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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer BERNAS: Jurnal Pengabdian Kepada Masyarakat Universitas Muhammadiyah Yogyakarta Undergraduate Conference Proceeding Ramatekno Cendikia: Jurnal Pendidikan dan Pengajaran Jurnal Polimesin NALAR FIQH: Jurnal Hukum Islam Journal of Applied Science and Advanced Engineering
Deni Kurnia
Politeknik Enjinering Indorama
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Journal : Jurnal Polimesin

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Real-Time Identification of Yarn Irregularities on DTY Machine Through Vibration Monitoring Kurnia, Deni; Sutanto, Agus; Fakhrurroja, Hanif; Roni Wibowo, Nanang
Jurnal Polimesin Vol 22, No 6 (2024): December
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jpl.v22i6.5847

Abstract

This paper presents an innovative real-time monitoring system for detecting yarn irregularities during the draw texturing process in DTY machine. The system uses advanced sensors to continuously measure vibration signals, which are then analyzed for anomalies. The system incorporates advanced sensors, controllers, and embedded software for monitoring the vibrations produced during the draw texturing process. Fast Fourier Transform (FFT) in LabVIEW converts these vibration signals into their frequency-domain representation. This helps identify anomalies that could indicate potential yarn irregularities. The results from the sensor data clearly indicate that amplitude values serve as a reliable measure for detecting yarn irregularities. For normal spindles, the amplitude ranges from 10.9 to 12.2 m/s², while abnormal spindles show significantly higher values, between 31.9 and 44.3 m/s². This distinction facilitates real-time classification of yarn quality. The system's ability to identify these amplitude variations promptly can significantly reduce waste and enhance quality control. Future developments will focus on integrating an intelligent early warning system that alerts operators immediately upon detecting irregularities, enabling quicker interventions and minimizing downtime.
A hybrid pareto–fishbone and IoT-based monitoring framework for reducing DTY yarn defects Kurnia, Deni; Fakhrurroja, Hanif; Marno, Marno; Joniko, Joniko
Jurnal Polimesin Vol 23, No 6 (2025): December
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jpl.v23i6.7678

Abstract

Quality Control (QC) challenges in the textile industry increasingly require data-driven and real-time solutions to reduce critical production defects. This research aims to develop a hybrid Pareto-Fishbone analysis integrated with an IoT-based monitoring framework to reduce the incidence of dominant defects in Draw Textured Yarn (DTY) yarns (X-stitch and Broken Filament). Defect data collected in 2024 (n=2,396) and early 2025 (n=1,177) were analyzed using Pareto charts, which identified X-stitch (40.15%) and Broken Filament (37.15%) as contributing 77.3% of total defects in 2024. Fishbone diagrams traced root causes to machine vibration and yarn tension anomalies. An IoT prototype was designed using ADXL345 vibration sensors (200 Hz sampling), tension monitoring, and MQTT communication to a Node-RED dashboard to enable real-time alerts. Preliminary testing achieved 95% MQTT transmission success and detected vibration anomalies correlating with 85% of X-stitch incidents. The proposed hybrid framework combines the diagnostic strength of Pareto–Fishbone analysis with the preventive capability of IoT monitoring, offering a scalable Industry 4.0-oriented solution for textile QC and predictive maintenance.
Co-Authors Adolf Asih Supriyanto Afzeri Afzeri Afzeri Afzeri Tamsir Agus Sutanto Dwi Kartini, Dwi Emmanuel Agung Nugroho Fakhrurroja, Hanif Friska Abadi Habibillah, M. Raihan Hadisantoso, Feri Siswoyo Hakim, Muhammad Nur Imam Muis H Irmani, Irmani Irvan Irvan Itqan Mazdadi, Muhammad Jalil, Lisya Auliyah Joniko, Joniko Kiki Pratiwi Koto, Muthya Khairunnisa Lovely Son, Lovely Marfinas, Zelki Marno Marno Maryani Maryani Nurmala , Indriani Siti Pratama, M. Ridho Radityo Adi Nugroho Sabrina Aisyah Putri Sinaga, M. Hendri Sugara Slamet Riyadi Slamet Riyadi Syahril, Ahmad Syamsiar, Syamsiar Tiawan Wibowo, Nanang Roni Widiawati, Windi