Control system for laser processing process of crease-resistant jean fabric

Abstract

The invention provides a control system and method for a laser processing process of an anti-wrinkle jean fabric, and the system comprises a preprocessing module which is used for carrying out the surface preprocessing of a target jean fabric, obtaining a preprocessed fabric, and obtaining a surface image of the preprocessed fabric; the initial parameter determination module is used for determining initial working parameters of laser equipment according to the surface image of the preprocessed fabric; the first working module is used for controlling laser equipment to scan and heat the pretreated fabric according to the initial working parameters so as to change the molecular structure of the pretreated fabric; the signal monitoring module is used for monitoring a temperature signal of the pretreated fabric in real time; the second working module is used for adjusting real-time working parameters of the laser equipment according to the temperature signal to obtain a processed fabric; and the post-processing module is used for cooling and shaping the processed fabric. Through real-time monitoring and dynamic adjustment of working parameters, high efficiency of the laser processing process is ensured, and unnecessary downtime is reduced.

Description

Technical Field

[0001] The 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 fabrics are widely used due to their wear resistance and fashion. However, denim fabrics are prone to wrinkles during use, which affects their appearance and wearing comfort. Traditional anti-wrinkle treatment methods mainly rely on chemical agents and physical pressing. Although these methods can improve the anti-wrinkle performance of fabrics 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' demand for harmless and environmentally friendly fabrics is increasing.

[0004] Physical pressing methods usually require high temperatures and pressures, which may damage or deform the fabric, affecting its appearance and service life. In addition, the treatment effects of physical methods are often not long-lasting, and the fabric is prone to wrinkles after washing or wearing.

[0005] As an emerging non-contact processing method, laser processing technology is efficient, precise and environmentally friendly. It can achieve fine processing of fabrics by directly acting on the surface of fabrics with high-energy laser beams. At present, the application of laser technology in the textile industry is mainly concentrated in pattern engraving and surface treatment, but the research and application of anti-wrinkle treatment is still relatively limited.

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

[0007] In summary, the existing anti-wrinkle treatment technology has 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, used for performing surface pretreatment on the target denim fabric to obtain the pretreated fabric and acquire a surface image of the pretreated fabric;

[0012] An initial parameter determination module, used to determine the initial working parameters of the laser device according to the surface image of the pre-treated fabric;

[0013] A first working module, used for controlling the laser device to scan and heat the pre-treated fabric according to the initial working parameters, so as to change the molecular structure of the pre-treated fabric;

[0014] A signal monitoring module, used for real-time monitoring of the temperature signal of the pre-treated fabric during the scanning and heating process;

[0015] A second working module, used for adjusting the real-time working parameters of the laser device according to the temperature signal, so as to realize the processing of the pre-treated 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 working 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 recognize 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 through 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, 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 in the corresponding filtering window when the number of noise points to be processed in the filtering window is greater than a preset threshold;

[0027] A detection unit, used for sliding 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 with any point on the denoised surface image as the center and calculate the grayscale average value 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 comprises:

[0033] The model building subunit is used to build a noise point detection model according to 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 gradient value of pixel x in the horizontal direction, ▽ y (x) represents the gradient value of pixel x in the vertical direction;

[0034] A detection subunit, used to detect each image point in 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 the corresponding image points greater than the similar noise value as the noise points to be processed.

[0036] Preferably, the denoising unit comprises:

[0037] The variance calculation subunit is used to calculate the pseudo pixel variance according to 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 area of ​​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 the 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 gray value of the pixel (a, b) after denoising, D is the adjustable coefficient, and x(a, b) represents the gray value of the pixel (a, b) in the filter window.

[0039] Preferably, the second working module includes:

[0040] A signal preprocessing submodule, used for smoothing and filtering the temperature signal to obtain a preprocessed signal;

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

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

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

[0044] A control method for 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 working parameters of the laser device according to the surface image of the pretreated fabric;

[0047] Controlling the laser device to scan and heat the pre-treated fabric according to the initial working parameters to change the molecular structure of the pre-treated 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 achieve processing of the pre-treated fabric to obtain processed fabric;

[0050] The processed fabric is cooled and shaped to obtain a 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 process of anti-wrinkle denim fabrics. The system includes: a pre-processing module, which is used to perform surface pre-processing on the target denim fabric to obtain the pre-processed fabric and obtain the surface image of the pre-processed fabric; an initial parameter determination module, which is used to determine the initial working parameters of the laser device according to the surface image of the pre-processed fabric; a first working module, which is used to control the laser device to scan and heat the pre-processed fabric according to the initial working parameters to change the molecular structure of the pre-processed fabric; a signal monitoring module, which is used to monitor the temperature signal of the pre-processed fabric in real time during the scanning and heating process; a second working module, which is used to adjust the real-time working parameters of the laser device according to the temperature signal to realize the processing of the pre-processed fabric and obtain the processed fabric; a post-processing module, which is used to cool and shape the processed fabric to obtain the processed product. 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; adjusts the working parameters of the laser device 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. 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 drawings required for use in the embodiments will be briefly introduced below. 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 creative labor.

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

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

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

[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, used for performing surface pretreatment on the target denim fabric to obtain the pretreated fabric and acquire a surface image of the pretreated fabric;

[0061] An initial parameter determination module, used to determine the initial working parameters of the laser device according to the surface image of the pre-treated fabric;

[0062] A first working module, used for controlling the laser device to scan and heat the pre-treated fabric according to the initial working parameters, so as to change the molecular structure of the pre-treated fabric;

[0063] A signal monitoring module, used for real-time monitoring of the temperature signal of the pre-treated fabric during the scanning and heating process;

[0064] A second working module, used for adjusting the real-time working parameters of the laser device according to the temperature signal, so as to realize the processing of the pre-treated 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 target denim fabric in the cleaning equipment and use appropriate detergent and warm water to clean it to remove dirt, grease and other impurities on the surface of the fabric. The cleaning process can be done by ultrasonic cleaning or mechanical stirring to ensure thorough cleaning.

[0069] Drying: After washing, place the fabric in a drying device 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 the fabric from deformation or damage.

[0070] Flattening: After drying, the fabric is flattened using a flattening device to ensure a smooth, wrinkle-free surface. This step can be achieved by heat pressing 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. Make sure 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 to ensure that the format and resolution of the image files 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 disk for backup 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 basis for subsequent laser processing.

[0079] Preferably, the initial working 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, usually expressed in watts (W). Laser power directly affects the heating effect of the laser on the fabric. Higher power can accelerate the heating process of the fabric, change its molecular structure, and thus improve the wrinkle resistance. However, too high power may cause the fabric to overheat, burn or be damaged, so it needs to be reasonably set according to the characteristics of the fabric and processing requirements.

[0082] 2. Scanning speed. Scanning speed refers to the speed at which the laser equipment moves during the processing, usually expressed in millimeters per second (mm / s). The scanning speed determines how fast the laser moves on the surface of the fabric. A faster scanning speed can improve processing efficiency, but may cause uneven heating and affect the processing effect; while a slower scanning speed can ensure sufficient heating, but may reduce processing efficiency. Therefore, it needs to be adjusted according to the characteristics of the fabric and the expected effect.

[0083] 3. Scanning path. Scanning path refers to the trajectory of the laser beam during the processing of the laser equipment. It can be in various forms such as straight lines, curves or grids. The design of the scanning path affects the coverage of the laser on the fabric and the uniformity of heating. A reasonable scanning path can ensure that the surface of the fabric is evenly heated to avoid local overheating or underheating. It is crucial to choose a suitable scanning path according to the shape of the fabric and the processing requirements.

[0084] 4. Scanning overlap rate, definition: Scanning overlap rate refers to the overlapping part between adjacent scanning paths during laser scanning, usually expressed as a percentage (%). Scanning overlap rate affects the heating uniformity and processing effect of the laser on the fabric. A higher overlap rate can ensure that the surface of the fabric is fully heated and reduce the appearance of unheated areas, but it may lead to longer processing time; while a lower overlap rate can increase the processing speed, but may cause uneven heating. Therefore, it needs to be reasonably set according to the characteristics of the fabric and the processing requirements.

[0085] By reasonably setting these initial working parameters, this embodiment can effectively improve the laser processing effect of the wrinkle-resistant denim fabric 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 recognize 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 through 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, image segmentation, etc.) to identify and extract the target area of ​​the fabric. The algorithm used in this embodiment includes Canny edge detection or threshold segmentation method to separate the target area in the surface image from the background. The output is the obtained fabric area image, accurately identifying the part that needs to be analyzed.

[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 information such as main color, saturation and brightness, and understand the color distribution of fabrics.

[0096] Shape features, extract the 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, which contains information such as texture, color and shape, is obtained as the input for subsequent model training.

[0098] Furthermore, the input of the training module of the present embodiment is: preset sample fabric surface features and sample working parameters. The present 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, the present embodiment uses the features 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, the present 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 input of the prediction module of the present embodiment is the surface feature vector of the fabric to be processed. The present embodiment inputs the extracted surface features of the fabric into the trained working parameter prediction model. Then the present embodiment performs prediction to calculate the initial working parameters required for the fabric, including laser power, scanning speed, scanning path, scanning overlap rate, etc. The output of the present embodiment is the initial working parameters for use in the subsequent laser processing process.

[0100] Preferably, the region identification submodule includes:

[0101] A noise point detection unit, 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 in the corresponding filtering window when the number of noise points to be processed in the filtering window is greater than a preset threshold;

[0103] A detection unit, used for sliding 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 with any point on the denoised surface image as the center and calculate the grayscale average value 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 comprises:

[0109] The model building subunit is used to build a noise point detection model according to 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 gradient value of pixel x in the horizontal direction, ▽y (x) represents the gradient value of pixel x in the vertical direction;

[0110] A detection subunit, used to detect each image point in 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 the corresponding image points greater than the similar noise value as the noise points to be processed.

[0112] Preferably, the denoising unit comprises:

[0113] The variance calculation subunit is used to calculate the pseudo pixel variance according to 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 area of ​​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 the 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 gray value of the pixel (a, b) after denoising, D is the adjustable coefficient, and x(a, b) represents the gray 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, used to determine the temperature value and temperature distribution state of the real-time temperature according to the preprocessing signal;

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

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

[0120] Specifically, in the second working module, first, the signal preprocessing submodule of the present embodiment smoothes and filters the real-time collected temperature signal to remove noise and instantaneous fluctuations and obtain a more stable preprocessing signal. Then, the temperature parameter determination submodule calculates the real-time temperature value according to the preprocessing signal, analyzes the temperature distribution state, and identifies the temperature changes in various areas of the fabric surface. Subsequently, the first adjustment submodule compares the real-time temperature value with the preset first preset threshold. If the temperature is lower than the lower threshold, the laser power is increased and the scanning speed is reduced to ensure that the fabric is fully heated; conversely, if the temperature is higher than 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 of different areas on the fabric surface according to the temperature distribution state, adjusts the scanning path and overlap rate, to ensure that the laser is evenly distributed on the fabric surface, avoids local overheating or unheated areas, and thus optimizes the processing effect. Through this series of adjustments, the stability of the laser processing process and the processing quality of the fabric are ensured.

[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 processes the original temperature signal by applying a smoothing filter algorithm (such as moving average filtering or Kalman filtering) to eliminate noise and instantaneous fluctuations and ensure the stability of the data. Then, the real-time temperature value of each temperature sensor is extracted using the processed temperature signal, and these sensors are distributed at different positions 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 the temperature distribution map, the temperature difference between different areas of the fabric surface can be identified, and it can be determined whether there are overheated or unheated areas, which provides a basis for subsequent real-time parameter adjustments. This process ensures comprehensive monitoring of the heating state of the fabric and helps to optimize the laser processing effect.

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

[0123] 1. Set the threshold. First, the system needs to preset two temperature thresholds: the lower threshold and the upper threshold. These thresholds should be set according to the characteristics of the fabric and the processing requirements. For example, for wrinkle-resistant denim fabrics, the lower threshold may be set to 60°C and the upper threshold to 80°C.

[0124] 2. Real-time temperature monitoring: During the laser processing, 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 is 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] Temperature is above 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 intensity of heating.

[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 fabric, such as Figure 2 As 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 working parameters of a laser device according to the surface image of the pre-treated fabric;

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

[0141] Step 400: During the scanning and heating process, real-time monitoring of the temperature signal of the pre-treated fabric;

[0142] Step 500: adjusting the real-time working parameters of the laser device according to the temperature signal to achieve processing of the pre-treated fabric to obtain 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, thereby 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] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the method disclosed in the embodiment, since it corresponds to the system disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the system part description.

[0151] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood 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, used for performing surface pretreatment on the target denim fabric to obtain the pretreated fabric and acquire a surface image of the pretreated fabric; An initial parameter determination module, used to determine the initial working parameters of the laser device according to the surface image of the pre-treated fabric; A first working module, used for controlling the laser device to scan and heat the pre-treated fabric according to the initial working parameters, so as to change the molecular structure of the pre-treated fabric; A signal monitoring module, used for real-time monitoring of the temperature signal of the pre-treated fabric during the scanning and heating process; A second working module, used for adjusting the real-time working parameters of the laser device according to the temperature signal, so as to realize the processing of the pre-treated fabric and obtain the processed fabric; The post-processing module is used to cool and shape the processed fabric to obtain the finished product.

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 initial working parameters include: laser power, scanning speed, scanning path and scanning overlap rate.

4. The control system for the laser processing of wrinkle-resistant denim fabric according to claim 3, characterized in that: The initial parameter determination module comprises: A region recognition submodule is used to recognize 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 through preset sample fabric surface features and sample working parameters to obtain a working parameter prediction model; 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.

5. The control system for the laser processing of wrinkle-resistant denim fabric according to claim 4, characterized in that: The region identification submodule comprises: A noise point detection unit, 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 in the corresponding filtering window when the number of noise points to be processed in the filtering window is greater than a preset threshold; A detection unit, used for sliding 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 with any point on the denoised surface image as the center and calculate the grayscale average value 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.

6. The control system for the laser processing of wrinkle-resistant denim fabric according to claim 5, characterized in that: The noise point detection unit comprises: The model building subunit is used to build a noise point detection model according to 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. represents the mean gradient of pixel x, is the grayscale median of all pixels in the filter window centered on pixel x, Represents the gradient value of pixel x in the horizontal direction. Represents the gradient value of pixel x in the vertical direction; A detection subunit, used to detect each image point in 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 the corresponding image points greater than the similar noise value as the noise points to be processed.

7. The control system for the laser processing of wrinkle-resistant denim fabric according to claim 6, characterized in that: The denoising unit comprises: The variance calculation subunit is used to calculate the pseudo pixel variance according to 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 area of ​​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 the 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: Among them, f(a, b) represents the gray value of the pixel (a, b) after denoising, D is the adjustable coefficient, and x(a, b) represents the gray value of the pixel (a, b) in the filter window.

8. The control system for the laser processing of wrinkle-resistant denim fabric according to claim 2, characterized in that: 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, used to determine the temperature value and temperature distribution state of the real-time temperature according to the preprocessing signal; A first adjustment submodule, used for adjusting the laser power and scanning speed in the real-time working parameters according to the temperature value and a preset first preset threshold value; The second adjustment submodule is used to adjust the scanning path and scanning overlap rate in the real-time working parameters according to the temperature distribution state.

9. A control method for the laser processing of wrinkle-resistant denim fabrics, characterized in that: include: Performing surface pretreatment on a target denim fabric to obtain a pretreated fabric, and acquiring a surface image of the pretreated fabric; Determining initial working parameters of the laser device according to the surface image of the pretreated fabric; Controlling the laser device to scan and heat the pre-treated fabric according to the initial working parameters to change the molecular structure of the pre-treated 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 achieve processing of the pre-treated fabric to obtain processed fabric; The processed fabric is cooled and shaped to obtain a finished product.

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