Yarn inspection and sorting system using robotic vision and machine learning

Nugroho, Emmanuel Agung; Setiawan, Joga Dharma; Kurnia, Deni; Wibowo, Nanang Roni

doi:10.11591/ijai.v15.i3.pp2325-2336

Akses terbuka Jurnal SINTA 1

Yarn inspection and sorting system using robotic vision and machine learning

IAES International Journal of Artificial Intelligence · Vol. 15 No. 3 (2026) · hlm. 2325–2336

DOI: 10.11591/ijai.v15.i3.pp2325-2336
ISSN: 2252-8938, 2089-4872
Terbit: 2026-06-01
Penerbit: Institute of Advanced Engineering and Science
Bahasa: eng
Lisensi: creativecommons.org/licenses/by-sa/4.0

Abstrak

The increasing demand for automation in the textile industry, particularly in quality inspection processes, underscores the need for intelligent and cost effective solutions. Conventional methods of yarn classification and sorting remain labor-intensive, time-consuming, and susceptible to human error, resulting in inconsistent quality control. This study introduces an automated system for yarn inspection and sorting that integrates robotic vision, machine learning, and position-based visual servoing (PBVS) for real-time motion control. The proposed system combines Raspberry Pi-based machine learning with computer vision utilizing a 4-degree-of-freedom (4-DOF) robotic manipulator and a webcam, enabling precise pick-and-place operations based on yarn classification into four categories: good, striped, moldy, and dirty. Experimental results validate the system’s effectiveness, achieving an average deviation of 0.375 mm along the x-axis, 0.69 mm along the y-axis, and 0.675 mm along the z-axis, resulting in an overall position error of 0.58 mm. These results demonstrate the system’s robustness and reliability in dynamic industrial environments. The novelty of this research lies in leveraging a low-cost embedded architecture with advanced visual servoing for textile automation, reducing operational errors, improving efficiency, and supporting industry 4.0 adoption.

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Politeknik Enjinering Indorama
Diponegoro University
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Organisasi sama
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Tampilkan sebagai tabel

Rekan penulis	Organisasi	Artikel bersama
Munadi Munadi	Diponegoro University	4
Emmanuel Agung Nugroho	Afiliasi tidak diketahui	2
Alifa Rustiyanti	Politeknik Enjinering Indorama	2
Rifky Ismail	Afiliasi tidak diketahui	1
Farika Tono Putri	Afiliasi tidak diketahui	1
Mochammad Ariyanto	Afiliasi tidak diketahui	1
Lovenda Samudro	Afiliasi tidak diketahui	1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.

Metadata asli (oai_dc)

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	<dc:title xml:lang="en-US">Yarn inspection and sorting system using robotic vision and machine learning</dc:title>
	<dc:creator>Nugroho, Emmanuel Agung</dc:creator>
	<dc:creator>Setiawan, Joga Dharma</dc:creator>
	<dc:creator>Kurnia, Deni</dc:creator>
	<dc:creator>Wibowo, Nanang Roni</dc:creator>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:subject xml:lang="en-US">4-DOF manipulator; Machine learning; Raspberry Pi; Robotic vision; Yarn inspection</dc:subject>
	<dc:description xml:lang="en-US">The increasing demand for automation in the textile industry, particularly in quality inspection processes, underscores the need for intelligent and cost effective solutions. Conventional methods of yarn classification and sorting remain labor-intensive, time-consuming, and susceptible to human error, resulting in inconsistent quality control. This study introduces an automated system for yarn inspection and sorting that integrates robotic vision, machine learning, and position-based visual servoing (PBVS) for real-time motion control. The proposed system combines Raspberry Pi-based machine learning with computer vision utilizing a 4-degree-of-freedom (4-DOF) robotic manipulator and a webcam, enabling precise pick-and-place operations based on yarn classification into four categories: good, striped, moldy, and dirty. Experimental results validate the system’s effectiveness, achieving an average deviation of 0.375 mm along the x-axis, 0.69 mm along the y-axis, and 0.675 mm along the z-axis, resulting in an overall position error of 0.58 mm. These results demonstrate the system’s robustness and reliability in dynamic industrial environments. The novelty of this research lies in leveraging a low-cost embedded architecture with advanced visual servoing for textile automation, reducing operational errors, improving efficiency, and supporting industry 4.0 adoption.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2026-06-01</dc:date>
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	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/29777</dc:identifier>
	<dc:identifier>10.11591/ijai.v15.i3.pp2325-2336</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 15, No 3: June 2026; 2325-2336</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v15.i3</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/29777/15206</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2026 Emmanuel Agung Nugroho, Joga Dharma Setiawan, Deni Kurnia, Nanang Roni Wibowo</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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