Process simulations of fiber reinforced polymer composites towards AI ages

Abstract

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.

Keywords

• composites
• process simulations
• artificial intelligence（ai）
• machine learning（ml）
• curing simulations
• process-induced deformation