Applied computer vision for composite material manufacturing by optimizing the impregnation velocity: An experimental approach

Abstract

The application of the cutting edge industrial solutions in composite manufacturing are optimizing the processes, quality control and resources usage. Computer vision is currently used in many quality control stages, although its potential advantages have not been applied in the process control. In this paper, computer vision is used to control the impregnation velocity in VARI (Vacuum Assisted Resin Infusion) process. As it is known, there is a relationship between the impregnation velocity and the final mechanical properties in LCM (Liquid Composite Manufacturing) processes. Constant and optimum flow front velocity mean optimum mechanical properties, although the nature of the process makes it difficult to keep these conditions. Then, a methodology has been proposed to identify and use the optimum velocity during the manufacturing process. Firstly, the flow front recognition algorithm was calibrated to be used in different reinforcement and fluid systems. Then, tensile and impact specimens have been manufactured and tested at different controlled and uncontrolled velocities. As a result, the tensile modulus has been increased up to 12.6%, as the tensile strength has increased up to 8.7%. Similarly, the maximum reaction force during the impact test has been increased up to 6.5%, as the damaged area has been reduced by 8.8%. For stitched laminates, force results increase up to 3.2%, as the damaged area has been reduced up to 31% when the optimum velocity is used. The experimental results have demonstrated the advantage of using mechanisms to control the impregnation process to achieve improved mechanical properties of composite materials.