A control system for a water jet loom

Abstract

The present application relates to a kind of control systems for water-jet loom, data acquisition module real-time acquisition weft tension, humidity and temperature data and transmission.Parameter calculation module, with fuzzy control algorithm, calculates the optimal parameters of water-jet time, water pressure and nozzle opening degree, is transmitted to water-jet actuator.Tension adjustment module, according to the comparison result of weft tension and preset threshold, adjusts nozzle opening degree and water-jet time.Nozzle optimization module optimizes nozzle design by computational fluid dynamics method.Pressure adjustment module monitors pipeline pressure by pressure sensor, and adjusts water pump speed and electromagnetic valve opening degree when threshold is exceeded.State detection module monitors needle and shuttle using machine vision technology, and adjusts water-jet parameters when abnormal.Data processing module uploads data to cloud platform, and optimizes control strategy using big data and machine learning to realize real-time monitoring and accurate control of various parameters of water-jet loom, to ensure weaving quality and efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent control, in particular to a control system for water-jet loom. BACKGROUND

[0002] During the high-speed operation of a water-jet loom, how to accurately control the timing and water volume of water spraying is a key technical problem. If water spraying is not timely or the water volume is insufficient, the weft yarn may not be inserted in place, while if water spraying is too early or the water volume is too large, it will cause waste and a decline in fabric quality. Meanwhile, different fabrics and yarns have different requirements for water spraying parameters, which need to be flexibly adjusted. In addition, the water spraying system also faces problems such as erosion and wear of components by high-pressure water flow, and nozzle blockage caused by long-time operation. How to ensure the weaving quality and efficiency while realizing accurate control, intelligent adjustment and reliable operation of the water spraying system is the core problem that needs to be considered and solved by the control system. SUMMARY

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

[0004] To achieve the above purpose, the present application provides a control system for water-jet loom, comprising:

[0005] A data acquisition module, the data acquisition module comprises a tension sensor, a humidity sensor and a temperature sensor installed on the weft yarn, for real-time acquisition of the tension, humidity and temperature data of the weft yarn, and transmission of the acquired data to the control system;

[0006] A parameter calculation module, the parameter calculation module dynamically calculates the optimal water spraying time, water pressure and nozzle opening parameters according to the changes of the weft yarn tension, humidity and temperature data, using a fuzzy control algorithm, and transmits the calculated parameters to the water spraying actuator;

[0007] A tension adjustment module, if the weft yarn tension exceeds the preset threshold, the control system determines that the weft yarn is too tight, and sends an instruction to increase the nozzle opening and 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 an instruction to reduce the nozzle opening and water spraying time;

[0008] A nozzle optimization module, the nozzle optimization module is used for optimizing the design of the nozzle by using computational fluid dynamics method, and determining the optimal nozzle structure parameters and layout scheme by simulation analysis;

[0009] A pressure regulating module is provided in the water circuit 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 electromagnetic valve opening are adjusted.

[0010] A state detection module is used to detect the motion state of the needle and shuttle in real time using machine vision technology. When an abnormality is detected in the needle or shuttle, an instruction is immediately sent through the control system to adjust the water spraying time and water pressure.

[0011] A data processing module is used to upload the loom production data and water spraying system operating parameters to a cloud platform, and to establish a water spraying optimization model using big data analysis technology and machine learning algorithms to iteratively optimize the water spraying control strategy.

[0012] Further, the data acquisition module and the parameter calculation module are connected by wired or wireless means.

[0013] Further, the parameter calculation module is based on a 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. The collected weft tension, humidity and temperature data are fuzzified by a Gaussian membership function, the language variables are calculated by a fuzzy inference machine, and the fuzzy values of water spraying time, water pressure and nozzle opening are converted into precise numerical parameters by a defuzzifier as control parameters of the water spraying device.

[0014] Further, the tension regulating module realizes self-adaptive adjustment of weft tension through closed-loop feedback control. When the weft tension exceeds the preset threshold, an instruction is sent to increase the nozzle opening and extend the water spraying time. When the weft tension is below the preset threshold, an instruction is sent to decrease the nozzle opening and shorten the water spraying time, and the weft tension is continuously monitored after adjustment. According to the deviation between the actual tension and the preset threshold, the nozzle opening and water spraying time are dynamically adjusted.

[0015] Further, the nozzle optimization module is based on the inner diameter, length and outlet shape of the nozzle and its layout position parameters on the loom, and uses computational fluid dynamics method for simulation analysis.

[0016] Further, the nozzle optimization module adopts a computational fluid dynamics method to establish a mathematical model of internal flow of the nozzle, and performs grid division and boundary condition setting, divides a hexahedral structure grid, and sets an inlet pressure and an outlet pressure; flow field distribution, velocity field and pressure field information under various nozzle parameters are obtained through numerical simulation, and a uniformity of water spraying and pressure loss are taken as optimization objectives to search for an optimal nozzle parameter combination under constraint conditions; after a best layout position of the optimized nozzle on the loom is determined, the nozzle is processed by a numerical control lathe, and actual water spraying tests are performed.

[0017] Further, the pressure sensor in the pressure regulation module transmits pipeline pressure data collected in real time to a control unit of the control system, and the control unit judges whether to send an alarm signal and regulates a water pump rotating speed and an electromagnetic valve opening degree according to a preset threshold value.

[0018] Further, the state detection module collects image information of the needle and the shuttle by an industrial camera, analyzes state information of a motion trajectory and a speed of the needle and the shuttle by using an image processing algorithm, and transmits a signal to the control unit of the control system to adjust water spraying time and water pressure when an abnormality is detected.

[0019] Further, the data processing module analyzes the relevance of loom parameters and water spraying parameters by using a correlation rule mining algorithm, and mines and analyzes data uploaded to a cloud platform; a water spraying optimization model is established by using a machine learning algorithm to continuously iteratively optimize a water spraying control strategy.

[0020] Further, the data processing module uploads data to the cloud platform, including but not limited to weft data, water spraying parameters, loom running time and fabric quality detection results, mines and analyzes the data in the cloud platform by using a big data analysis technology, and optimizes the water spraying control strategy by using a machine learning algorithm.

[0021] The control system for the water jet loom has the following beneficial effects: by integrating the controller, the sensor and the actuator, real-time monitoring and accurate control of various parameters of the water jet loom are realized. The control system is designed in a modular manner, and function modules can be flexibly configured according to different weaving requirements. By optimizing key parameters such as water spraying pressure and nozzle angle, the weaving efficiency and fabric quality are effectively improved. Meanwhile, the control system also has fault diagnosis and remote monitoring functions, and greatly improves the reliability and maintainability of the equipment. The application of the control system significantly improves the overall performance of the water jet loom, and provides strong support for the intelligent upgrading of the textile industry. BRIEF DESCRIPTION OF DRAWINGS

[0022] Figure 1 is a structural block diagram of the control system for the water jet loom in an embodiment of the present application;

[0023] Figure 2 is a flowchart of a control method for a water jet loom according to an embodiment of the present application.

[0024] The implementation, functional features and advantages of the present application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and not to limit the present application.

[0026] Reference Figure 1 A structural block diagram of a control system for a water jet loom according to the present application is shown in the figure, which comprises:

[0027] A data acquisition module, which comprises a tension sensor, a humidity sensor and a temperature sensor installed on the weft yarn, is used to acquire the tension, humidity and temperature data of the weft yarn in real time and transmit the acquired data to the control system;

[0028] A parameter calculation module, which dynamically calculates the optimal water jet time, water jet pressure and nozzle opening degree parameters by using a fuzzy control algorithm according to the changes of the weft yarn tension, humidity and temperature data, and transmits the calculated parameters to the water jet actuator;

[0029] A tension adjustment module, if the weft yarn tension exceeds the preset threshold value, the control system determines that the weft yarn is too tight and sends an instruction to increase the nozzle opening degree and water jet time, if the weft yarn tension is lower than the preset threshold value, the control system determines that the weft yarn is too loose and sends an instruction to reduce the nozzle opening degree and water jet time;

[0030] A nozzle optimization module, which is used to optimize the design of the nozzle by using computational fluid dynamics method and determine the optimal nozzle structure parameters and layout scheme by simulation analysis;

[0031] A pressure adjustment module, which sets a pressure sensor in the waterway system to monitor the pipeline pressure in real time, when the pressure exceeds the preset threshold value, sends an alarm signal through the control system, and adjusts the water pump speed and electromagnetic valve opening degree at the same time;

[0032] A state detection module, which is used to detect the motion state of the needle and the shuttle in real time by using machine vision technology, and immediately sends an instruction to adjust the water jet time and water pressure through the control system when an abnormality of the needle or the shuttle is detected;

[0033] The data processing module is configured to upload the loom production data and the water jet system operation parameters to a cloud platform, establish a water jet optimization model by using a big data analysis technique and a machine learning algorithm, and iteratively optimize a water jet control strategy.

[0034] As described above, 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. 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, excessive tension may cause the weft yarn to break, and insufficient tension may cause the fabric to relax. By monitoring the weft yarn tension in real time, the system can provide key data for subsequent judgment and adjustment of the loom operation state. The humidity sensor is used to monitor the humidity of the environment in which the weft yarn is located. Humidity has a significant impact on the performance of the weft yarn. For example, excessive humidity may cause the weft yarn to absorb moisture and deform, affecting its strength and dimensional stability; insufficient humidity may cause the weft yarn to become too dry, increasing the possibility of static electricity generation, and thus affecting the weaving process. The temperature sensor is used to sense the temperature of the environment around the weft yarn. Changes in temperature will also affect the physical properties of the weft yarn. For example, excessive temperature may change the performance of the weft yarn material, affecting its tensile strength and elasticity; insufficient temperature may cause the weft yarn to become hard and brittle, easily breaking. By obtaining temperature data, the system can better grasp the state of the weft yarn and provide a basis for optimizing the weaving process. The main function of this module is to continuously obtain various data of the weft yarn through the sensors and transmit these collected data to the control system for subsequent analysis and processing. The data transmission method can be wired connection (such as transmitting data through physical lines such as cables), or wireless connection (such as using Bluetooth, Wi-Fi, and other wireless communication technologies to transmit data).

[0035] As described above, the parameter calculation module calculates the optimal water spraying time, water pressure and nozzle opening degree according to the changes of the weft tension, humidity and temperature data, and transmits the calculated parameters to the water spraying execution mechanism. Based on the weft tension, humidity and temperature data collected by the data acquisition module, the fuzzy control algorithm is used to dynamically calculate the optimal water spraying time, water pressure and nozzle opening degree. The fuzzy control algorithm can handle complex and difficult-to-model systems by simulating human thinking, fuzzy processing of input data, and reasoning based on a pre-set fuzzy rule base. In the water jet loom, the loom state and water spraying requirements are complex, and the fuzzy control algorithm can consider multiple factors to dynamically calculate the appropriate parameters. The dynamic refers to the fact that the parameters are not fixed and can be adjusted in real time according to the weft state. For example, when the weft tension is high and the humidity is high, the water spraying time, water pressure and opening degree need to be adjusted to ensure smooth weaving of the weft. The calculated parameters are transmitted to the water spraying execution mechanism. The execution mechanism adjusts the water spraying operation accordingly to achieve precise control of the water spraying process of the loom and ensure weaving quality and efficiency.

[0036] As described above, if the weft tension exceeds the pre-set threshold value, the control system determines that the weft is too tight and sends instructions to increase the nozzle opening degree and water spraying time. If the weft tension is lower than the pre-set threshold value, the control system determines that the weft is too loose and sends instructions to reduce the nozzle opening degree and water spraying time. The system pre-sets a standard value for weft tension, i.e. the pre-set threshold value. When the actual weft tension is measured, it is compared with the pre-set threshold value. If the weft tension exceeds the pre-set threshold value, the control system determines that the weft is in a tight state. To restore the weft tension to the appropriate range, the system sends instructions to increase the nozzle opening degree and extend the water spraying time. By increasing the water spraying amount and the water spraying time, the water force on the weft is used to appropriately loosen the tight weft to ensure smooth weaving. If the weft tension is lower than the pre-set threshold value, the control system determines that the weft is too loose. At this time, the system sends opposite instructions to reduce the nozzle opening degree and shorten the water spraying time. Reducing the water spraying amount and the water spraying time can reduce the water force on the weft, allowing the weft to be appropriately tightened to correct the loose weft condition and maintain the weft tension requirements of the weaving process.

[0037] As described above, the nozzle optimization module is used to optimize the design of the nozzle by employing computational fluid dynamics methods to determine the optimal nozzle structural parameters and layout scheme through simulation analysis. The fluid dynamics (CFD) method is an analysis of a system containing fluid flow and heat conduction and other related physical phenomena 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, combined with the basic equations of fluid mechanics (such as continuity equation, momentum equation and energy equation, etc.), the flow law of 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 spraying effect. Different inner diameters determine the flow rate and flow rate of the water flow; the length will affect the pressure distribution and energy loss of the water flow inside the nozzle; the outlet shape is related to the atomization effect, jet angle, etc. After the water flow is sprayed. Through CFD simulation analysis of the water flow under different structural parameters, such as velocity field, pressure field distribution, etc., the parameter combination that can make the water spraying achieve the best effect is found, for example, to make the water flow spray more uniform, the speed more stable, etc. In addition to the structure of the nozzle itself, the layout position on the loom is also crucial. The layout scheme involves the relative position of the nozzle and the needle, shuttle and other components, as well as the spacing and arrangement of the nozzles. A reasonable layout can ensure that the weft yarn is evenly sprayed during weaving, avoiding the situation of local over-spraying or under-spraying. Through simulation analysis of the water spraying coverage, the effect of water flow on the weft yarn, etc. under different layouts, the nozzle layout that can achieve the optimal water spraying effect is determined, and the stability of the entire weaving process and the fabric quality are improved.

[0038] As described above, the pressure regulating module is provided with a pressure sensor in the waterway system to monitor the pipeline pressure in real time. When the pressure exceeds the preset threshold, the control system sends an alarm signal and adjusts the water pump speed and electromagnetic valve opening degree. The pressure sensor installed in the waterway system can monitor the pressure in the pipeline in real time and accurately. The system has a preset pressure standard value, i.e. 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 waterway system pressure is abnormal, which may adversely affect the operation of the loom and needs to be paid attention to and handled in time. At the same time, the system automatically takes adjustment measures. The water pump speed and electromagnetic valve opening degree are two key factors for regulating the pipeline pressure. By adjusting the water pump speed, the water pump's pumping or water delivery capacity can be changed, thereby affecting the water flow in the pipeline to achieve the purpose of pressure regulation. For example, reducing the water pump speed, the water flow decreases, and the pipeline pressure may decrease. Adjusting the electromagnetic valve opening degree is to control the flow rate of water through the valve. Increasing the electromagnetic valve opening degree increases the water flow; otherwise, it decreases. By adjusting the water pump speed and electromagnetic valve opening degree, the pipeline pressure is restored to the normal range as soon as possible to ensure the stable operation of the water jet loom.

[0039] As described above, the state detection module is used to detect the motion state of the needle and the shuttle in real time by using machine vision technology. When an abnormality is detected in the needle or the shuttle, the control system immediately sends an instruction to adjust the water jet time and water pressure. The machine vision technology is similar to the human visual system, which obtains image information through optical equipment (such as an industrial camera) and analyzes and understands the image by using image processing algorithms. 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 needle and the shuttle, and determines 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 needle or the shuttle exceed this range, it is determined to be abnormal. For example, the needle motion speed suddenly slows down or the shuttle deviates from the predetermined trajectory, which is an abnormal situation. When an abnormality is detected, the state detection module quickly sends an instruction through the control system. The water jet time and water pressure are adjusted by the instruction, because the change of the water jet time and water pressure can affect the tension and motion state of the weft yarn to a certain extent, thereby adjusting the abnormal motion of the needle and the shuttle to a certain extent, avoiding loom failure or fabric quality problems caused by abnormal needle or shuttle, and ensuring the stable operation of the loom.

[0040] As described above, the data processing module is used to upload the loom production data and the water jet system operation parameters to the cloud platform, establish a water jet optimization model using big data analysis technology and machine learning algorithm, and iteratively optimize the water jet control strategy. Among them, the loom production data includes loom speed, running time, fabric yield, etc., reflecting the overall production status of the loom; the water jet system operation parameters such as water jet time, water pressure, nozzle opening degree, etc., reflect the working state of the water jet link. These data comprehensively record the key information in the loom production process. The above data is transmitted to the cloud platform, which has strong data storage and processing capacity. Use big data analysis technology to mine a large amount of data in the cloud platform. By analyzing the correlation between data, such as the potential relationship between water jet parameters and fabric quality, loom running state, the law hidden behind the data is found. With the help of machine learning algorithm to establish water jet optimization model, the model is based on historical data for learning, through continuously adjusting model parameters, the model can accurately predict the optimal water jet parameters under different production conditions. For example, according to the loom speed, fabric type, etc. Input, predict the best water jet time and water pressure. With the continuous production, new data is constantly coming in. Use these new data 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, continuously adjust the water jet control strategy to achieve fine control of the water jet process, improve the loom production efficiency and product quality.

[0041] In one embodiment, the running state data of the loom is obtained, including the speed, position, vibration and other parameters of the loom, and whether the loom is in a stable running state is judged according to a preset threshold value. If the loom is not running stably, the water jet time and pressure are adjusted until the loom returns to stable.

[0042]

[0043] S represents the loom running state index, 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 that the running is unstable.

[0044] The parameter data of the fabric is obtained, including the density, thickness, texture and other parameters of the fabric, and the best water jet flow and water jet angle are determined according to the fabric parameters, and the opening and direction of the nozzle are adjusted to realize accurate water jet control of the fabric.

[0045] Q = k1p + k2h + k3T

[0046] Q represents the optimal water flow rate, p represents the fabric density, h represents the fabric thickness, T represents the fabric texture characteristics, and k1, k2, and k3 are weight coefficients. The optimal water flow rate is automatically calculated based on the fabric parameters.

[0047] During the water spraying process, the water spraying pressure and flow rate are monitored in real time. If the pressure or flow rate exceeds the preset range, the opening of the nozzle is adjusted in a timely manner to ensure that the water spraying pressure and flow rate are always within the optimal range, thereby ensuring the stability of the fabric quality. A machine learning algorithm is used to establish a correlation model between the loom operating state, fabric parameters, and water spraying parameters based on historical data. The optimal water spraying timing, pressure, and flow rate are predicted through the model to achieve intelligent control of the water spraying process.

[0048]

[0049] y represents the predicted water spraying parameters (timing, pressure, or flow rate), x i represents the input features (such as loom speed, fabric density, etc.), w i represents the feature weights, b represents the bias term, and n represents the number of features. A support vector machine algorithm is used to train the model to predict the optimal water spraying parameters.

[0050] ΔP = α (P - P0)

[0051] 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.

[0052] During the water spraying process, high-speed cameras are used to capture image data of the fabric surface. Image processing algorithms are 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 returns to uniformity.

[0053]

[0054] H represents the fabric surface texture uniformity, 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 number of gray levels. The fabric surface uniformity is evaluated by calculating the entropy of the gray level co-occurrence matrix, and the water spraying parameters are dynamically adjusted accordingly.

[0055] For different types of fabric, an optimal database of water spraying parameters is established in advance. The optimal water spraying parameters corresponding to the type and parameters of the fabric are obtained from the database as the initial water spraying parameters, and the water spraying process is dynamically optimized based on real-time data. The parameter data of the water spraying process are uploaded to the cloud server in real time. Through big data analysis technology, the correlation between water spraying parameters and fabric quality is mined, and the water spraying control algorithm is continuously optimized to achieve the intelligence and efficiency of the water jet loom.

[0056] The data acquisition module and the parameter calculation module are connected by wired or wireless mode. The weft tension sensor collects weft tension data in real time, the sampling frequency is 1 kHz, the range is 0-100 N, and the accuracy is 0.5% FS. The temperature and humidity sensor collects the temperature and humidity data of the loom environment, the temperature measurement range is -20℃-80℃, the humidity measurement range is 0-100% RH, and the accuracy is ±0.5℃ and ±3% RH. The weft tension, humidity and temperature data are obtained through the data acquisition module and transmitted to the parameter calculation module in the control system.

[0057] After the parameter calculation module receives the weft data, based on the fuzzy PID control algorithm, the fuzzy subsets of weft tension, temperature and humidity are set as {NB, NM, NS, ZO, PS, PM, PB}, the domain range is {0-100 N, -20℃-80℃, 0-100% RH}, and the fuzzy rule base is established, and the fuzzy subsets of water spraying time, water pressure and nozzle opening degree are {NB, NM, NS, ZO, PS, PM, PB}, the domain range is {0-500 ms, 0-1 MPa, 0-100%}. According to the preset fuzzy rule base, the collected weft tension, humidity and temperature data are fuzzified, that is, the numerical data are converted into language variables according to the preset fuzzy rule base and membership function.

[0058]

[0059] μ A (x) represents the membership function, x is the input variable, c is the membership function center, and σ is the width parameter. The formula defines the Gaussian membership function, which is used to fuzzify the input variable.

[0060] The language variables are calculated by the fuzzy inference engine, and the fuzzy values of water spraying time, water pressure and nozzle opening degree are obtained according to the correlation between weft tension, humidity and temperature. The defuzzifier is used to convert the fuzzy values of water spraying time, water pressure and nozzle opening degree into accurate numerical parameters as the control parameters of the water spraying device. For example, if the weft tension exceeds 80 N, the environmental temperature is lower than 20℃, and the humidity is higher than 80% RH, the fuzzy control rule judges that the weft is too tight, and the output water spraying time is 400 ms, the water pressure is 0.8 MPa, and the nozzle opening degree is 90%.

[0061] If the weft tension exceeds the preset threshold, the tension adjustment module judges that the weft is too tight, and sends instructions to the water spraying actuator to increase the nozzle opening degree and prolong the water spraying time; if the weft tension is lower than the preset threshold, it is judged that the weft is too loose, and instructions are sent to reduce the nozzle opening degree and shorten the water spraying time, so as to realize the self-adaptive adjustment of weft tension through closed-loop feedback control.

[0062] The nozzle optimization module uses computational fluid dynamics method to simulate and analyze based on the inner diameter, length, outlet shape and layout position parameters of the nozzle on the loom. According to the loom parameters and water spraying performance requirements, the objective function and constraint conditions of nozzle optimization are determined, for example, the objective function of nozzle optimization is determined as the water spraying uniformity greater than 95%, while the nozzle inner diameter is constrained between 0.2-0.8mm, and the length is between 2-10mm.

[0063]

[0064] u represents the fluid velocity vector, t represents time, p represents fluid density, p represents pressure, and v represents kinematic viscosity. The formula is the Navier-Stokes equation for describing the fluid motion inside the nozzle.

[0065] A mathematical model of the flow inside the nozzle is established using computational fluid dynamics method, such as using ANSYS Fluent software to establish the Navier-Stokes equation mathematical model of the flow inside the nozzle, and to perform grid division and boundary condition setting, hexahedral structure grid is divided, and the inlet pressure is set to 0.2MPa, and the outlet pressure is set to standard atmospheric pressure. Through numerical simulation, the flow field distribution, velocity field and pressure field information under different nozzle parameters are obtained, and comparative analysis is carried out, and the flow field distribution cloud diagram under different inner diameter, length and outlet shape is obtained. According to the simulation results, the influence law of nozzle inner diameter, length and outlet shape on water spraying 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. Using optimization algorithm such as genetic algorithm or particle swarm optimization algorithm, the optimal nozzle parameter combination is searched under the constraint condition, using multi-objective genetic algorithm, the water spraying uniformity and pressure loss are used as optimization target, the optimal parameter combination is searched under the constraint condition, the nozzle inner diameter is 0.38mm, the length is 4.7mm, and the outlet angle is 60°. According to the optimized nozzle parameters, the best layout position on the loom is determined, and the nozzle is processed and installed, according to the structure characteristics of the loom, the best layout of the nozzle in the loom is determined as a straight line arrangement with a distance of 50mm from the weaving area and a spacing of 10mm. Through actual water spraying test, whether the performance of the optimized nozzle meets the water spraying requirements of the loom is verified, the nozzle is processed by numerical control lathe, the precision is controlled within ±0.01mm, and the high-speed camera is used to shoot the water spraying process, the spray angle, fragmentation degree and other indexes are analyzed, and it is verified that the performance of the optimized nozzle meets the water spraying uniformity requirements of the loom. If necessary, further parameter adjustment is carried out, and the nozzle optimization scheme is finally determined, and finally the water supply pressure and nozzle height are adjusted to realize the optimal control of the loom water spraying system and improve the fabric quality.

[0066] The pressure sensor in the pressure regulating module collects pressure data in the pipeline in real time. The pressure sensor uses a high-precision strain pressure sensor with a range of 0-1 MPa and an accuracy level of 0.5. It transmits the pressure data to the control unit of the control system through an RS485 bus with a cycle of 50 ms. The control unit uses 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 (preset pressure threshold upper limit 1 MPa and lower limit 0.8 MPa). 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, it determines the adjustment range of the water pump speed and the electromagnetic valve opening through the PID algorithm, calculates the adjustment value of the water pump speed and the electromagnetic valve opening, and sends the control command to the water pump control module and the electromagnetic valve control module.

[0067]

[0068] u(t) represents the control output, e(t) represents the error signal, K p , K i , K d are the proportional, integral, and differential coefficients respectively. This formula is the mathematical expression of the PID controller, which is used to adjust the water pump speed and the electromagnetic valve opening.

[0069] The water pump control module adjusts the speed of the water pump motor through the frequency converter according to the received speed adjustment command, thereby changing the water flow rate of the water pump and achieving dynamic regulation of the pipeline pressure. The electromagnetic valve control module adjusts the opening of the valve by controlling the on-off time ratio of the electromagnetic valve according to the received valve opening adjustment command, thereby changing the flow rate of the water flow through the valve and achieving precise control of the pipeline pressure in cooperation with the water pump speed adjustment. During the pressure regulation process, the control unit continuously receives real-time pressure data from the pressure sensor and constantly corrects the water pump speed and the electromagnetic valve opening through closed-loop feedback control, so that the pipeline pressure is stabilized within the preset threshold range, for example, the pressure is controlled at 0.9±0.05 MPa, ensuring the normal operation of the water jet loom.

[0070] The state detection module utilizes machine vision technology to collect the motion images of the needles and the shuttles in real time through a high-speed camera. A high-speed CMOS industrial camera with a pixel resolution of 2048x2048 is used to collect the motion images of the needles and the shuttles at a speed of 100 frames per second during the operation of the loom. The obtained image frame data is preprocessed, including image denoising (such as sequentially performing median filtering to remove salt and pepper noise), enhancement (using gamma transformation to improve contrast), and normalization (scaling to 512x512 pixels to speed up subsequent processing), and the like, to improve image quality. A region-based image segmentation algorithm, such as a region growing-based segmentation algorithm, is used to accurately segment the motion regions of the needles and the shuttles from the preprocessed images, using the mean gray values of the needles and the shuttles as seed points and 8-neighborhood growth. For the segmented motion regions of the needles and the shuttles, key information such as contour features (such as Freeman chain code contour features) and centroid coordinates is extracted. According to the changes in the centroid coordinates of the needles and the shuttles in consecutive image frames, the real-time motion trajectories and instantaneous speeds thereof are calculated. The real-time motion parameters of the needles and the shuttles are compared with preset normal working thresholds (the normal needle speed should be between 0.5-2.5 m / s, and the shuttle speed should be between 5-10 m / s), and if they are outside the threshold range, it is determined that an abnormal state exists. When the motion of the needles or the shuttles is detected to be abnormal, for example, when the needle speed is detected to be lower than 0.3 m / s or the shuttle speed is detected to be lower than 4 m / s, an abnormal signal is transmitted in a timely manner to a control unit, and the control unit dynamically adjusts the water spraying time and the water spraying pressure accordingly, such as extending the water spraying time by 20% and increasing the water spraying pressure to 0.4 MPa, to ensure the normal and efficient operation of the loom.

[0071] The data processing module uploads the loom production parameters and the water spraying system operation data to the cloud platform. The data uploaded to the cloud platform includes but is not limited to weft data, water spraying parameters, loom operation time, and fabric quality detection results. Using big data analysis technology, the association rule mining algorithm Apriori is used to analyze the association between the loom parameters and the water spraying parameters, with a support setting of 0.05 and a confidence setting of 0.8. The data in the cloud platform is mined and analyzed. Through machine learning algorithms, such as the decision tree algorithm C4.5, a water spraying optimization model is established, and the model parameters are updated once every 1000 m of fabric produced, and the model parameters are iteratively updated to continuously optimize the water spraying control strategy and improve the intelligent level of the loom.

[0072] For example, the loom running state data is collected in real time by sensors, including parameters such as speed, displacement, and vibration frequency. The system presets the speed threshold as 1000 rpm, the displacement threshold as 2 mm, and the vibration frequency threshold as 50 Hz. When the loom speed is lower than 950 rpm, the displacement is greater than 2.2 mm, or the vibration frequency exceeds 55 Hz, it is determined that the loom is running unstably. At this time, the system automatically adjusts the water spraying time, delays the water spraying by 10 ms, and increases the water spraying pressure by 0.2 MPa. After 3 iterations of adjustment, the loom returns to stable operation. The fabric parameters are collected by optical sensors and pressure sensors, including density, thickness, and texture characteristics. When the fabric density is between 200-300 g / m 2 , the thickness is between 0.5-1.0 mm, and the texture is twill, the system determines the optimal water spraying flow as 1.5 L / min and the water spraying angle as 30°. The nozzle opening and direction are controlled by a 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 2.5 MPa or the flow is lower than 1.2 L / min, the system automatically adjusts the nozzle opening, with a pressure adjustment step of 0.1 MPa and a flow adjustment step of 0.2 L / min, to ensure that the water spraying parameters are always within the optimal range. The system uses a support vector machine (SVM) algorithm to train the correlation model based on 5000 historical data, inputs the loom speed, fabric density, and texture characteristics, predicts the optimal water spraying time, pressure, and flow, and the prediction accuracy reaches 95%. A high-speed camera collects fabric surface images at a speed of 500 fps, and the texture uniformity feature is extracted by 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 0.05 MPa and an angle adjustment step of 2°. After 5 iterations of optimization, the fabric uniformity is improved to more than 95%. For 10 common fabrics such as pure cotton, polyester cotton, and silk, the system pre-establishes a water spraying parameter database, storing 100 optimal parameters. When a new fabric type is identified, the system retrieves the corresponding parameters from the database as the initial values, and then dynamically optimizes the water spraying process based on the real-time collected fabric parameters through the fuzzy control algorithm, with an optimization period of 200 ms. The system uploads parameters such as water spraying pressure, flow, time, 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 spraying pressure and fabric uniformity is 85%, based on which the water spraying control strategy is optimized to improve the intelligence level of the loom. Through the above intelligent transformation, the production efficiency of the water spraying loom is improved by 20%, the fabric quality pass rate is improved by 5%, and the intelligent and efficient production of the loom is realized.

[0073] In summary, the present application provides a control system for water jet loom, comprising: a data acquisition module for real-time acquisition of weft yarn tension, humidity and temperature data, and transmitting the collected data to the control system; a parameter calculation module for dynamically calculating the optimal water jet time, water jet pressure and nozzle opening parameters according to the changes of weft yarn tension, humidity and temperature data, and transmitting the calculated parameters to the water jet actuator; a tension adjustment module, if the weft yarn tension exceeds the preset threshold, the control system determines that the weft yarn is too tight, and sends instructions to increase the nozzle opening and water jet 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 instructions to reduce the nozzle opening and water jet time; a nozzle optimization module for optimizing the design of the nozzle by using computational fluid dynamics method, and determining the optimal nozzle structure parameters and layout scheme by simulation analysis; a pressure adjustment module, a pressure sensor is arranged in the waterway system for real-time monitoring of pipeline pressure, when the pressure exceeds the preset threshold, the control system sends an alarm signal, and adjusts the water pump speed and electromagnetic valve opening degree; a state detection module for real-time detection of the motion state of the needle and the shuttle by using machine vision technology, when the needle or the shuttle is detected to be abnormal, the control system immediately sends instructions to adjust the water jet time and water pressure; a data processing module for uploading the loom production data and water jet system operating parameters to the cloud platform, establishing a water jet optimization model by using big data analysis technology and machine learning algorithm, and iteratively optimizing the water jet control strategy to realize real-time monitoring and accurate control of various parameters of the water jet loom, and ensure the weaving quality and efficiency.

[0074] Referring to Figure 2 , a flowchart of a control method for water jet loom is provided, comprising:

[0075] Real-time acquisition of weft yarn tension, humidity and temperature data, and transmitting the collected data to the control system;

[0076] According to the changes of weft yarn tension, humidity and temperature data, the optimal water jet time, water jet pressure and nozzle opening parameters are dynamically calculated by using fuzzy control algorithm, and the calculated parameters are transmitted to the water jet actuator;

[0077] If the weft yarn tension exceeds the preset threshold, the control system determines that the weft yarn is too tight, and sends instructions to increase the nozzle opening and water jet 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 instructions to reduce the nozzle opening and water jet time;

[0078] The nozzle is optimized by using computational fluid dynamics method, and the optimal nozzle structure parameters and layout scheme are determined by simulation analysis;

[0079] A pressure sensor is arranged in the waterway system to monitor the pipeline pressure in real time. When the pressure exceeds the preset threshold, the control system sends an alarm signal and adjusts the water pump speed and the electromagnetic valve opening degree.

[0080] Machine vision technology is used to detect the motion state of the needle and the shuttle in real time. When an abnormality is detected in the needle or the shuttle, the control system immediately sends an instruction to adjust the water spraying time and water pressure.

[0081] The loom production data and the water spraying system operation parameters are uploaded to a cloud platform. A water spraying optimization model is established using big data analysis technology and machine learning algorithms to iteratively optimize the water spraying control strategy.

[0082] The above description is only a preferred embodiment of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation, or direct or indirect application in other related technical fields, based on the content of the specification and drawings, is also included in the patent protection scope of the present application.

Claims

1. A control system for a water-jet loom, characterized in that, include: The data acquisition module includes a tension sensor, a humidity sensor, and a temperature sensor installed on the weft yarn, which are used to acquire the tension, humidity, and temperature data of the weft yarn in real time and transmit the acquired data to the control system. The parameter calculation module dynamically calculates the optimal water spraying time, water spraying pressure, and nozzle opening parameters based on the changes in weft tension, humidity, and temperature data using a fuzzy control algorithm, and transmits the calculated parameters to the water spraying actuator. The parameter calculation module is based on a fuzzy PID control algorithm. It sets fuzzy subsets and universes of discourse for weft tension, temperature, and humidity, and establishes a fuzzy rule base. It outputs fuzzy subsets and universes of discourse for water spraying time, water pressure, and nozzle opening. The collected weft tension, humidity, and temperature data are fuzzified using a Gaussian membership function. The linguistic variables are inferred and calculated using a fuzzy inference engine. Finally, the fuzzy values ​​of water spraying time, water spraying pressure, and nozzle opening are converted into precise numerical parameters as control parameters for the water spraying device. The tension adjustment module determines that the weft yarn tension is too tight if the weft yarn tension exceeds a preset threshold, and sends a command to increase the nozzle opening and water spraying time. If the weft yarn tension is lower than the preset threshold, the control system determines that the weft yarn tension is too loose, and sends a command to decrease the nozzle opening and water spraying time. The nozzle optimization module is used to optimize the nozzle design by employing computational fluid dynamics methods and to determine the optimal nozzle structure parameters and layout scheme through simulation analysis. The nozzle optimization module employs computational fluid dynamics to establish a mathematical model of the internal flow of the nozzle, and performs mesh generation and boundary condition setting. It creates a hexahedral mesh and sets the inlet and outlet pressures. Through numerical simulation, it obtains flow field distribution, velocity field, and pressure field information under various nozzle parameters. With water spray uniformity and pressure loss as optimization objectives, it searches for the optimal combination of nozzle parameters under constraints. After determining the optimal layout position of the optimized nozzle on the loom, the nozzle is machined using a CNC lathe and tested through actual water spraying experiments. The pressure regulation module is equipped with a pressure sensor in the water system to monitor the pipeline pressure in real time. When the pressure exceeds a preset threshold, the control system sends an alarm signal and simultaneously adjusts the water pump speed and the opening of the solenoid valve. The status detection module is used to detect the movement status of the knitting needle and shuttle in real time using machine vision technology. When an abnormality is detected in the knitting needle or 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 loom production data and water spray system operating parameters to the cloud platform, and to establish a water spray optimization model using big data analysis technology and machine learning algorithms, and to iteratively optimize the water spray control strategy.

2. The control system for a water-jet loom according to claim 1, characterized in that, The data acquisition module and the parameter calculation module are connected via wired or wireless means.

3. The control system for a water-jet loom according to claim 1, characterized in that, The tension adjustment module achieves adaptive adjustment of weft tension through closed-loop feedback control. When the weft tension exceeds the preset threshold, it sends a command to increase the nozzle opening and extend the water spraying time; when the weft tension is lower than the preset threshold, it sends a command to decrease the nozzle opening and shorten the water spraying time. After adjustment, it continuously monitors the weft tension and dynamically adjusts the nozzle opening and water spraying time according to the deviation between the actual tension and the preset threshold.

4. The 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 nozzle's inner diameter, length, outlet shape, and layout parameters on the loom.

5. The control system for a water-jet loom according to claim 1, characterized in that, The pressure sensor in the pressure regulation module transmits the real-time collected pipeline pressure data 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 solenoid valve opening based on a preset threshold.

6. The control system for a water-jet loom according to claim 1, characterized in that, The status detection module acquires image information of the knitting needles and shuttles through an industrial camera, and uses image processing algorithms to analyze the motion trajectory and speed status information 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.

7. The control system for a water-jet loom according to claim 1, characterized in that, The data processing module utilizes big data analytics and employs association rule mining algorithms to analyze the correlation between loom parameters and water spray parameters, and performs data mining and analysis on the data uploaded to the cloud platform. Through machine learning algorithms, it establishes a water spray optimization model and continuously iterates and optimizes the water spray control strategy.

8. The 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 includes, but is not limited to, weft yarn data, water spray parameters, loom running time, and fabric quality test results.

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