Abstract

During the manufacturing process of a carbon fiber (CFRP) part in the aircraft industry defects occur. These defects are identified through inspections carried out in intermediate steps and even at the end of the manufacturing process. Due to these defects reworks need to be performed and even futilities. The aims of this project is the inline control of the defects that will be produced along the manufacturing  of a stiffned surface panel. The ZAero control system consists on sensors integrated in the lay-up machine and sensors used during the infusion and resin curing processes. These sensors will detect defects that are outside the acceptance range and a response will occur. This response may be a rework as in the case of defects produced in the lamination stage or the variation of parameters produced in the infusion and resin curing stage due to the process monitoring. Three demonstrators will be manufactured. The degree of complexity will be increased in each demonstrator. The control system through sensors will make a quality control and when a defect that is outside the range of acceptance a response will occur.

Abstract

The preforming consists of processing the reinforcement to obtain a geometry of the dry fiber, close to that of the final product before being impregnated by the resin during the injection process. During the process, the fibers are manipulated to reach the desired shape are subjected to tensile, shear and torsion stresees that can cause changes in the orientation of the fibers, misalignments,or other defects such as wrinkles or fiber breakage that can affect the performance of the preform . In this work a solution for the automation of the quality control of the preforming process by artificial vision is investigated.. Two approaches are proposed, 3D vision for the geometric and superficial inspection of the fibers and machine learning aproach based on Deep Learning (DL) to detect irregularities in the orientation. The solution based on 3D vision automatically detects defects based on the comparison of a theoretical 3D model and a 3D reconstruction of the real part. A structured light system is used to generate dense and precise point clouds. Once aligned, the dissimilarities between both surfaces are analyzed. The solution is complemented by a 2D vision system based on DL that classifies the irregularities in the orientations of the fabric. The model is trained with images of classified fiber orientations to automatically identify areas of fiber whose orientation does not correspond to the expected one. The combination of both technologies allows to give a complete solution to the automated quality inspection of preforms for manufacturing or defects.