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M. Munadi
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Automotive Engineering Control & Systems Engineering Engineering Materials Science & Nanotechnology Mechanical Engineering

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Enhancing textile quality control with the application of teachable machine and Raspberry Pi as machine learning-based image processing Emmanuel Agung Nugroho; Joga Dharma Setiawan; M. Munadi; M. Diki
Jurnal Polimesin Vol 22, No 5 (2024): October
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jpl.v22i5.5308

Abstract

The adoption of image processing-based technologies in the textile sector is rising. This technology is commonly utilized to replace traditional sensor systems that are limited to a single function while also improving product quality control functions. Defects during the manufacturing process are a common problem in the textile business, particularly with fabric products. This study created a fabric quality control system that detects fabric problems using machine learning-based picture classification techniques. A D320p web camera detects rare and slap flaws, which are classified using open-source Google teaching machine software and processed on a Raspberry Pi 3B device. The laboratory-scale measurement was carried out on a prototype cloth rolling machine using the confusion matrix method. The test results reveal an average inference speed of 143.5 milliseconds, a frame rate of 6.45 fps, and a 98.56% accuracy rate. These results demonstrate that the proposed system is effective and efficient for detecting fabric defects, offering a promising solution for enhancing quality control in the textile industry. Future research could focus on scaling the system for industrial use and enhancing real-time performance.
Co-Authors Emmanuel Agung Nugroho Joga Dharma Setiawan M. Diki