Monitoring Technology for Yarn Feeding Status in Circular Knitting Machines
Feb 21, 2024
In the knitting process of circular knitting machines, timely monitoring of yarn conveying status is essential to diagnose common faults like yarn breakage and running. This paper analyzes a method for monitoring yarn feeding on circular knitting machines and proposes an external monitoring scheme based on the infrared sensitization principle to address this need. Utilizing photoelectric signal processing technology, we design a comprehensive framework for yarn motion monitoring, including key hardware circuits and software algorithms. Through experimental tests and on-machine debugging, our proposed scheme demonstrates the capability to monitor yarn movement characteristics in real-time, improving fault diagnosis accuracy and promoting dynamic detection technology in Chinese-made circular welt knitting machines.
Introduction:
In recent years, advancements in high-speed mechanical sensors, piezoelectric sensors, capacitive sensors, and efficient yarn breakage detection have revolutionized yarn monitoring in circular knitting machines. However, existing methods have limitations in accurately detecting yarn movement during operation. This paper explores innovative approaches to address these challenges and enhance the reliability of yarn monitoring systems in circular knitting machines.
Current Challenges and Limitations:
While piezoelectric sensors can detect yarn breakage, they often require direct contact with the yarn, leading to additional tension and affecting product quality. Similarly, electro-mechanical sensors have limitations in accurately detecting yarn movement, particularly in complex working conditions. Capacitive and fluid sensors, while capable of detecting yarn faults, are sensitive to environmental factors and may not be suitable for circular weft machines.
Proposed Solution:
To overcome these limitations, our proposed solution utilizes an external monitoring scheme based on the infrared sensitization principle. This approach allows for non-contact monitoring of yarn feeding status, minimizing interference with the knitting process and improving fault diagnosis accuracy. Leveraging photoelectric signal processing technology, we design a robust framework for yarn motion monitoring, incorporating advanced hardware circuits and software algorithms.
Innovative Approach:
Unlike traditional sensors, our method enables real-time monitoring of yarn movement characteristics without direct contact with the yarn. By analyzing infrared signals, we can accurately detect yarn breakage and running, enhancing product quality and production efficiency. Moreover, our solution is cost-effective and adaptable to the complex working conditions of circular weft machines.
Conclusion:
The proposed monitoring technology represents a significant advancement in yarn monitoring for circular knitting machines. By addressing the limitations of existing methods and introducing innovative approaches, we can improve fault diagnosis accuracy and promote the adoption of dynamic detection technology in Chinese-made circular welt knitting machines.
Introduction:
In recent years, advancements in high-speed mechanical sensors, piezoelectric sensors, capacitive sensors, and efficient yarn breakage detection have revolutionized yarn monitoring in circular knitting machines. However, existing methods have limitations in accurately detecting yarn movement during operation. This paper explores innovative approaches to address these challenges and enhance the reliability of yarn monitoring systems in circular knitting machines.
Current Challenges and Limitations:
While piezoelectric sensors can detect yarn breakage, they often require direct contact with the yarn, leading to additional tension and affecting product quality. Similarly, electro-mechanical sensors have limitations in accurately detecting yarn movement, particularly in complex working conditions. Capacitive and fluid sensors, while capable of detecting yarn faults, are sensitive to environmental factors and may not be suitable for circular weft machines.
Proposed Solution:
To overcome these limitations, our proposed solution utilizes an external monitoring scheme based on the infrared sensitization principle. This approach allows for non-contact monitoring of yarn feeding status, minimizing interference with the knitting process and improving fault diagnosis accuracy. Leveraging photoelectric signal processing technology, we design a robust framework for yarn motion monitoring, incorporating advanced hardware circuits and software algorithms.
Innovative Approach:
Unlike traditional sensors, our method enables real-time monitoring of yarn movement characteristics without direct contact with the yarn. By analyzing infrared signals, we can accurately detect yarn breakage and running, enhancing product quality and production efficiency. Moreover, our solution is cost-effective and adaptable to the complex working conditions of circular weft machines.
Conclusion:
The proposed monitoring technology represents a significant advancement in yarn monitoring for circular knitting machines. By addressing the limitations of existing methods and introducing innovative approaches, we can improve fault diagnosis accuracy and promote the adoption of dynamic detection technology in Chinese-made circular welt knitting machines.
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