Design of a Machine for the Production of Advanced Fabrics

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 subsequently 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.

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Publication Details

Published
2024-10-17
DOI
https://doi.org/10.1109/uemcon62879.2024.10754768
Primary Topic
Assembly Line Balancing Optimization
Type
article
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article

Design of a Machine for the Production of Advanced Fabrics

Andrés Flores, Sario Angel Chamorro Quijano, R. A. Torres, Roberto Belarmino Quispe Cabana et al.
Assembly Line Balancing Optimization
article

Design of a Machine for the Production of Advanced Fabrics

Andrés Flores, Sario Angel Chamorro Quijano, R. A. Torres, Roberto Belarmino Quispe Cabana, Luis Enrique Soto Otivo
article en

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 subsequently 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.

Universidad Continental (PE)
Openalex Percentile: Top 70%
Assembly Line Balancing Optimization
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