Control system for water-jet loom

Abstract

The invention relates to a control system for a water-jet loom. A data acquisition module acquires and transmits weft tension, humidity and temperature data in real time. The parameter calculation module calculates the optimal parameters of the water spraying time, the water pressure and the nozzle opening degree through a fuzzy control algorithm according to data changes and transmits the optimal parameters to a water spraying execution mechanism. The tension adjusting module adjusts the nozzle opening degree and the water spraying time according to the comparison result of the weft yarn tension and the preset threshold value. The nozzle optimization module optimizes the nozzle design by means of a computational fluid dynamics approach. The pressure adjusting module monitors pipeline pressure through a pressure sensor, and alarms and adjusts the rotating speed of a water pump and the opening degree of an electromagnetic valve if the pipeline pressure exceeds a threshold value. The state detection module monitors knitting needles and shuttles through the machine vision technology, and water spraying parameters are adjusted when abnormity occurs. The data processing module uploads data to a cloud platform, and big data and machine learning are utilized to optimize a control strategy so as to realize real-time monitoring and accurate control of various parameters of the water-jet loom and guarantee weaving quality and efficiency.

Description

Technical Field

[0001] The invention relates to the technical field of intelligent control, and in particular to a control system for a water jet loom. Background Art

[0002] How to accurately control the timing and amount of water spraying during the high-speed operation of the water jet loom is a key technical problem. Untimely water spraying or insufficient water volume will result in inadequate insertion of the weft yarn, while premature water spraying or excessive water volume will cause waste and deterioration of fabric quality. At the same time, different fabrics and yarns have different requirements for water spray parameters, which need to be flexibly adjusted. In addition, the water spray system also faces the problems of scouring and wear of components by high-pressure water flow, as well as nozzle blockage caused by long-term operation and other faults. How to achieve precise control, intelligent adjustment and reliable operation of the water spray system while ensuring weaving quality and efficiency is a core issue that the control system needs to comprehensively consider and solve. Summary of the invention

[0003] The main purpose of the present invention is to provide a control system for a water jet loom, so as to achieve real-time monitoring and precise control of various parameters of the water jet loom, and ensure the weaving quality and efficiency.

[0004] To achieve the above object, the present invention provides a control system for a water jet loom, comprising: A data acquisition module, wherein the data acquisition module includes a tension sensor, a humidity sensor and a temperature sensor installed on the weft yarn, and is used to collect the tension, humidity and temperature data of the weft yarn in real time, and transmit the collected data to the control system; A parameter calculation module, which dynamically calculates the optimal water spraying time, water spraying pressure and nozzle opening parameters using a fuzzy control algorithm according to changes in weft yarn tension, humidity and temperature data, and transmits the calculated parameters to a water spraying actuator; A tension adjustment module, if the weft yarn tension exceeds a preset threshold, the control system determines that the weft yarn is too tight, and sends a command to increase the nozzle opening and the water spraying time; if the weft yarn tension is lower than the preset threshold, the control system determines that the weft yarn is too loose, and sends a command to reduce the nozzle opening and the water spraying time; A nozzle optimization module, wherein the nozzle optimization module is used to optimize the nozzle design by using a computational fluid dynamics method and determine the best nozzle structural parameters and layout scheme by using simulation analysis; A pressure regulating module, wherein a pressure sensor is arranged in the water system to monitor the pipeline pressure in real time. When the pressure exceeds a preset threshold, an alarm signal is sent through the control system, and the water pump speed and the solenoid valve opening are adjusted at the same time; A state detection module, which is used to detect the motion state of the knitting needle and the shuttle in real time using machine vision technology. When an abnormality is detected in the knitting needle or the shuttle, the control system immediately sends a command to adjust the water spraying time and water pressure; The data processing module is used to upload the loom production data and the operating parameters of the water spray system to the cloud platform, establish a water spray optimization model using big data analysis technology and machine learning algorithms, and iteratively optimize the water spray control strategy.

[0005] Furthermore, the data acquisition module is connected to the parameter calculation module via a wired or wireless manner.

[0006] Furthermore, the parameter calculation module sets the fuzzy subsets and domain ranges of weft tension, temperature and humidity based on the fuzzy PID control algorithm, and establishes a fuzzy rule base to output the fuzzy subsets and domain ranges of water spraying time, water pressure and nozzle opening; the collected weft tension, humidity and temperature data are fuzzified through a Gaussian membership function, the language variables are inferred and calculated through a fuzzy inference engine, and the fuzzy values ​​of water spraying time, water spraying pressure and nozzle opening are converted into precise numerical parameters by a defuzzifier as control parameters of the water spraying device.

[0007] Furthermore, the tension adjustment module realizes adaptive adjustment of the weft yarn tension through closed-loop feedback control. When the weft yarn tension exceeds a preset threshold, an instruction is sent to increase the nozzle opening and extend the water spraying time; when the weft yarn tension is lower than the preset threshold, an instruction is sent to reduce the nozzle opening and shorten the water spraying time, and the weft yarn tension is continuously monitored after adjustment, and the nozzle opening and water spraying time are dynamically adjusted according to the deviation between the actual tension and the preset threshold.

[0008] Furthermore, the nozzle optimization module uses computational fluid dynamics to perform simulation analysis based on the inner diameter, length and outlet shape of the nozzle and the layout position parameters on the loom.

[0009] Furthermore, the nozzle optimization module adopts computational fluid dynamics methods to establish a mathematical model of the flow inside the nozzle, and performs grid division and boundary condition setting, divides the hexahedral structure grid, and sets the inlet pressure and outlet pressure; obtains the flow field distribution, velocity field and pressure field information under various nozzle parameters through numerical simulation, takes water spray uniformity and pressure loss as optimization goals, and searches for the optimal nozzle parameter combination under constraints; after determining the optimal layout position of the optimized nozzle on the loom, the nozzle is processed by a CNC lathe and passes an actual water spray test.

[0010] Furthermore, the pressure sensor in the pressure regulating module transmits the pipeline pressure data collected in real time to the control unit of the control system, and the control unit determines whether to send an alarm signal and adjust the water pump speed and the solenoid valve opening according to a preset threshold.

[0011] Furthermore, the status detection module collects image information of the knitting needles and shuttles through an industrial camera, and uses an image processing algorithm to analyze the status information of the motion trajectory and speed of the knitting needles and shuttles. When an abnormality is detected, the signal is transmitted to the control unit of the control system to adjust the water spraying time and water pressure.

[0012] Furthermore, the data processing module uses big data analysis technology and an association rule mining algorithm to analyze the correlation between loom parameters and water spray parameters, and performs mining and analysis on the data uploaded to the cloud platform; through a machine learning algorithm, a water spray optimization model is established, and the water spray control strategy is continuously iterated and optimized.

[0013] Furthermore, the data uploaded to the cloud platform by the data processing module includes but is not limited to weft yarn data, water jet parameters, loom operation time and fabric quality test results. Big data analysis technology is used to mine and analyze the data in the cloud platform, and the water jet control strategy is optimized through machine learning algorithms.

[0014] The control system for a water jet loom provided by the present invention has the following beneficial effects: by integrating a controller, a sensor and an actuator, real-time monitoring and precise control of various parameters of the water jet loom are realized. The present invention adopts a modular design and flexibly configures functional modules according to different weaving requirements. By optimizing key parameters such as water spray pressure and nozzle angle, the weaving efficiency and fabric quality are effectively improved. At the same time, the present invention also has fault diagnosis and remote monitoring functions, which greatly improves the reliability and maintainability of the equipment. The application of this control system significantly improves the overall performance of the water jet loom and provides strong support for the intelligent upgrade of the textile industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a block diagram of a control system for a water jet loom according to an embodiment of the present invention; Figure 2 It is a flow chart of a control method for a water jet loom in one embodiment of the present invention.

[0016] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0018] Reference Figure 1 , is a structural block diagram of a control system for a water jet loom proposed by the present invention, comprising: A data acquisition module, wherein the data acquisition module includes a tension sensor, a humidity sensor and a temperature sensor installed on the weft yarn, and is used to collect the tension, humidity and temperature data of the weft yarn in real time, and transmit the collected data to the control system; A parameter calculation module, which dynamically calculates the optimal water spraying time, water spraying pressure and nozzle opening parameters using a fuzzy control algorithm according to changes in weft yarn tension, humidity and temperature data, and transmits the calculated parameters to a water spraying actuator; A tension adjustment module, if the weft yarn tension exceeds a preset threshold, the control system determines that the weft yarn is too tight, and sends a command to increase the nozzle opening and the water spraying time; if the weft yarn tension is lower than the preset threshold, the control system determines that the weft yarn is too loose, and sends a command to reduce the nozzle opening and the water spraying time; A nozzle optimization module, wherein the nozzle optimization module is used to optimize the nozzle design by using a computational fluid dynamics method and determine the best nozzle structural parameters and layout scheme by using simulation analysis; A pressure regulating module, wherein a pressure sensor is arranged in the water system to monitor the pipeline pressure in real time. When the pressure exceeds a preset threshold, an alarm signal is sent through the control system, and the water pump speed and the solenoid valve opening are adjusted at the same time; A state detection module, which is used to detect the motion state of the knitting needle and the shuttle in real time using machine vision technology. When an abnormality is detected in the knitting needle or the shuttle, the control system immediately sends a command to adjust the water spraying time and water pressure; The data processing module is used to upload the loom production data and the operating parameters of the water spray system to the cloud platform, establish a water spray optimization model using big data analysis technology and machine learning algorithms, and iteratively optimize the water spray control strategy.

[0019] As described in the above data acquisition module, the data acquisition module includes a tension sensor, a humidity sensor and a temperature sensor installed on the weft yarn, which are used to collect the tension, humidity and temperature data of the weft yarn in real time, and transmit the collected data to the control system. Among them, the tension sensor is used to measure the tension of the weft yarn. During the operation of the water jet loom, the change of the weft yarn tension will affect the weaving quality. For example, too much tension may cause the weft yarn to break, and too little tension may cause the fabric to sag. By real-time monitoring of the weft yarn tension, key data is provided for the subsequent system to judge and adjust the operation status of the loom. The humidity sensor is used to monitor the humidity of the environment in which the weft yarn is located. The humidity has a significant impact on the performance of the weft yarn. For example, too high humidity may cause the weft yarn to absorb moisture and deform, affecting its strength and dimensional stability; too low humidity may cause the weft yarn to be too dry, increase the possibility of static electricity, and thus affect the weaving process. The temperature sensor is used to sense the temperature of the surrounding environment of the weft yarn. Changes in temperature will also affect the physical properties of the weft yarn. For example, too high temperature may change the material properties of the weft yarn, affecting its tensile strength and elasticity; too low temperature may cause the weft yarn to become hard and brittle and easy to break. By acquiring temperature data, the system can better understand the state of the weft yarn and provide a basis for optimizing the weaving process. The main function of this module is to continuously acquire various data of the weft yarn through sensors and transmit the collected data to the control system for subsequent modules to analyze and process. Data transmission can be done by wired connection (such as transmitting data through physical lines such as cables) or wireless connection (such as transmitting data using wireless communication technologies such as Bluetooth and Wi-Fi).

[0020] As described in the parameter calculation module, the parameter calculation module dynamically calculates the optimal water spray time, water spray pressure and nozzle opening parameters according to the changes in the weft tension, humidity and temperature data using the fuzzy control algorithm, and transmits the calculated parameters to the water spray actuator. Based on the weft tension, humidity and temperature data obtained by the data acquisition module, the fuzzy control algorithm is used to dynamically obtain the optimal water spray time, water spray pressure and nozzle opening parameters. Among them, the fuzzy control algorithm simulates human thinking, fuzzifies the input data, and performs reasoning based on the preset fuzzy rule library. It can cope with complex systems that are difficult to accurately model. In the water jet loom, the loom state and water spray demand are complex. The fuzzy control algorithm can comprehensively consider multiple factors and dynamically calculate the appropriate parameters. The dynamic means that the parameters are not fixed and can be adjusted in real time with the weft state. For example, when the weft tension is large and the humidity is high, the water spray time, water pressure and opening need to be adjusted to ensure smooth weaving of the weft. The calculated parameters are transmitted to the water spray actuator. The actuator adjusts the water spray operation accordingly to achieve precise control of the loom water spray link and ensure weaving quality and efficiency.

[0021] As described in the tension adjustment module, if the weft tension exceeds the preset threshold, the control system determines that the weft is too tight and sends an instruction to increase the nozzle opening and the water spraying time. If the weft tension is lower than the preset threshold, the control system determines that the weft is too loose and sends an instruction to reduce the nozzle opening and the water spraying time. The system pre-sets a standard value of the weft tension, i.e., the preset threshold. When the actual tension of the weft is measured, it will be compared with the preset threshold. If the weft tension exceeds the preset threshold, the control system will determine that the weft is in an overtight state. In order to restore the weft tension to the appropriate range, the system will issue an instruction to increase the nozzle opening and extend the water spraying time. By increasing the amount of water sprayed and the time of water spraying, the force of water on the weft is used to appropriately loosen the overtight weft to ensure the smooth weaving process. If the weft tension is lower than the preset threshold, the control system determines that the weft is too loose. At this time, the system will issue an opposite instruction to reduce the nozzle opening and shorten the water spraying time. Reducing the amount and time of water spraying can reduce the effect of water on the weft yarn, tighten the weft yarn appropriately, thereby correcting the condition of the weft yarn being too loose and maintaining the weft yarn tension requirements of the weaving process.

[0022] As described in the nozzle optimization module, the nozzle optimization module is used to optimize the nozzle design by using the computational fluid dynamics method, and determine the optimal nozzle structural parameters and layout scheme by simulation analysis. The fluid dynamics (CFD) method is an analysis of a system containing related physical phenomena such as fluid flow and heat conduction through computer numerical calculation and image display. In the nozzle optimization scenario, it can simulate the flow of fluid (water) inside and around the nozzle. By constructing a mathematical model and combining the basic equations of fluid mechanics (such as the continuity equation, momentum equation and energy equation, etc.), the flow law of the fluid in the nozzle is described. The structural parameters of the nozzle, such as the inner diameter, length, outlet shape, etc., have a key influence on the water spray effect. Different inner diameters determine the flow rate and flow rate of the water flow; the length affects the pressure distribution and energy loss of the water flow inside the nozzle; the outlet shape is related to the atomization effect and spray angle of the water flow after it is sprayed out. Through CFD simulation and analysis of the water flow conditions under different structural parameters, such as velocity field and pressure field distribution, find a parameter combination that can achieve the best effect of water spraying, such as making the water flow spray more uniform and the speed more stable. In addition to the structure of the nozzle itself, the layout position on the loom is also crucial. The layout plan involves the relative position of the nozzle and the needles, shuttles and other components, as well as the spacing and arrangement between the nozzles. A reasonable layout can ensure that the weft yarn is evenly watered during the weaving process, avoiding the situation where too much or too little water is sprayed locally. By simulating and analyzing the water spray coverage under different layouts, the effect of water flow on the weft yarn, etc., the nozzle layout that can achieve the optimal water spray effect is determined, thereby improving the stability of the entire weaving process and the quality of the fabric.

[0023] As described in the above-mentioned pressure regulating module, the pressure regulating module sets a pressure sensor in the water system for real-time monitoring of the pipeline pressure. When the pressure exceeds the preset threshold, the control system sends an alarm signal and adjusts the water pump speed and the solenoid valve opening at the same time. The pressure sensor is installed in the water system to monitor the pressure in the pipeline in real time and accurately. The system pre-sets a pressure standard value, that is, the preset threshold. When the pressure sensor detects that the pipeline pressure exceeds this preset threshold, the control system immediately sends an alarm signal to remind the operator that the water system pressure is abnormal, which may have an adverse effect on the operation of the loom and requires timely attention and processing. At the same time as the alarm is issued, the system automatically takes adjustment measures. The water pump speed and the solenoid valve opening are two key factors for regulating the pipeline pressure. By adjusting the water pump speed, the water pumping or water delivery capacity of the water pump can be changed, thereby affecting the water flow in the pipeline and achieving the purpose of regulating the pressure. For example, by reducing the water pump speed, the water flow decreases, and the pipeline pressure may decrease accordingly. Adjusting the solenoid valve opening is to control the flow rate of water through the valve. Increasing the solenoid valve opening increases the water flow rate; otherwise, it decreases. By collaboratively adjusting the water pump speed and the solenoid valve opening, the pipeline pressure can be restored to the normal range as quickly as possible to ensure the stable operation of the water jet loom.

[0024] As described in the above-mentioned state detection module, the state detection module is used to use machine vision technology to detect the motion state of the knitting needle and the shuttle in real time. When an abnormality is detected in the knitting needle or the shuttle, the control system immediately sends instructions to adjust the water spray time and water pressure. Among them, the machine vision technology is similar to the human visual system, which obtains image information through optical equipment (such as industrial cameras) and uses image processing algorithms to analyze and understand the image. In the water jet loom scene, it can capture the motion of the key components of the loom in real time. The state detection module uses machine vision technology to continuously monitor the motion state of the knitting needle and the shuttle, and judges whether it is in a normal working state according to the preset normal motion parameter range (such as speed, position, trajectory, etc.). Once the motion parameters of the knitting needle or the shuttle are detected to exceed this range, it is determined that an abnormality has occurred. For example, the knitting needle movement speed suddenly slows down or the shuttle deviates from the predetermined trajectory, which are all abnormal situations. When an abnormality is detected, the state detection module quickly sends instructions through the control system. The water spraying time and water pressure are adjusted through instructions, because the changes in water spraying time and water pressure can affect the tension and movement state of the weft yarn to a certain extent, thereby playing a certain regulatory role in the abnormal movement of the knitting needles and shuttles, avoiding loom failures or fabric quality problems caused by abnormal knitting needles or shuttles, and ensuring the stable operation of the loom.

[0025] As described in the above data processing module, the data processing module is used to upload the loom production data and the water spray system operating parameters to the cloud platform, establish a water spray optimization model using big data analysis technology and machine learning algorithms, and iteratively optimize the water spray control strategy. Among them, the loom production data includes loom speed, running time, fabric output, etc., reflecting the overall production status of the loom; the water spray system operating parameters such as water spray time, water pressure, nozzle opening, etc. reflect the working status of the water spray link. These data comprehensively record the key information in the loom production process. The above data is transmitted to the cloud platform, which has powerful data storage and processing capabilities. The large amount of data in the cloud platform is mined using big data analysis technology. By analyzing the correlation between data, such as the potential connection between water spray parameters and fabric quality and loom operation status, the rules hidden behind the data are discovered. With the help of machine learning algorithms, a water spray optimization model is established. The model is learned based on historical data. By continuously adjusting the model parameters, the model can accurately predict the optimal water spray parameters under different production conditions. For example, based on inputs such as loom speed and fabric type, the optimal water spray time and water pressure are predicted. As production continues, new data continues to flow in. The new data is used to iteratively train the water jet optimization model, so that the model can continuously adapt to changes in production and more accurately predict the optimal water jet parameters. Based on the optimized model, the water jet control strategy is continuously adjusted to achieve refined control of the water jet process and improve loom production efficiency and product quality.

[0026] In one embodiment, the operating status data of the loom is obtained, including parameters such as the speed, position, and vibration of the loom, and whether the loom is in a stable operating state is determined based on a preset threshold. If the loom is not operating stably, the timing and pressure of the water spray are adjusted until the loom returns to stability.

[0027]

[0028] S represents the loom operation status indicator, v represents the current speed, v_0 represents the preset speed threshold, d represents the current displacement, d_0 represents the preset displacement threshold, f represents the current vibration frequency, and f_0 represents the preset vibration frequency threshold. When S is greater than the preset threshold, it is determined to be unstable operation.

[0029] Obtain fabric parameter data, including fabric density, thickness, texture, etc., determine the optimal water spray flow and spray angle based on fabric parameters, and achieve precise water spray control of the fabric by adjusting the opening and direction of the nozzle.

[0030]

[0031] Q represents the optimal water spray flow rate, ρ represents fabric density, h represents fabric thickness, T represents fabric texture characteristics, and k1, k2, and k3 are weight coefficients. The optimal water spray flow rate is automatically calculated based on fabric parameters.

[0032] During the water spraying process, the water spraying pressure and flow are monitored in real time. If the pressure or flow exceeds the preset range, the nozzle opening is adjusted in time to ensure that the water spraying pressure and flow are always within the optimal range to ensure the stability of fabric quality. A machine learning algorithm is used to establish a correlation model between the loom operation status, fabric parameters and water spraying parameters based on historical data. The model is used to predict the optimal water spraying time, pressure and flow, and realize intelligent control of the water spraying process.

[0033]

[0034] y represents the predicted water spray parameters (timing, pressure or flow), x i represents the input features (such as loom speed, fabric density, etc.), w i Represents the feature weight, b represents the bias term, and n represents the number of features. The support vector machine algorithm is used to train the model and predict the optimal water spraying parameters.

[0035]

[0036] ΔP represents the pressure adjustment amount, P represents the current pressure, P0 represents the target pressure, and α represents the adjustment coefficient. The nozzle opening is dynamically adjusted based on the real-time monitored pressure deviation.

[0037] During the water spraying process, a high-speed camera is used to collect image data of the fabric surface, and an image processing algorithm is used to analyze the uniformity of the fabric. If the fabric is found to be uneven, the water spraying parameters are dynamically adjusted until the fabric becomes uniform again.

[0038]

[0039] H represents the uniformity of fabric surface texture, p(i,j) represents the element value of the i-th row and j-th column in the gray-level co-occurrence matrix, and G represents the gray-level level. The entropy of the gray-level co-occurrence matrix is ​​calculated to evaluate the uniformity of the fabric surface and dynamically adjust the water spray parameters accordingly.

[0040] For different types of fabrics, an optimal database of water spray parameters is established in advance. According to the type and parameters of the fabric, the corresponding optimal water spray parameters are obtained from the database as the initial water spray parameters, and then the water spray process is dynamically optimized according to real-time data. The various parameter data of the water spray process are uploaded to the cloud server in real time. Through big data analysis technology, the correlation between water spray parameters and fabric quality is explored, and the water spray control algorithm is continuously optimized to realize the intelligence and efficiency of the water jet loom.

[0041] The data acquisition module is connected to the parameter calculation module by wire or wireless means. The weft tension sensor collects weft tension data in real time, with a sampling frequency of 1kHz, a range of 0-100N, and an accuracy of 0.5%FS. The temperature and humidity sensor collects loom environment temperature and humidity data, with a temperature measurement range of -20℃~80℃, a humidity measurement range of 0~100%RH, and an accuracy of ±0.5℃ and ±3%RH. The weft tension, humidity and temperature data are obtained through the data acquisition module, and the collected data is transmitted to the parameter calculation module in the control system.

[0042] After receiving the weft data, the parameter calculation module sets the fuzzy subsets of weft tension, temperature and humidity as {NB, NM, NS, ZO, PS, PM, PB} based on the fuzzy PID control algorithm, and the domain ranges are {0 - 100N, - 20℃ ~ 80℃, 0 ~ 100%RH}, and establishes a fuzzy rule base to output the fuzzy subsets of water spray time, water pressure and nozzle opening {NB, NM, NS, ZO, PS, PM, PB}, with a domain range of {0 - 500ms, 0 - 1MPa, 0 - 100%}. According to the preset fuzzy rule base, the collected weft tension, humidity and temperature data are fuzzified, that is, the numerical data is converted into language variables according to the preset fuzzy rule base and membership function.

[0043]

[0044] μ A (x) represents the membership function, x is the input variable, c is the membership function center, and σ is the width parameter. This formula defines a Gaussian membership function for fuzzifying input variables.

[0045] The fuzzy inference engine is used to infer and calculate the language variables, and the fuzzy values ​​of water spray time, water spray pressure and nozzle opening are obtained according to the correlation between weft tension, humidity and temperature. The defuzzifier is used to convert the fuzzy values ​​of water spray time, water spray pressure and nozzle opening into precise numerical parameters as the control parameters of the water spray device. For example, if the weft tension exceeds 80N, the ambient temperature is lower than 20℃, and the humidity is higher than 80%RH, the fuzzy control rule determines that the weft is too tight, and the output water spray time is 400ms, the water pressure is 0.8MPa, and the nozzle opening is 90%.

[0046] If the weft yarn tension exceeds the preset threshold, the tension adjustment module determines that the weft yarn is too tight, and sends a command to the water spray actuator to increase the nozzle opening and extend the water spray time; if the weft yarn tension is lower than the preset threshold, it is judged that the weft yarn is too loose, and sends a command to reduce the nozzle opening and shorten the water spray time, thereby realizing adaptive adjustment of the weft yarn tension through closed-loop feedback control.

[0047] The nozzle optimization module uses computational fluid dynamics to simulate and analyze the nozzle's inner diameter, length, outlet shape, and layout position parameters on the loom. According to the loom parameters and water jet performance requirements, the objective function and constraints of nozzle optimization are determined. For example, the objective function of nozzle optimization is determined to be a water jet uniformity greater than 95%, while constraining the nozzle inner diameter to be between 0.2 and 0.8 mm and the length to be between 2 and 10 mm.

[0048]

[0049] u represents the fluid velocity vector, t represents time, ρ represents fluid density, p represents pressure, and ν represents kinematic viscosity. This formula is the Navier-Stokes equation, which is used to describe the movement of fluid inside the nozzle.

[0050] Computational fluid dynamics methods are used to establish a mathematical model of the flow inside the nozzle. For example, ANSYS Fluent software is used to establish a mathematical model of the Navier-Stokes equation for the flow inside the nozzle, and meshing and boundary conditions are set. The hexahedral structure mesh is divided, and the inlet pressure is set to 0.2MPa and the outlet pressure is set to standard atmospheric pressure. Through numerical simulation, information such as flow field distribution, velocity field and pressure field under different nozzle parameters is obtained, and comparative analysis is performed to obtain flow field distribution cloud maps under different inner diameters, lengths and outlet shapes. According to the simulation results, the influence of the nozzle inner diameter, length and outlet shape on the water spray performance is judged, and the optimization direction of each parameter is determined. It is found that when the inner diameter is 0.4mm, the length is 5mm, and the outlet is conical, the nozzle outlet velocity distribution is the most uniform and the pressure loss is the smallest. An optimization algorithm, such as a genetic algorithm or a particle swarm algorithm, is used to search for the optimal combination of nozzle parameters under constraints. A multi-objective genetic algorithm is used, with water spray uniformity and pressure loss as optimization targets, to search for the optimal parameter combination under constraints, and the optimization result is that the nozzle inner diameter is 0.38 mm, the length is 4.7 mm, and the outlet angle is 60°. According to the optimized nozzle parameters, the optimal layout position on the loom is determined, and the nozzle is processed and installed. According to the structural characteristics of the loom, the optimal layout of the nozzle in the loom is determined to be a linear arrangement with a distance of 50 mm from the weaving area and a spacing of 10 mm. Through actual water spray tests, it is verified whether the performance of the optimized nozzle meets the water spray requirements of the loom. The nozzle is processed by a CNC lathe with an accuracy of ±0.01 mm, and the water spray process is filmed with a high-speed camera. The spray angle, fragmentation and other indicators are analyzed to verify that the performance of the optimized nozzle meets the water spray uniformity requirements of the loom. Further parameter fine-tuning is carried out when necessary to finally determine the nozzle optimization plan. Finally, by adjusting the water supply pressure and nozzle height, the optimal control of the loom water spray system is achieved to improve the quality of the fabric.

[0051] The pressure sensor in the pressure regulating module collects the pressure data in the pipeline in real time. The pressure sensor adopts a high-precision strain gauge pressure sensor with a range of 0-1MPa and an accuracy level of 0.5. The pressure data is transmitted to the control unit of the control system through the RS485 bus with a cycle of 50ms. The control unit adopts a PLC programmable controller with a built-in PID control algorithm module. After receiving the pipeline pressure data from the pressure sensor, it compares it with the preset threshold (the preset pressure threshold upper limit 1MPa and lower limit 0.8MPa). If the pipeline pressure data exceeds the preset threshold range, the control unit sends an alarm signal. At the same time, according to the degree of deviation of the pressure data from the threshold, the PID algorithm is used to determine the adjustment range of the water pump speed and the solenoid valve opening, calculate the adjustment value of the water pump speed and the solenoid valve opening, and send the control command to the water pump control module and the solenoid valve control module.

[0052]

[0053] u(t) represents the control output, e(t) represents the error signal, K p , K i , K d are proportional, integral and differential coefficients respectively. This formula is the mathematical expression of PID controller, which is used to adjust the speed of water pump and the opening of solenoid valve.

[0054] The water pump control module adjusts the speed of the water pump motor through the frequency converter according to the speed adjustment command received, thereby changing the water flow rate of the water pump and realizing dynamic adjustment of the pipeline pressure. The solenoid valve control module adjusts the valve opening according to the valve opening adjustment command received, by controlling the on-off time ratio of the solenoid valve, changing the flow rate of water through the valve, and coordinating with the water pump speed adjustment to realize precise control of the pipeline pressure. During the pressure adjustment process, the control unit continuously receives real-time pressure data from the pressure sensor, and through closed-loop feedback control, continuously corrects the water pump speed and the solenoid valve opening to stabilize the pipeline pressure within the preset threshold range, for example, to control the pressure at 0.9 ± 0.05MPa, to ensure the normal operation of the water jet loom.

[0055] The state detection module uses machine vision technology to collect the motion images of the knitting needles and shuttles in real time through a high-speed camera. A high-speed CMOS industrial camera with a pixel resolution of 2048×2048 is used to collect the motion images of the knitting needles and shuttles in real time at a speed of 100 frames per second during the operation of the loom. The acquired image frame data is preprocessed, including image denoising (such as median filtering to remove salt and pepper noise in sequence), enhancement (using gamma transformation to improve contrast) and normalization (scaling to 512×512 pixels to speed up subsequent processing) to improve image quality. A region-based image segmentation algorithm, such as a region-growing-based segmentation algorithm, is used. The grayscale mean of the knitting needles and shuttles is used as the seed point, and 8 neighborhoods are grown to accurately segment the motion areas of the knitting needles and shuttles from the preprocessed image. For the segmented knitting needle and shuttle motion areas, key information such as their contour features (such as Freeman chain code contour features) and centroid coordinates are extracted. According to the changes in the centroid coordinates of the knitting needles and shuttles in continuous image frames, their real-time motion trajectory and instantaneous speed are calculated. The real-time motion parameters of the knitting needle and shuttle are compared with the preset normal working threshold (normal knitting needle speed should be between 0.5-2.5m / s, and shuttle speed should be between 5-10m / s). If it exceeds the threshold range, it is judged as an abnormal state. When the movement of the knitting needle or shuttle is detected to be abnormal, for example, the knitting needle speed is detected to be lower than 0.3m / s or the shuttle speed is lower than 4m / s, the abnormal signal is transmitted to the control unit in time, and the control unit dynamically adjusts the water spraying time and water spraying pressure accordingly, such as extending the water spraying time by 20% and increasing the water spraying pressure to 0.4MPa, to ensure the normal and efficient operation of the loom.

[0056] The data processing module uploads the loom production parameters and water spray system operation data to the cloud platform. The data uploaded to the cloud platform include but are not limited to weft yarn data, water spray parameters, loom operation time and fabric quality test results. Using big data analysis technology, the association rule mining algorithm Apriori is used to analyze the correlation between loom parameters and water spray parameters. The support is set to 0.05 and the confidence is set to 0.8 to mine and analyze the data in the cloud platform. Through machine learning algorithms, such as the decision tree algorithm C4.5, a water spray optimization model is established. The model parameters are updated every time 1000m of fabric is produced. The model parameters are continuously updated iteratively, the water spray control strategy is continuously optimized, and the intelligence level of the loom is improved.

[0057] For example, the loom operation status data is collected in real time through sensors, including parameters such as speed, displacement and vibration frequency. The system presets the speed threshold to 1000rpm, the displacement threshold to 2mm, and the vibration frequency threshold to 50Hz. When the loom speed is lower than 950rpm, the displacement is greater than 2.2mm or the vibration frequency exceeds 55Hz, it is judged to be unstable. At this time, the system automatically adjusts the water spraying timing, delays the water spraying by 10ms, and increases the water spraying pressure by 0.2MPa. After 3 iterative adjustments, the loom resumes stable operation. Fabric parameters are collected through optical sensors and pressure sensors, including density, thickness and texture characteristics. According to the fabric density of 200-300g / m², the thickness of 0.5-1.0mm, and the texture of twill, the system determines that the optimal water spraying flow rate is 1.5L / min and the water spraying angle is 30°. The nozzle opening and direction are controlled by the servo motor to achieve precise water spraying. During the water spraying process, the pressure sensor and flow meter monitor the water spraying pressure and flow in real time. When the pressure exceeds 2.5MPa or the flow is lower than 1.2L / min, the system automatically adjusts the nozzle opening, with a pressure adjustment step of 0.1MPa and a flow adjustment step of 0.2L / min, to ensure that the water spraying parameters are always within the optimal range. The system uses the support vector machine (SVM) algorithm to train the association model based on 5,000 sets of historical data, inputs the loom speed, fabric density and texture features, and predicts the optimal water spraying time, pressure and flow, with a prediction accuracy of 95%. The high-speed camera captures fabric surface images at a speed of 500fps, and extracts texture uniformity features through the gray-level co-occurrence matrix (GLCM) algorithm. When the uniformity is lower than 90%, the system dynamically adjusts the water spraying pressure and angle, with a pressure adjustment step of 0.05MPa and an angle adjustment step of 2°. After 5 iterations of optimization, the fabric uniformity is increased to more than 95%. For 10 common fabrics such as pure cotton, polyester-cotton, and silk, the system pre-establishes a water spray parameter database and stores 100 sets of optimal parameters. When a new fabric type is identified, the system retrieves the corresponding parameters from the database as the initial value, and then dynamically optimizes the water spray process through the fuzzy control algorithm based on the real-time collected fabric parameters, with an optimization cycle of 200ms. The system uploads parameters such as water spray pressure, flow, timing, and fabric uniformity to the cloud in real time, and uses the Apriori association rule mining algorithm to find that the correlation confidence between water spray pressure and fabric uniformity is 85%. Based on this, the water spray control strategy is optimized to improve the intelligence level of the loom. Through the above intelligent transformation, the production efficiency of the water jet loom has increased by 20%, and the fabric quality pass rate has increased by 5%, realizing the intelligent and efficient production of the loom.

[0058] In summary, the present invention provides a control system for a water jet loom, comprising: a data acquisition module, used for real-time acquisition of weft tension, humidity and temperature data, and transmitting the acquired data to the control system; a parameter calculation module, which dynamically calculates the optimal water spraying time, water spraying pressure and nozzle opening parameters using a fuzzy control algorithm according to changes in weft tension, humidity and temperature data, and transmits the calculated parameters to a water jet actuator; a tension adjustment module, if the weft tension exceeds a preset threshold, the control system determines that the weft is too tight, and sends an instruction to increase the nozzle opening and the water spraying time; if the weft tension is lower than the preset threshold, the control system determines that the weft is too loose, and sends an instruction to reduce the nozzle opening and the water spraying time; a nozzle optimization module, which is used to optimize the design of the nozzle by using a computational fluid dynamics method. The optimal nozzle structure parameters and layout scheme are determined by simulation analysis; a pressure regulation module is provided with a pressure sensor in the water system to monitor the pipeline pressure in real time. When the pressure exceeds the preset threshold, an alarm signal is sent through the control system, and the water pump speed and the solenoid valve opening are adjusted at the same time; a state detection module is used to use machine vision technology to detect the movement state of the knitting needles and shuttles in real time. When an abnormality is detected in the knitting needles or shuttles, an instruction is immediately sent through the control system to adjust the water spray time and water pressure; a data processing module is used to upload the loom production data and the water spray system operation parameters to the cloud platform, and a water spray optimization model is established by using big data analysis technology and machine learning algorithms. The water spray control strategy is iteratively optimized to achieve real-time monitoring and precise control of various parameters of the water jet loom to ensure the weaving quality and efficiency.

[0059] Reference Figure 2 , which is a flow chart of a control method for a water jet loom proposed by the present invention, comprising: Collect the tension, humidity and temperature data of the weft yarn in real time and transmit the collected data to the control system; According to the changes in weft yarn tension, humidity and temperature data, the fuzzy control algorithm is used to dynamically calculate the optimal water spraying time, water spraying pressure and nozzle opening parameters, and the calculated parameters are transmitted to the water spraying actuator; If the weft yarn tension exceeds the preset threshold, the control system determines that the weft yarn is too tight and sends a command to increase the nozzle opening and the water spraying time; if the weft yarn tension is lower than the preset threshold, the control system determines that the weft yarn is too loose and sends a command to reduce the nozzle opening and the water spraying time; The nozzle is optimized by using computational fluid dynamics method, and the best nozzle structure parameters and layout scheme are determined by simulation analysis; A pressure sensor is set in the water system to monitor the pipeline pressure in real time. When the pressure exceeds a preset threshold, an alarm signal is sent through the control system, and the water pump speed and the solenoid valve opening are adjusted at the same time; The motion state of the knitting needle and the shuttle is detected in real time by using machine vision technology. When an abnormality is detected in the knitting needle or the shuttle, the control system immediately sends instructions to adjust the water spraying time and water pressure; The loom production data and water spray system operating parameters are uploaded to the cloud platform, and the water spray optimization model is established using big data analysis technology and machine learning algorithms to iteratively optimize the water spray control strategy.

[0060] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A control system for a water jet loom, characterized in that: include: A data acquisition module, wherein the data acquisition module includes a tension sensor, a humidity sensor and a temperature sensor installed on the weft yarn, and is used to collect the tension, humidity and temperature data of the weft yarn in real time, and transmit the collected data to the control system; A parameter calculation module, which dynamically calculates the optimal water spraying time, water spraying pressure and nozzle opening parameters using a fuzzy control algorithm according to changes in weft yarn tension, humidity and temperature data, and transmits the calculated parameters to a water spraying actuator; A tension adjustment module, if the weft yarn tension exceeds a preset threshold, the control system determines that the weft yarn is too tight, and sends a command to increase the nozzle opening and the water spraying time; if the weft yarn tension is lower than the preset threshold, the control system determines that the weft yarn is too loose, and sends a command to reduce the nozzle opening and the water spraying time; A nozzle optimization module, wherein the nozzle optimization module is used to optimize the nozzle design by using a computational fluid dynamics method and determine the best nozzle structural parameters and layout scheme by using simulation analysis; A pressure regulating module, wherein a pressure sensor is arranged in the water system to monitor the pipeline pressure in real time. When the pressure exceeds a preset threshold, an alarm signal is sent through the control system, and the water pump speed and the solenoid valve opening are adjusted at the same time; A state detection module, which is used to detect the motion state of the knitting needle and the shuttle in real time using machine vision technology. When an abnormality is detected in the knitting needle or the shuttle, the control system immediately sends a command to adjust the water spraying time and water pressure; The data processing module is used to upload the loom production data and the operating parameters of the water spray system to the cloud platform, establish a water spray optimization model using big data analysis technology and machine learning algorithms, and iteratively optimize the water spray control strategy.

2. A control system for a water jet loom according to claim 1, characterized in that: The data acquisition module is connected to the parameter calculation module via a wired or wireless method.

3. A control system for a water jet loom according to claim 1, characterized in that: The parameter calculation module is based on the fuzzy PID control algorithm, sets the fuzzy subsets and domain ranges of weft tension, temperature and humidity, and establishes a fuzzy rule base to output the fuzzy subsets and domain ranges of water spraying time, water pressure and nozzle opening; fuzzy processing is performed on the collected weft tension, humidity and temperature data through a Gaussian membership function, and the language variables are inferred and calculated through a fuzzy inference engine, and then the fuzzifier is used to convert the fuzzy values ​​of water spraying time, water spraying pressure and nozzle opening into precise numerical parameters as control parameters of the water spraying device.

4. A control system for a water jet loom according to claim 1, characterized in that: The tension adjustment module realizes adaptive adjustment of the weft tension through closed-loop feedback control. When the weft tension exceeds a preset threshold, an instruction is sent to increase the nozzle opening and extend the water spraying time; when the weft tension is lower than the preset threshold, an instruction is sent to reduce the nozzle opening and shorten the water spraying time. After adjustment, the weft tension is continuously monitored, and the nozzle opening and the water spraying time are dynamically adjusted according to the deviation between the actual tension and the preset threshold.

5. A control system for a water jet loom according to claim 1, characterized in that: The nozzle optimization module uses computational fluid dynamics to perform simulation analysis based on the inner diameter, length and outlet shape of the nozzle and the layout position parameters on the loom.

6. A control system for a water jet loom according to claim 1, characterized in that: The nozzle optimization module adopts computational fluid dynamics method to establish a mathematical model of the flow inside the nozzle, and performs grid division and boundary condition setting, divides the hexahedral structure grid, and sets the inlet pressure and outlet pressure; obtains the flow field distribution, velocity field and pressure field information under various nozzle parameters through numerical simulation, takes water spray uniformity and pressure loss as optimization goals, and searches for the optimal nozzle parameter combination under constraint conditions; after determining the optimal layout position of the optimized nozzle on the loom, the nozzle is processed by a CNC lathe and passes an actual water spray test.

7. A control system for a water jet loom according to claim 1, characterized in that: The pressure sensor in the pressure regulating module transmits the pipeline pressure data collected in real time to the control unit of the control system. The control unit determines whether to send an alarm signal and adjusts the water pump speed and the solenoid valve opening according to a preset threshold.

8. A control system for a water jet loom according to claim 1, characterized in that: The status detection module collects image information of the knitting needles and shuttles through an industrial camera, and uses an image processing algorithm to analyze the status information of the motion trajectory and speed of the knitting needles and shuttles. When an abnormality is detected, the signal is transmitted to the control unit of the control system to adjust the water spraying time and water pressure.

9. A control system for a water jet loom according to claim 1, characterized in that: The data processing module uses big data analysis technology and an association rule mining algorithm to analyze the correlation between loom parameters and water spray parameters, and mines and analyzes the data uploaded to the cloud platform; through a machine learning algorithm, a water spray optimization model is established, and the water spray control strategy is continuously iterated and optimized.

10. A control system for a water jet loom according to claim 1, characterized in that: The data uploaded to the cloud platform by the data processing module include but are not limited to weft yarn data, water jet parameters, loom operation time and fabric quality test results. Big data analysis technology is used to mine and analyze the data in the cloud platform, and the water jet control strategy is optimized through machine learning algorithms.

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