Infrared Yarn Monitoring and Circular Knitting Machine

Monitoring yarn feed during the knitting process in circular knitting machines has been challenging, particularly with the timely detection of common issues such as yarn breakage and running yarn. This paper examines a method to monitor yarn feeding in circular knitting machines and proposes an external monitoring scheme based on the principle of infrared sensitization.

Using photoelectric signal processing technology, the study designs an overall framework for yarn motion monitoring, including key hardware circuits and software algorithms. Through experimental tests and on-machine debugging, the proposed scheme demonstrates the ability to monitor yarn movement characteristics during knitting, improving the accuracy of diagnosing common faults such as yarn breakage and yarn running. This advancement also enhances the dynamic detection technology of yarn in circular knitting machines.

In recent years, advancements in high-speed mechanical sensors, piezoelectric sensors, capacitive sensors, and fluid sensors have significantly improved the detection of yarn breakage in circular knitting machines. These sensors play a crucial role in accurately monitoring yarn movement. Piezoelectric sensors detect yarn breakage by changing the signal level during operation, which is critical in detecting the movement of yarn. Electro-mechanical sensors detect yarn breakage based on dynamic signal characteristics but require contact with the yarn, increasing additional tension.

Currently, yarn status is primarily determined by the swinging or rotation of electronic components, which trigger yarn break alarms and affect product quality. These sensors often fail to accurately determine yarn movement. Capacitive sensors detect faults by capturing electrostatic charge effects during yarn transport, while fluid sensors detect changes in fluid flow caused by yarn breakage. However, both capacitive and fluid sensors are sensitive to external environmental factors and cannot adapt to the complex conditions of circular weft machines.

Image detection sensors analyze yarn movement images to determine faults but are expensive. A single knitting weft machine would require dozens or hundreds of image detection sensors to function properly, making widespread use impractical.

To overcome these challenges, this paper proposes an external yarn monitoring scheme based on infrared sensitization. The scheme leverages photoelectric signal processing technology to create a robust framework for monitoring yarn motion. The study designs key hardware circuits and software algorithms to enhance fault detection accuracy.

Experimental tests and on-machine debugging demonstrate that this infrared-based monitoring scheme effectively tracks yarn movement characteristics, significantly improving the diagnosis of common faults like yarn breakage and yarn running. This approach not only enhances fault detection accuracy but also promotes the use of dynamic yarn detection technology in circular knitting machines.

Conclusion

The proposed infrared sensitization monitoring scheme represents a significant improvement over traditional sensor technologies. By providing timely and accurate monitoring of yarn movement, it addresses the limitations of existing sensors and supports more efficient and reliable knitting processes. This advancement holds promise for the future of circular knitting machines, particularly those produced in China.