has been widely used in simulations of the lay-up of reinforcements/prepregs，the infiltration flows of resin，the curing behaviours of thermosetting resin，the heat transmission and exchange，and the nonlinear mechanics including residual stress and curing deformation predictions. Recently，artificial intelligence（AI）technologies have rapidly developed，its technical basis machine learning（ML），in combination with artificial neural networks（ANN），has been used in the field of lay-up process of fiber reinforcements，liquid molding processes，and autoclave processes，which aimed for data mining and developing reduced-order models. The former can establish relationships between process conditions and the curing quality or mechanical properties of the composite parts，while the latter can improve computational efficiency of the process simulation. However，due to the complexity，immeasurability，and high cost of manufacturing fiber-reinforced resin matrix composites，at the beginning of the AI age，it is difficult to meet the requirements of ML only by relying on the amount of data obtained by experiments. Also，data-driven AI technology faces uncertain issues regarding the representativeness，generalisability，and interpretability of the models. Therefore，traditional process simulation based on physicochemical mechanisms can provide a large amount of reliable data for data-driven ML simulation，and then through AI，more quantitative models describing the composite process can be established to expand the computable scope of process simulation. At the same time，as AI technology enhances the computational efficiency，the process simulation that meets real-time requirements can evolve into digital twins（DT）of the composite manufacturing process，which can provide new technical support for reducing the composite costs and improving the scientific whole-life cycle management.

The composites were separately prepared by a domestic T700-grade carbon fiber used for aviation and four types of bismaleimide resin. The composite laminates were treated by three hygrothermal conditions, including 100 ℃ boiling water, 70 ℃ water immersion and 70 ℃/85% relative humidity to study the hygrothermal mechanical properties of the composite laminates related to interface and matrix under ambient temperature and 150 ℃. The results show that the moisture absorption decreases the mechanical properties of composites, and the effect on mechanical properties under high temperature is significant. Furthermore, 90o tensile property, 0o compressive strength, flexural strength and open-hole compressive strength are sensitive to moisture absorption and temperature, while the sensitivities of 0o compressive modulus, flexural modulus and open-hole tensile strength are small. By comparing the changes of the mechanical properties of composite after different hygrothermal treatment conditions, it is found that the degree of degradation after 100 ℃ boiling water is larger, which is believed to be related to its higher equilibrium moisture absorption rate. Based on fracture morphology of composite after 90o tensile testing using scanning electron microscope, it is considered that the hygrothermal property of interface is the critical factor to determine the hygrothermal property of domestic carbon fiber composite.