Alberdi et al 2021a - Revision history

Materiales.Compuestos at 23:40, 26 November 2022

2022-11-26T23:40:52Z

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Materiales.Compuestos at 23:34, 26 November 2022

2022-11-26T23:34:08Z

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Scipediacontent at 13:13, 7 May 2022

2022-05-07T13:13:43Z

Scipediacontent at 12:10, 7 May 2022

2022-05-07T12:10:28Z

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Scipediacontent: Scipediacontent moved page Draft Content 846350697 to Alberdi et al 2021a

2022-03-31T19:07:56Z

Scipediacontent moved page Draft Content 846350697 to Alberdi et al 2021a

Scipediacontent: Created page with "== 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 t..."

2022-03-31T19:07:53Z

Created page with "== 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 t..."

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== 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.

== Full document ==
<pdf>Media:Draft_Content_846350697native-articles-323.pdf</pdf>

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 El preformado consiste en procesar el refuerzo para obtener una geometría de la fibra seca, cercana a la del producto final antes de ser impregnado por la resina durante el proceso de inyección. Durante el proceso, las fibras son manipuladas para alcanzar la forma deseada sufriendo esfuerzos de tracción, cortadura y torsión que pueden provocar cambios en la orientación de las fibras, desalineamientos, defectos como arrugas o rotura de fibras que pueden afectar a las prestaciones de la preforma.

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