An embroidery thread tension control system and method for an embroidery machine

Abstract

The application discloses an embroidery thread tension control system and method, and relates to the technical field of tension control, which 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 to predict the tension at a future time point in combination with past tension changes and current tension. The tension prediction unit can predict the tension at the future time point, and can timely adjust the embroidery thread tension. The tension optimization unit optimizes the current time tension adjustment amount, and adjusts the predicted tension weight according to the adaptive prediction adjustment factor result, so that the weight of the prediction and the error can be adjusted according to the historical feedback. The application avoids excessive dependence on the predicted tension, reduces error accumulation and excessive adjustment, improves the precision of the embroidery thread tension control, and improves the quality of the final embroidery product.

Description

Technical Field

[0001] This invention relates to the field of tension control technology, specifically to a tension control system and method for embroidery thread in an embroidery machine. Background Technology

[0002] An embroidery machine is a machine used to create patterns on textiles. It achieves the embroidery function through sewing or embroidery machines. With technological advancements, embroidery machines have gradually evolved into computerized embroidery machines. These are advanced devices that combine artificial intelligence embroidery software with precise mechanical control. Computerized embroidery machines can achieve high-speed, high-efficiency embroidery and can fulfill the "multi-layered, multi-functional" requirements that are impossible to achieve with hand embroidery. In modern custom clothing, computerized embroidery machines are commonly used for various embroidery techniques such as flat embroidery, three-dimensional embroidery, appliqué embroidery, and towel embroidery. These techniques not only inherit the characteristics of traditional Chinese hand-embroidered silk threads but also incorporate buttonhole designs from lace, making garments more exquisite 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 can also cause problems such as thread breakage and loop jumping. Traditional control methods cannot adapt to complex dynamic changes in a timely manner, resulting in untimely tension adjustment and an inability to adjust based on the adjustment effect. Consequently, the precision of embroidery thread tension control is low, leading to poor embroidery quality. Summary of the Invention

[0004] The purpose of this invention is to provide a tension control system and method for embroidery thread in an embroidery machine, which solves the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: 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 predict the tension at future time points by combining past tension changes and current tension. The dynamic adjustment unit, by combining the predicted tension at future time points and the target tension parameter, derives the current time tension adjustment amount ΔT. adj (t), the tension optimization unit is used to adjust the current time tension amount ΔT by combining historical adjustment effects. adj (t) is optimized to obtain the final adjustment amount ΔT. final ; The execution module is based on the final adjustment amount ΔTfinal Adjust the current tension of the embroidery thread.

[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, the signal processing unit including filtering, amplification and sampling processing.

[0007] Optionally, the tension prediction unit's prediction process 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) represents the current time-predicted tension; ΔT avg (t) is the average rate of change of tension over a past period of time; α is a smoothing factor, ranging 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. Specifically: ; Where n is the number of historical time points; By predicting the tension of embroidery thread in the future based on historical data, it is possible to foresee the trend of changes in embroidery thread tension over a period of time.

[0008] Optionally, the adjustment process of the dynamic adjustment unit is as follows: ; Where ΔT adj (t) represents the current time tension adjustment amount; K(t) is a dynamic adjustment coefficient used to control the dynamic adjustment intensity, and its value ranges from 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 current time tension error; β is the predicted tension weight, with a value ranging from 0 to 1; γ is the tension error weight, with a value ranging from 0 to 1; Specifically: ; Where T tar It 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 This is the final adjustment amount; ΔT adj (t) represents the current time tension adjustment amount; T pred (t+1) is the predicted tension at time t+1; T tar It is the target tension; PAF is an adaptive predictive adjustment factor used to dynamically adjust tension based on historical adjustment effects and current prediction bias. Specifically: ; Where n is the number of historical time points; ΔT adj (i) represents the tension adjustment amount at the i-th historical time point; T i It is the actual tension at the i-th historical point in time; W is a weighting factor used to adjust the PAF's dependence on historical data, and its value ranges from 0 to 1. e is a positive number to avoid the denominator being zero; If the adaptive prediction adjustment factor PAF is positive, 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 negative, 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 positive, 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. In this case, increasing the predicted tension weight β in the tension prediction unit helps to enhance the role of predicted tension in the final adjustment. When the adaptive prediction adjustment factor PAF is negative, it indicates that the historical adjustment effect is inconsistent with the direction of tension change. In this case, decreasing the predicted tension weight β reduces the impact of predicted tension on the adjustment. Specifically: ; Where β new It is a new predicted tension weight; d is an adjustment constant used to control the sensitivity of PAF to β adjustment; When the adaptive prediction adjustment factor PAF is positive, β new Increase, and vice versa.

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

[0012] Optionally, a data storage module is also included, which is used to store the embroidery machine's operating data, including sensor data, control parameters, system status, historical logs, and fault records, for later use.

[0013] This invention provides the following technical solution: a method for controlling the tension of embroidery thread in an embroidery machine, comprising the following steps: S1. Real-time monitoring of embroidery thread tension via tension detection module; S2. By using the tension prediction unit and combining past tension changes with the current tension, predict the tension at future time points; S3. By using the dynamic adjustment unit and combining the predicted tension at future time points with the target tension parameters, the current time tension adjustment amount ΔT is obtained. adj (t); S4. Adjust the current time tension amount ΔT using the tension optimization unit and in conjunction with historical adjustment effects. adj (t) is optimized to obtain the final adjustment amount ΔT. final ; S5. The execution module determines the final adjustment amount ΔT. final Adjust the current tension of the embroidery thread.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: I. The tension prediction unit of this invention predicts the tension at future points in time by combining the average rate of change of tension in the past with the current tension. By predicting the tension of the embroidery thread in the future, the staff can effectively deal with unexpected situations and make timely adjustments before the tension of the embroidery thread deviates from the set value. This reduces response time and delays in the control process, avoids the accumulation of errors, prevents the system from having a lag reaction, ensures more precise tension control during the embroidery process, improves the quality of the embroidery, and by setting a smoothing factor α, it can be adjusted according to the actual embroidery situation, improving the flexibility of the system and ensuring the quality of the embroidery.

[0015] Second, this invention obtains the required adjustment value of the embroidery thread tension through a dynamic adjustment unit, i.e., the current time tension adjustment amount. Then, the tension optimization unit optimizes the current time tension adjustment amount and combines it with historical adjustment effects to obtain the final adjustment amount. 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 weight of prediction and error can be adjusted according to historical feedback. This avoids over-reliance on predicted tension, avoids repeating past mistakes, reduces error accumulation and over-adjustment, ensures the stability of the embroidery thread tension adjustment process, and improves the accuracy of embroidery thread tension control. The various algorithms are interconnected and influence each other, jointly improving the quality of the final embroidery. Attached Figure Description

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

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] For examples, please refer to 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 combines past tension changes with the current tension to predict the tension at future points in time. The dynamic adjustment unit combines the predicted tension at future points in time with the target tension parameter to derive the current tension adjustment amount ΔT. adj (t), the tension optimization unit is used to adjust the current time tension amount ΔT by combining historical adjustment effects. adj (t) is optimized to obtain the final adjustment amount ΔT. final ; The execution module is based on the final adjustment amount ΔT final Adjust the current tension of the embroidery thread.

[0019] In this embodiment, the tension detection module monitors the tension data of the embroidery thread in real time during the operation of the embroidery machine. 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 rate of tension change with 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, allows staff to effectively respond to unexpected situations, effectively avoids the accumulation of errors, prevents the system from having a lag response, ensures more precise tension control during the embroidery process, and improves the embroidery quality. Then, the dynamic adjustment unit predicts the tension T based on time t+1. pred Using (t+1) and the target tension of the embroidery thread, we can determine the value that the embroidery thread tension needs to be adjusted, i.e., the tension adjustment amount ΔT at the current time. adj (t), and finally the tension adjustment amount ΔT at the current time is adjusted by the tension optimization unit. adj(t) is optimized, and the final adjustment amount 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 prevented from being repeated, error accumulation and over-adjustment are reduced, ensuring the smoothness of the embroidery thread tension adjustment process and improving the accuracy of embroidery thread tension control.

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

[0021] Specifically, the data conversion unit converts the embroidery thread tension data for subsequent processing. Then, the signal processing unit filters, amplifies, and samples the data. Filtering removes electromagnetic interference, vibration noise, and other contaminants from the tension data, ensuring a clearer and more accurate signal. Amplification increases signal strength and avoids distortion. Sampling converts continuous-time analog signals into discrete-time digital signals for easier digital processing. Thus, the signal processing unit can process the acquired embroidery thread tension data to improve data quality for subsequent analysis and enhance control precision.

[0022] Furthermore, the tension prediction unit's prediction process 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) represents the current time-predicted tension; ΔT avg (t) is the average rate of change of tension over a past period of time; α is a smoothing factor, ranging 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. Specifically: ; Where n is the number of historical time points; Specifically, by predicting the embroidery thread tension in the future based on historical data, the trend of embroidery thread tension changes over a period of time can be predicted in advance. This allows for adjustments to be made before the embroidery thread tension deviates from the set value, reducing response time and delays in the control process, avoiding the accumulation of errors, preventing system lag, ensuring more precise tension control during embroidery, improving embroidery quality, and allowing for adjustment based on actual embroidery conditions by setting a smoothing factor α. If the embroidery quality is good under historical tension conditions, the α value can be appropriately reduced to improve the flexibility of the system. Furthermore, the adjustment process of the dynamic adjustment unit is as follows: ; Where ΔT adj (t) represents the current time tension adjustment amount; K(t) is a dynamic adjustment coefficient used to control the dynamic adjustment intensity, and its value ranges from 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 current time tension error; β is the predicted tension weight, with a value ranging from 0 to 1; γ is the tension error weight, with a value ranging from 0 to 1; Specifically: ; Where T tar It is the target tension; T(t) is the actual tension at the current time.

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

[0024] Furthermore, the optimization process of the tension optimization unit is as follows: ; Where ΔT final This is the final adjustment amount; ΔT adj (t) represents the current time tension adjustment amount; T pred (t+1) is the predicted tension at time t+1; T tar It is the target tension; PAF is an adaptive predictive adjustment factor used to dynamically adjust tension based on historical adjustment effects and current prediction bias. Specifically: ; Where n is the number of historical time points; ΔT adj (i) represents the tension adjustment amount at the i-th historical time point; T i It is the actual tension at the i-th historical point in time; W is a weighting factor used to adjust the PAF's dependence on historical data, and its value ranges from 0 to 1. e is a positive number to avoid the denominator being zero; Specifically, if the adaptive prediction adjustment factor (PAF) is positive, it indicates that the adjustment direction is consistent with the actual tension change direction, and the PAF will enhance the adjustment effect. When PAF is negative, it indicates that the adjustment direction is incorrect, and the PAF will reduce the adjustment effect. The tension optimization unit combines historical adjustment effects with current prediction deviations, enabling the embroidery tension control system to adjust the tension more accurately and reduce error accumulation. During the operation of the embroidery machine, the tension fluctuates greatly due to factors such as machine movement and material properties. PAF can adjust the adjustment strategy in real time based on historical adjustment data and the current predicted tension trend, thereby improving the system's adaptability to sudden changes. By adjusting the weights of prediction and error through historical feedback, it avoids over-reliance on predicted tension, reduces error accumulation and over-adjustment, ensures the smoothness of the embroidery tension adjustment process, and improves the accuracy of embroidery tension control.

[0025] Furthermore, when the adaptive prediction adjustment factor PAF is positive, 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 adjustment. In this case, increasing the predicted tension weight β in the tension prediction unit helps to enhance the role of predicted tension in the final adjustment. When the adaptive prediction adjustment factor PAF is negative, it indicates that the historical adjustment effect is inconsistent with the direction of tension change. In this case, decreasing the predicted tension weight β reduces the impact of predicted tension on adjustment. Specifically: ; Where β new It is a new predicted tension weight; d is an adjustment constant used to control the sensitivity of PAF to β adjustment; Specifically, when the adaptive prediction adjustment factor PAF is positive, β new The system can adaptively adjust the tension of the embroidery thread based on the actual effect of the tension, thereby maintaining high adjustment accuracy in complex operating environments. This avoids inaccurate control due to outdated or unsuitable parameters and improves the quality of the final embroidery.

[0026] Furthermore, the display module is used to display the embroidery machine's operating data to the staff in real time. 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 presents this data in the form of graphics, numbers, and graphs, so that the staff can quickly understand the current tension of the embroidery thread. The control panel provides manual adjustment of parameters, starting or stopping the equipment, setting the operating mode, etc., so that in case of emergencies, the staff can manually adjust the machine and stop the damage in time.

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

[0029] Specifically, by setting up a data storage module, the operating data of the embroidery machine thread tension control system is ensured to be retained for a long time. Staff can review historical data at any time, view past operation records, sensor outputs and system status, so as to facilitate later maintenance, fault diagnosis and performance analysis.

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

Claims

1. A thread tension control system for an embroidery machine, characterized in that, It includes 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 configured to predict the tension at a future time point in combination with past tension changes and the current tension, and the dynamic adjustment unit is configured to derive a current time tension adjustment amount ΔT adj (t) by combining the predicted tension at the future time point and a target tension parameter adj The tension optimization unit is configured to optimize the current time tension adjustment amount ΔT final (t) in combination with historical adjustment effects to obtain a final adjustment amount ΔT The execution module is based on the final adjustment amount ΔT final Adjust the current tension of the embroidery thread.

2. The embroidery thread tension control system for an embroidery machine 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, 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.

3. The embroidery thread tension control system for an embroidery machine according to claim 2, characterized in that: The tension prediction unit's prediction process 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) represents the current time-predicted tension; ΔT avg (t) is the average rate of change of tension over a past period of time; α is a smoothing factor, ranging 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; By predicting the tension of embroidery thread in the future based on historical data, it is possible to foresee the trend of embroidery thread tension changes in the future. The adjustment process of the dynamic adjustment unit is as follows: ； Where ΔT adj (t) represents the current time tension adjustment amount; K(t) is a dynamic adjustment coefficient used to control the dynamic adjustment intensity, and its value ranges from 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, with a value ranging from 0 to 1; γ is the tension error weight, and its value ranges from 0 to 1; E(t) is the current time tension error, expressed as: ; Where T tar It is the target tension; T(t) is the actual tension at the current time.

4. The embroidery thread tension control system for an embroidery machine according to claim 3, characterized in that: The optimization process of the tension optimization unit is as follows: ; Where ΔT final This is the final adjustment amount; ΔT adj (t) represents the current time tension adjustment amount; T pred (t+1) is the predicted tension at time t+1; T tar It is the target tension; PAF is an adaptive predictive adjustment factor used to dynamically adjust tension based on historical adjustment effects and current prediction bias. Specifically: ; Where n is the number of historical time points; ΔT adj (i) represents the tension adjustment amount at the i-th historical time point; T i It is the actual tension at the i-th historical point in time; W is a weighting factor used to adjust the PAF's dependence on historical data, and its value ranges from 0 to 1. e is a positive number used to avoid the denominator being zero.

5. The embroidery thread tension control system for an embroidery machine according to claim 4, characterized in that: When the adaptive prediction adjustment factor PAF is positive, 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 negative, it means that the adjustment direction is incorrect, and PAF will reduce the adjustment effect.

6. The embroidery thread tension control system for an embroidery machine according to claim 5, characterized in that: When the adaptive prediction adjustment factor PAF is positive, 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. In this case, increasing the predicted tension weight β in the tension prediction unit helps to enhance the role of predicted tension in the final adjustment. When the adaptive prediction adjustment factor PAF is negative, it indicates that the historical adjustment effect is inconsistent with the direction of tension change. In this case, decreasing the predicted tension weight β reduces the impact of predicted tension on the adjustment. Specifically: ； Where β new It is a new predicted tension weight; d is an adjustment constant used to control the sensitivity of PAF to β adjustment; When the adaptive prediction adjustment factor PAF is positive, β new Increase, and vice versa.

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

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

9. The control method of the embroidery thread tension control system of the embroidery machine according to claim 1, characterized in that, Includes the following steps: S1. Real-time monitoring of embroidery thread tension via tension detection module; S2. By using the tension prediction unit and combining past tension changes with the current tension, predict the tension at future time points; S3. By using the dynamic adjustment unit and combining the predicted tension at future time points with the target tension parameters, the current time tension adjustment amount ΔT is obtained. adj (t); S4. Adjust the current time tension amount ΔT by using the tension optimization unit and combining historical adjustment effects. adj (t) is optimized to obtain the final adjustment amount ΔT. final ; S5. The execution module determines the final adjustment amount ΔT. final Adjust the current tension of the embroidery thread.

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