CN114839129B

CN114839129B - Online detection method, device and system

Info

Publication number: CN114839129B
Application number: CN202210451190.3A
Authority: CN
Inventors: 王永兴; 王海涛; 史晓慧; 王晶; 于海燕; 李姝佳; 宋东鹏; 马振武; 马腾飞
Original assignee: Jiangsu Saichong Intelligent Equipment Co ltd; Jinan Yongxin New Material Technology Co ltd; Donghua University
Current assignee: Jiangsu Saichong Intelligent Equipment Co ltd; Jinan Yongxin New Material Technology Co ltd; Donghua University
Priority date: 2022-04-26
Filing date: 2022-04-26
Publication date: 2024-08-16
Anticipated expiration: 2042-04-26
Also published as: CN114839129A

Abstract

The invention discloses an on-line detection method, device and system, which are used for detecting the quality of cloth on line in the production process of spun-laced non-woven cloth. Relates to the technical field of quality detection of spunlaced non-woven fabrics. The method comprises the following steps: every preset time interval, collecting real-time images of the spunlaced nonwoven running on the production line; performing image processing on the real-time image of the spunlaced nonwoven fabric to obtain real-time nominal porosity; solving a real-time nominal porosity average value in a preset time period to obtain average porosity; and comparing the average porosity with the standard porosity to obtain a comparison result. By implementing the technical scheme disclosed by the invention, the method for judging the quality of the spunlaced non-woven fabric is simplified; the on-line quality detection function of the spun-laced non-woven fabric is realized; and raw materials and process parameters in the production process can be timely adjusted according to the detection result, so that the produced cloth is ensured to have excellent performance and quality consistency.

Description

Online detection method, device and system

Technical Field

The invention relates to the technical field of quality detection of spunlaced nonwovens, in particular to an on-line detection method, device and system.

Background

Hydroentanglement is a process in which fibers in a web are moved, bent, extruded, and entangled with each other by spraying the web with a high-pressure water jet, thereby forming a nonwoven fabric having certain mechanical properties, as shown in fig. 1. The nonwoven fabric manufactured by the hydroentangling process does not need to add any adhesive, the fibers are used as the composition of the nonwoven fabric, and the mutual entanglement state among the fibers influences the tensile mechanical properties of the hydroentangled nonwoven fabric and the product quality of the hydroentangled nonwoven fabric.

Because of the complex fiber state in the spun-laced nonwoven fabric, it is difficult to directly establish the relationship between the fiber state and the tensile mechanical properties. At present, a monofilament drawing method can be adopted for researching the fiber condition in the spunlaced nonwoven fabric, and the relation between the fiber state and the tensile mechanical property is analyzed according to the drawing force (such as the non-patent literature: the high-hydrophilicity terylene spunlaced fabric performance and the research on the fiber entanglement mechanism); there are also image processing techniques used to measure the mesoscopic fibrous structure parameters of hydroentangled nonwovens, such as: fiber length, fiber cross-sectional shape (as described in non-patent documents ：Microstructural analysis of non-woven fabrics using scanning electron microscopy and image processing.Part 1:Development and verification of the methods, and Microstructural analysis of non-woven fabrics using scanning electron microscopy and image processing.Part 2:Application to hydroentangled fabrics), fiber orientation distribution (as described in non-patent document ：Measuring fiber alignment in electro spun scaffolds:auser's guide to the 2D fast Fourier transform approach, and application of fourier transform in nanofiber orientation measurement), fiber curl, and the like; there are also methods for observing a microscopic fiber structure by using a scanning electron microscope (as described in non-patent document: researching microstructure of a wool fabric by using an infrared spectrometer and a scanning electron microscope), and for performing a nonwoven fabric stretching test by using a general-purpose instrument such as a strength tester and a tensile tester (as described in non-patent document: test evaluation of structure and mechanical properties of two different base fabric aramid needled nonwoven materials, and as described in patent document: CN 210513924U). Without exception, the method needs to use a special testing instrument to perform a series of complex off-line tests on the spun-laced nonwoven fabric sample, and some of the method even needs to perform destructive experiments on the sample to evaluate the fiber state of the spun-laced nonwoven fabric, which is not suitable for real-time detection on a production line.

Therefore, a method for online real-time detection of the tensile mechanical properties of the spun-laced nonwoven fabric in the production process of the spun-laced nonwoven fabric is needed, and raw materials and technological parameters are adjusted in time according to the real-time detection result so as to control the produced spun-laced nonwoven fabric to have excellent properties and quality.

Disclosure of Invention

In order to solve the problems in the prior art, the embodiment of the invention provides an on-line detection method, an on-line detection device and an on-line detection system, which are used for overcoming the defect of the prior art that in the production process of spunlaced non-woven fabrics, real-time on-line detection is carried out on the quality of the fabrics, and further timely parameter adjustment is carried out on the quality fluctuation problem of the fabrics in the production process according to the detection result, so that the quality consistency and the quality superiority of the produced fabrics are ensured.

In order to solve one or more of the above technical problems, the technical solution adopted by the present invention is as follows:

In a first aspect, an on-line detection method is provided, for detecting quality of a fabric on line in a production process of a spun-laced nonwoven fabric, the method comprising:

Every preset time interval, collecting real-time images of the spunlaced nonwoven running on the production line; wherein the hydroentangled nonwoven pattern comprises projections of surface fibers and interstices between the surface fibers in a sampling plane;

Performing image processing on the real-time image of the spunlaced nonwoven fabric to obtain real-time nominal porosity; the real-time nominal porosity represents the ratio of the projection area of the pores among all surface fibers in a sampling plane to the total sampling area in the real-time image range of the spunlaced nonwoven fabric;

solving a real-time nominal porosity average value in a preset time period to obtain average porosity; the average porosity is used for representing the ratio of the projection area of the pores among all surface layer fibers in the spunlaced non-woven fabric surface to the total area of the fabric surface;

And comparing the average porosity with the standard porosity to obtain a comparison result.

Further, the method may include obtaining a standard porosity.

Further, obtaining the standard porosity includes:

Sampling the standard sample of the spunlaced nonwoven fabric along the advancing direction of the spunlaced nonwoven fabric on production equipment to obtain a rectangular standard sample with a preset size; the standard sample at least comprises a long side with a preset length and a wide side with a preset width, and the traveling direction of the long side and the spunlaced nonwoven fabric on production equipment is consistent;

Uniformly applying stress along the long side direction of the standard sample on the wide side of the standard sample; wherein the stress on the unit width is the same as the stress applied on the unit width when the spun-laced non-woven fabric advances on the production equipment;

Randomly selecting N sampling positions on a standard sample to acquire a sample surface layer image; wherein N is a positive integer;

Carrying out image processing on the surface layer images of the standard sample one by one to obtain the nominal porosity of the sample corresponding to the surface layer images; the nominal porosity of the sample represents the ratio of the projection area of the pores among all surface fibers in the sampling plane to the total sampling area in the image area of the standard sample;

Solving a statistical average value of the nominal porosity of the sample to obtain a standard porosity; the standard porosity is used for representing the ratio of the projection area of the pores among all surface layer fibers of the standard sample in the surface of the standard sample to the total area of the test area of the standard sample.

Further, performing image processing includes:

Setting an image binarization threshold of GRAYTHRESH functions in MATLAB;

converting the image to be processed into a binarized image by using GRAYTHRESH functions; the image to be processed comprises a spunlaced non-woven fabric image or a sample surface layer image, and the binarized image consists of black pixel points and/or white pixel points;

Calculating the ratio of the number of black pixels in the binarized image to the number of total pixels in the image to obtain the nominal porosity corresponding to the image to be processed; wherein the nominal porosity comprises real-time nominal porosity or sample nominal porosity.

Further, comparing the average porosity with the standard porosity, the obtaining a comparison result includes:

Solving the difference between the average porosity and the standard porosity to obtain a porosity difference value;

And solving the percentage of the porosity difference value to the standard porosity to obtain the fluctuation percentage as a comparison result.

Further, the method further comprises adjusting production equipment parameter settings according to the comparison result, and the method comprises the following steps:

if the absolute value of the comparison result is smaller than the monitoring threshold value, keeping the raw material parameters and the process parameters of the production equipment unchanged;

and if the absolute value of the comparison result is larger than the monitoring threshold, adjusting the raw material parameter or the process parameter of the production equipment to enable the absolute value of the comparison result to be smaller than the monitoring threshold.

In a second aspect, an on-line detecting device is provided, which is used for detecting the quality of cloth on line in the production process of a spun-laced nonwoven fabric, and the device comprises: the device comprises an image acquisition module, a real-time porosity acquisition module, an average porosity acquisition module and a porosity comparison module;

The image acquisition module is used for acquiring real-time images of the spunlaced non-woven fabric running on the production line at preset time intervals; wherein the hydroentangled nonwoven pattern comprises projections of surface fibers and interstices between the surface fibers in a sampling plane;

The real-time porosity acquisition module is used for performing image processing on the real-time image of the spunlaced nonwoven fabric to acquire real-time nominal porosity; the real-time nominal porosity represents the ratio of the projection area of the pores among all surface fibers in a sampling plane to the total sampling area in the real-time image range of the spunlaced nonwoven fabric;

the average porosity acquisition module is used for solving the average value of the real-time nominal porosity in a preset time period to obtain average porosity; the average porosity is used for representing the ratio of the projection area of the pores among all surface layer fibers in the spunlaced non-woven fabric surface to the total area of the fabric surface;

And the porosity comparison module is used for comparing the average porosity with the standard porosity to obtain a comparison result.

Further, the apparatus further comprises a standard porosity acquisition module comprising: the device comprises a sample acquisition sub-module, a stress application sub-module, an image acquisition sub-module, a porosity acquisition sub-module and a numerical value calculation sub-module;

The sample acquisition submodule is used for sampling the standard sample of the spunlaced nonwoven fabric along the advancing direction of the spunlaced nonwoven fabric on the production equipment to acquire a rectangular standard sample with a preset size; the standard sample at least comprises a long side with a preset length and a wide side with a preset width, and the traveling direction of the long side and the spunlaced nonwoven fabric on production equipment is consistent;

The stress applying submodule is used for uniformly applying stress along the long side direction of the standard sample on the wide side of the standard sample; wherein the stress on the unit width is the same as the stress applied on the unit width when the spun-laced non-woven fabric advances on the production equipment;

The image acquisition sub-module is used for arbitrarily selecting N sampling positions on the standard sample to acquire the surface layer image of the sample;

The porosity acquisition submodule is used for carrying out image processing on the surface layer images of the standard sample one by one to obtain the nominal porosity of the sample corresponding to the surface layer images; the nominal porosity of the sample represents the ratio of the projection area of the pores among all surface fibers in the sampling plane to the total sampling area in the image area of the standard sample;

The numerical calculation sub-module is used for solving the statistical average value of the nominal porosity of the sample to obtain the standard porosity; the standard porosity is used for representing the ratio of the projection area of the pores among all surface layer fibers of the standard sample in the surface of the standard sample to the total area of the test area of the standard sample.

Further, the apparatus further includes an image processing unit for performing image processing, including: a threshold setting subunit, an image conversion subunit, a calculation subunit;

a threshold setting subunit, configured to set an image binarization threshold of GRAYTHRESH functions in MATLAB;

An image conversion subunit for converting the image to be processed into a binarized image using GRAYTHRESH functions; the image to be processed comprises a spunlaced non-woven fabric image or a sample surface layer image, and the binarized image consists of black pixel points and/or white pixel points;

The calculating subunit is used for calculating the ratio of the number of black pixels in the binarized image to the number of total pixels in the image to obtain the nominal porosity corresponding to the image to be processed; wherein the nominal porosity comprises real-time nominal porosity or sample nominal porosity.

In a third aspect, an on-line detecting system is provided for detecting the quality of a fabric on line in the production process of a spun-laced nonwoven fabric, the system comprising: at least one camera, data transmission device, computer;

the camera is used for collecting real-time images of the spunlaced nonwoven fabric running on the production line;

the data transmission equipment is used for transmitting the spun-laced nonwoven pattern image which is acquired by the camera and runs on the production line to the computer;

a computer configured to execute the steps of an online detection method according to the first aspect.

The technical scheme provided by the embodiment of the invention has the beneficial effects that:

1. By implementing the technical scheme disclosed by the invention, the complex process of judging the quality of the cloth by detecting the mechanical properties of the spun-laced non-woven fabric is converted into a simple method for judging the quality of the cloth by image detection, so that the method for judging the quality of the spun-laced non-woven fabric is simplified;

2. the method for judging the quality of the cloth by utilizing the image detection realizes the on-line quality detection function of the spun-laced non-woven cloth;

3. Through the quality detection of the on-line spun-laced non-woven fabric, raw materials and process parameters in the production process can be timely adjusted according to detection results, and the produced fabric is ensured to have excellent performance and quality consistency.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a schematic illustration of the forming principle of a spunlaced nonwoven;

FIG. 2 is a schematic flow chart of an on-line detection method according to an embodiment of the present invention;

FIG. 3 is a schematic view of a camera disposed above a cloth cover according to an embodiment of the present invention;

Fig. 4 is a schematic diagram of a camera with M parts above a cloth cover according to an embodiment of the present invention;

FIG. 5 is a schematic diagram of sampling a standard sample of a cloth cover according to an embodiment of the present invention;

FIG. 6 is a schematic diagram of a standard sample testing device according to an embodiment of the present invention;

FIG. 7 is an exemplary diagram of a binarized image obtained by binarization processing according to an embodiment of the present invention;

FIG. 8 is a schematic diagram of an on-line detection device according to an embodiment of the present invention;

FIG. 9 is a schematic diagram of an in-line inspection system including a camera according to an embodiment of the present invention;

FIG. 10 is a schematic diagram of an on-line inspection system with inspection function according to an embodiment of the present invention;

FIG. 11 is a schematic diagram of an on-line inspection system including M cameras according to an embodiment of the present invention;

Fig. 12 is a schematic view of a production flow of a spun-laced nonwoven fabric including a detection point according to an embodiment of the present invention.

Detailed Description

For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some examples of the present invention, not all examples. 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.

Unless defined otherwise, technical or scientific terms used in this disclosure should be given the ordinary meaning as understood by one of ordinary skill in the art to which this disclosure belongs. The terms "first," "second," and the like, as used in this disclosure, do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. Likewise, the terms "a," "an," or "the" and similar terms do not denote a limitation of quantity, but rather denote the presence of at least one. The numerals in the drawings of the specification merely denote distinction of respective functional components or modules, and do not denote logical relationships between the components or modules. The word "comprising" or "comprises", and the like, means that elements or items preceding the word are included in the element or item listed after the word and equivalents thereof, but does not exclude other elements or items. The terms "connected" or "connected," and the like, are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "upper", "lower", "left", "right", etc. are used merely to indicate relative positional relationships, which may also be changed when the absolute position of the object to be described is changed.

Hereinafter, various embodiments according to the present disclosure will be described in detail with reference to the accompanying drawings. Note that in the drawings, the same reference numerals are given to constituent parts having substantially the same or similar structures and functions, and repeated description thereof will be omitted.

Aiming at the problems that in the prior art, the mechanical property detection method of the spunlaced nonwoven fabric is complex and can only detect in an off-line manner, so that the production parameters of the fabric can not be adjusted in time according to the quality of the fabric surface to perform real-time quality control. The invention provides an on-line detection method, device and system, which are used for realizing real-time on-line detection of cloth quality in the production process of spun-laced non-woven cloth, and further timely adjusting production parameters according to detection results so as to ensure consistency and excellence of the cloth quality. The specific technical scheme is as follows:

In one embodiment, as shown in fig. 2, an on-line detection method includes:

Step S1: every preset time interval, collecting real-time images of the spunlaced nonwoven running on the production line; wherein the hydroentangled nonwoven pattern comprises projections of the surface fibers and interstices between the surface fibers in the sampling plane.

The device for collecting the real-time images is a camera which is arranged above the spunlaced non-woven fabric and has an optical axis perpendicular to the fabric surface, and as shown in fig. 3, a camera with an optical axis perpendicular to the fabric surface is arranged above the spunlaced non-woven fabric.

Preferably, the camera is a high-speed camera and is provided with a micro lens. The area of the window in which the camera can capture the effective image is 2.5mm by 1.5mm.

The preset time interval may be preset, or may be obtained through experimental statistics, which is not limited in this embodiment.

The image acquisition by the camera shown in fig. 3 is carried out, and a real-time image of the spunlaced nonwoven fabric is obtained corresponding to each acquisition time.

Step S2: performing image processing on the real-time image of the spunlaced nonwoven fabric to obtain real-time nominal porosity; wherein, the real-time nominal porosity represents the ratio of the projection area of the pores among all surface layer fibers in the sampling plane to the total sampling area in the real-time image range of the spunlaced nonwoven fabric.

And obtaining a value of the real-time nominal porosity according to the real-time image corresponding to each acquisition time.

Step S3: solving a real-time nominal porosity average value in a preset time period to obtain average porosity; wherein, the average porosity is used for representing the ratio of the projection area of the pores among all surface layer fibers in the spun-laced non-woven fabric surface to the total area of the fabric surface.

The preset time period can be selected from a historical time period of 5 minutes or 10 minutes from the current acquisition time, or a time period comprising a plurality of preset time intervals from the current acquisition time, or a time when the cloth cover of a roll of spunlaced non-woven fabric passes through the camera for the first time from the initial acquisition time, and the whole historical time period from the initial acquisition time to the current acquisition time is taken as the preset time period. This embodiment is not limited thereto. When calculating the average porosity, the corresponding average porosity is calculated according to the real-time nominal porosity quantity contained in the selected time period.

In one embodiment, the production rate of the spunlaced nonwoven is 200m/min, the total length of the entire roll of spunlaced nonwoven is about 3000m, and the time to complete a roll of spunlaced nonwoven is about 15 minutes.

Preferably, the preset time period is: in the production process of each roll of spunlaced nonwoven fabric, images are collected from the moment when the fabric surface passes through a camera for the first time to the current collection moment. The average porosity is performed throughout the production of a roll of spunlaced nonwoven.

As shown in fig. 4, if M cameras are disposed along the width direction of the fabric, there are M real-time images of the spun-laced nonwoven fabric corresponding to each collection time, and there are M nominal porosity values corresponding to each collection time, at this time, the M nominal porosity values corresponding to a certain collection time need to be averaged first, and then the average porosity in a corresponding preset time period needs to be obtained.

Step S4: and comparing the average porosity with the standard porosity to obtain a comparison result.

Preferably, an on-line detection method further comprises:

step S0: standard porosity was obtained.

Specifically, step S0: obtaining a standard porosity comprising:

step S01: sampling the standard sample of the spunlaced nonwoven fabric along the advancing direction of the spunlaced nonwoven fabric on production equipment to obtain a rectangular standard sample with a preset size; the standard sample at least comprises a long side with a preset length and a wide side with a preset width, and the traveling direction of the long side and the spunlaced nonwoven fabric on production equipment is consistent.

Fig. 5 shows a schematic diagram of a standard sample collection position for collecting a standard sample from a cloth cover. The standard sample size was 50mm (width) x 200mm (length); in view of the clamping requirement, the length of the cut sample is increased to 300mm, wherein 50mm each of the two ends in the long side direction is used for being held by a test fixture. A plurality of samples are respectively and equidistantly cut on the same piece of cloth along the upper width direction and the lower width direction, the samples in the upper width direction are sequentially marked as A-S1, A-S2, A-S3 and A-S4, and the samples in the lower width direction are marked as B-S1, B-S2, B-S3 and B-S4.

Step S02: uniformly applying stress along the long side direction of the standard sample on the wide side of the standard sample; wherein the stress is the same as the stress applied to the unit width when the spun-laced nonwoven fabric travels on the production equipment.

Fig. 6 shows a schematic diagram of a standard sample testing device. The testing device comprises four modules: the device comprises a fixed clamping module, a motion control module, a measurement module, a data acquisition module and an image acquisition module. Wherein:

and (3) fixing and clamping the module: comprises a supporting frame, a fixing plate and a clamp. Adopting an aluminum profile with the thickness of 40mm multiplied by 40mm to install the aluminum profile into a supporting frame; the clamp with the mutually crossed tooth-shaped surfaces at the clamping positions is self-developed, so that the non-woven fabric sample is ensured to be uniformly stressed and not to slip at the clamped positions within the range of the tensile rate of the experiment;

A motion control module: comprises a guide rail, a sliding block, two ball screw modules, a servo motor and a motion controller. The clamp B is arranged on the sliding block of one module, and the screw rod drives the sliding block and the clamp B to linearly move together; the optical displacement sensor is mounted on the slider of another module to move it. The motion controller controls the motion of the two ball screw modules;

And a measurement module: comprises an optical displacement sensor, a force sensor and a sensor controller. One end of the force sensor is fixed on the fixed plate, and the other end of the force sensor is connected with the clamp A through a bolt; the optical displacement sensor is arranged on the sliding block of the module, so that the transverse shrinkage of the sample can be measured at different positions;

And the data and image acquisition module is used for: comprises an LMS data acquisition instrument, a microscope and a computer. The data acquisition instrument acquires data of the force sensor and the displacement sensor. The microscope is arranged on the sliding block of the guide rail, and the surface layer image acquisition of the sample at different positions is completed.

The stress applied on the standard sample is determined by the force applied to the spunlaced nonwoven in the machine output direction when the spunlaced nonwoven moves on the production equipment, and the test state is required to reproduce the stress condition of the spunlaced nonwoven when the spunlaced nonwoven moves on the production equipment. Taking fig. 4 or 5 as an example, the roller for transporting the conveyor mat is kept at a fixed linear speed while rolling, typically set at 200m/min. The force used for pulling the cloth to run is required to keep the cloth running at a linear speed of 200m/min.

Step S03: randomly selecting N sampling positions on a standard sample to acquire a sample surface layer image; wherein N is a positive integer.

If in step S01, a standard sample is selected for testing, N sampling positions are arbitrarily selected on the standard sample for image acquisition.

In another embodiment, a plurality of standard samples are selected for testing, and then N sampling positions are selected arbitrarily on each standard sample for image acquisition.

Step S04: carrying out image processing on the surface layer images of the standard sample one by one to obtain the nominal porosity of the sample corresponding to the surface layer images; the nominal porosity of the sample represents the ratio of the projection area of the pores among all surface fibers in the sampling plane to the total sampling area in the image area of the standard sample;

Step S05: solving a statistical average value of the nominal porosity of the sample to obtain a standard porosity; the standard porosity is used for representing the ratio of the projection area of the pores among all surface layer fibers of the standard sample in the surface of the standard sample to the total area of the test area of the standard sample.

If a plurality of standard samples are selected for testing, solving a statistical average value of nominal porosities of samples corresponding to the standard samples to obtain the standard porosities.

The stress of each standard sample along the travelling direction of the cloth surface corresponds to the nominal porosity of the corresponding sample. And (3) obtaining the average value to obtain the standard porosity, wherein the standard porosity is used for detecting the mechanical property of the on-line spunlaced nonwoven fabric, and the mechanical property is a main investigation index of the quality of the spunlaced nonwoven fabric. Thus, standard porosity was used as a measure of the quality of the hydroentangled nonwoven.

Specifically, the image processing in step S2 and step S04 includes:

An image binarization threshold of GRAYTHRESH functions in MATLAB is set.

The selection rule of the binarization threshold value is as follows: the fiber image and the gap between the fibers can be accurately reflected, and specific values of the binarization threshold and the binarization threshold are accurately obtained, and are determined according to the type of the cloth, the content of the raw material, and the like, and are not limited in this embodiment.

Converting the image to be processed into a binarized image by using GRAYTHRESH functions; the image to be processed comprises a spunlaced non-woven fabric image or a sample surface layer image, and the binarized image consists of black pixel points and/or white pixel points.

The fiber position is usually represented by white pixels, the void position is represented by black pixels, and the binary image is composed of black pixels and white pixels, as shown in fig. 7. For extreme cases where the camera takes a photograph, for example: and acquiring hole images of the cloth cover, fiber close-packed images and the like, wherein the binarized images correspond to single black pixel points and single white pixel points respectively.

Specifically, step S4: comparing the average porosity with the standard porosity, and obtaining a comparison result comprises:

Step S41: solving the difference between the average porosity and the standard porosity to obtain a porosity difference value;

step S42: and solving the percentage of the porosity difference value to the standard porosity to obtain the fluctuation percentage as a comparison result.

Preferably, the method further comprises: step S5: adjusting production equipment parameter setting according to the comparison result;

specifically, step S5: adjusting production equipment parameter settings according to the comparison result comprises:

step S51: if the absolute value of the comparison result is smaller than the monitoring threshold value, the raw material parameters and the process parameters of the production equipment are kept unchanged.

For production batches with high cloth quality requirements, the value of the monitoring threshold is small, the quality fluctuation of each part of the cloth is small, and the quality consistency is high; for production batches with low cloth quality requirements, the value of the monitoring threshold value can be properly improved.

Step S52: and if the absolute value of the comparison result is larger than the monitoring threshold, adjusting the raw material parameter or the process parameter of the production equipment to enable the absolute value of the comparison result to be smaller than the monitoring threshold.

In another embodiment, as shown in fig. 8, an in-line detecting device is provided for in-line detecting the quality of cloth during the production process of a spun-laced nonwoven fabric, the device comprising: the device comprises an image acquisition module, a real-time porosity acquisition module, an average porosity acquisition module and a porosity comparison module;

Preferably, the apparatus further comprises a standard porosity acquisition module comprising: the device comprises a sample acquisition sub-module, a stress application sub-module, an image acquisition sub-module, a porosity acquisition sub-module and a numerical value calculation sub-module;

Preferably, the apparatus further comprises an image processing unit for performing image processing, comprising: a threshold setting subunit, an image conversion subunit, a calculation subunit;

In another embodiment, as shown in fig. 9, an in-line detecting system is provided for in-line detecting the quality of cloth during the production of a spun-laced nonwoven fabric, the system comprising: at least one camera 11, a data transmission device 12, a computer 13;

a camera 11 for collecting real-time images of the spun-laced nonwoven fabric running on the production line;

the camera 11 is arranged above the spun-laced nonwoven fabric, and the optical axis of the camera is perpendicular to the fabric surface. The camera is provided with at least one part, and when the camera is provided as one part, the camera 11 is arranged at a fixed position and is used for collecting images at a position with a fixed width of the cloth cover.

In another embodiment, as shown in fig. 10, a camera 11 is provided to reciprocate in the width direction of the cloth cover and collect images at the same time to realize inspection of the cloth cover.

In another embodiment, as shown in fig. 11, a camera with M fixed positions is disposed in the width direction of the cloth cover, where M is a positive integer. So as to realize the image acquisition at the position of the specified width of the cloth cover.

A data transmission device 12 for transmitting the spun-laced nonwoven pattern acquired by the camera and running on the production line to a computer;

a computer 13 for executing the steps of an on-line detection method according to the first aspect.

Fig. 12 shows a schematic of a production flow of a spun-laced nonwoven fabric, which includes a detection point 1, a detection point 2, a detection point 3, and a detection point 4. An on-line detection system may be provided at any one of the detection points or may be provided in combination at any one of the detection points, as desired.

Any combination of the above optional solutions may be adopted to form an optional embodiment of the present invention, which is not described herein.

Example 1

An on-line detection method is specifically described below in conjunction with fig. 2-7.

As shown in fig. 2, the method includes:

The camera is a high-speed camera and is provided with a micro lens. The area of the window in which the camera can capture the effective image is 2.5mm by 1.5mm. The preset time interval is 1s.

And (3) presetting a time period, and selecting the whole historical time period from the initial acquisition time to the current acquisition time. When calculating the average porosity, the corresponding average porosity is calculated according to the real-time nominal porosity of the whole historical time period from the initial acquisition time to the current acquisition time.

The on-line detection method further comprises the following steps:

step S0: standard porosity was obtained.

Specifically, step S0: obtaining a standard porosity comprising:

Specifically, the image processing in step S2 and step S04 includes:

An image binarization threshold of GRAYTHRESH functions in MATLAB is set.

The fiber position is usually represented by white pixels, the void position is represented by black pixels, and the binary image is composed of black pixels and white pixels, as shown in fig. 7.

The method further comprises the steps of: step S5: adjusting production equipment parameter setting according to the comparison result;

And selecting a monitoring threshold value of 2% for producing the cloth with high-quality requirement batch.

Example two

An in-line inspection apparatus is described in detail below with reference to fig. 8.

As shown in fig. 8, an on-line detecting device is provided for detecting the quality of cloth on line in the production process of spun-laced nonwoven fabric, the device comprising: the device comprises an image acquisition module, a real-time porosity acquisition module, an average porosity acquisition module and a porosity comparison module;

The apparatus further includes an image processing unit for performing image processing, including: a threshold setting subunit, an image conversion subunit, a calculation subunit;

Example III

An in-line detection system for in-line detection of fabric quality during the production of hydroentangled nonwoven fabrics is specifically described below in conjunction with fig. 9, the system comprising: a camera 11, a data transmission device 12, a computer 13;

As shown in fig. 9, an on-line detecting system is provided for detecting the quality of cloth on line in the production process of spun-laced nonwoven fabric, the system comprising: a camera 11, a data transmission device 12, a computer 13;

Example IV

An in-line detection system is described in detail below in conjunction with fig. 10, the system comprising: a camera 11, a data transmission device 12, a computer 13;

In this embodiment, the camera 11 can reciprocate along the width direction of the cloth cover, and collect images at the same time, so as to realize inspection of the cloth cover.

Example five

An in-line detection system is described in detail below in conjunction with FIG. 11, the system comprising: an M-part camera 11, a data transmission device 12, a computer 13;

In this embodiment, a camera with M fixed positions is disposed in the width direction of the cloth cover, where M is a positive integer. So as to realize the image acquisition at the position of the specified width of the cloth cover.

In particular, according to embodiments of the present application, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present application include a computer program product comprising a computer program loaded on a computer readable medium, the computer program comprising program code for performing the method shown in the flowcharts. In such an embodiment, the computer program may be downloaded and installed from a network via a communication device, or from memory, or from ROM. The above-described functions defined in the method of the embodiment of the present application are performed when the computer program is executed by an external processor.

It should be noted that, the computer readable medium of the embodiments of the present application may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In embodiments of the present application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in embodiments of the present application, the computer-readable signal medium may comprise a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (Radio Frequency), and the like, or any suitable combination thereof.

The computer readable medium may be contained in the server; or may exist alone without being assembled into the server. The computer readable medium carries one or more programs which, when executed by the server, cause the server to: acquiring a frame rate of an application on the terminal in response to detecting that a peripheral mode of the terminal is not activated; when the frame rate meets the screen-extinguishing condition, judging whether a user is acquiring screen information of the terminal; and controlling the screen to enter an immediate dimming mode in response to the judgment result that the user does not acquire the screen information of the terminal.

Computer program code for carrying out operations for embodiments of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).

In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for a system or system embodiment, since it is substantially similar to a method embodiment, the description is relatively simple, with reference to the description of the method embodiment being made in part. The systems and system embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.

The foregoing has outlined rather broadly the more detailed description of the application in order that the detailed description of the application that follows may be better understood, and in order that the present principles and embodiments may be better understood; also, it is within the scope of the present application to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the application.

The foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the invention are intended to be included within the scope of the invention.

Claims

1. An on-line detection method for detecting the quality of cloth on line in the production process of a spun-laced non-woven fabric, which is characterized by comprising the following steps:

Performing image processing on the real-time image of the spunlaced nonwoven fabric to obtain real-time nominal porosity; wherein the real-time nominal porosity represents the ratio of the projection area of the pores among all surface fibers in a sampling plane to the total sampling area in the real-time image range of the spunlaced nonwoven fabric;

Solving the real-time nominal porosity average value in a preset time period to obtain average porosity; wherein the average porosity is used for representing the ratio of the projection area of the pores among all surface layer fibers in the spun-laced non-woven fabric surface to the total area of the fabric surface;

And comparing the average porosity with the standard porosity to obtain a comparison result, wherein the standard porosity is used for representing the ratio of the projection area of the pores among all surface layer fibers of the standard sample in the surface of the standard sample to the total area of the test area of the standard sample, the standard sample at least comprises a long side with a preset length and a wide side with a preset width, and the long side is consistent with the advancing direction of the spunlaced non-woven fabric on production equipment.

2. The in-line detection method of claim 1, further comprising obtaining a standard porosity.

3. An in-line detection method according to claim 2, wherein said obtaining a standard porosity comprises:

sampling the standard sample of the spunlaced nonwoven fabric along the advancing direction of the spunlaced nonwoven fabric on production equipment to obtain a rectangular standard sample with a preset size; uniformly applying stress along the long side direction of the standard sample on the wide side of the standard sample; wherein the stress is the same as the stress applied to the unit width when the spun-laced non-woven fabric advances on the production equipment;

randomly selecting N sampling positions on the standard sample to acquire a sample surface layer image; wherein N is a positive integer;

Carrying out image processing on the surface layer images of the standard sample one by one to obtain the nominal porosity of the sample corresponding to the surface layer images; the nominal porosity of the sample represents the ratio of the projection area of the pores among all surface fibers in the sampling surface to the total sampling area in the image area of the standard sample;

and solving a statistical average value of the nominal porosity of the sample to obtain the standard porosity.

4. An on-line detection method according to claim 1 or 3, wherein said performing image processing comprises:

Setting an image binarization threshold of GRAYTHRESH functions in MATLAB;

Converting the image to be processed into a binarized image by using GRAYTHRESH functions; the image to be processed comprises a spunlaced non-woven fabric image or a sample surface layer image, and the binarization image consists of black pixel points and/or white pixel points;

5. The in-line detection method according to claim 1, wherein the comparing the average porosity with a standard porosity to obtain a comparison result comprises:

6. The on-line inspection method of claim 1, further comprising adjusting production facility parameter settings based on the comparison result, comprising:

and if the absolute value of the comparison result is larger than the monitoring threshold, adjusting the raw material parameters or the process parameters of the production equipment to enable the absolute value of the comparison result to be smaller than the monitoring threshold.

7. An on-line detection device for detecting the quality of cloth on line in the production process of a spun-laced nonwoven fabric, characterized in that the device comprises: the device comprises an image acquisition module, a real-time porosity acquisition module, an average porosity acquisition module and a porosity comparison module;

the image acquisition module is used for acquiring real-time images of the spunlaced non-woven fabric running on the production line at preset time intervals;

The real-time porosity acquisition module is used for performing image processing on the real-time image of the spunlaced nonwoven fabric to acquire real-time nominal porosity;

the average porosity acquisition module is used for solving the real-time nominal porosity average value in a preset time period to obtain average porosity;

The porosity comparison module is used for comparing the average porosity with the standard porosity to obtain a comparison result, wherein the standard porosity is used for representing the ratio of the projection area of the pores among all surface fibers of the standard sample in the surface of the standard sample to the total area of the test area of the standard sample, the standard sample at least comprises a long side with a preset length and a wide side with a preset width, and the traveling direction of the long side is consistent with that of the spunlaced nonwoven fabric on production equipment.

8. The in-line inspection device of claim 7, further comprising a standard porosity acquisition module comprising: the device comprises a sample acquisition sub-module, a stress application sub-module, an image acquisition sub-module, a porosity acquisition sub-module and a numerical value calculation sub-module;

The sample acquisition submodule is used for sampling the standard sample of the spunlaced non-woven fabric along the advancing direction of the spunlaced non-woven fabric on production equipment to acquire a rectangular standard sample with a preset size;

The stress applying submodule is used for uniformly applying stress along the long side direction of the standard sample on the wide side of the standard sample;

The image acquisition submodule is used for acquiring surface layer images of the sample at any N sampling positions selected from the standard sample;

The porosity acquisition submodule is used for carrying out image processing on the surface layer images of the standard sample one by one to obtain the nominal porosity of the sample corresponding to the surface layer images;

and the numerical calculation submodule is used for solving the statistical average value of the nominal porosity of the sample to obtain the standard porosity.

9. An in-line detection apparatus according to claim 7 or 8, characterized in that the apparatus further comprises an image processing unit for performing image processing, comprising: a threshold setting subunit, an image conversion subunit, a calculation subunit;

The threshold setting subunit is used for setting an image binarization threshold of GRAYTHRESH functions in MATLAB;

The image conversion subunit is used for converting the image to be processed into a binary image by using GRAYTHRESH functions;

The calculating subunit is used for calculating the ratio of the number of black pixels in the binarized image to the number of total pixels in the image to obtain the nominal porosity corresponding to the image to be processed.

10. An on-line detection system for detecting the quality of a fabric on line during the production of a spun-laced nonwoven fabric, the system comprising: at least one camera, data transmission device, computer;

the data transmission equipment is used for transmitting the spun-laced nonwoven pattern image which is acquired by the camera and runs on the production line to a computer;

the computer being adapted to perform the steps of an on-line detection method as claimed in any one of claims 1-6.