Design of a machine for the production of advanced fabrics

• Autores: Meza Torres, Raul Oswaldo, Hinostroza Flores, Anthony Jesus, Soto Otivo, Luis Enrique, Chamorro Quijano, Sario Angel, Quispe Cabana, Roberto Belarmino
• Formato: tesis de grado
• Fecha de Publicación: 2025
• Institución: Universidad Continental
• Repositorio: CONTINENTAL-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.continental.edu.pe:20.500.12394/17336
• Enlace del recurso: https://hdl.handle.net/20.500.12394/17336; https://doi.org/10.1109/UEMCON62879.2024.10754768
• Nivel de acceso: acceso abierto
• Materia: Industria textil; Textile industry; Fibras textiles; Textile fibers; Automatización industrial; Industrial automation; Máquinas; Machines; https://purl.org/pe-repo/ocde/ford#2.03.00

Abstract — In this project, a design for an automated mechanism is presented for implementation in textile manufacturing processes, where an integrated system for textile fiber recognition based on artificial intelligence (AI) was added. The mechanical design of the machine was studied in terms of force, providing a robust and efficient structure that supports the integration of sensors and cameras for real - time image capture of the processed fibers. Additionally, this design included a precise feeding system and a transport mechanism that ensured the stability and correct positioning of the fibers during analysis. In parallel, an AI model was implemented to identify and classify textile fibers based on their color characteristics. A simulated dataset was generated , where each type of fiber was represented by a specific color (red, green, blue, yellow), and a simple neural network was trained to recognize these color patterns. The model was optimized to achieve high accuracy in fiber classification and was subsequen tly evaluated with a test set. The system also included a visualization functionality that allowed the recognized fiber color to be displayed along with its classification, providing visual validation of the process. This comprehensive approach, combining advanced mechanical design with AI, proved effective in improving the accuracy and efficiency of automatic textile fiber identification, significantly contributing to the optimization of production processes in the textile industry.