A control system for the laser processing of wrinkle-resistant denim fabrics

Abstract

The present invention provides a control system and method for the laser processing of wrinkle-resistant denim fabric. The system includes: a pre-processing module for performing surface pre-processing on a target denim fabric to obtain a pre-processed fabric and acquire a surface image of the pre-processed fabric; an initial parameter determination module for determining the initial operating parameters of a laser device based on the surface image of the pre-processed fabric; a first operating module for controlling the laser device to scan and heat the pre-processed fabric based on the initial operating parameters to change the molecular structure of the pre-processed fabric; a signal monitoring module for real-time monitoring of the temperature signal of the pre-processed fabric; a second operating module for adjusting the real-time operating parameters of the laser device based on the temperature signal to obtain the processed fabric; and a post-processing module for cooling and shaping the processed fabric. By real-time monitoring and dynamic adjustment of operating parameters, the present invention ensures the efficiency of the laser processing process and reduces unnecessary downtime.

Description

Technical Field

[0001] The present invention relates to the technical field of textile processing, in particular to a control system for a laser processing process of wrinkle-resistant denim fabrics. Background Art

[0002] In the modern textile industry, denim fabric is widely used for its wear resistance and fashionable design. However, denim fabric is prone to wrinkling during use, affecting its appearance and comfort. Traditional wrinkle-removing treatments rely primarily on chemical agents and physical compression. While these methods can improve the fabric's wrinkle resistance to a certain extent, they also have some shortcomings.

[0003] Many anti-wrinkle agents contain chemical ingredients that may cause potential harm to the human body and the environment. With the improvement of environmental awareness, consumers are increasingly demanding harmless and environmentally friendly fabrics.

[0004] Physical pressing methods typically require high temperatures and pressures, which can damage or deform the fabric, affecting its appearance and service life. Furthermore, the treatment effects of physical methods are often not long-lasting, and the fabric easily regains wrinkles after washing or wearing.

[0005] Laser processing technology, an emerging non-contact processing method, is characterized by high efficiency, precision, and environmental friendliness. It uses a high-energy laser beam directly on the fabric surface, enabling fine processing of fabrics. Currently, the application of laser technology in the textile industry is primarily focused on pattern engraving and surface treatment, but research and application in wrinkle-resistant treatments remains limited.

[0006] As consumers' demands for clothing quality and functionality continue to rise, the market demand for wrinkle-resistant denim fabrics is growing. Developing a new wrinkle-resistant treatment technology can meet market demand and enhance product competitiveness.

[0007] In summary, existing anti-wrinkle treatment technologies have certain limitations, and a new, environmentally friendly and effective anti-wrinkle treatment method is urgently needed. Summary of the Invention

[0008] In order to overcome the deficiencies of the prior art, the purpose of the present invention is to provide a control system and method for the laser processing process of wrinkle-resistant denim fabrics, which can improve the anti-wrinkle effect and fabric quality while ensuring the high efficiency of the laser processing process.

[0009] To achieve the above object, the present invention provides the following solutions:

[0010] A control system for the laser processing of wrinkle-resistant denim fabrics, comprising:

[0011] A pretreatment module is used to perform surface pretreatment on the target denim fabric to obtain a pretreated fabric and acquire a surface image of the pretreated fabric;

[0012] An initial parameter determination module, configured to determine initial operating parameters of the laser device based on the surface image of the pretreated fabric;

[0013] a first working module, configured to control the laser device to scan and heat the pretreated fabric according to the initial working parameters, so as to change the molecular structure of the pretreated fabric;

[0014] A signal monitoring module, used to monitor the temperature signal of the pretreated fabric in real time during the scanning and heating process;

[0015] a second working module, configured to adjust the real-time working parameters of the laser device according to the temperature signal, so as to process the pretreated fabric and obtain the processed fabric;

[0016] The post-processing module is used to cool and shape the processed fabric to obtain the finished product.

[0017] Preferably, the surface pretreatment includes: cleaning, drying and surface flattening.

[0018] Preferably, the initial operating parameters include: laser power, scanning speed, scanning path and scanning overlap rate.

[0019] Preferably, the initial parameter determination module includes:

[0020] A region recognition submodule is used to identify and extract a target region of the surface image to obtain a fabric region image;

[0021] A feature extraction submodule is used to extract features from the fabric area image to obtain fabric surface features;

[0022] A training module is used to train the initial decision tree model using preset sample fabric surface features and sample working parameters to obtain a working parameter prediction model;

[0023] The prediction module is used to use the working parameter prediction model to predict the surface characteristics of the fabric to obtain the initial working parameters.

[0024] Preferably, the region identification submodule includes:

[0025] A noise point detection unit is used to detect noise points on the surface image using a filter window to obtain noise points to be processed;

[0026] a denoising unit, configured to denoise the surface image within the corresponding filtering window when the number of noise points to be processed within the filtering window is greater than a preset threshold;

[0027] a detection unit, configured to slide the filter window to perform image detection until the entire surface image is traversed to obtain a denoised surface image;

[0028] A grayscale calculation unit is used to take a neighborhood window centered at any point on the denoised surface image and calculate the grayscale average of all pixels in the neighborhood window;

[0029] A mean image acquisition unit is used to take the grayscale average of the corresponding pixel points as the output of the central pixel point to obtain a mean surface image;

[0030] a threshold determination unit, configured to obtain an optimal segmentation threshold according to a correlation between the mean surface image and the denoised surface image;

[0031] The segmentation unit is used to perform image segmentation on the denoised surface image using an optimal segmentation threshold to obtain the fabric area image.

[0032] Preferably, the noise point detection unit includes:

[0033] The model building subunit is used to build a noise point detection model based on the mean and median of each image point in the filtering window; the noise point detection model is: Among them, f(x) represents the similar noise value of pixel x, u(x) represents the gray value of pixel x, and u mean (x) represents the grayscale mean of all pixels in the filter window centered on pixel x, ▽u(x) represents the gradient mean of pixel x, ▽u mean (x) is the grayscale median of all pixels in the filter window centered on pixel x, ▽ x (x) represents the horizontal gradient value of pixel x, ▽ y (x) represents the gradient value of pixel x in the vertical direction;

[0034] A detection subunit, configured to detect each image point within the filtering window using the noise point detection model to obtain a similar noise value for each image point;

[0035] The noise point determination subunit is used to take corresponding image points with a value greater than the similar noise value as noise points to be processed.

[0036] Preferably, the denoising unit includes:

[0037] The variance calculation subunit is used to calculate the pseudo pixel variance based on the grayscale median of all pixels in the filter window; wherein the pseudo pixel variance calculation formula is: in, represents the pseudo pixel variance of the pixel point (a, b) in the filter window with a size of (2n+1)×(2n+1), mean(a,b) represents the grayscale median of the pixel point (a, b) in the filter window, and x(k,l) represents the grayscale value of the pixel point at position (k,l);

[0038] The denoising model subunit is used to construct a window denoising model using the pseudo pixel variance; the formula of the window denoising model is: Among them, f(a,b) represents the grayscale value of the pixel (a, b) after denoising, D is the adjustable coefficient, and x(a,b) represents the grayscale value of the pixel (a, b) in the filter window.

[0039] Preferably, the second working module includes:

[0040] A signal preprocessing submodule, configured to smooth and filter the temperature signal to obtain a preprocessed signal;

[0041] a temperature parameter determination submodule, configured to determine a temperature value and a temperature distribution state of a real-time temperature according to the preprocessing signal;

[0042] A first adjustment submodule, configured to adjust the laser power and scanning speed in the real-time working parameters according to the temperature value and a preset first threshold value;

[0043] The second adjustment submodule is configured to adjust the scanning path and the scanning overlap ratio in the real-time working parameters according to the temperature distribution state.

[0044] A method for controlling a laser processing process of wrinkle-resistant denim fabrics, comprising:

[0045] Performing surface pretreatment on a target denim fabric to obtain a pretreated fabric, and acquiring a surface image of the pretreated fabric;

[0046] determining initial operating parameters of the laser device according to the surface image of the pretreated fabric;

[0047] Controlling the laser device to scan and heat the pretreated fabric according to the initial working parameters to change the molecular structure of the pretreated fabric;

[0048] During the scanning and heating process, the temperature signal of the pretreated fabric is monitored in real time;

[0049] adjusting the real-time working parameters of the laser device according to the temperature signal to process the pre-treated fabric and obtain the processed fabric;

[0050] The processed fabric is cooled and shaped to obtain the finished product.

[0051] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0052] The present invention provides a control system and method for the laser processing of wrinkle-resistant denim fabric. The system includes: a pre-processing module for performing surface pre-processing on a target denim fabric to obtain a pre-processed fabric and acquire a surface image of the pre-processed fabric; an initial parameter determination module for determining initial operating parameters of a laser device based on the surface image of the pre-processed fabric; a first operating module for controlling the laser device to scan and heat the pre-processed fabric according to the initial operating parameters to change the molecular structure of the pre-processed fabric; a signal monitoring module for monitoring the temperature signal of the pre-processed fabric in real time during the scanning and heating process; a second operating module for adjusting the real-time operating parameters of the laser device based on the temperature signal to process the pre-processed fabric and obtain the processed fabric; and a post-processing module for cooling and shaping the processed fabric to obtain the finished product. The present invention ensures the efficiency of the laser processing process and reduces unnecessary downtime by real-time monitoring and dynamic adjustment of operating parameters. Adjusting the operating parameters of the laser device based on the real-time temperature signal ensures that the fabric is processed within an optimal temperature range, improving the wrinkle-resistant effect and fabric quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0054] Figure 1 A diagram of the system module structure provided by an embodiment of the present invention;

[0055] Figure 2 A flowchart of a method provided by an embodiment of the present invention.

[0056] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0057] The purpose of the present invention is to provide a control system and method for the laser processing process of anti-wrinkle denim fabric, which can improve the anti-wrinkle effect and fabric quality while ensuring the high efficiency of the laser processing process.

[0058] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0059] Figure 1 The system module structure diagram provided by the embodiment of the present invention is as follows: Figure 1 As shown, the present invention provides a control system for the laser processing process of wrinkle-resistant denim fabric, comprising:

[0060] A pretreatment module is used to perform surface pretreatment on the target denim fabric to obtain a pretreated fabric and acquire a surface image of the pretreated fabric;

[0061] An initial parameter determination module, configured to determine initial operating parameters of the laser device based on the surface image of the pretreated fabric;

[0062] a first working module, configured to control the laser device to scan and heat the pretreated fabric according to the initial working parameters, so as to change the molecular structure of the pretreated fabric;

[0063] A signal monitoring module, used to monitor the temperature signal of the pretreated fabric in real time during the scanning and heating process;

[0064] a second working module, configured to adjust the real-time working parameters of the laser device according to the temperature signal, so as to process the pretreated fabric and obtain the processed fabric;

[0065] The post-processing module is used to cool and shape the processed fabric to obtain the finished product.

[0066] Preferably, the surface pretreatment includes: cleaning, drying and surface flattening.

[0067] Specifically, the surface pretreatment of this embodiment includes:

[0068] Cleaning: First, place the denim fabric in a cleaning machine and use an appropriate detergent and warm water to remove dirt, grease, and other impurities from the fabric surface. Ultrasonic cleaning or mechanical agitation can be used during the cleaning process to ensure thorough cleaning.

[0069] Drying: After washing, place the fabric in a drying machine and use hot air drying or vacuum drying to quickly remove moisture from the fabric. The drying temperature and time should be adjusted according to the material and thickness of the fabric to prevent deformation or damage to the fabric.

[0070] Flattening: After drying, the fabric is flattened using a flattening device to ensure a smooth, wrinkle-free surface. This step can be achieved through heat or mechanical flattening to improve the accuracy of subsequent image acquisition.

[0071] Furthermore, the specific steps of acquiring the fabric surface image in this embodiment are as follows:

[0072] 1. Use a high-resolution camera or scanner to photograph the pre-treated fabric. The camera should have good optical performance to capture detailed images. Ensure that the distance between the camera and the fabric is appropriate to obtain a clear image.

[0073] 2. During the shooting process, ensure that the surface of the fabric is evenly illuminated. You can use a ring light or evenly distributed LED light for lighting to avoid shadows and reflections that affect the image quality.

[0074] 3. After acquiring the image, use image processing software to perform preliminary preprocessing on the image, including denoising, contrast enhancement, and brightness adjustment. These processes can improve the readability of the image and facilitate subsequent feature extraction and analysis.

[0075] 4. Data storage and management

[0076] Image storage: Save the processed images to the database, ensuring that the image file format and resolution are suitable for subsequent analysis. This embodiment uses JPEG or PNG format for storage and records relevant fabric information (such as type, thickness, cleaning status, etc.) for easy management.

[0077] 5. Back up image data regularly to prevent data loss. You can use cloud storage or external hard drive to back up to ensure data security and accessibility.

[0078] Through the above steps, this embodiment can effectively perform surface pretreatment on the target denim fabric and obtain high-quality surface images, providing a reliable foundation for subsequent laser processing.

[0079] Preferably, the initial operating parameters include: laser power, scanning speed, scanning path and scanning overlap rate.

[0080] Specifically, the working parameters of this embodiment are as follows:

[0081] 1. Laser power. Laser power refers to the energy intensity of the laser beam emitted by the laser device, typically expressed in watts (W). Laser power directly affects the laser's heating effect on the fabric. Higher power can accelerate the heating process of the fabric, changing its molecular structure and thus improving its wrinkle resistance. However, excessive power may cause the fabric to overheat, burn, or otherwise damage. Therefore, it is necessary to appropriately set the power based on the fabric's characteristics and processing requirements.

[0082] 2. Scanning speed. Scanning speed refers to the speed at which the laser equipment moves during processing, typically expressed in millimeters per second (mm / s). This speed determines how quickly the laser moves across the fabric surface. Faster scanning speeds improve processing efficiency but can lead to uneven heating, impacting the results. Slower scanning speeds ensure adequate heating but may reduce processing efficiency. Therefore, the speed should be adjusted based on the fabric's characteristics and desired results.

[0083] 3. Scan path. The scan path refers to the path of the laser beam during processing. It can be a straight line, a curve, or a grid. The design of the scan path affects the laser's coverage of the fabric and the uniformity of heating. A reasonable scan path ensures uniform heating of the fabric surface, avoiding localized overheating or underheating. Choosing the appropriate scan path is crucial, based on the fabric shape and processing requirements.

[0084] 4. Scan overlap. Definition: Scan overlap refers to the overlap between adjacent scan paths during the laser scanning process, typically expressed as a percentage. Scan overlap affects the uniformity of laser heating and the processing effect on the fabric. A higher overlap ensures sufficient heating of the fabric surface and reduces the occurrence of unheated areas, but may increase processing time. A lower overlap increases processing speed but may result in uneven heating. Therefore, it should be appropriately set based on the fabric's characteristics and processing requirements.

[0085] By reasonably setting these initial working parameters, this embodiment can effectively improve the laser processing effect of wrinkle-resistant denim fabrics and ensure the quality and performance of the final product.

[0086] Preferably, the initial parameter determination module includes:

[0087] A region recognition submodule is used to identify and extract a target region of the surface image to obtain a fabric region image;

[0088] A feature extraction submodule is used to extract features from the fabric area image to obtain fabric surface features;

[0089] A training module is used to train the initial decision tree model using preset sample fabric surface features and sample working parameters to obtain a working parameter prediction model;

[0090] The prediction module is used to use the working parameter prediction model to predict the surface characteristics of the fabric to obtain the initial working parameters.

[0091] Specifically, the input of the region recognition submodule of this embodiment is the acquired surface image of the pre-treated fabric.

[0092] The surface image is processed using image processing techniques (such as edge detection and image segmentation) to identify and extract the target area of ​​the fabric. This embodiment uses algorithms such as Canny edge detection or threshold segmentation to separate the target area from the background in the surface image. The output is an image of the resulting fabric area, accurately identifying the portion requiring analysis.

[0093] Furthermore, the input of the feature extraction submodule of this embodiment is the fabric area image. The feature extraction method is as follows:

[0094] Texture features,using gray-level co-occurrence matrix (GLCM), local binary pattern (LBP) and other methods to extract texture features,reflecting the roughness and texture structure of the fabric surface.

[0095] Color features, calculate color histogram, extract main hue, saturation and brightness information, and understand the color distribution of fabrics.

[0096] Shape features, extracting shape features of the fabric surface (such as contour, shape closure, etc.).

[0097] The output of the feature extraction submodule is: the fabric surface feature vector containing information such as texture, color and shape, which serves as the input for subsequent model training.

[0098] Furthermore, the input of the training module of this embodiment is: preset sample fabric surface features and sample working parameters. This embodiment first constructs a training data set, collects the surface features of multiple sample fabrics and their corresponding initial working parameters (laser power, scanning speed, etc.), and organizes them into a training data set. Select a suitable machine learning algorithm, such as a decision tree, random forest or support vector machine (SVM), to construct an initial decision tree model. Then, this embodiment uses the characteristics of the sample fabric as input and the corresponding working parameters as output, and uses a supervised learning method to train the model to create a working parameter prediction model. Finally, this embodiment uses cross-validation or other methods to evaluate the accuracy of the model and adjust the model parameters until a satisfactory prediction effect is achieved.

[0099] Furthermore, the prediction module of this embodiment takes as input the surface feature vector of the fabric to be processed. This embodiment inputs the extracted surface features into a trained operating parameter prediction model. This embodiment then performs a prediction to calculate the initial operating parameters required for the fabric, including laser power, scanning speed, scanning path, and scanning overlap. The output of this embodiment is the initial operating parameters, which are used in subsequent laser processing.

[0100] Preferably, the region identification submodule includes:

[0101] A noise point detection unit is used to detect noise points on the surface image using a filter window to obtain noise points to be processed;

[0102] a denoising unit, configured to denoise the surface image within the corresponding filtering window when the number of noise points to be processed within the filtering window is greater than a preset threshold;

[0103] a detection unit, configured to slide the filter window to perform image detection until the entire surface image is traversed to obtain a denoised surface image;

[0104] A grayscale calculation unit is used to take a neighborhood window centered at any point on the denoised surface image and calculate the grayscale average of all pixels in the neighborhood window;

[0105] A mean image acquisition unit is used to take the grayscale average of the corresponding pixel points as the output of the central pixel point to obtain a mean surface image;

[0106] a threshold determination unit, configured to obtain an optimal segmentation threshold according to a correlation between the mean surface image and the denoised surface image;

[0107] The segmentation unit is used to perform image segmentation on the denoised surface image using an optimal segmentation threshold to obtain the fabric area image.

[0108] Preferably, the noise point detection unit includes:

[0109] The model building subunit is used to build a noise point detection model based on the mean and median of each image point in the filtering window; the noise point detection model is: Among them, f(x) represents the similar noise value of pixel x, u(x) represents the gray value of pixel x, and u mean (x) represents the grayscale mean of all pixels in the filter window centered on pixel x, ▽u(x) represents the gradient mean of pixel x, ▽u mean (x) is the grayscale median of all pixels in the filter window centered on pixel x, ▽ x (x) represents the horizontal gradient value of pixel x, ▽y (x) represents the gradient value of pixel x in the vertical direction;

[0110] A detection subunit, configured to detect each image point within the filtering window using the noise point detection model to obtain a similar noise value for each image point;

[0111] The noise point determination subunit is used to take corresponding image points with a value greater than the similar noise value as noise points to be processed.

[0112] Preferably, the denoising unit includes:

[0113] The variance calculation subunit is used to calculate the pseudo pixel variance based on the grayscale median of all pixels in the filter window; wherein the pseudo pixel variance calculation formula is: in, represents the pseudo pixel variance of the pixel point (a, b) in the filter window with a size of (2n+1)×(2n+1), mean(a,b) represents the grayscale median of the pixel point (a, b) in the filter window, and x(k,l) represents the grayscale value of the pixel point at position (k,l);

[0114] The denoising model subunit is used to construct a window denoising model using the pseudo pixel variance; the formula of the window denoising model is: Among them, f(a,b) represents the grayscale value of the pixel (a, b) after denoising, D is the adjustable coefficient, and x(a,b) represents the grayscale value of the pixel (a, b) in the filter window.

[0115] Preferably, the second working module includes:

[0116] A signal preprocessing submodule, used for smoothing and filtering the temperature signal to obtain a preprocessed signal;

[0117] a temperature parameter determination submodule, configured to determine a temperature value and a temperature distribution state of a real-time temperature according to the preprocessing signal;

[0118] A first adjustment submodule, configured to adjust the laser power and scanning speed in the real-time working parameters according to the temperature value and a preset first threshold value;

[0119] The second adjustment submodule is configured to adjust the scanning path and the scanning overlap ratio in the real-time working parameters according to the temperature distribution state.

[0120] Specifically, in the second working module, the signal preprocessing submodule of this embodiment first smoothes and filters the real-time temperature signal to remove noise and transient fluctuations, resulting in a more stable preprocessed signal. Next, the temperature parameter determination submodule calculates the real-time temperature value based on the preprocessed signal and analyzes the temperature distribution to identify temperature variations across various regions of the fabric surface. Subsequently, the first adjustment submodule compares the real-time temperature value with a preset first threshold. If the temperature is below the lower threshold, the laser power is increased and the scanning speed is reduced to ensure adequate heating of the fabric. Conversely, if the temperature is above the upper threshold, the laser power is reduced and the scanning speed is increased to prevent overheating. Finally, the second adjustment submodule evaluates the heating uniformity across different regions of the fabric surface based on the temperature distribution and adjusts the scanning path and overlap ratio to ensure uniform laser light distribution across the fabric surface, avoiding localized overheating or unheated areas, thereby optimizing the processing effect. This series of adjustments ensures the stability of the laser processing process and the quality of the fabric processed.

[0121] Specifically, in the process of calculating the real-time temperature value and analyzing the temperature distribution state based on the preprocessed signal, first, the signal preprocessing applies a smoothing filter algorithm (such as a moving average filter or a Kalman filter) to the original temperature signal to eliminate noise and instantaneous fluctuations and ensure data stability. Then, the processed temperature signal is used to extract the real-time temperature value of each temperature sensor, which is distributed at different locations on the fabric surface to obtain comprehensive temperature information. Subsequently, the temperature distribution state analysis generates a temperature distribution map of the fabric surface by spatially interpolating the temperature values ​​of each sensor (such as bilinear interpolation or Kriging interpolation), which intuitively displays the temperature changes in each area. By analyzing this temperature distribution map, the temperature differences between different areas of the fabric surface can be identified, and it can be determined whether there are overheated or unheated areas, providing a basis for subsequent real-time parameter adjustments. This process ensures comprehensive monitoring of the fabric heating state and helps optimize the laser processing effect.

[0122] Furthermore, the adjustment steps of this embodiment are as follows:

[0123] 1. Set thresholds. First, the system needs to preset two temperature thresholds: a lower threshold and an upper threshold. These thresholds should be set based on the fabric's characteristics and processing requirements. For example, for wrinkle-resistant denim fabric, the lower threshold might be set at 60°C and the upper threshold at 80°C.

[0124] 2. Real-time temperature monitoring: During the laser processing process, the system continuously monitors the real-time temperature value of the fabric and obtains the latest data from the temperature sensor.

[0125] 3. Temperature comparison: Whenever a new real-time temperature value is obtained, the system compares the value with the preset lower and upper thresholds:

[0126] Below the lower threshold: If the real-time temperature value is below the lower threshold, it means that the fabric has not reached the required processing temperature.

[0127] Above upper threshold: If the real-time temperature value is above the upper threshold, it means the fabric may be overheated.

[0128] 4. Parameter adjustment:

[0129] When the temperature falls below the lower threshold, increase the laser power: the system increases the power setting of the laser device, for example, from 100W to 150W, to enhance the heating effect.

[0130] Reduce scanning speed: At the same time, the system reduces the scanning speed, for example, from 200mm / s to 150mm / s, to increase the laser's residence time on the fabric surface and ensure that the fabric is fully heated.

[0131] The temperature is higher than the upper threshold:

[0132] Reduce laser power: The system lowers the power setting of the laser device, for example, from 100W to 80W, to reduce the heating intensity.

[0133] Increase scanning speed: At the same time, the system increases the scanning speed, for example, from 200mm / s to 250mm / s, to reduce the laser's residence time on the fabric surface and prevent overheating.

[0134] 5. Real-time feedback and adjustment:

[0135] After each parameter adjustment, the system continues to monitor the real-time temperature value and continuously compares and adjusts it during the subsequent processing, forming a closed-loop control system to ensure the stability of the processing and the quality of the fabric.

[0136] Through the above steps, the system of this embodiment can dynamically adjust the working parameters of the laser equipment according to real-time temperature changes, ensuring that the fabric is processed within the optimal temperature range, thereby improving the anti-wrinkle effect and processing quality.

[0137] Corresponding to the above method, this embodiment also provides a control method for the laser processing process of anti-wrinkle denim fabrics, such as Figure 2 Shown, including:

[0138] Step 100: performing surface pretreatment on a target denim fabric to obtain a pretreated fabric, and acquiring a surface image of the pretreated fabric;

[0139] Step 200: determining initial operating parameters of the laser device according to the surface image of the pre-treated fabric;

[0140] Step 300: controlling the laser device to scan and heat the pretreated fabric according to the initial working parameters to change the molecular structure of the pretreated fabric;

[0141] Step 400: During the scanning and heating process, the temperature signal of the pretreated fabric is monitored in real time;

[0142] Step 500: adjusting the real-time operating parameters of the laser device according to the temperature signal to process the pre-treated fabric and obtain the processed fabric;

[0143] Step 600: Cooling and shaping the processed fabric to obtain a finished product.

[0144] The beneficial effects of the present invention are as follows:

[0145] (1) The present invention ensures the high efficiency of the laser processing process and reduces unnecessary downtime by real-time monitoring and dynamic adjustment of working parameters.

[0146] (2) The present invention adjusts the working parameters of the laser equipment according to the real-time temperature signal to ensure that the fabric is processed within the optimal temperature range, thereby improving the anti-wrinkle effect and fabric quality.

[0147] (3) The present invention avoids excessive heating and reduces energy consumption by precisely controlling laser power and scanning speed, thus meeting environmental protection requirements.

[0148] (4) The present invention utilizes image processing and machine learning technology to realize intelligent analysis of fabric properties and improve the automation level of the system.

[0149] (5) The present invention can flexibly adjust working parameters according to different types of denim fabrics and processing requirements to adapt to diverse market demands.

[0150] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. The methods disclosed in the embodiments are described briefly because they correspond to the systems disclosed in the embodiments. For relevant parts, refer to the description of the systems.

[0151] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only intended to help understand the method and core concept of the present invention. At the same time, those skilled in the art will find that the specific implementation methods and application scopes may vary based on the concept of the present invention. In summary, the contents of this specification should not be construed as limiting the present invention.

Claims

1. A control system for the laser processing of wrinkle-resistant denim fabrics, characterized in that: include: A pretreatment module is used to perform surface pretreatment on the target denim fabric to obtain a pretreated fabric and acquire a surface image of the pretreated fabric; An initial parameter determination module, configured to determine initial operating parameters of the laser device based on the surface image of the pretreated fabric; a first working module, configured to control the laser device to scan and heat the pretreated fabric according to the initial working parameters, so as to change the molecular structure of the pretreated fabric; A signal monitoring module, used to monitor the temperature signal of the pretreated fabric in real time during the scanning and heating process; a second working module, configured to adjust the real-time working parameters of the laser device according to the temperature signal, so as to process the pretreated fabric and obtain the processed fabric; The post-processing module is used to cool and shape the processed fabric to obtain the finished product; The initial working parameters include: laser power, scanning speed, scanning path and scanning overlap rate; The initial parameter determination module includes: A region recognition submodule is used to identify and extract a target region of the surface image to obtain a fabric region image; A feature extraction submodule is used to extract features from the fabric area image to obtain fabric surface features; A training module is used to train the initial decision tree model using preset sample fabric surface features and sample working parameters to obtain a working parameter prediction model; A prediction module, configured to use the working parameter prediction model to predict the surface characteristics of the fabric to obtain the initial working parameters; The second working module includes: A signal preprocessing submodule, used for smoothing and filtering the temperature signal to obtain a preprocessed signal; a temperature parameter determination submodule, configured to determine a temperature value and a temperature distribution state of a real-time temperature according to the preprocessing signal; A first adjustment submodule, configured to adjust the laser power and scanning speed in the real-time working parameters according to the temperature value and a preset first threshold value; The second adjustment submodule is configured to adjust the scanning path and the scanning overlap ratio in the real-time working parameters according to the temperature distribution state.

2. The control system for the laser processing of wrinkle-resistant denim fabric according to claim 1, characterized in that: The surface pretreatment includes: cleaning, drying and surface flattening.

3. The control system for the laser processing of wrinkle-resistant denim fabric according to claim 1, characterized in that: The region identification submodule includes: A noise point detection unit is used to detect noise points on the surface image using a filter window to obtain noise points to be processed; a denoising unit, configured to denoise the surface image within the corresponding filtering window when the number of noise points to be processed within the filtering window is greater than a preset threshold; a detection unit, configured to slide the filter window to perform image detection until the entire surface image is traversed to obtain a denoised surface image; A grayscale calculation unit is used to take a neighborhood window centered at any point on the denoised surface image and calculate the grayscale average of all pixels in the neighborhood window; A mean image acquisition unit is used to take the grayscale average of the corresponding pixel points as the output of the central pixel point to obtain a mean surface image; a threshold determination unit, configured to obtain an optimal segmentation threshold according to a correlation between the mean surface image and the denoised surface image; The segmentation unit is used to perform image segmentation on the denoised surface image using an optimal segmentation threshold to obtain the fabric area image.

4. The control system for the laser processing of wrinkle-resistant denim fabric according to claim 3, characterized in that: The noise point detection unit includes: The model building subunit is used to build a noise point detection model based on the mean and median of each image point in the filtering window; the noise point detection model is: ;in, Represents the similar noise value of pixel x, Represents the grayscale value of pixel x, Represents the grayscale mean of all pixels in the filter window centered on pixel x, represents the mean gradient of pixel x, is the grayscale median of all pixels in the filter window centered on pixel x, Indicates the gradient value of pixel x in the horizontal direction, Indicates the gradient value of pixel x in the vertical direction; A detection subunit, configured to detect each image point within the filtering window using the noise point detection model to obtain a similar noise value for each image point; The noise point determination subunit is used to take corresponding image points with a value greater than the similar noise value as noise points to be processed.

5. The control system for the laser processing of wrinkle-resistant denim fabric according to claim 4, characterized in that: The denoising unit includes: The variance calculation subunit is used to calculate the pseudo pixel variance based on the grayscale median of all pixels in the filter window; wherein the pseudo pixel variance calculation formula is: ;in, Indicates that the size of the pixel (a, b) in the filter window is The pseudo pixel variance in the region of Represents the grayscale median of the pixel (a, b) in the filter window, Represents the grayscale value of the pixel at position (k, l); The denoising model subunit is used to construct a window denoising model using the pseudo pixel variance; the formula of the window denoising model is: ;in, Represents the grayscale value of the pixel (a, b) after denoising, D is an adjustable coefficient, Represents the grayscale value of the pixel (a, b) within the filter window.

6. A method for controlling the laser processing process of wrinkle-resistant denim fabric, characterized in that: A control system applied to a laser processing process of wrinkle-resistant denim fabric according to any one of claims 1 to 5, the method comprising: Performing surface pretreatment on a target denim fabric to obtain a pretreated fabric, and acquiring a surface image of the pretreated fabric; determining initial operating parameters of the laser device according to the surface image of the pretreated fabric; Controlling the laser device to scan and heat the pretreated fabric according to the initial working parameters to change the molecular structure of the pretreated fabric; During the scanning and heating process, the temperature signal of the pretreated fabric is monitored in real time; adjusting the real-time working parameters of the laser device according to the temperature signal to process the pre-treated fabric and obtain the processed fabric; The processed fabric is cooled and shaped to obtain the finished product.

Images