Orclever Proceedings of Research and Development

An Integrated Deep Learning Framework for Automated Quality Control and Process Optimization in Slasher Indigo Dyeing

Mohammad Muttaqi

Prosmh R&D Centre

https://orcid.org/0009-0004-2635-199X

Gizem Daskaya

Prosmh R&D Centre

https://orcid.org/0009-0008-7585-3493

Kerem Cakir

Prosmh R&D Centre

https://orcid.org/0009-0009-5060-595X

DOI: https://doi.org/10.56038/oprd.v7i1.694

Keywords: Slasher Indigo, Industry 4.0, Textile Automation, Machine Learning, Deep Learning, Computer Vision

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Abstract

This paper presents the development of a multi-step, multi-disciplinary automation framework designed to enhance quality assurance and process control in slasher indigo dyeing machines. The system integrates two complementary subsystems: (1) a real-time yarn defect detection module employing deep learning-based computer vision, and (2) a process optimization module utilizing chromaticity analysis for colour stability and chemical balance control. The defect detection system uses four moving cameras strategically placed across the machine to identify broken yarns and irregular density patterns with high accuracy. The colour monitoring subsystem, developed in collaboration with Agteks, continuously records yarn colour in the CIELAB colour space and recommends corrective pH or reduction agent (Hydro) adjustments when deviations occur. Experimental results demonstrate a detection accuracy of 92.4%, with significant improvements in production speed, consistency, and operator workload reduction. The proposed system represents a comprehensive step toward fully autonomous dyeing operations aligned with Industry 4.0 objectives.

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