Automatic system for yarn quality analysis by image processing

Caldas, Pedro; Sousa, Filipe Lage de; Pereira, Filipe; Lopes, Helena; Machado, José

doi:10.1007/s40430-022-03875-3

Cited by 6 publications

(7 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The mechatronic prototype designed for this project contains a developed image acquisition system and it is intended to be inserted into a designed textile yarn winding and unwinding machine (Figure 26) [35].…”

Section: Developed Prototypementioning

confidence: 99%

“…Table 2. Nomenclature of mechanical system components [35]. The mechanical system of this prototype consists of a subsystem (A) that initially pulls the thread, from top to bottom, from the assembled bobbin (2) and guides it inside the machine with the aid of ceramic guides (3).…”

Section: Alphanumeric Reference Numerical Referencementioning

confidence: 99%

“…Finally, after the wire passes through the second positive feeder (9), the wire enters the winding phase (C), being led out of the box and into the grooved cylinder (11), which is aligned with the winding spool. winding (12) and winds the yarn around it, controlling the winding direction [35].…”

Section: Alphanumeric Reference Numerical Referencementioning

confidence: 99%

See 2 more Smart Citations

Intelligent Computer Vision System for Analysis and Characterization of Yarn Quality

et al. 2023

Self Cite

View full text Add to dashboard Cite

The quality of yarn is essential in the control of the fabrics processes. There is some commercial equipment that measures the quality of yarn based on sensors, of different types, used for collecting data about some textile yarn characteristic parameters. The irregularity of the textile thread influences its physical properties/characteristics and there may be a possibility of a break in the textile thread during the fabric manufacturing process. This can contribute to the occurrence of unwanted patterns in fabrics that deteriorate their quality. The existing equipment, for the above-mentioned purpose, is characterized by its high size and cost, and for allowing the analysis of only few yarn quality parameters. The main findings/results of the study are the yarn analysis method as well as the developed algorithm, which allows the analysis of defects in a more precise way. Thus, this paper presents the development and results obtained with the design of a mechatronic prototype integrating a computer vision system that allows, among other parameters, the analysis and classification, in real time, of the hairs of the yarn using artificial intelligence techniques. The system also determines other characteristics inherent to the yarn quality analysis, such as: linear mass, diameter, volume, twist orientation, twist step, average mass deviation, coefficient of variation, hairiness coefficient, average hairiness deviation, and standard hairiness deviation, as well as performing spectral analysis. A comparison of the obtained results with the designed system and a commercial equipment was performed validating the undertaken methodology.

show abstract

Section: Developed Prototypementioning

confidence: 99%

Section: Alphanumeric Reference Numerical Referencementioning

confidence: 99%

See 1 more Smart Citation

Intelligent Computer Vision System for Analysis and Characterization of Yarn Quality

et al. 2023

Self Cite

View full text Add to dashboard Cite

show abstract

Section: Developed Prototypementioning

confidence: 99%

“…Finally, after the wire passes through the second positive feeder ( 9), the wire enters the winding phase (C), being led out of the box and into the grooved cylinder (11), which is aligned with the winding spool. winding (12) and winds the yarn around it, controlling the winding direction [32].…”

Section: Textile Yarn Winding and Unwinding Processmentioning

confidence: 99%

Intelligent Computer Vision System for Analysis and Characterization of Yarn Quality

Pereira¹,

Macêdo²,

Ferreira-Pinto³

et al. 2022

Preprint

View full text Add to dashboard Cite

The quality of yarn is essential in the control of the fabrics processes. There is some commercial equipment that measures the quality of yarn based on sensors, of different types, used for collecting data about some yarn characteristic parameters. The existing equipment, for the above-mentioned purpose, is characterized by its high size and cost, and for allowing the analysis of only few yarn quality parameters. Thus, this paper presents the development and results obtained with the design of a mechatronic prototype integrating a computer vision system that allows, among other parameters, the analysis and classification, in real time, of the hairs of the yarn using artificial intelligence techniques. The system also determines other characteristics inherent to the yarn quality analysis, such as: linear mass, diameter, volume, twist orientation, twist step, average mass deviation, coefficient of variation, hairiness coefficient, average hairiness deviation and standard hairiness deviation, as well as performs spectral analysis. A comparison of the obtained results with the designed system and a commercial equipment was performed validating the undertaken methodology.

show abstract