Robotics and Automation in Textile Industry

Jindal, Harsh; Kaur, Spinder

doi:10.32628/ijsrset21839

Cited by 8 publications

(1 citation statement)

References 6 publications

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“…Robotics has expanded beyond its initial domain, finding universal application in both industrial and textile industries, yielding substantial benefits. Particularly in the textile sector, robotics is employed to reduce labor-intensive processes, and today, automation is synonymous with robotics, driven by computers enhancing efficiency and productivity, especially in textile-related applications [23].…”

Section: Integrating Robotics For Transformative Innovations Into Gar...mentioning

confidence: 99%

Revolutionizing the Garment Industry 5.0: Embracing Closed-Loop Design, E-Libraries, and Digital Twins

Donmezer,

Demircioglu,

Bogrekci

et al. 2023

Sustainability

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This study presents an innovative approach for modernizing the garment industry through the fusion of digital human modeling (DHM), virtual modeling for fit sizing, ergonomic body-size data, and e-library resources. The integration of these elements empowers manufacturers to revolutionize their clothing design and production methods, leading to the delivery of unparalleled fit, comfort, and personalization for a wide range of body shapes and sizes. DHM, known for its precision in representing human bodies virtually and integrating anthropometric data, including ergonomic measurements, enhances the shopping experience by providing valuable insights. Consumers gain access to the knowledge necessary for making tailored clothing choices, thereby enhancing the personalization and satisfaction of their shopping experience. The incorporation of e-library resources takes the garment design approach to a data-driven and customer-centric level. Manufacturers can draw upon a wealth of information regarding body-size diversity, fashion trends, and customer preferences, all sourced from e-libraries. This knowledge supports the creation of a diverse range of sizes and styles, promoting inclusivity and relevance. Beyond improving garment fit, this comprehensive integration streamlines design and production processes by reducing the reliance on physical prototypes. This not only enhances efficiency but also contributes to environmental responsibility, fostering a more sustainable and eco-friendly future for the garment industry and embracing the future of fashion, where technology and data converge to create clothing that authentically fits, resonates with consumers, and aligns with the principles of sustainability. This study developed the mobile application integrating with the information in cloud database in order to present the best-suited garment for the user.

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Section: Integrating Robotics For Transformative Innovations Into Gar...mentioning

confidence: 99%

Revolutionizing the Garment Industry 5.0: Embracing Closed-Loop Design, E-Libraries, and Digital Twins

Donmezer,

Demircioglu,

Bogrekci

et al. 2023

Sustainability

View full text Add to dashboard Cite

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Manipulation of Deformable Objects with a Multi-robot System

Valdeolmillos,

Sagüés,

Herguedas

2024

Lecture Notes in Networks and Systems

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Lightweight bobbin yarn detection model for auto-coner with yarn bank

Dai,

Hu,

et al. 2024

Sci Rep

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The automated replacement of empty tubes in the yarn bank is a critical step in the process of automatic winding machines with yarn banks, as the real-time detection of depleted yarn on spools and accurate positioning of empty tubes directly impact the production efficiency of winding machines. Addressing the shortcomings of traditional methods, such as poor adaptability and low sensitivity in optical and visual tube detection, and aiming to reduce the computational and detection time costs introduced by neural networks, this paper proposes a lightweight yarn spool detection model based on YOLOv8. The model utilizes Darknet-53 as the backbone network, and due to the dense spatial distribution of yarn spool targets, it incorporates large selective kernel units to enhance the recognition and positioning of dense targets. To address the issue of excessive focus on local features by convolutional neural networks, a bi-level routing attention mechanism is introduced to capture long-distance dependencies dynamically. Furthermore, to balance accuracy and detection speed, a FasterNeck is constructed as the neck network, replacing the original convolutional blocks with Ghost convolutions and integrating with FasterNet. This design minimizes the sacrifice of detection accuracy while achieving a significant improvement in inference speed. Lastly, the model employs weighted IoU with a dynamic focusing mechanism as the bounding box loss function. Experimental results on a custom yarn spool dataset demonstrate a notable improvement over the baseline model, with a high-confidence mAP of 94.2% and a compact weight size of only 4.9 MB. The detection speed reaches 223FPS, meeting the requirements for industrial deployment and real-time detection.

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Robotics and Automation in Textile Industry

Cited by 8 publications

References 6 publications

Revolutionizing the Garment Industry 5.0: Embracing Closed-Loop Design, E-Libraries, and Digital Twins

Revolutionizing the Garment Industry 5.0: Embracing Closed-Loop Design, E-Libraries, and Digital Twins

Manipulation of Deformable Objects with a Multi-robot System

Lightweight bobbin yarn detection model for auto-coner with yarn bank

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