Embroidery thread tension control system and method of embroidery machine

Abstract

The invention discloses an embroidery thread tension control system and method of an embroidery machine, and relates to the technical field of tension control, the embroidery thread tension control system comprises a tension detection module, a tension control module, an execution module and a display module, the tension control module comprises a tension prediction unit, a dynamic adjustment unit and a tension optimization unit, the tension prediction unit is used for predicting the tension at the future time point by combining the past tension change and the current tension, the tension at the future time point is predicted through the tension prediction unit, the tension of the embroidery thread can be adjusted in time, the tension adjustment amount at the current time is optimized through the tension optimization unit, and the adjustment accuracy of the embroidery thread is improved. Meanwhile, the prediction tension weight is adjusted according to the self-adaptive prediction adjustment factor result, so that the prediction and error weight can be adjusted according to historical feedback, excessive dependence on the prediction tension is avoided, error accumulation and excessive adjustment are reduced, the embroidery thread tension control precision is improved, and the final embroidery quality is improved.

Description

Technical Field

[0001] The invention relates to the technical field of tension control, in particular to an embroidery thread tension control system and method for an embroidery machine. Background Art

[0002] An embroidery machine is a machine used to create patterns on textiles. It achieves the embroidery function by means of a sewing machine or an embroidery machine. With the development of technology, embroidery machines have gradually evolved into computer embroidery machines, which are advanced equipment that combines artificial intelligence embroidery software and precision mechanical control. Computer embroidery machines can achieve high-speed and high-efficiency embroidery, and can meet the "multi-level and multi-functional" requirements that manual embroidery cannot achieve. In modern clothing customization, computer embroidery machines are often used for a variety of embroidery techniques such as flat embroidery, three-dimensional embroidery, applique embroidery, and towel embroidery. These techniques not only inherit the characteristics of traditional Chinese hand-made silk embroidery, but also absorb the buttonhole design of lace, making the clothing more refined and stylish.

[0003] During the embroidery process, the tension of the embroidery thread is prone to fluctuations due to changes in factors such as machine movement, stitch density, embroidery speed, and material properties. Tension fluctuations not only lead to poor embroidery quality, but may also cause problems such as thread breakage and coil jumping. Traditional control methods cannot adapt to complex dynamic changes in time, resulting in untimely tension adjustment and inability to adjust according to the adjustment effect. The embroidery thread tension control accuracy is low, resulting in poor embroidery quality. Summary of the invention

[0004] The object of the present invention is to provide an embroidery thread tension control system and method for an embroidery machine, which solves the problems raised in the above-mentioned background technology.

[0005] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: an embroidery thread tension control system for an embroidery machine, comprising a tension detection module, a tension control module, an execution module, and a display module; The tension detection module includes a sensor unit, a data conversion unit and a signal processing unit. The sensor unit is used to monitor the tension of the embroidery thread in real time. The tension control module includes a tension prediction unit, a dynamic adjustment unit and a tension optimization unit; The tension prediction unit is used to combine past tension changes and current tension to predict the tension at a future time point. The dynamic adjustment unit obtains the current time tension adjustment value ΔT by combining the predicted tension at the future time point and the target tension parameter. adj (t), the tension optimization unit is used to adjust the current time tension ΔT in combination with the historical adjustment effect adj (t) is optimized to obtain the final adjustment value ΔT final ; The execution module is based on the final adjustment amount ΔTfinal Adjust the current embroidery thread tension.

[0006] Optionally, the data conversion unit is used to convert the data detected by the sensor unit into an electrical signal, and the signal processing unit is used to improve the quality of the electrical signal, and the signal processing unit includes filtering, amplification and sampling processing.

[0007] Optionally, the tension prediction unit predicts the process as follows: ; Where T pred (t+1) is the predicted tension at time t+1; T(t) is the actual tension at the current time; T pred (t) is the predicted tension at the current time; ΔT avg (t) is the average rate of change of tension over the past period of time; α is the smoothing factor, which ranges from 0 to 1. A large α value indicates a strong dependence on the actual tension at the current time, while a small α value indicates a strong dependence on historical data; Specific: ; Where n is the number of historical time points; By predicting the embroidery thread tension in the future based on historical data, the changing trend of the embroidery thread tension in the future can be predicted in advance.

[0008] Optionally, the dynamic adjustment unit adjustment process is as follows: ; Where ΔT adj (t) is the tension adjustment at the current time; K(t) is the dynamic adjustment coefficient, which is used to control the dynamic adjustment strength, and its value range is 0 to 1; T pred (t+1) is the predicted tension at time t+1; T(t) is the actual tension at the current time; E(t) is the tension error at the current time; β is the predicted tension weight, ranging from 0 to 1; γ is the tension error weight, ranging from 0 to 1; Specific: ; Where T tar is the target tension; T(t) is the actual tension at the current time.

[0009] Optionally, the optimization process of the tension optimization unit is as follows: ; Where ΔT final is the final adjustment amount; ΔT adj (t) is the tension adjustment at the current time; T pred (t+1) is the predicted tension at time t+1; T tar is the target tension; PAF is an adaptive prediction adjustment factor, which is used to dynamically adjust tension based on historical adjustment effects and current prediction deviations; Specifically: ; Where n is the number of historical time points; ΔT adj (i) is the tension adjustment at the i-th historical time point; T i is the actual tension at the ith historical time point; W is the weight factor, which is used to adjust the degree of dependence of PAF on historical data, and its value range is 0 to 1; e is a positive number used to avoid the denominator being zero; If the adaptive prediction adjustment factor PAF is a positive number, it means that the adjustment direction is consistent with the actual tension change direction, and the adaptive prediction adjustment factor PAF will enhance the adjustment effect. When PAF is a negative number, it means that the adjustment direction is wrong, and PAF will reduce the adjustment effect.

[0010] Optionally, when the adaptive prediction adjustment factor PAF is a positive number, it indicates that the historical adjustment effect is consistent with the direction of tension change, and the predicted value of future tension has a greater impact on the adjustment. At this time, the predicted tension weight β in the tension prediction unit is increased, and increasing β helps to enhance the role of the predicted tension in the final adjustment. When the adaptive prediction adjustment factor PAF is a negative number, it indicates that the historical adjustment effect is inconsistent with the direction of tension change. At this time, the predicted tension weight β is reduced to reduce the impact of the predicted tension on the adjustment, specifically: ; where β new is the new predicted tension weight; d is a tuning constant that controls the sensitivity of the PAF to β adjustment; When the adaptive prediction adjustment factor PAF is positive, β new Increases, otherwise decreases.

[0011] Optionally, the display module is used to display the embroidery machine operation data to the staff in real time, and the display module includes a graphical interface and a control panel.

[0012] Optionally, it also includes a data storage module, which is used to store the embroidery machine operation data, including sensor data, control parameters, system status, historical logs, and fault records, for subsequent use.

[0013] The present invention provides the following technical solution: a method for controlling the tension of embroidery thread of an embroidery machine, comprising the following steps: S1, real-time monitoring of embroidery thread tension through the tension detection module; S2, predicting the tension at a future time point by combining past tension changes and current tension through a tension prediction unit; S3, by dynamically adjusting the unit and combining the predicted tension at the future time point and the target tension parameter, the current time tension adjustment value ΔT is obtained adj (t); S4. The tension adjustment value ΔT at the current time is adjusted by the tension optimization unit and combined with the historical adjustment effect. adj (t) is optimized to obtain the final adjustment value ΔT final ; S5, the execution module is used to adjust the final amount ΔT final Adjust the current embroidery thread tension.

[0014] Compared with the prior art, the present invention has the following beneficial effects: 1. The tension prediction unit of the present invention predicts the tension at a future time point by combining the past average tension change rate and the current tension. By predicting the embroidery thread tension at a future time, the staff can effectively respond to emergencies and make corresponding adjustments in time before the embroidery thread tension deviates from the set value, thereby reducing the response time and the delay in the control process, avoiding the accumulation of errors, preventing the system from having a lagging reaction, ensuring that the tension control during the embroidery process is more accurate, and improving the embroidery quality. By setting the smoothing factor α, it can be adjusted according to the actual embroidery situation, thereby improving the flexibility of the system and ensuring the quality of the embroidery.

[0015] 2. The present invention obtains the value of the embroidery thread tension that needs to be adjusted, that is, the current time tension adjustment amount, through the dynamic adjustment unit, and then optimizes the current time tension adjustment amount through the tension optimization unit, and obtains the final adjustment amount in combination with the historical adjustment effect. At the same time, the predicted tension weight in the tension prediction unit is adjusted through the adaptive prediction adjustment factor PAF result, so that the weights of the prediction and error can be adjusted according to historical feedback, avoiding excessive reliance on predicted tension, avoiding repeating past mistakes, reducing error accumulation and excessive adjustment, ensuring the stability of the embroidery thread tension adjustment process, and improving the accuracy of embroidery thread tension control. The algorithms are interconnected and influence each other, and jointly improve the quality of the final embroidery product. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a system block diagram of the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] For examples, see Figure 1 ,This embodiment provides an embroidery machine thread tension control system, including a tension detection module, a tension control module, an execution module, and a display module; The tension detection module includes a sensor unit, a data conversion unit and a signal processing unit. The sensor unit is used to monitor the tension of the embroidery thread in real time. The tension control module includes a tension prediction unit, a dynamic adjustment unit and a tension optimization unit; The tension prediction unit is used to combine the past tension changes and the current tension to predict the tension at a future time point. The dynamic adjustment unit combines the predicted tension at a future time point and the target tension parameter to obtain the current time tension adjustment value ΔT. adj (t), the tension optimization unit is used to adjust the current tension value ΔT based on the historical adjustment effect adj (t) is optimized to obtain the final adjustment value ΔT final ; The execution module is based on the final adjustment value ΔT final Adjust the current embroidery thread tension.

[0019] In this embodiment, the tension detection module monitors the embroidery thread tension data in real time during the operation of the embroidery machine, and after data conversion and signal processing, the quality of the collected data is improved. Then, the tension prediction unit predicts the tension at a future time point by combining the past average tension change rate and the current tension, and obtains the predicted tension T at time t+1. pred (t+1), by predicting the embroidery thread tension in the future, the staff can effectively respond to emergencies, effectively avoid the accumulation of errors, prevent the system from lagging, ensure more accurate tension control during the embroidery process, and improve the embroidery quality. Then, the tension T is predicted according to time t+1 through the dynamic adjustment unit. pred (t+1) and the target tension of the embroidery thread, and the value of the embroidery thread tension that needs to be adjusted is obtained, that is, the tension adjustment value at the current time ΔT adj (t), and finally the tension optimization unit adjusts the current time tension by ΔT adj(t) is optimized and the final adjustment value ΔT is obtained by combining the historical adjustment effect final , by adjusting the weights of prediction and error through historical feedback, over-reliance on predicted tension is avoided, past mistakes are avoided, error accumulation and over-adjustment are reduced, the stability of the embroidery thread tension adjustment process is ensured, and the accuracy of embroidery thread tension control is improved.

[0020] Furthermore, the data conversion unit is used to convert the data detected by the sensor unit into an electrical signal, and the signal processing unit is used to improve the quality of the electrical signal. The signal processing unit includes filtering, amplification and sampling processing.

[0021] Specifically, the embroidery thread tension data is converted through a data conversion unit for subsequent processing, and then the data is filtered, amplified and sampled through a signal processing unit. The filtering removes electromagnetic interference, vibration noise, etc. in the tension data to ensure that the required signal is clearer and more accurate. The amplification process is to increase the signal strength and avoid distortion. Sampling is the process of converting continuous-time analog signals into discrete-time digital signals for easy digital processing. The collected embroidery thread tension data can be processed through the signal processing unit to improve data quality for subsequent analysis and improve control accuracy.

[0022] Furthermore, the prediction process of the tension prediction unit is as follows: ; Where T pred (t+1) is the predicted tension at time t+1; T(t) is the actual tension at the current time; T pred (t) is the predicted tension at the current time; ΔT avg (t) is the average rate of change of tension over the past period of time; α is the smoothing factor, which ranges from 0 to 1. A large α value indicates a strong dependence on the actual tension at the current time, while a small α value indicates a strong dependence on historical data; Specific: ; Where n is the number of historical time points; Specifically, by predicting the embroidery thread tension in the future based on historical data, the changing trend of the embroidery thread tension in the future can be predicted in advance, so that corresponding adjustments can be made before the embroidery thread tension deviates from the set value, reducing the response time and the delay in the control process, avoiding the accumulation of errors, and preventing the system from having a lagging reaction, ensuring that the tension control during the embroidery process is more precise and the embroidery quality is improved. By setting the smoothing factor α, it can be adjusted according to the actual embroidery situation. If the embroidery quality is good under the historical tension conditions, the α value can be appropriately reduced to improve the flexibility of the system. Furthermore, the dynamic adjustment unit adjustment process is as follows: ; Where ΔT adj (t) is the tension adjustment at the current time; K(t) is the dynamic adjustment coefficient, which is used to control the dynamic adjustment strength, and its value range is 0 to 1; T pred (t+1) is the predicted tension at time t+1; T(t) is the actual tension at the current time; E(t) is the tension error at the current time; β is the predicted tension weight, ranging from 0 to 1; γ is the tension error weight, ranging from 0 to 1; Specific: ; Where T tar is the target tension; T(t) is the actual tension at the current time.

[0023] Specifically, the predicted tension weight β and the tension error weight γ are adjusted according to different situations, so that the system can adapt to different working conditions, such as load changes, material properties, etc. By considering the current error and the predicted error at the same time, the system state can be adjusted in real time more accurately, reducing the error accumulation caused by system fluctuations. Compared with traditional static adjustment, the adaptability of this system is improved.

[0024] Furthermore, the optimization process of the tension optimization unit is as follows: ; Where ΔT final is the final adjustment amount; ΔT adj (t) is the tension adjustment at the current time; T pred (t+1) is the predicted tension at time t+1; T tar is the target tension; PAF is an adaptive prediction adjustment factor, which is used to dynamically adjust tension based on historical adjustment effects and current prediction deviations; Specifically: ; Where n is the number of historical time points; ΔT adj (i) is the tension adjustment at the i-th historical time point; T i is the actual tension at the ith historical time point; W is the weight factor, which is used to adjust the degree of dependence of PAF on historical data, and its value range is 0 to 1; e is a positive number used to avoid the denominator being zero; Specifically, if the adaptive prediction adjustment factor PAF is a positive number, it means that the adjustment direction is consistent with the actual tension change direction, and the adaptive prediction adjustment factor PAF will enhance the adjustment effect. When PAF is a negative number, it means that the adjustment direction is wrong. At this time, PAF will reduce the adjustment effect. The tension optimization unit combines the historical adjustment effect with the current prediction deviation, so that the embroidery thread tension control system can adjust the tension more accurately and reduce error accumulation. During the operation of the embroidery machine, due to the influence of factors such as machine movement and material properties, the tension fluctuates greatly. PAF can adjust the adjustment strategy in real time according to the historical adjustment data and the current predicted tension trend, thereby improving the system's adaptability to sudden changes. The weights of prediction and error are adjusted through historical feedback, avoiding excessive reliance on predicted tension, reducing error accumulation and excessive adjustment, ensuring the stability of the embroidery thread tension adjustment process, and improving the accuracy of embroidery thread tension control.

[0025] Furthermore, when the adaptive prediction adjustment factor PAF is positive, it means that the historical adjustment effect is consistent with the direction of tension change, and the predicted value of future tension has a greater impact on the adjustment. At this time, the predicted tension weight β in the tension prediction unit is increased. Increasing β helps to enhance the role of predicted tension in the final adjustment. When the adaptive prediction adjustment factor PAF is negative, it means that the historical adjustment effect is inconsistent with the direction of tension change. At this time, the predicted tension weight β is reduced to reduce the impact of predicted tension on the adjustment. Specifically: ; where β new is the new predicted tension weight; d is a tuning constant that controls the sensitivity of the PAF to β adjustment; Specifically, when the adaptive prediction adjustment factor PAF is positive, β new Increase, otherwise decrease, so that the system can be adaptively adjusted according to the actual effect of embroidery thread tension, so that the system can maintain a high adjustment accuracy in a complex operating environment, avoid inaccurate control due to outdated or unsuitable parameters, and improve the quality of the final embroidery.

[0026] Furthermore, the display module is used to display the embroidery machine operation data to the staff in real time, and the display module includes a graphical interface and a control panel.

[0027] Specifically, the graphical interface is used to display the real-time data, system status, alarm information, etc. of the embroidery machine, and present these data and information in the form of graphics, numbers, curve charts, etc., so that the staff can quickly understand the current embroidery thread tension of the embroidery machine. The control panel provides manual adjustment of parameters, start or stop equipment, set operating mode and other operating functions to prevent emergencies. The staff can manually adjust the machine and stop the loss in time.

[0028] Furthermore, it is also used to store the embroidery machine operation data, including sensor data, control parameters, system status, historical logs, and fault records, for subsequent use.

[0029] Specifically, by setting up a data storage module, the operating data of the embroidery machine thread tension control system can be retained for a long time. The staff can trace back historical data at any time to view past operation records, sensor outputs and system status for subsequent maintenance, fault diagnosis and performance analysis.

[0030] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An embroidery thread tension control system for an embroidery machine, characterized in that: It includes tension detection module, tension control module, execution module and display module; The tension detection module includes a sensor unit, a data conversion unit and a signal processing unit, and the sensor unit is used to monitor the tension of the embroidery thread in real time; The tension control module includes a tension prediction unit, a dynamic adjustment unit and a tension optimization unit; The tension prediction unit is used to combine past tension changes and current tension to predict the tension at a future time point. The dynamic adjustment unit obtains the current time tension adjustment value ΔT by combining the predicted tension at the future time point and the target tension parameter. adj (t), the tension optimization unit is used to adjust the current time tension ΔT in combination with the historical adjustment effect adj (t) is optimized to obtain the final adjustment value ΔT final ; The execution module is based on the final adjustment amount ΔT final Adjust the current embroidery thread tension.

2. The embroidery thread tension control system according to claim 1, characterized in that: The data conversion unit is used to convert the data detected by the sensor unit into an electrical signal. The signal processing unit is used to improve the quality of the electrical signal. The signal processing unit includes filtering, amplification and sampling processing.

3. The embroidery thread tension control system according to claim 2, characterized in that: The prediction process of the tension prediction unit is as follows: ； Where T pred (t+1) is the predicted tension at time t+1; T(t) is the actual tension at the current time; T pred (t) is the predicted tension at the current time; ΔT avg (t) is the average rate of change of tension over the past period of time; α is the smoothing factor, which ranges from 0 to 1. A large α value indicates a strong dependence on the actual tension at the current time, while a small α value indicates a strong dependence on historical data; ； Where n is the number of historical time points; Predicting the embroidery thread tension in the future based on historical data can predict the changing trend of embroidery thread tension in the future in advance; The dynamic adjustment unit adjustment process is as follows: ； Where ΔT adj (t) is the tension adjustment at the current time; K(t) is the dynamic adjustment coefficient, which is used to control the dynamic adjustment strength, and its value range is 0 to 1; T pred (t+1) is the predicted tension at time t+1; T(t) is the actual tension at the current time; β is the predicted tension weight, ranging from 0 to 1; γ is the tension error weight, ranging from 0 to 1; E(t) is the tension error at the current time, expressed as: ; Where T tar is the target tension; T(t) is the actual tension at the current time.

4. The embroidery thread tension control system according to claim 3, characterized in that: The optimization process of the tension optimization unit is as follows: ; Where ΔT final is the final adjustment amount; ΔT adj (t) is the tension adjustment at the current time; T pred (t+1) is the predicted tension at time t+1; T tar is the target tension; PAF is an adaptive prediction adjustment factor, which is used to dynamically adjust tension based on historical adjustment effects and current prediction deviations; Specifically: ; Where n is the number of historical time points; ΔT adj (i) is the tension adjustment at the i-th historical time point; T i is the actual tension at the ith historical time point; W is the weight factor, which is used to adjust the degree of dependence of PAF on historical data, and its value range is 0 to 1; e is a positive number used to avoid the denominator being zero.

5. The embroidery thread tension control system according to claim 4, characterized in that: When the adaptive prediction adjustment factor PAF is a positive number, it means that the adjustment direction is consistent with the actual tension change direction, and the adaptive prediction adjustment factor PAF will enhance the adjustment effect. When PAF is a negative number, it means that the adjustment direction is wrong, and PAF will reduce the adjustment effect.

6. The embroidery thread tension control system according to claim 5, characterized in that: When the adaptive prediction adjustment factor PAF is a positive number, it means that the historical adjustment effect is consistent with the direction of tension change, and the predicted value of future tension has a greater impact on the adjustment. At this time, the predicted tension weight β in the tension prediction unit is increased. Increasing β helps to enhance the role of predicted tension in the final adjustment. When the adaptive prediction adjustment factor PAF is a negative number, it means that the historical adjustment effect is inconsistent with the direction of tension change. At this time, the predicted tension weight β is reduced to reduce the impact of predicted tension on the adjustment, specifically: ； where β new is the new predicted tension weight; d is a tuning constant that controls the sensitivity of the PAF to β adjustment; When the adaptive prediction adjustment factor PAF is positive, β new Increases, otherwise decreases.

7. The embroidery thread tension control system according to claim 1, characterized in that: The display module is used to display the embroidery machine operation data to the staff in real time, and the display module includes a graphical interface and a control panel.

8. The embroidery thread tension control system of claim 1, characterized in that: It also includes a data storage module, which is used to store the operating data of the embroidery machine, including sensor data, control parameters, system status, historical logs, and fault records, for subsequent use.

9. The control method of the embroidery thread tension control system of an embroidery machine according to claim 1, characterized in that: The following steps are involved: S1, real-time monitoring of embroidery thread tension through the tension detection module; S2, predicting the tension at a future time point by combining past tension changes and current tension through a tension prediction unit; S3, by dynamically adjusting the unit and combining the predicted tension at the future time point and the target tension parameter, the current time tension adjustment value ΔT is obtained adj (t); S4. The tension adjustment value ΔT at the current time is adjusted by the tension optimization unit and combined with the historical adjustment effect. adj (t) is optimized to obtain the final adjustment value ΔT final ; S5, the execution module is used to adjust the final amount ΔT final Adjust the current embroidery thread tension.

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