CN119703388A - Control system for crease-resistant jean fabric laser processing process - Google Patents
Control system for crease-resistant jean fabric laser processing process Download PDFInfo
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Abstract
本发明提供了一种抗皱牛仔面料镭射加工过程的控制系统及方法，系统包括：预处理模块，用于对目标牛仔面料进行表面预处理，得到预处理面料，并获取预处理面料的表面图像；初始参数确定模块，用于根据预处理面料的表面图像确定镭射设备的初始工作参数；第一工作模块，用于根据初始工作参数控制镭射设备对预处理面料进行扫描和加热，以改变预处理面料的分子结构；信号监控模块，用于实时监控预处理面料的温度信号；第二工作模块，用于根据温度信号调整镭射设备的实时工作参数，得到加工后的面料；后处理模块，用于对加工后的面料进行冷却和定型处理。本发明通过实时监控和动态调整工作参数，确保镭射加工过程的高效性，减少不必要的停机时间。
The present invention provides a control system and method for the laser processing process of wrinkle-resistant 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 acquire 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; a second working module, which is used to adjust the real-time working parameters of the laser device according to the temperature signal to obtain the processed fabric; and a post-processing module, which is used to cool and shape the processed fabric. 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.
Description
Technical Field
The invention relates to the technical field of textile processing, in particular to a control system for a laser processing process of crease-resistant jean fabric.
Background
In modern textile industry, denim fabric is widely used due to its wear resistance and fashion. However, the jean fabric is easy to wrinkle in the use process, and the appearance and wearing comfort of the jean fabric are affected. Conventional anti-wrinkle treatment methods mainly rely on chemical agents and physical pressing, and although the methods can improve the anti-wrinkle performance of the fabric to a certain extent, the methods have some defects.
Many anti-wrinkle agents contain chemical components that can potentially harm the human body and the environment. Along with the improvement of environmental awareness, consumers have increasingly demanded harmless and environmental-friendly fabrics.
The physical pressing method generally requires high temperature and high pressure, and may cause damage or deformation of the fabric, affecting the appearance and service life thereof. In addition, the physical method often has an insufficient and durable treatment effect, and the fabric is easy to recover wrinkles after washing or wearing.
The laser processing technology is an emerging non-contact processing mode and has the characteristics of high efficiency, precision and environmental protection. The high-energy laser beam directly acts on the surface of the fabric, so that the fine processing of the fabric can be realized. Currently, the application of laser technology in textile industry is mainly focused on pattern engraving and surface treatment, but the research and application in crease-resistant treatment are still limited.
With the continuous increase of the requirements of consumers on the quality and the functionality of clothes, the market demand for crease-resistant jean fabrics is increasing. Develop a novel anti-wrinkle treatment technology, can satisfy market demand and promote product competitiveness.
In view of the foregoing, there is a limitation in the existing anti-wrinkle treatment technology, and there is a need for a new, environment-friendly and effective anti-wrinkle treatment method.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide a control system and a control method for the laser processing process of crease-resistant jean fabric, which can improve crease-resistant effect and fabric quality on the premise of ensuring the high efficiency of the laser processing process.
In order to achieve the above object, the present invention provides the following solutions:
a control system for a laser processing process of crease-resistant jean fabric comprises:
the pretreatment module is used for carrying out surface pretreatment on the target jean fabric to obtain a pretreated fabric, and obtaining a surface image of the pretreated fabric;
The initial parameter determining module is used for determining initial working parameters of the laser equipment according to the surface image of the pretreated fabric;
the first working module is used for controlling the 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 the temperature signal of the pretreated fabric in real time in the scanning and heating process;
the second working module is used for adjusting real-time working parameters of the laser equipment according to the temperature signals so as to process the pretreated fabric and obtain the processed fabric;
and the post-treatment module is used for cooling and shaping the processed fabric to obtain a processed finished product.
Preferably, the surface pretreatment includes cleaning, drying and surface flattening.
Preferably, the initial operating parameters include laser power, scan speed, scan path and scan overlap rate.
Preferably, the initial parameter determining module includes:
The region identification sub-module is used for carrying out target region identification extraction on the surface image to obtain a fabric region image;
the characteristic extraction submodule is used for extracting characteristics of the fabric area image to obtain fabric surface characteristics;
The training module is used for training the initial decision tree model through preset sample fabric surface characteristics and sample working parameters to obtain a working parameter prediction model;
And the prediction module is used for predicting the surface characteristics of the fabric by using the working parameter prediction model to obtain the initial working parameters.
Preferably, the region identification submodule includes:
The noise point detection unit is used for detecting noise points on the surface image by using the filter window to obtain noise points to be processed;
the denoising unit is used for denoising the surface image in the corresponding filter window when the number of noise points to be processed in the filter window is greater than a preset threshold value;
the detection unit is used for sliding the filtering window to detect the image until the whole surface image is traversed, and a denoised surface image is obtained;
The gray level calculation unit is used for taking a neighborhood window with any point on the denoised surface image as the center and calculating the gray level average value of all pixel points in the neighborhood window;
The average value image acquisition unit is used for taking the gray average value of the corresponding pixel point as the output of the central pixel point to obtain an average value surface image;
The threshold determining unit is used for obtaining an optimal segmentation threshold according to the correlation between the mean value surface image and the denoised surface image;
And the segmentation unit is used for carrying out image segmentation on the denoised surface image by utilizing an optimal segmentation threshold value to obtain the fabric area image.
Preferably, the noise point detection unit includes:
The model construction subunit is used for constructing a noise point detection model according to the mean value and the median value of each image point in the filtering window, and the noise point detection model is as follows: Wherein f (x) represents a similar noise value of the pixel point x, u (x) represents a gray value of the pixel point x, u mean (x) represents a gray average value of all the pixel points in a filtering window with the pixel point x as a center, u (x) represents a gradient average value of the pixel point x, u mean (x) is a gray median value of all the pixel points in the filtering window with the pixel point x as a center, x (x) represents a gradient value of the pixel point x in a horizontal direction, and y (x) represents a gradient value of the pixel point x in a vertical direction;
The detection subunit is used for detecting each image point in the filtering window by using the noise point detection model to obtain a similar noise value of each image point;
And the noise point determining subunit is used for taking the corresponding image point which is larger than the similar noise value as a noise point to be processed.
Preferably, the denoising unit includes:
The variance calculating subunit is used for calculating a pseudo pixel variance according to the gray median value of all pixel points in the filtering window, wherein the pseudo pixel variance calculating formula is as follows: Wherein, Representing the pseudo pixel variance of the pixel point (a, b) in the area with the size of (2n+1) x (2n+1) of the filter window, mean (a, b) represents the gray median of the pixel point (a, b) in the filter window, and x (k, l) represents the gray value of the pixel point in the (k, l) position;
The denoising model subunit is used for constructing a window denoising model by utilizing the pseudo pixel variance, and the formula of the window denoising model is as follows: wherein f (a, b) represents the gray value of the pixel point (a, b) after denoising, D is an adjustable coefficient, and x (a, b) represents the gray value of the pixel point (a, b) in the filter window.
Preferably, the second working module includes:
The signal preprocessing sub-module is used for smoothing and filtering the temperature signal to obtain a preprocessed signal;
the temperature parameter determining submodule is used for determining a temperature value and a temperature distribution state of the real-time temperature according to the preprocessing signal;
The first adjusting sub-module is used for adjusting laser power and scanning speed in the real-time working parameters according to the temperature value and a preset first preset threshold value;
And the second adjusting sub-module is used for adjusting the scanning path and the scanning overlapping rate in the real-time working parameters according to the temperature distribution state.
A control method for a laser processing process of crease-resistant jean fabric comprises the following steps:
carrying out surface pretreatment on the target jean fabric to obtain a pretreated fabric, and obtaining a surface image of the pretreated fabric;
Determining initial working parameters of laser equipment according to the surface image of the pretreated fabric;
controlling the 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;
Monitoring the temperature signal of the pretreated fabric in real time in the scanning and heating processes;
Adjusting real-time working parameters of the laser equipment according to the temperature signals to realize the processing of the pretreated fabric, and obtaining the processed fabric;
And (5) cooling and shaping the processed fabric to obtain a processed finished product.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
The invention provides a control system and method for a laser processing process of crease-resistant jean fabric, wherein the system comprises a pretreatment module, an initial parameter determination module, a first working module, a signal monitoring module and a second working module, wherein the pretreatment module is used for carrying out surface pretreatment on a target jean fabric to obtain a pretreated fabric and obtaining a surface image of the pretreated fabric, the initial parameter determination module is used for determining initial working parameters of laser equipment according to the surface image of the pretreated fabric, the first working module is used for controlling the 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 temperature signals of the pretreated fabric in real time in the scanning and heating processes, the second working module is used for adjusting the real-time working parameters of the laser equipment according to the temperature signals so as to realize the processing of the pretreated fabric to obtain a processed fabric, and the post-treatment module is used for carrying out cooling and shaping treatment on the processed fabric to obtain a processed product. The invention ensures the high efficiency of the laser processing process and reduces unnecessary downtime by monitoring and dynamically adjusting the working parameters in real time, and ensures the processing of the fabric in the optimal temperature range according to the working parameters of the laser equipment adjusted by the real-time temperature signals, thereby improving the anti-wrinkle effect and the quality of the fabric.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a block diagram of a system module according to an embodiment of the present invention;
fig. 2 is a flowchart of a method according to an embodiment of the present invention.
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention aims to provide a control system and a control method for a laser processing process of crease-resistant jean fabric, which can improve crease-resistant effect and fabric quality on the premise of ensuring high efficiency of the laser processing process.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
Fig. 1 is a block diagram of a system module provided by an embodiment of the present invention, and as shown in fig. 1, the present invention provides a control system for a laser processing process of an anti-wrinkle jean fabric, including:
the pretreatment module is used for carrying out surface pretreatment on the target jean fabric to obtain a pretreated fabric, and obtaining a surface image of the pretreated fabric;
The initial parameter determining module is used for determining initial working parameters of the laser equipment according to the surface image of the pretreated fabric;
the first working module is used for controlling the 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 the temperature signal of the pretreated fabric in real time in the scanning and heating process;
the second working module is used for adjusting real-time working parameters of the laser equipment according to the temperature signals so as to process the pretreated fabric and obtain the processed fabric;
and the post-treatment module is used for cooling and shaping the processed fabric to obtain a processed finished product.
Preferably, the surface pretreatment includes cleaning, drying and surface flattening.
Specifically, the surface pretreatment of the present embodiment includes:
Firstly, putting the target jean fabric into cleaning equipment, and cleaning by using proper cleaning agent and warm water to remove dirt, grease and other impurities on the surface of the fabric. The cleaning process can adopt ultrasonic cleaning or mechanical stirring and other modes to ensure thorough cleaning.
And (3) drying, namely placing the fabric into drying equipment after cleaning, and rapidly removing the moisture in the fabric by adopting a hot air drying or vacuum drying method. The drying temperature and time should be adjusted according to the material and thickness of the fabric to prevent the fabric from deforming or damaging.
And (3) flattening, namely flattening the fabric by using flattening equipment after the drying is finished, so as to ensure that the surface of the fabric is smooth and has no wrinkles. This step may be achieved by thermal compression or mechanical flattening to improve the accuracy of subsequent image acquisition.
Further, the specific steps of acquiring the surface image of the fabric in this embodiment are as follows:
1. the pretreated fabric is photographed using a high resolution camera or scanner. The camera should have good optical performance to capture detailed images. The distance between the camera and the fabric is moderate, so that a clear image is obtained.
2. In the shooting process, uniform illumination on the surface of the fabric is ensured. An annular lamp or uniformly distributed LED lamps may be used for illumination to avoid shadows and reflections affecting image quality.
3. After the image is acquired, the image is subjected to preliminary preprocessing by using image processing software, including denoising, contrast enhancement, brightness adjustment and the like. These processes may improve the readability of the image, facilitating subsequent feature extraction and analysis.
4. Data storage and management
And storing the processed image into a database to ensure that the format and resolution of the image file are suitable for subsequent analysis. The present embodiment uses a JPEG or PNG format for storage and records related face material information (e.g., type, thickness, cleaning status, etc.) for ease of management.
5. The image data is regularly backed up to prevent the data from being lost. The cloud storage or an external hard disk can be used for backup, so that the safety and accessibility of the data are ensured.
Through the steps, the method can effectively perform surface pretreatment on the target jean fabric, acquire high-quality surface images and provide a reliable basis for the subsequent laser processing process.
Preferably, the initial operating parameters include laser power, scan speed, scan path and scan overlap rate.
Specifically, the working parameters of this embodiment are as follows:
1. Laser power, which refers to the energy intensity of a laser beam emitted by a laser device, is typically expressed in units of watts (W). The laser power directly influences the heating effect of the laser on the fabric. The higher power can accelerate the heating process of the fabric and change the molecular structure of the fabric, thereby improving the crease resistance. However, excessive power may cause overheating, scorching or damage of the fabric, and thus needs to be reasonably set according to the characteristics and processing requirements of the fabric.
2. Scanning speed, which refers to the speed at which the laser apparatus moves during processing, is typically expressed in millimeters per second (mm/s). The scanning speed determines the speed of the laser moving on the surface of the fabric. A faster scan speed may increase the processing efficiency but may result in uneven heating, affecting the processing results, while a slower scan speed may ensure adequate heating but may decrease the processing efficiency. Therefore, the adjustment is required according to the characteristics and the expected effect of the fabric.
3. The scanning path refers to the moving track of the laser beam of the laser device in the processing process, and can be in various forms such as a straight line, a curve or a grid. The design of the scanning path influences the coverage range and heating uniformity of the laser on the fabric. The reasonable scanning path can ensure that the surface of the fabric is uniformly heated, and the local overheating or unheated condition is avoided. The selection of a proper scanning path is important according to the shape and processing requirements of the fabric.
4. Scan overlap ratio, defined as the overlap between adjacent scan paths during a laser scan, is generally expressed in percent (%). The scanning overlapping rate influences the heating uniformity and the treatment effect of the laser on the fabric. The higher overlapping rate can ensure that the surface of the fabric is sufficiently heated, reduce the occurrence of unheated areas, but possibly lead to prolonged processing time, while the lower overlapping rate can improve the processing speed, but possibly lead to uneven heating. Therefore, the fabric needs to be reasonably arranged according to the characteristics and the processing requirements of the fabric.
According to the embodiment, the initial working parameters are reasonably set, so that the laser processing effect of the crease-resistant jean fabric can be effectively improved, and the quality and performance of a final product are ensured.
Preferably, the initial parameter determining module includes:
The region identification sub-module is used for carrying out target region identification extraction on the surface image to obtain a fabric region image;
the characteristic extraction submodule is used for extracting characteristics of the fabric area image to obtain fabric surface characteristics;
The training module is used for training the initial decision tree model through preset sample fabric surface characteristics and sample working parameters to obtain a working parameter prediction model;
And the prediction module is used for predicting the surface characteristics of the fabric by using the working parameter prediction model to obtain the initial working parameters.
Specifically, the input of the region identification submodule in this embodiment is an acquired surface image of the pretreated fabric.
The surface image is processed using image processing techniques (e.g., edge detection, image segmentation, etc.) to identify and extract target areas of the fabric. The algorithm applied in this embodiment includes Canny edge detection or threshold segmentation methods to separate the target region from the background in the surface image. Outputting the image of the area of the fabric to accurately identify the part to be analyzed.
Further, the input of the feature extraction submodule of the embodiment is a fabric area image. The feature extraction method specifically comprises the following steps:
Texture features are extracted by using a gray level co-occurrence matrix (GLCM), a Local Binary Pattern (LBP) and other methods, and the roughness and texture structure of the surface of the fabric are reflected.
And calculating a color histogram, extracting information such as dominant hue, saturation and brightness, and knowing the color distribution of the fabric.
And extracting shape characteristics (such as outline, shape closure degree and the like) of the surface of the fabric.
The output of the feature extraction submodule is that a fabric surface feature vector is obtained, and the fabric surface feature vector contains information such as texture, color, shape and the like and is used as the input of subsequent model training.
Furthermore, the training module of the embodiment is input with preset sample fabric surface characteristics and sample working parameters. In the embodiment, firstly, a training data set is constructed, and the surface characteristics of a plurality of sample fabrics and corresponding initial working parameters (laser power, scanning speed and the like) are collected and arranged into the training data set. An initial decision tree model is constructed by selecting an appropriate machine learning algorithm, such as a decision tree, random forest, or Support Vector Machine (SVM). Then, the embodiment takes the characteristics of the sample fabric as input and the corresponding working parameters as output, and trains the model by using a supervised learning method so as to create a working parameter prediction model. Finally, the embodiment uses cross-validation or other methods to evaluate the accuracy of the model and adjusts the model parameters until a satisfactory predictive effect is achieved.
Further, the input of the prediction module in this embodiment is the surface feature vector of the fabric to be processed. The embodiment inputs the extracted surface features of the fabric into a trained working parameter prediction model. Then, the embodiment predicts to calculate the initial working parameters required by the fabric, including laser power, scanning speed, scanning path, scanning overlap ratio, etc. The output of this embodiment is the initial operating parameters for use in subsequent laser processing.
Preferably, the region identification submodule includes:
The noise point detection unit is used for detecting noise points on the surface image by using the filter window to obtain noise points to be processed;
the denoising unit is used for denoising the surface image in the corresponding filter window when the number of noise points to be processed in the filter window is greater than a preset threshold value;
the detection unit is used for sliding the filtering window to detect the image until the whole surface image is traversed, and a denoised surface image is obtained;
The gray level calculation unit is used for taking a neighborhood window with any point on the denoised surface image as the center and calculating the gray level average value of all pixel points in the neighborhood window;
The average value image acquisition unit is used for taking the gray average value of the corresponding pixel point as the output of the central pixel point to obtain an average value surface image;
The threshold determining unit is used for obtaining an optimal segmentation threshold according to the correlation between the mean value surface image and the denoised surface image;
And the segmentation unit is used for carrying out image segmentation on the denoised surface image by utilizing an optimal segmentation threshold value to obtain the fabric area image.
Preferably, the noise point detection unit includes:
The model construction subunit is used for constructing a noise point detection model according to the mean value and the median value of each image point in the filtering window, and the noise point detection model is as follows: Wherein f (x) represents a similar noise value of the pixel point x, u (x) represents a gray value of the pixel point x, u mean (x) represents a gray average value of all the pixel points in a filtering window with the pixel point x as a center, u (x) represents a gradient average value of the pixel point x, u mean (x) is a gray median value of all the pixel points in the filtering window with the pixel point x as a center, x (x) represents a gradient value of the pixel point x in a horizontal direction, and y (x) represents a gradient value of the pixel point x in a vertical direction;
The detection subunit is used for detecting each image point in the filtering window by using the noise point detection model to obtain a similar noise value of each image point;
And the noise point determining subunit is used for taking the corresponding image point which is larger than the similar noise value as a noise point to be processed.
Preferably, the denoising unit includes:
The variance calculating subunit is used for calculating a pseudo pixel variance according to the gray median value of all pixel points in the filtering window, wherein the pseudo pixel variance calculating formula is as follows: Wherein, Representing the pseudo pixel variance of the pixel point (a, b) in the area with the size of (2n+1) x (2n+1) of the filter window, mean (a, b) represents the gray median of the pixel point (a, b) in the filter window, and x (k, l) represents the gray value of the pixel point in the (k, l) position;
The denoising model subunit is used for constructing a window denoising model by utilizing the pseudo pixel variance, and the formula of the window denoising model is as follows: wherein f (a, b) represents the gray value of the pixel point (a, b) after denoising, D is an adjustable coefficient, and x (a, b) represents the gray value of the pixel point (a, b) in the filter window.
Preferably, the second working module includes:
The signal preprocessing sub-module is used for smoothing and filtering the temperature signal to obtain a preprocessed signal;
the temperature parameter determining submodule is used for determining a temperature value and a temperature distribution state of the real-time temperature according to the preprocessing signal;
The first adjusting sub-module is used for adjusting laser power and scanning speed in the real-time working parameters according to the temperature value and a preset first preset threshold value;
And the second adjusting sub-module is used for adjusting the scanning path and the scanning overlapping rate in the real-time working parameters according to the temperature distribution state.
Specifically, in the second working module, first, the signal preprocessing sub-module in this embodiment performs smoothing and filtering processing on the temperature signal acquired in real time, so as to remove noise and transient fluctuation, and obtain a more stable preprocessed signal. And then, the temperature parameter determination submodule calculates a real-time temperature value according to the pretreatment signal, analyzes the temperature distribution state and identifies the temperature change condition of each area on the surface of the fabric. And then, the first adjusting submodule compares the real-time temperature value with a preset first preset threshold value, if the temperature is lower than a lower limit threshold value, laser power is increased and scanning speed is reduced to ensure that the fabric is fully heated, and otherwise, if the temperature is higher than an upper limit threshold value, the laser power is reduced and the scanning speed is increased to prevent overheating. And finally, the second adjusting submodule evaluates the heating uniformity of different areas on the surface of the fabric according to the temperature distribution state, adjusts the scanning path and the overlapping rate so as to ensure that the laser is uniformly distributed on the surface of the fabric, and avoids local overheating or unheated areas, thereby optimizing the processing effect. Through the series of adjustment, the stability of the laser processing process and the processing quality of the fabric are ensured.
Specifically, in the process of calculating a real-time temperature value and analyzing a temperature distribution state according to a pre-processing signal, first, the signal pre-processing processes an original temperature signal by applying a smoothing filtering algorithm (such as a moving average filtering or a kalman filtering) to eliminate noise and transient fluctuations, thereby ensuring stability of data. And then, extracting real-time temperature values of each temperature sensor by using the processed temperature signals, wherein the sensors are distributed at different positions on the surface of the fabric so as to acquire comprehensive temperature information. And then, the temperature distribution state analysis generates a temperature distribution map of the surface of the fabric by carrying out spatial interpolation (such as bilinear interpolation or kriging interpolation) on the temperature values of the sensors, and intuitively displays the temperature change condition of each area. By analyzing the temperature distribution map, the temperature difference of different areas on the surface of the fabric can be identified, whether the overheated or unheated area exists or not is judged, and a basis is provided for subsequent real-time parameter adjustment. The process ensures the comprehensive monitoring of the heating state of the dough and is beneficial to optimizing the laser processing effect.
Further, the adjustment steps of the present embodiment are as follows:
1. Setting a threshold, firstly, the system needs to preset two temperature thresholds, namely a lower limit threshold and an upper limit threshold. These thresholds should be set according to the characteristics of the fabric and the processing requirements, for example, for crease-resistant jean fabric, a lower threshold of 60 ℃ and an upper threshold of 80 ℃ may be set.
2. And (3) monitoring the temperature in real time, wherein in the laser processing process, the system continuously monitors the real-time temperature value of the fabric, and acquires the latest data from the temperature sensor.
3. Temperature comparison, each time a new real-time temperature value is obtained, the system compares this value with preset lower and upper thresholds:
and if the real-time temperature value is lower than the lower threshold, the fabric does not reach the required processing temperature.
Above the upper threshold, if the real-time temperature value is above the upper threshold, this indicates that the fabric may overheat.
4. Parameter adjustment:
The temperature is below the lower threshold and laser power is increased by the system increasing the power setting of the laser device, for example from 100W to 150W, to enhance the heating effect.
And the scanning speed is reduced, for example, from 200mm/s to 150mm/s by the system, so that the residence time of the laser on the surface of the fabric is prolonged, and the fabric is fully heated.
The temperature is above the upper threshold:
Reducing laser power the system reduces the power setting of the laser device, for example from 100W to 80W, to reduce the heating intensity.
And the scanning speed is increased, and the system increases the scanning speed from 200mm/s to 250mm/s, so as to reduce the residence time of the laser on the surface of the fabric and prevent overheating.
5. Real-time feedback and adjustment:
After each parameter adjustment, the system continuously monitors the real-time temperature value, and continuously compares and adjusts the real-time temperature value in the subsequent processing process to form a closed-loop control system so as to ensure the stability of the processing process and the quality of the fabric.
Through the steps, the system of the embodiment can dynamically adjust the working parameters of the laser equipment according to the real-time temperature change, and ensure that the fabric is processed in the optimal temperature range, so that the anti-wrinkle effect and the processing quality are improved.
Corresponding to the above method, the embodiment also provides a control method for the laser processing process of the crease-resistant jean fabric, as shown in fig. 2, including:
Step 100, carrying out surface pretreatment on a target jean fabric to obtain a pretreated fabric, and obtaining a surface image of the pretreated fabric;
step 200, determining initial working parameters of laser equipment according to the surface image of the pretreated fabric;
Step 300, controlling the 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;
step 400, monitoring temperature signals of the pretreated fabric in real time in the scanning and heating processes;
Step 500, adjusting real-time working parameters of the laser equipment according to the temperature signal to process the pretreated fabric, so as to obtain the processed fabric;
And 600, cooling and shaping the processed fabric to obtain a processed finished product.
The beneficial effects of the invention are as follows:
(1) The invention ensures the high efficiency of the laser processing process and reduces unnecessary downtime by monitoring and dynamically adjusting the working parameters in real time.
(2) According to the invention, the working parameters of the laser equipment are adjusted according to the real-time temperature signals, so that the fabric is ensured to be processed in the optimal temperature range, and the anti-wrinkle effect and the quality of the fabric are improved.
(3) The invention can avoid overheating, reduce energy consumption and meet the environmental protection requirement by accurately controlling laser power and scanning speed.
(4) The invention utilizes image processing and machine learning technology to realize intelligent analysis of the characteristics of the fabric and improve the automation level of the system.
(5) According to the jean fabric and the processing requirements of different types, the working parameters can be flexibly adjusted, and the method is suitable for diversified market requirements.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer 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 points refer to the system part.
The principles and embodiments of the present invention have been described herein with reference to specific examples, which are intended to facilitate an understanding of the principles and concepts of the invention and are to be varied in scope and detail by persons of ordinary skill in the art based on the teachings herein. In view of the foregoing, this description should not be construed as limiting the invention.
Claims (9)
1. A control system for a laser processing process of crease-resistant jean fabric is characterized by comprising the following steps:
the pretreatment module is used for carrying out surface pretreatment on the target jean fabric to obtain a pretreated fabric, and obtaining a surface image of the pretreated fabric;
The initial parameter determining module is used for determining initial working parameters of the laser equipment according to the surface image of the pretreated fabric;
the first working module is used for controlling the 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 the temperature signal of the pretreated fabric in real time in the scanning and heating process;
the second working module is used for adjusting real-time working parameters of the laser equipment according to the temperature signals so as to process the pretreated fabric and obtain the processed fabric;
and the post-treatment module is used for cooling and shaping the processed fabric to obtain a processed finished product.
2. The control system for laser processing of crease-resistant denim fabric of claim 1, wherein the surface pretreatment comprises cleaning, drying and surface flattening.
3. The control system of the anti-wrinkle jean fabric laser processing process according to claim 1, wherein the initial operating parameters comprise laser power, scanning speed, scanning path and scanning overlap ratio.
4. The control system of anti-wrinkle jean fabric laser processing process according to claim 3, wherein the initial parameter determination module comprises:
The region identification sub-module is used for carrying out target region identification extraction on the surface image to obtain a fabric region image;
the characteristic extraction submodule is used for extracting characteristics of the fabric area image to obtain fabric surface characteristics;
The training module is used for training the initial decision tree model through preset sample fabric surface characteristics and sample working parameters to obtain a working parameter prediction model;
And the prediction module is used for predicting the surface characteristics of the fabric by using the working parameter prediction model to obtain the initial working parameters.
5. The control system of the anti-wrinkle jean fabric laser processing process according to claim 4, wherein the area identification submodule comprises:
The noise point detection unit is used for detecting noise points on the surface image by using the filter window to obtain noise points to be processed;
the denoising unit is used for denoising the surface image in the corresponding filter window when the number of noise points to be processed in the filter window is greater than a preset threshold value;
the detection unit is used for sliding the filtering window to detect the image until the whole surface image is traversed, and a denoised surface image is obtained;
The gray level calculation unit is used for taking a neighborhood window with any point on the denoised surface image as the center and calculating the gray level average value of all pixel points in the neighborhood window;
The average value image acquisition unit is used for taking the gray average value of the corresponding pixel point as the output of the central pixel point to obtain an average value surface image;
The threshold determining unit is used for obtaining an optimal segmentation threshold according to the correlation between the mean value surface image and the denoised surface image;
And the segmentation unit is used for carrying out image segmentation on the denoised surface image by utilizing an optimal segmentation threshold value to obtain the fabric area image.
6. The control system of the anti-wrinkle jean fabric laser processing process according to claim 5, wherein the noise point detection unit comprises:
The model construction subunit is used for constructing a noise point detection model according to the mean value and the median value of each image point in the filtering window, and the noise point detection model is as follows: wherein f (x) represents a similar noise value of the pixel x, u (x) represents a gray value of the pixel x, u mean (x) represents a gray average value of all the pixels in the filter window centered on the pixel x, Represents the gradient mean value of the pixel point x,The median gray value of all pixels in the filter window centered on pixel x,Represents the gradient value of the pixel x in the horizontal direction,Gradient values of the pixel points x in the vertical direction are represented;
The detection subunit is used for detecting each image point in the filtering window by using the noise point detection model to obtain a similar noise value of each image point;
And the noise point determining subunit is used for taking the corresponding image point which is larger than the similar noise value as a noise point to be processed.
7. The control system of the anti-wrinkle jean fabric laser processing process according to claim 6, wherein the denoising unit comprises:
The variance calculating subunit is used for calculating a pseudo pixel variance according to the gray median value of all pixel points in the filtering window, wherein the pseudo pixel variance calculating formula is as follows: Wherein, Representing the pseudo pixel variance of the pixel point (a, b) in the area with the size of (2n+1) x (2n+1) of the filter window, mean (a, b) represents the gray median of the pixel point (a, b) in the filter window, and x (k, l) represents the gray value of the pixel point in the (k, l) position;
The denoising model subunit is used for constructing a window denoising model by utilizing the pseudo pixel variance, and the formula of the window denoising model is as follows: wherein f (a, b) represents the gray value of the pixel point (a, b) after denoising, D is an adjustable coefficient, and x (a, b) represents the gray value of the pixel point (a, b) in the filter window.
8. The control system of the anti-wrinkle jean fabric laser processing process according to claim 2, wherein the second working module comprises:
The signal preprocessing sub-module is used for smoothing and filtering the temperature signal to obtain a preprocessed signal;
the temperature parameter determining submodule is used for determining a temperature value and a temperature distribution state of the real-time temperature according to the preprocessing signal;
The first adjusting sub-module is used for adjusting laser power and scanning speed in the real-time working parameters according to the temperature value and a preset first preset threshold value;
And the second adjusting sub-module is used for adjusting the scanning path and the scanning overlapping rate in the real-time working parameters according to the temperature distribution state.
9. The control method of the crease-resistant jean fabric laser processing process is characterized by comprising the following steps of:
carrying out surface pretreatment on the target jean fabric to obtain a pretreated fabric, and obtaining a surface image of the pretreated fabric;
Determining initial working parameters of laser equipment according to the surface image of the pretreated fabric;
controlling the 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;
Monitoring the temperature signal of the pretreated fabric in real time in the scanning and heating processes;
Adjusting real-time working parameters of the laser equipment according to the temperature signals to realize the processing of the pretreated fabric, and obtaining the processed fabric;
And (5) cooling and shaping the processed fabric to obtain a processed finished product.
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