A rubber glove defect detection method and system based on nondestructive testing technology

Abstract

The present disclosure provides a rubber glove defect detection method and system based on nondestructive testing technology, relating to the field of defect detection technology, which comprises: starting a photoetching detection device; obtaining the preparation material information of the first rubber glove to be detected; outputting the first light transmittance; outputting the first control parameter; controlling the photoetching detection device according to the first control parameter, carrying out image acquisition based on the image acquisition device embedded on the photoetching detection device, and outputting the light detection image; carrying out defect analysis on the light detection image, and outputting the defect detection result, solving the technical problem that the light control parameter is not accurate due to insufficient detailed analysis of the material information of the rubber glove in the prior art, affecting the accuracy of the rubber glove defect detection, achieving the technical effect of improving the accuracy of the light control parameter and improving the accuracy of the rubber glove defect detection.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of defect detection, in particular to a rubber glove defect detection method and system based on non-destructive testing technology. BACKGROUND

[0002] In the production of rubber gloves, it is necessary to identify defects on the rubber gloves, such as scratches, stains, bubbles, etc. The traditional defect identification method uses the human eye to identify defects. With the development of science and technology, non-destructive testing technology gradually replaces the human eye to identify defects in rubber gloves. Photolithography detection technology is one of the commonly used detection methods in non-destructive testing technology.

[0003] At present, in the prior art, there is a technical problem that the light control parameters set are not accurate due to insufficient analysis of the material information of the rubber gloves, which affects the accuracy of the defect detection of the rubber gloves. SUMMARY

[0004] The present disclosure provides a rubber glove defect detection method and system based on non-destructive testing technology to solve the technical problem that the light control parameters set are not accurate due to insufficient analysis of the material information of the rubber gloves, which affects the accuracy of the defect detection of the rubber gloves.

[0005] According to a first aspect of the present disclosure, a rubber glove defect detection method based on non-destructive testing technology is provided, comprising: starting the photolithography detection device, wherein the photolithography detection device comprises a first placement platform prepared by a light transmission plate, the upper side of which is used for placing rubber gloves, and the lower side of which comprises a light control module, the light control module is used for light transmission detection of the rubber gloves by controlling light parameters; obtaining the preparation material information of the first rubber glove to be detected; performing light transmission rate analysis according to the preparation material information, and outputting the first light transmission rate; inputting the first light transmission rate into the light control module in the photolithography detection device, and outputting the first control parameter, wherein the first control parameter is a light control parameter, including light intensity, light area and light duration; controlling the photolithography detection device according to the first control parameter, image acquisition based on the image acquisition device embedded in the photolithography detection device, and outputting the light detection image; performing defect analysis on the light detection image, and outputting the defect detection result.

[0006] According to a second aspect of the present disclosure, a rubber glove defect detection system based on non-destructive testing technology is provided, comprising: a photolithography detection device starting module for starting the photolithography detection device, wherein the photolithography detection device comprises a first placement platform prepared by a light transmission plate, the upper side of which is used for placing a rubber glove, and the lower side comprises a light illumination control module for light transmission detection of the rubber glove by controlling light illumination parameters; a preparation material information acquisition module for acquiring preparation material information of a first rubber glove to be inspected; a light transmission rate analysis module for light transmission rate analysis according to the preparation material information, outputting a first light transmission rate; a first control parameter output module for inputting the first light transmission rate into the light illumination control module in the photolithography detection device, outputting a first control parameter, wherein the first control parameter is a light illumination control parameter, including light illumination intensity, light illumination area and light illumination time; a light illumination detection image acquisition module for controlling the photolithography detection device according to the first control parameter, and acquiring images based on an image acquisition device embedded in the photolithography detection device, outputting a light illumination detection image; and a defect analysis module for defect analysis of the light illumination detection image, outputting a defect detection result.

[0007] According to a rubber glove defect detection method based on non-destructive testing technology adopted by the present disclosure, the present disclosure analyzes the preparation material information of a first rubber glove to be inspected, analyzes the content of the first principal component and the remaining components other than the first principal component, and analyzes the influence of the remaining components on the light transmission rate, so as to obtain the first light transmission rate according to the analysis result, ensure the accuracy of the light transmission rate, facilitate subsequent light illumination control, ensure the accuracy of the light illumination control parameter, and improve the accuracy of defect detection. Further, the first light transmission rate is input into the light illumination control module in the photolithography detection device, a first control parameter is output, the intensity of ambient light is analyzed, a light illumination regulation network layer is generated according to the light variation characteristics, which is used for optimizing the light illumination analysis model in the light illumination control module, improving the accuracy of the light illumination control parameter, and then controlling the photolithography detection device according to the light illumination control parameter, acquiring images based on the image acquisition device embedded in the photolithography detection device, outputting a light illumination detection image, and performing defect analysis on the light illumination detection image, so as to improve the accuracy of the light illumination control parameter and further improve the accuracy of the rubber glove defect detection.

[0008] It should be understood that the content described in this part is not intended to identify key or important features of the embodiments of the present disclosure, nor is it used to limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the present disclosure or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only exemplary, and other drawings can be obtained by those skilled in the art without creative effort on the basis of the provided drawings.

[0010] Figure 1 A flowchart of a rubber glove defect detection method based on nondestructive testing technology provided by the embodiments of the present disclosure;

[0011] Figure 2 A flowchart of outputting a first light transmittance according to the first principal component of the light transmittance information in the embodiments of the present disclosure;

[0012] Figure 3 A flowchart of outputting a defect recognition result in the embodiments of the present disclosure;

[0013] Figure 4 A structural diagram of a rubber glove defect detection system based on nondestructive testing technology provided by the embodiments of the present disclosure.

[0014] Legend of reference signs: photolithography detection device starting module 11, prepared material information acquisition module 12, light transmittance analysis module 13, first control parameter output module 14, light detection image acquisition module 15, defect analysis module 16. DETAILED DESCRIPTION

[0015] The exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to help understanding, which should be considered only as exemplary. Therefore, those skilled in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Also, in order to be clear and concise, the description below omits the description of well-known functions and structures.

[0016] In order to solve the technical problem in the prior art that the light control parameters set are inaccurate due to insufficient analysis of the material information of the rubber gloves, thereby affecting the accuracy of the rubber glove defect detection, the inventors of the present disclosure have made creative efforts and obtained a rubber glove defect detection method and system based on nondestructive testing technology.

[0017] Embodiment 1

[0018] Figure 1A rubber glove defect detection method based on nondestructive testing technology is provided for the embodiments of the present disclosure. The method is applied to a quality management system of a rubber glove. The system is in communication connection with a photolithography detection device. As shown in Figure 1 The method comprises the following steps:

[0019] Step S100: starting the photolithography detection device. The photolithography detection device comprises a first placement platform prepared by a light transmission plate. The upper side of the first placement platform is used for placing a rubber glove. The lower side of the first placement platform comprises a light control module. The light control module is used for performing light transmission detection on the rubber glove by controlling light parameters.

[0020] Specifically, the above-mentioned quality management system of a rubber glove is a system platform for defect detection of a rubber glove and quality management according to the defect detection result. The system is in communication connection with a photolithography detection device, and can realize interactive transmission of information. The above-mentioned photolithography detection device is a device for light transmission detection of a rubber glove, such as a photolithography machine. However, the photolithography machine is generally used for preparing microelectronic materials. In the embodiments of the present disclosure, the photolithography detection device is mainly used for light transmission detection of a rubber glove. Subsequently, defect recognition of the rubber glove is performed according to the light transmission detection image of the rubber glove. The photolithography detection device is started. The photolithography detection device comprises a first placement platform. The first placement platform is a region for placing a rubber glove. The first placement platform is prepared by a light transmission plate. The light transmission plate is a transparent glass or quartz plate, etc. The first placement platform is composed of a transparent glass or quartz plate, etc. so that light can pass through and only irradiate on the rubber glove. The upper side of the first placement platform is used for placing a rubber glove. The lower side of the first placement platform comprises a light control module. The intensity, area and irradiation time of light emitted by the photolithography detection device are controlled by the light control module, so as to control the light transmission detection.

[0021] Step S200: obtaining preparation material information of a first rubber glove to be detected.

[0022] Specifically, the first rubber glove to be detected refers to any type of rubber glove, such as a medical rubber glove, a disposable rubber glove, etc. The preparation material information of the first rubber glove to be detected is obtained. The preparation material information refers to the composition of the first rubber glove to be detected, such as acetonitrile, rubber, silicone, etc. The preparation material information also comprises content information of various components.

[0023] Step S300: performing light transmission rate analysis according to the preparation material information, and outputting a first light transmission rate.

[0024] As shown in Figure 2 The step S300 of the embodiments of the present disclosure further comprises the following steps:

[0025] Step S310: obtaining material component information and material content information according to the preparation material information;

[0026] Step S320: performing principal component identification according to the material component information and the material content information, and outputting a first principal component, wherein the first principal component is a component with the largest content;

[0027] Step S330: outputting the first light transmittance according to the light transmittance attribute information of the first principal component.

[0028] In the step S330, the method further comprises:

[0029] Step S331: setting a first preset content threshold by identifying the content of the first principal component;

[0030] Step S332: determining whether the content corresponding to the remaining components except the first principal component is within the first preset content threshold;

[0031] Step S333: if the content of the remaining components except the first principal component is within the first preset content threshold, analyzing the light transmittance attribute information of the first principal component, and outputting the first light transmittance.

[0032] In the step S332, the method further comprises:

[0033] Step S3321: if the content of the remaining components except the first principal component is not within the first preset content threshold, identifying the components not within the first preset content threshold, and outputting N identified components;

[0034] Step S3322: determining whether there is a component with a content greater than or equal to a second preset content threshold in the N identified components;

[0035] Step S3323: if there is a component with a content greater than or equal to the second preset content threshold in the N identified components, obtaining a secondary identified component;

[0036] Step S3324: outputting a first influence coefficient according to the influence degree of the secondary identified component on the light transmittance;

[0037] Step S3325: if the first influence coefficient is greater than a preset influence coefficient, performing light transmittance determination on the first rubber glove to be detected, and outputting the first light transmittance.

[0038] Specifically, the embodiment of the present disclosure utilizes photolithography detection technology to detect defects of the rubber glove. The principle of photolithography detection technology is to detect the rubber glove by a photolithography detection device. If the light parameter is not accurately set, it will affect the light transmission detection effect, and further affect the accuracy of defect detection. Therefore, it is necessary to analyze the light transmission rate of the first rubber glove to be detected. The light transmission rate is related to the preparation material information of the first rubber glove to be detected. Different materials have different absorption intensities of light. The higher the absorption intensity of light, the lower the corresponding light transmission rate. Based on this, the absorption intensity of light of the material composition in the preparation material information is analyzed, and then the first light transmission rate is obtained according to the light absorption intensity. The light transmission rate represents the ability of light to pass through the rubber glove. When the light intensity of the rubber glove is certain, the greater the absorption intensity of light of the rubber glove, the smaller the intensity of the transmitted light. The light transmission rate can be calculated as follows: The ability of light to pass through the rubber glove is the first light transmission rate T, The light intensity of the rubber glove, The absorption intensity of light of the rubber glove. The value of the first light transmission rate is a percentage, and the value range is 0-100%.

[0039] Specifically, the material composition information and the material content information are extracted from the preparation material information. The material composition information and the material content information have a corresponding relationship. The content of any component is divided by the total component content to obtain the content. According to the material composition information and the material content information, the principal component is identified. In short, the material composition information and the material content information are compared, and the material component with the largest material content information is selected as the first principal component. In other words, the first principal component is the component with the largest content. According to the light transmission attribute information of the first principal component, the light transmission attribute information refers to the absorption intensity of light of the first principal component. According to the absorption intensity of light of the first principal component, the first light transmission rate is output.

[0040] Specifically, the first preset content threshold is a reference index for judging the content of the components in the material preparation information. The first preset content threshold is generally set to be small, so as to ensure that the content of the first main component is much greater than that of other residual components. For example, the content of the first main component is 98%, and the first preset content threshold can be set to 1%. As long as the content of other residual components is less than 1%, the influence of these components on the light transmittance can be ignored. Therefore, it is necessary to further judge whether the content corresponding to the residual components other than the first main component is within the first preset content threshold. If it is within the first preset content threshold, it indicates that the content of other residual components is small, and the influence of these components on the light transmittance is small, so the influence of these components on the light transmittance can be ignored. Only the light transmittance attribute information of the first main component needs to be analyzed, that is, the absorption intensity of the first main component to light is analyzed according to the content of the first main component, and the first light transmittance is output. By analyzing the content corresponding to the residual components other than the first main component, the accuracy of the light transmittance analysis is ensured.

[0041] Specifically, it is judged whether the content corresponding to the residual components other than the first main component is within the first preset content threshold. If the content of the residual components other than the first main component is not within the first preset content threshold, at least one of the components is marked, for example, marked with green, and N marked components are output, N being an integer greater than 0. A second preset content threshold is further set, which is slightly larger than the first preset content threshold. The N marked components are further judged to determine whether the content of any one of the N marked components is greater than or equal to the second preset content threshold. If so, the component whose content is greater than or equal to the second preset content threshold is marked again, for example, marked with red. The multiple components marked again are taken as secondary marked components. For example, the first preset content threshold can be set to 1%, and the second preset content threshold can be set to 5%. That is, if the content corresponding to the residual components other than the first main component is greater than 1%, the component whose content is greater than 1% is marked for the first time, for example, 5 components are marked for the first time. The content of the 5 components is further judged to determine whether the content of the 5 components is greater than or equal to 5%. If the content of any one of the 5 components is greater than or equal to 5%, the component is marked again, which indicates that the influence of the component on the light transmittance cannot be ignored, and the influence of the secondary marked component on the light transmittance needs to be analyzed.

[0042] According to the components and contents of the secondary identification component, the intensity of light absorption of the secondary identification component is analyzed. The greater the intensity of light absorption of the secondary identification component, the greater the degree of influence on the light transmittance, and the greater the first influence coefficient. The first influence coefficient represents the degree of influence of the secondary identification component on the light transmittance of the rubber glove. Then, the first influence coefficient and the preset influence coefficient are compared. The preset influence coefficient is a reference index for judging the first influence coefficient and can be set by oneself. That is, different materials have different light absorption intensities, and if the content of the secondary identification component is greater than the second preset content threshold, it means that the influence on the light transmittance may be greater. However, if the light absorption intensity of the secondary identification component is small, the influence on the light transmittance is also small and can be ignored. Therefore, the first influence coefficient and the preset influence coefficient need to be compared. If the first influence coefficient is greater than the preset influence coefficient, it is confirmed that the influence of the secondary identification component on the light transmittance cannot be ignored. The light transmittance of the first rubber glove to be detected is determined by using the existing light transmittance detection instrument on the market, and the first light transmittance is output. The model of the light transmittance detection instrument can be selected according to the actual situation, which is not limited here. The light transmittance detection is performed by using the light transmittance detection instrument because the influence of the secondary identification component on the light transmittance cannot be ignored, and a chemical reaction may occur in the process of combining the first main component and the secondary identification component to obtain the rubber glove. Only by analyzing the light transmittance according to the light absorption intensity of the first main component and the secondary identification component, the light transmittance analysis result may be inaccurate. Therefore, the light transmittance detection is performed by using the light transmittance detection instrument, which can ensure the accuracy of the light transmittance. At the same time, according to the degree of influence of the secondary identification component on the light transmittance, it is selected whether to use the light transmittance detection instrument to detect the light transmittance, which can control the cost under the condition of ensuring the accuracy of the light transmittance.

[0043] Step S400: input the first light transmittance into the light control module in the photolithography detection device, and output a first control parameter, wherein the first control parameter is a light control parameter, including light intensity, light area and light duration;

[0044] In the embodiment of the present disclosure, step S400 further includes:

[0045] Step S410: obtaining the light controllable parameters of the light control module;

[0046] Step S420: taking the light controllable parameters as a controlled variable and the first light transmittance as an input variable to build a light analysis model, wherein the light analysis model includes a fitness function, and the light analysis model is obtained by training a plurality of groups of data to convergence, and the plurality of groups of data include light training parameters, light transmittance training parameters, light test parameters and light transmittance test parameters.

[0047] In the embodiment of the present disclosure, step S420 further includes:

[0048] Step S421: Obtain the first environmental information when the photolithography detection device detects;

[0049] Step S422: Analyze the light changes of the first environmental information, and output the light change characteristics;

[0050] Step S423: According to the light change characteristics, generate a light control network layer;

[0051] Step S424: The light control network layer is used as a feedback network layer to optimize the light analysis model.

[0052] Specifically, the light control module is a module in the photolithography detection device for controlling the emitted light. The light control module analyzes and identifies the first transmittance and outputs the corresponding first control parameter. The first control parameter is a light control parameter, including light intensity, light area and light duration. That is, when detecting defects in rubber gloves, the light control module controls the photolithography detection device to emit corresponding light to the rubber gloves. The light transmits through the rubber gloves, and the image of the rubber gloves after transmitting the light is collected to analyze the existing defects. That is, if the light control parameter is not accurately set, it may cause the rubber gloves to have poor light transmission effect, resulting in failure to identify defects.

[0053] Specifically, the light control module embeds a light analysis model, which obtains the light control parameter through the light analysis model. The light analysis model is built as follows: first, obtain the light controllable parameters of the light control module, including light intensity, light area and light duration. The light controllable parameters are used as controlled variables, including light intensity, light area and light duration. The first transmittance is used as an input variable. The light analysis model is built, the input of the light analysis model is the first transmittance, and the output is the light control parameter. The light analysis model includes a fitness function for evaluating the control effect of the light control parameter. In short, the light control parameter is obtained to control the photolithography monitoring device to emit light that can transmit through the rubber gloves, facilitating defect detection. Different combinations of light intensity, light area and light duration will result in different costs. There may be multiple sets of light control parameters that can achieve the desired effect. The fitness function is used to evaluate multiple control parameters to find the light control parameter with the lowest cost and the desired control effect as the first control parameter. This ensures the accuracy of the first control parameter while reducing costs.

[0054] The illumination analysis model is obtained by training a plurality of sets of data to convergence, and the plurality of sets of data include illumination training parameters, transmittance training parameters, and illumination test parameters and transmittance test parameters. Specifically, the transmittance training parameters are input into the illumination analysis model, the output of the illumination analysis model is supervised and adjusted through the corresponding illumination training parameters, so that the output of the illumination analysis model is consistent with the illumination training parameters, all the illumination training parameters and the transmittance training parameters are trained, and then the illumination test parameters and the transmittance test parameters are used to test the accuracy of the illumination analysis model. The transmittance test parameters are input into the illumination analysis model to obtain output data, the output data and the illumination test parameters are compared to determine whether they are consistent, all the test parameters are statistically analyzed to obtain the accuracy of the illumination analysis model, and if the accuracy meets a preset requirement, such as 95%, the illumination analysis model is completed, otherwise the illumination analysis model needs to be retrained and tested to obtain an illumination analysis model meeting the expected requirement.

[0055] Specifically, first environment information when the photolithography detection device detects is obtained, the first environment information refers to the intensity of ambient light when the photolithography detection device detects, the first environment information is dynamically changed with the change of detection time, the change of light of the first environment information is analyzed, and light change characteristics are output. Specifically, the intensity of ambient light in the first environment information is taken as the ordinate, and the detection time is taken as the abscissa, and a light change curve is drawn. The light change curve is taken as the light change characteristics, and a light illumination control network layer is generated according to the light change characteristics. Simply, the ambient light becomes dark, which will cause the accuracy of the photolithography detection device in the light transmission detection of the rubber glove, and the first control parameter needs to be adjusted according to the light change characteristics, for example, after the light becomes dark, the intensity, area and time of the light in the first control parameter need to be adjusted appropriately to ensure the accuracy of the light transmission detection. Based on this, the light illumination control network layer is taken as a feedback network layer to optimize the light analysis model, so as to improve the accuracy of the light transmission detection.

[0056] Step S500: controlling the photolithography detection device according to the first control parameter, and performing image acquisition based on an image acquisition device embedded on the photolithography detection device to output a light detection image;

[0057] Specifically, the photolithography detection device is controlled through the first control parameter, the photolithography detection device emits light with the same intensity, area and time as the first control parameter to the first rubber glove to be detected, the photolithography detection device is embedded with an image acquisition device, the image acquisition device is used for image acquisition of the first rubber glove to be detected after the light transmission detection, and a light detection image is obtained. The light detection image is the rubber glove to be detected after the light transmission detection.

[0058] Step S600: performing defect analysis on the light detection image, and outputting a defect detection result.

[0059] In the step S600, the method further comprises: Figure 3

[0060] Step S610: obtaining a first light transmission contour region by performing edge detection on the light detection image.

[0061] Step S620: performing gray unit recognition on the first light transmission contour region, and calculating gray values of each gray unit, and outputting a gray calculation result.

[0062] Step S630: performing abnormal gray value recognition according to the gray calculation result, and outputting an abnormal gray unit.

[0063] Step S640: outputting the defect detection result according to a proportion of the abnormal gray unit in total gray units of the first light transmission contour region.

[0064] Specifically, the light detection image is subjected to gray recognition, and defect analysis is performed according to the gray recognition result. Whether there are defects such as scratches, bubbles, spots, stains and the like on the first rubber glove to be inspected is analyzed, and a defect area is obtained as the defect detection result.

[0065] ​Specifically, by performing edge detection on the light detection image, the edge detection is a method of analyzing images in image processing and computer vision, and the purpose of edge detection is to find a set of pixel points with sharp changes in brightness in the light detection image, which is manifested as the image contour, which is used as the first light transmission contour area, which can effectively reduce the data operation amount and improve the detection efficiency. Further, the first light transmission contour area is identified by a gray unit, which refers to a pixel point, and the gray value refers to the color depth of the color, which can be considered as the color depth of the pixel point in the black and white image, and the range is generally from 0 to 255, white is 255, and black is 0. Specifically, the first light transmission contour area can be identified by a gray unit by using an image processing software, such as NIVISION, HALCON, etc., to obtain the gray value of each gray unit. According to the gray calculation result, the abnormal gray value is identified, and the abnormal gray unit is output. Specifically, the light detection image of the rubber glove without any defect and completely identical to the first rubber glove to be detected can be obtained, and the gray value of each pixel point is obtained as a standard gray identification result. The range of the allowed change of the gray value of each pixel point in the standard gray identification result is obtained, and it is judged whether the multiple gray values contained in the gray calculation result are within the allowed change range of the gray value of each pixel point. If not, it indicates that the gray value is an abnormal gray value, and the pixel point corresponding to the abnormal gray value is determined as an abnormal gray unit. The total gray unit refers to the total number of pixel points in the first light transmission contour area. The abnormal gray unit is divided by the total gray unit of the first light transmission contour area to obtain the proportion result. According to the proportion result, the defect area is obtained as a defect detection result, thereby realizing accurate identification of the defect, facilitating quality control of the rubber glove, and taking timely measures to solve the defective rubber glove to prevent greater losses.

[0066] Based on the above analysis, this disclosure provides a method for detecting defects in rubber gloves using non-destructive testing technology. In this embodiment, by analyzing the material information of the first rubber glove to be inspected, the content of the first principal component and the remaining components other than the first principal component are analyzed, and the influence of the remaining components on light transmittance is analyzed. The first light transmittance is obtained based on the analysis results, ensuring the accuracy of the light transmittance. This facilitates subsequent illumination control, ensuring the accuracy of the illumination control parameters, thereby improving the accuracy of defect detection. Furthermore, the first light transmittance is input into the illumination control module in the photolithography inspection device, outputting the first control parameters. The ambient light intensity is analyzed, and an illumination regulation network layer is generated based on the light change characteristics. This layer is used to optimize the illumination analysis model in the illumination control module, improving the accuracy of the illumination control parameters. The photolithography inspection device is then controlled according to the illumination control parameters. Images are acquired using the image acquisition device embedded in the photolithography inspection device, and illumination inspection images are output. Defect analysis is performed on the illumination inspection images, achieving the technical effect of improving the accuracy of the illumination control parameters and thus enhancing the accuracy of rubber glove defect detection.

[0067] Example 2

[0068] Based on the same inventive concept as the rubber glove defect detection method based on non-destructive testing technology in the foregoing embodiments, such as Figure 4 As shown, this disclosure also provides a rubber glove defect detection system based on non-destructive testing technology. The system is communicatively connected to a photolithography inspection device, and the system includes:

[0069] A photolithography detection device start-up module 11 is used to start the photolithography detection device. The photolithography detection device includes a first placement platform, which is prepared by a light-transmitting plate. The upper side of the first placement platform is used to place a rubber glove, and the lower side includes a light control module. The light control module is used to perform light transmission detection on the rubber glove by controlling the light parameters.

[0070] The material preparation information acquisition module 12 is used to acquire the material preparation information of the first rubber glove to be inspected.

[0071] Transmittance analysis module 13 is used to perform transmittance analysis based on the information of the prepared material and output a first transmittance.

[0072] The first control parameter output module 14 is used to input the first transmittance into the illumination control module in the photolithography detection device and output the first control parameter, wherein the first control parameter is an illumination control parameter, including illumination intensity, illumination area and illumination duration.

[0073] The illumination detection image acquisition module 15 is configured to control the photolithography detection device according to the first control parameter, acquire an image based on an image acquisition device embedded in the photolithography detection device, and output an illumination detection image.

[0074] The defect analysis module 16 is configured to analyze the illumination detection image and output a defect detection result.

[0075] Further, the system further comprises:

[0076] The component content information acquisition module is configured to acquire material component information and material content information according to the preparation material information.

[0077] The main component identification module is configured to identify a main component according to the material component information and the material content information, and output a first main component, wherein the first main component is a component with the largest content.

[0078] The light transmission property analysis module is configured to output the first light transmission rate according to light transmission property information of the first main component.

[0079] Further, the system further comprises:

[0080] The first preset content threshold setting module is configured to set a first preset content threshold by identifying the content of the first main component.

[0081] The component content judgment module is configured to judge whether the content corresponding to the remaining components except the first main component is within the first preset content threshold.

[0082] The second light transmission property analysis module is configured to analyze the light transmission property information of the first main component and output the first light transmission rate if the content of the remaining components except the first main component is within the first preset content threshold.

[0083] Further, the system further comprises:

[0084] The one-time identification module is configured to identify the component not within the first preset content threshold and output N identified components if the content of the remaining components except the first main component is not within the first preset content threshold.

[0085] An identification component content judgment module is configured to judge whether there is an identification component with a content greater than or equal to a second preset content threshold in the N identification components.

[0086] A secondary identification module is configured to obtain a secondary identification component if there is an identification component with a content greater than or equal to the second preset content threshold in the N identification components.

[0087] An influence degree analysis module is configured to output a first influence coefficient according to the influence degree of the secondary identification component on the light transmittance.

[0088] A light transmittance measurement module is configured to measure the light transmittance of the first rubber glove to be tested and output the first light transmittance if the first influence coefficient is greater than a preset influence coefficient.

[0089] Further, the system further comprises:

[0090] An illumination controllable parameter acquisition module is configured to acquire an illumination controllable parameter of the illumination control module.

[0091] An illumination analysis model building module is configured to build an illumination analysis model by taking the illumination controllable parameter as a controlled variable and taking the first light transmittance as an input variable, wherein the illumination analysis model comprises a fitness function, and the illumination analysis model is obtained by training a plurality of sets of data to convergence, and the plurality of sets of data comprise illumination training parameters, light transmittance training parameters, and illumination test parameters, and light transmittance test parameters.

[0092] Further, the system further comprises:

[0093] A first environmental information acquisition module is configured to acquire first environmental information when the photolithography detection device is detected.

[0094] A light change analysis module is configured to analyze the light change of the first environmental information and output a light change feature.

[0095] An illumination regulation network layer generation module is configured to generate an illumination regulation network layer according to the light change feature.

[0096] An illumination analysis model optimization module is configured to optimize the illumination analysis model by taking the illumination regulation network layer as a feedback network layer.

[0097] Further, the system further comprises:

[0098] An edge detection module is configured to acquire a first light transmission profile region by performing edge detection on the illumination detection image.

[0099] A gray scale unit identification module is configured to perform gray scale unit identification on the first light transmission profile region and calculate gray scale values of each gray scale unit, and output a gray scale calculation result.

[0100] An abnormal gray scale value identification module is configured to perform abnormal gray scale value identification according to the gray scale calculation result, and output an abnormal gray scale unit.

[0101] A defect detection result output module is configured to output the defect detection result according to a proportion of the abnormal gray scale unit in total gray scale units of the first light transmission profile region.

[0102] The specific example of the rubber glove defect detection method based on the non-destructive testing technology in the foregoing embodiment one is also applicable to the rubber glove defect detection system based on the non-destructive testing technology in the present embodiment. Through the foregoing detailed description of the rubber glove defect detection method based on the non-destructive testing technology, those skilled in the art can clearly understand the rubber glove defect detection system based on the non-destructive testing technology in the present embodiment. Therefore, for the sake of brevity of the specification, no further detailed description is given herein. For the device disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple, and the relevant part is described in the method part.

[0103] It should be understood that the various forms of flow shown above can be reordered, added or deleted steps. For example, each step described in the present disclosure can be executed in parallel, sequentially or in a different order, as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved, which is not limited herein.

[0104] The foregoing detailed description does not constitute a limitation on the protection scope of the present disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements and improvements within the spirit and principles of the present disclosure should be included in the protection scope of the present disclosure.

Claims

1. A method for detecting defects in rubber gloves based on non-destructive testing technology, characterized in that, The method is applied to a quality management system for rubber gloves, the system being communicatively connected to a photolithography inspection device, and the method includes: The photolithography detection device is activated, wherein the photolithography detection device includes a first placement platform, which is prepared by a light-transmitting plate. The upper side of the first placement platform is used to place a rubber glove, and the lower side includes a light control module. The light control module is used to perform light transmission detection on the rubber glove by controlling the light parameters. Obtain information on the materials used in the manufacture of the first rubber glove to be inspected; Based on the information about the prepared material, transmittance analysis is performed, and the first transmittance is output. The first transmittance is input into the illumination control module in the photolithography detection device, and the first control parameter is output. The first control parameter is an illumination control parameter, including illumination intensity, illumination area and illumination duration. The photolithography detection device is controlled according to the first control parameter, and an image is acquired based on the image acquisition device embedded in the photolithography detection device to output an illumination detection image. Defect analysis is performed on the illumination detection image, and the defect detection results are output.

2. The method as described in claim 1, characterized in that, Based on the information about the prepared material, transmittance analysis is performed to output a first transmittance. The method further includes: Based on the material preparation information, obtain the material composition information and material content information; Based on the material composition information and the material content information, principal component identification is performed, and a first principal component is output, wherein the first principal component is the component with the largest content; Based on the light transmittance information of the first principal component, the first light transmittance is output.

3. The method as described in claim 2, characterized in that, Based on the light transmittance property information of the first principal component, the first light transmittance is output. The method further includes: By identifying the content of the first principal component, a first preset content threshold is set; Determine whether the content of the remaining components other than the first principal component is within the first preset content threshold. If the contents of all remaining components other than the first principal component are within the first preset content threshold, the light transmittance information of the first principal component is analyzed, and the first light transmittance is output.

4. The method as described in claim 3, characterized in that, The method for determining whether the content of the remaining components other than the first principal component is within the first preset content threshold also includes: If the content of at least one of the remaining components other than the first principal component is not at the first preset content threshold, the component that is not at the first preset content threshold is identified, and N identified components are output. Determine whether any of the N identified components have a content greater than or equal to a second preset content threshold; If any of the N identified components has a content greater than or equal to the second preset content threshold, then a secondary identified component is obtained; Based on the influence of the secondary labeling components on light transmittance, the first influence coefficient is output; If the first influence coefficient is greater than the preset influence coefficient, the transmittance of the first rubber glove to be tested is measured, and the first transmittance is output.

5. The method as described in claim 1, characterized in that, The method further includes: Obtain the controllable illumination parameters of the illumination control module; Using the controllable illumination parameters as the variables to be controlled and the first transmittance as the input variable, an illumination analysis model is built. The illumination analysis model includes a fitness function. The illumination analysis model is trained to convergence using multiple sets of data, including illumination training parameters, transmittance training parameters, illumination test parameters, and transmittance test parameters.

6. The method as described in claim 5, characterized in that, The method for building the illumination analysis model further includes: Obtain the first environmental information during the photolithography inspection process; Analyze the light changes in the first environmental information and output the light change characteristics; Based on the aforementioned light change characteristics, an illumination control network layer is generated; The illumination control network layer is used as a feedback network layer to optimize the illumination analysis model.

7. The method as described in claim 1, characterized in that, The method includes performing defect analysis on the illumination detection image and outputting defect detection results. The first light-transmitting contour region is obtained by performing edge detection on the illumination detection image; The grayscale unit of the first light-transmitting contour region is identified, and the grayscale value of each grayscale unit is calculated, and the grayscale calculation result is output. Based on the grayscale calculation results, abnormal grayscale values ​​are identified, and abnormal grayscale units are output. The defect detection result is output based on the proportion of the abnormal grayscale units to the total grayscale units of the first light-transmitting contour area.

8. A rubber glove defect detection system based on non-destructive testing technology, characterized in that, The system is communicatively connected to the photolithography inspection device, and the system includes: A photolithography detection device start-up module is used to start the photolithography detection device. The photolithography detection device includes a first placement platform, which is prepared by a light-transmitting plate. The upper side of the first placement platform is used to place a rubber glove, and the lower side includes an illumination control module. The illumination control module is used to perform light transmission detection on the rubber glove by controlling the illumination parameters. A material preparation information acquisition module is used to acquire the material preparation information of the first rubber glove to be inspected. A transmittance analysis module is used to perform transmittance analysis based on the information of the prepared material and output a first transmittance. The first control parameter output module is used to input the first transmittance into the illumination control module in the photolithography detection device and output the first control parameter, wherein the first control parameter is an illumination control parameter, including illumination intensity, illumination area and illumination duration; An illumination detection image acquisition module is used to control the photolithography detection device according to the first control parameters, acquire images based on the image acquisition device embedded in the photolithography detection device, and output an illumination detection image. The defect analysis module is used to perform defect analysis on the illumination detection image and output the defect detection results.

Images