PVC sheath material performance detection method and system based on image processing

Abstract

The invention relates to the technical field of image processing, in particular to a PVC sheath material performance detection method and system based on image processing, and the method comprises the following steps: obtaining a plurality of granular PVC sheath material samples with the same weight, and respectively placing the granular PVC sheath material samples in a closed heating space; acquiring an image of the sample in a heating temperature rise stage, and calculating a comprehensive temperature rise performance index according to data such as color and glossiness of the sheath material; acquiring an image of the sample in a melting stage, and calculating a comprehensive melting performance index according to data such as flow uniformity and bubble area variance; the release of the hydrogen chloride gas is judged by detecting whether ammonium chloride precipitates are generated; acquiring and processing an ammonium chloride image to obtain a volatility index; obtaining and treating the cooled sample to obtain a thermogravimetric analysis index; and finally, calculating a comprehensive performance index for measuring the overall performance stability of the sample. According to the invention, the image of the PVC sheath material is processed, so that the detection efficiency of the performance stability of the sheath material in the heating stage and the melting stage is improved.

Description

Technical Field

[0001] The invention relates to the technical field of image processing, and in particular to a method and system for detecting performance of PVC sheathing materials based on image processing. Background Art

[0002] Granular PVC sheath material is a material used to make the outer protective layer of wires and cables. It is made of polyvinyl chloride (PVC) resin and other additives (such as plasticizers, stabilizers, fillers and lubricants, etc.) mixed and processed into granules for further processing and use. Although image processing technology itself is not directly used to measure material properties, it can be used as an auxiliary means to detect surface morphology changes, crack detection, color analysis, etc. of materials during various performance tests, and to more intuitively understand and analyze test results.

[0003] In existing research, image processing technology has been used to conduct a series of performance tests on PVC sheathing materials, including tensile resistance, heat resistance, weather resistance, insulation, corrosion resistance and aging resistance. However, as an industrial semi-finished product, granular PVC sheathing materials will undergo further processing such as extrusion and injection molding according to application requirements. Granular PVC sheathing materials will be in a high-temperature molten state for a long time. Evaluating their performance stability during heating and melting stages is a key step to ensure whether they can maintain quality and performance consistency during further processing, which needs to be deeply explored and analyzed.

[0004] Therefore, a PVC sheath material performance detection method and system based on image processing is proposed. Summary of the invention

[0005] The purpose of the present invention is to provide a PVC sheath material performance detection method and system based on image processing, by setting multiple groups of samples to perform sheath material performance stability detection in the heating temperature rising stage and in the molten state, through the comprehensive heating performance index, comprehensive melting performance index, volatility index and thermogravimetric analysis index, the performance of the PVC sheath material is evaluated from different angles and in multiple aspects; by calculating the comprehensive performance index, the above-mentioned multiple key performance indicators are integrated together to intuitively reflect the overall performance stability of the sample.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A method for detecting performance of PVC sheathing materials based on image processing, comprising:

[0008] Step S1: obtain J portions of granular PVC sheath material samples of the same weight and place them in J closed heating spaces respectively.

[0009] Furthermore, the sample is laid flat in the closed heating space, and the J closed heating spaces are respectively set with J different inclination angles, and the inclination angles belong to [R1, R2], where R1 represents a first angle threshold, and R2 represents a second angle threshold; the different inclination angles are used to simulate different positions and directions of the granular PVC sheathing material during the processing.

[0010] Step S2: Acquire and process the image of the sample during the heating stage to obtain first data, wherein the first data includes the color, glossiness and surface roughness of the sheath material during the heating process, and calculate the comprehensive heating performance index of the sample based on the first data.

[0011] Furthermore, fixed shooting angle images of the PVC sheath material at K intervals of fixed temperature in the heating stage are obtained and image processing is performed to obtain first data of the granular PVC sheath material, wherein the first data includes the color, glossiness and surface roughness of the sheath material during the heating process; the heating performance index is calculated according to the first data, and the calculation formula of the heating performance index is:

[0012]

[0013] Where E k represents the heating performance index at the kth temperature, α1 represents the weight coefficient of the color difference change, M represents the number of pixels, represents the R value of the pixel at the kth temperature, represents the R value of the pixel at the k-1th temperature, represents the G value of the pixel at the kth temperature, represents the G value of the pixel at the k-1th temperature, represents the B value of the pixel at the kth temperature, represents the B value of the pixel at the k-1th temperature, α2 represents the weight coefficient of glossiness, I re Represents the reflected light intensity, I in represents the incident light intensity, α3 represents the weight coefficient of roughness, z i Represents the gray value of the pixel. represents the average gray value;

[0014] The calculation formula of the comprehensive heating performance index is:

[0015]

[0016] Wherein, W1 represents the comprehensive heating performance index, J represents the number of the closed heating spaces, K represents the number of images taken at fixed temperature intervals, Indicates that the jth enclosed heating space is at Tk The weight coefficient of the heating performance index at temperature, Represents the heating performance index of the jth enclosed heating space at the kth temperature.

[0017] Further, T0 represents the initial temperature.

[0018] Step S3: Obtain an image of the sample in the melting stage and process it to obtain second data, wherein the second data includes the expansion speed of the sheath material, flow uniformity, the number and area of ​​bubbles, cracks, and the color of the sheath material during the melting process; calculate the comprehensive melting performance index based on the second data.

[0019] Furthermore, the melting stage refers to the process from the beginning of the sample melting to the complete melting of the sample. An image of the sample in the melting stage is acquired and the boundary of the sample is obtained by image processing technology;

[0020] The flow uniformity is calculated according to the expansion speed of the boundary of the sample, and the calculation formula of the flow uniformity is:

[0021]

[0022] Among them, σ j represents the flow uniformity of the sample in the jth closed heating space, H represents the number of sampling points, and v h represents the expansion speed of the hth sampling position, Represents the average expansion speed.

[0023] Furthermore, an image of the sample in the melting stage is obtained and the image is binarized, bubbles are detected using connected domain analysis, and the bubble area variance is calculated according to the number of bubbles and the bubble area. j represents the bubble area variance of the sample in the jth closed heating space.

[0024] Through edge detection and morphological operations, cracks are identified and crack characteristic index is calculated; the calculation formula of the crack characteristic index is:

[0025]

[0026] Among them, LW j represents the crack characteristic index of the sample in the jth closed heating space, β1 represents the length weight coefficient, P represents the total number of cracks, L p represents the length of the pth crack, β2 represents the width weight coefficient, S p represents the width of the pth crack.

[0027] Furthermore, images of the sample in the melting stage are acquired and processed at N different time points to obtain the second data, and the comprehensive melting performance index is calculated according to the second data. The calculation formula of the comprehensive melting performance index is:

[0028]

[0029] Wherein, W2 represents the comprehensive melting performance index, N represents the number of images, θ n represents the weight coefficient at the nth time node, J represents the number of the closed heating space, ω1 represents the weight coefficient of flow uniformity, represents the flow uniformity of the sample in the jth closed heating space at the nth time node, ω2 represents the weight coefficient of the bubble area variance, represents the bubble area variance of the sample in the jth closed heating space at the nth time node, ω3 represents the weight coefficient of the crack characteristic index, LW n j represents the crack characteristic index of the sample in the jth closed heating space at the nth time node, ω4 represents the weight coefficient of the melting color difference, It represents the melting color difference of the sample in the jth closed heating space at the nth time node.

[0030] Step S4: Filling the closed heating space with ammonia gas, and indirectly judging the release of hydrogen chloride gas by detecting whether ammonium chloride precipitation is generated; acquiring an ammonium chloride image and processing it using image processing technology to obtain a volatility index.

[0031] Furthermore, the enclosed heating space is filled with ammonia gas of a preset concentration, and the release of hydrogen chloride gas is determined by detecting ammonium chloride precipitation; an image of the ammonium chloride precipitation taken by a high-resolution camera is obtained and grayscaled and denoised to obtain a first image; the first image is further processed to identify white pixels in the first image; the area of ​​the ammonium chloride precipitation is obtained according to the number of white pixels, and the proportion of the ammonium chloride precipitation in the jth sample is obtained according to the area. The volatility index is calculated according to the ratio, and the calculation formula of the volatility index is:

[0032]

[0033] Wherein, W3 represents the volatility index, J represents the number of the first images, γ j Represents the weight coefficient of the j-th ratio.

[0034] Step S5: obtaining and processing the cooled sample to obtain a thermogravimetric analysis index.

[0035] Further, the cooled sample is obtained and washed, air-dried and weighed to obtain a weight of the cooled sample G2, the weight of the sample is obtained and the thermogravimetric analysis index is calculated, and the calculation formula of the thermogravimetric analysis index is:

[0036]

[0037] Wherein, W4 represents the thermogravimetric analysis index, and G1 represents the weight of the sample.

[0038] Step S6: Obtain and process the data from step S2 to step S5 to obtain a comprehensive performance index for measuring the performance stability of the sample.

[0039] Further, the comprehensive heating performance index, the comprehensive melting performance index, the volatility index and the thermogravimetric analysis index are obtained and processed to obtain the comprehensive performance index, and the calculation formula of the comprehensive performance index is:

[0040] W=λ1·W1+λ2·W2+λ3·(1-W3)+λ4·W4;

[0041] Wherein, W represents the comprehensive performance index, λ1 represents the weight coefficient of the comprehensive heating performance index, λ2 represents the weight coefficient of the comprehensive melting performance index, λ3 represents the weight coefficient of the volatility index, and λ4 represents the weight coefficient of the thermogravimetric analysis index.

[0042] A PVC sheath material performance detection system based on image processing, comprising: an experimental setting module, for obtaining J portions of granular PVC sheath materials of the same weight, and placing them in J closed heating spaces respectively;

[0043] A heating performance index module is used to obtain and process the image of the granular PVC sheath material during the heating stage to obtain first data, wherein the first data includes the color, glossiness and surface roughness of the sheath material during the heating process, and calculate a comprehensive heating performance index based on the first data;

[0044] A melting performance index module is used to obtain and process the image of the granular PVC sheathing material in the melting stage to obtain second data, wherein the second data includes the expansion speed of the sheathing material, flow uniformity, the number and area of ​​bubbles, cracks and the color of the sheathing material during the melting process; and calculate the comprehensive melting performance index according to the second data;

[0045] The volatile gas detection module is used to fill the closed heating space with ammonia gas, and indirectly judge the release of hydrogen chloride gas by detecting whether ammonium chloride precipitation is generated; obtain the ammonium chloride image and process it using image processing technology to obtain a volatility index;

[0046] Thermogravimetric analysis module, used to obtain and process the cooled PVC sheath material to obtain thermogravimetric analysis index;

[0047] The comprehensive performance index module obtains and processes the data of the heating performance index module, the melting performance index module, the volatile gas detection module and the thermogravimetric analysis module to obtain a comprehensive performance index for measuring the performance stability of the sample.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] 1. By taking images at different temperatures and analyzing the performance data of granular PVC sheathing materials, its performance changes during the entire heating process can be obtained; the color difference change calculates the color change between the current image and the image taken at the previous temperature, and then combines multiple performance data such as the surface gloss and surface roughness of the granular PVC sheathing material to form a heating performance index; the heating performance index obtained at different temperatures is weighted, and the weight takes into account that the higher the temperature, the greater the impact on the granular PVC sheathing material. By calculating a comprehensive heating performance index during the heating process, the thermal stability and heat resistance of the material during the heating process can be more comprehensively and accurately evaluated.

[0050] 2. By calculating the expansion speed and average expansion speed at different boundary positions, the flow uniformity of granular PVC sheathing material during the melting process can be quantified, which helps to identify possible uneven flow problems; the detection and analysis of the number, area and surface defects such as cracks of bubbles can reveal possible internal defects of the sheathing material during the melting process; by calculating the color difference, analyzing the color change of the sheathing material during the melting process, it is helpful to evaluate the thermal stability of the sheathing material; by calculating the comprehensive melting performance index, multiple performance parameters can be integrated to form an overall performance evaluation index, reflecting the overall melting performance of the sheathing material, and realizing a comprehensive and accurate evaluation of the thermal stability of the sheathing material during the melting stage.

[0051] 3. The toxic gas hydrogen chloride that may be produced by granular PVC sheathing materials in the molten state is difficult to detect directly, but by reacting with ammonia to generate ammonium chloride precipitation, it is possible to indirectly determine whether hydrogen chloride gas is released. This method uses the high sensitivity of chemical reactions to accurately detect the presence of hydrogen chloride gas; by calculating the volatility index, it can be determined whether the sheathing material produces volatile degradation products such as hydrogen chloride during the heating process, thereby evaluating the thermal stability of the material. Integrating multiple key performance indicators together and comprehensively evaluating the overall performance of the sheathing material from multiple perspectives avoids the deviation that may be caused by a single performance indicator and provides a more comprehensive performance evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1A flow chart of a method for detecting the performance of PVC sheathing material based on image processing provided in Example 1 of the present invention;

[0053] Figure 2 A schematic diagram of the structure for calculating the comprehensive melting performance index provided in Example 1 of the present invention;

[0054] Figure 3 A schematic structural diagram of a PVC sheath material performance detection system based on image processing provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] Example 1

[0057] In order to detect the performance stability of the PVC sheath material it produces, a cable manufacturer introduced a performance detection method based on image processing. The method flow chart is as follows: Figure 1 The problem encountered by the manufacturer during the production process is that due to slight differences in process control, different batches of PVC sheath materials have fluctuations in performance, affecting the quality stability of the final product. Therefore, the factory decided to use the method provided by the present invention to perform performance testing to ensure that each batch of products can meet the quality requirements for further processing and treatment.

[0058] Step S1: obtain J portions of granular PVC sheath material samples of the same weight and place them in J closed heating spaces respectively.

[0059] Furthermore, the manufacturer first randomly selected several portions of granular PVC sheathing materials of the same weight from the current production batch. In order to simulate different environments, these samples were laid flat in multiple closed heating spaces. J different inclination angles were set in J closed heating spaces, and the inclination angles belonged to [R1, R2], where R1 represents the first angle threshold and R2 represents the second angle threshold. The different inclination angles are used to simulate different positions and directions of the granular PVC sheathing material during the processing.

[0060] Setting different tilt angles can effectively simulate the effects of gravity, fluidity and heat conduction on granular PVC sheathing materials in different positions and directions during actual processing, such as extrusion or injection molding. This simulation can help more accurately evaluate the performance of materials under different processing conditions.

[0061] Step S2: Acquire and process the image of the sample during the heating stage to obtain first data, wherein the first data includes the color, glossiness and surface roughness of the sheath material during the heating process, and calculate the comprehensive heating performance index of the sample based on the first data.

[0062] Further, images are taken at fixed temperature intervals, fixed shooting angle images of the PVC sheath material at K fixed temperature intervals in the heating stage are obtained and image processing is performed to obtain first data of the granular PVC sheath material, the first data including the color, glossiness and surface roughness of the sheath material during the heating process; the heating performance index is calculated according to the first data, and the calculation formula of the heating performance index is:

[0063]

[0064] Where E k represents the heating performance index at the kth temperature, α1 represents the weight coefficient of the color difference change, M represents the number of pixels, represents the R value of the pixel at the kth temperature, represents the R value of the pixel at the k-1th temperature, represents the G value of the pixel at the kth temperature, represents the G value of the pixel at the k-1th temperature, represents the B value of the pixel at the kth temperature, represents the B value of the pixel at the k-1th temperature, α2 represents the weight coefficient of glossiness, I re Represents the reflected light intensity, I in represents the incident light intensity, α3 represents the weight coefficient of roughness, z i It represents the gray value of the pixel after the image is converted into a gray image, and z represents the average gray value; as shown in Table 1, the heating performance index of samples in different closed heating spaces at a certain temperature.

[0065] After calculating the T k After obtaining the heating performance index at the temperature, all heating performance indexes are obtained, and the comprehensive heating performance index is calculated. The calculation formula of the comprehensive heating performance index is:

[0066]

[0067] Wherein, W1 represents the comprehensive heating performance index, J represents the number of the closed heating spaces, K represents the number of images taken at fixed temperature intervals, represents the weight coefficient of the heating performance index of the jth enclosed heating space at the kth temperature, E k jRepresents the heating performance index of the jth enclosed heating space at the kth temperature.

[0068] Table 1. Heating performance index of samples in different closed heating spaces at a certain temperature

[0069] Enclosed Heating Space No. Heating performance index Weight coefficient 1 0.95 0.1 2 0.98 0.1 3 0.92 0.1 4 0.98 0.1 5 0.91 0.1 6 0.99 0.1 7 0.93 0.1 8 0.96 0.1 9 0.97 0.1 10 0.94 0.1

[0070] Furthermore, the weighting takes into account that the higher the temperature, the greater the impact on the granular PVC sheath material, so the higher the temperature, the greater the weighting coefficient. The bigger.

[0071] By analyzing the data of multiple dimensions such as color, glossiness and surface roughness of the sheath material during the heating process, the performance changes of the material at different temperatures can be fully reflected; setting a fixed shooting angle and interval temperature for image acquisition and processing ensures that the conditions of each experiment are consistent, thereby improving the repeatability and comparability of the experimental results; by calculating the comprehensive heating performance index and combining the test results of multiple closed heating spaces, the overall performance of the material at different temperatures is evaluated by weighted average, which can fully reflect the performance of the material under actual processing conditions.

[0072] Step S3: Obtain and process the image of the sample in the melting stage to obtain second data, wherein the second data includes the expansion speed of the sheath material, flow uniformity, the number and area of ​​bubbles, cracks and the color of the sheath material during the melting process; calculate the comprehensive melting performance index according to the second data, such as Figure 2 Shown is a schematic diagram of the structure for calculating the comprehensive melt performance index.

[0073] Furthermore, the melting stage refers to the process from the beginning of melting of the sample to the complete melting of the sample; the design of different angles of the closed heating space is to observe the flow of the sample in different directions when it melts; the image of the sample in the melting stage is obtained and the boundary of the sample is obtained through image processing technology;

[0074] The flow uniformity is calculated according to the expansion speed of the boundary of the sample, and the calculation formula of the flow uniformity is:

[0075]

[0076] Among them, σ j represents the flow uniformity of the sample in the jth closed heating space, H represents the number of sampling points, and v h represents the expansion speed of the hth sampling position, Represents the average expansion speed.

[0077] Furthermore, taking the center of the image as the starting point of the ray, H rays are evenly projected in all directions, and there are H intersections with the edge of the sample, namely the sampling positions. The sampling photos of the current time node and the previous time node of the same sample are obtained, and the expansion speed of the sampling position of the sample at this time point is calculated according to the displacement of the sampling position, and the average expansion speed at this time point is calculated using the expansion speeds of all sampling positions.

[0078] By acquiring images of granular PVC sheathing materials at the melting stage at multiple time points and calculating the expansion speed of the material boundary, the fluidity of the material can be accurately evaluated. Multi-point sampling data can reduce the impact of measurement errors and make the test results more representative and accurate.

[0079] Furthermore, an image of the sample in the melting stage is obtained and the image is binarized, bubbles are detected using connected domain analysis, and the bubble area variance is calculated based on the number of bubbles and the bubble area. represents the bubble area variance of the sample in the jth closed heating space.

[0080] Furthermore, cracks may appear in the PVC jacket material during the thermal expansion process. On the one hand, if the melting temperature is insufficient, the material cannot be completely melted, resulting in stress concentration in the incompletely melted area, which may cause cracks or reduce the mechanical properties of the material; on the other hand, the PVC jacket material may contain a variety of additives such as plasticizers, stabilizers and fillers. Inappropriate formula ratios or uneven distribution of certain additives may cause uneven stress when the material is melted, thereby forming cracks. In particular, if the plasticizer content in the PVC formula is insufficient, the material is not flexible enough during the melting process, and it may be more likely to crack in the stressed area.

[0081] Through edge detection and morphological operations, cracks are identified and crack characteristic index is calculated; the calculation formula of the crack characteristic index is:

[0082]

[0083] Among them, LW j represents the crack characteristic index of the sample in the jth closed heating space, β1 represents the length weight coefficient, P represents the total number of cracks, L p represents the length of the pth crack, β2 represents the width weight coefficient, S p represents the width of the pth crack.

[0084] By binarizing and connecting domain analyzing the images in the melting stage, bubbles and cracks in the material can be accurately detected and their characteristics can be quantitatively analyzed, which helps to clarify the size, quantity and distribution of bubbles and cracks, thus providing specific indicators for material quality control.

[0085] Furthermore, images of the sample in the melting stage are acquired and processed at N different time points to obtain the second data, and the comprehensive melting performance index is calculated according to the second data. The calculation formula of the comprehensive melting performance index is:

[0086]

[0087] Wherein, W2 represents the comprehensive melting performance index, N represents the number of images, θ n represents the weight coefficient at the nth time node, J represents the number of the closed heating space, ω1 represents the weight coefficient of flow uniformity, represents the flow uniformity of the sample in the jth closed heating space at the nth time node, ω2 represents the weight coefficient of the bubble area variance, represents the bubble area variance of the sample in the jth closed heating space at the nth time node, ω3 represents the weight coefficient of the crack characteristic index, LW n j represents the crack characteristic index of the sample in the jth closed heating space at the nth time node, ω4 represents the weight coefficient of the melting color difference, It represents the melting color difference of the sample in the jth closed heating space at the nth time node.

[0088] Furthermore, the color difference change of the sample in the jth closed heating space at the nth time node is The calculation method is to obtain the image pixel data of the nth time node and the image pixel data of the previous time node, and calculate the difference between the corresponding pixels, and calculate the color difference change based on the difference of all pixels.

[0089] Multiple key performance parameters such as flow uniformity, bubble area variance, crack characteristic index and color difference are quantified and weight coefficients are introduced so that the importance of each parameter can be reasonably considered; by calculating the comprehensive melting performance index, the various performances of the PVC sheath material in the molten state can be comprehensively evaluated, intuitively reflecting the overall quality and processing stability of the material.

[0090] Step S4: Filling the closed heating space with ammonia gas, and indirectly judging the release of hydrogen chloride gas by detecting whether ammonium chloride precipitation is generated; acquiring an ammonium chloride image and processing it using image processing technology to obtain a volatility index.

[0091] Furthermore, PVC materials will thermally decompose under high temperature conditions and release toxic gases, among which hydrogen chloride gas is the main toxic gas, so the thermal decomposition performance of the sample is tested in this step. The enclosed heating space is filled with ammonia gas of a preset concentration, and the release of hydrogen chloride gas is determined by detecting ammonium chloride precipitation; the image of the ammonium chloride precipitation taken by a high-resolution camera is obtained and grayscaled and denoised to obtain a first image; the first image is further processed to identify white pixels in the first image; the area of ​​the ammonium chloride precipitation is obtained according to the number of white pixels, and the proportion of the ammonium chloride precipitation in the jth sample is obtained by the area. The volatility index is calculated according to the ratio, and the calculation formula of the volatility index is:

[0092]

[0093] Wherein, W3 represents the volatility index, J represents the number of the first images, γ j Represents the weight coefficient of the j-th ratio.

[0094] Furthermore, the heating is stopped after the last time point in step S3, and the heated enclosed space is naturally cooled to room temperature; after the heated enclosed space is cooled to room temperature, a front image of the sample is collected, and the above operation is performed to obtain a volatility index.

[0095] By introducing ammonia and detecting the precipitation of ammonium chloride, the release of volatile hydrogen chloride gas from PVC sheathing materials during heating can be indirectly but effectively evaluated. The volatility index is calculated by combining image processing technology with grayscale, denoising, threshold segmentation and binarization, which further improves the sensitivity and accuracy of detection.

[0096] Step S5: obtaining and processing the cooled sample to obtain a thermogravimetric analysis index.

[0097] Further, the cooled sample is obtained and washed, air-dried and weighed to obtain a weight of the cooled sample G2, the weight of the sample is obtained and the thermogravimetric analysis index is calculated, and the calculation formula of the thermogravimetric analysis index is:

[0098]

[0099] Wherein, W4 represents the thermogravimetric analysis index, and G1 represents the weight of the sample. Table 2 shows the weight of the sample after partial cooling.

[0100] Table 2. Sample weight after cooling

[0101] Sample No. Initial sample weight (g) Sample weight after cooling (g) Sample 1 100.00 97.23 Sample 2 100.00 98.56 Sample 3 100.00 96.89 Sample 4 100.00 98.98 Sample 5 100.00 97.76 Sample 6 100.00 95.98 Sample 7 100.00 98.26 Sample 8 100.00 96.58 Sample 9 100.00 99.30 Sample 10 100.00 97.02

[0102] After obtaining the weight of all cooled samples, the thermogravimetric analysis index was calculated to be 0.977. The thermogravimetric analysis index can accurately evaluate the weight change of PVC sheath materials during heating and cooling, which helps to determine the weight loss of materials during heating, and then evaluate the durability and stability of materials in high temperature environments.

[0103] Step S6: Obtain and process the data from step S2 to step S5 to obtain a comprehensive performance index for measuring the performance stability of the sample.

[0104] Further, the comprehensive heating performance index, the comprehensive melting performance index, the volatility index and the thermogravimetric analysis index are obtained and processed to obtain the comprehensive performance index, and the calculation formula of the comprehensive performance index is:

[0105] W=λ1·W1+λ2·W2+λ3·(1-W3)+λ4·W4;

[0106] Wherein, W represents the comprehensive performance index, λ1 represents the weight coefficient of the comprehensive heating performance index, λ2 represents the weight coefficient of the comprehensive melting performance index, λ3 represents the weight coefficient of the volatility index, and λ4 represents the weight coefficient of the thermogravimetric analysis index.

[0107] Through calculation, the comprehensive performance index of this batch of samples is 0.98, which meets the quality requirements in the high-temperature processing process. By weighting and integrating multiple key performance indicators such as the comprehensive heating performance index, comprehensive melting performance index, volatility index and thermogravimetric analysis index, the overall performance of the PVC sheath material can be comprehensively evaluated, and the performance stability of the PVC sheath material in the heating and melting stages can be accurately analyzed; the comprehensive performance index allows the weight coefficients of different performance indicators to be adjusted to flexibly adapt to the needs of different application scenarios. For example, the weight of volatility or thermogravimetric index can be adjusted according to the application scenario to ensure the best performance of the material in a specific application.

[0108] The present embodiment provides a PVC sheath material performance detection method based on image processing, which quantifies and evaluates the performance of the PVC sheath material from multiple dimensions such as heating, melting, volatility and thermogravimetry through detailed image processing analysis and performance index calculation; by acquiring and analyzing sample data in different closed heating spaces and performing weighted fusion, it is possible to more accurately simulate and monitor the performance of the PVC sheath material in the actual production process; the calculation method of the comprehensive performance index integrates multiple key performance indicators, which can intuitively reflect the overall performance stability of the sample during the heating process and in the melting state.

[0109] Example 2

[0110] In order to verify whether the produced granular PVC sheathing material meets the requirements for further processing, a PVC sheathing material manufacturer uses a PVC sheathing material performance detection system based on image processing provided by the present invention to perform performance detection of PVC sheathing material. The system structure is as follows: Figure 3 shown.

[0111] The manufacturer first randomly selected several samples of granular PVC jacket material of the same weight from the current production batch. In order to simulate different processing environments, these samples were placed in several closed heating spaces. These heating spaces are designed with different inclination angles to simulate the influence of different positions and directions on material properties during further processing and injection molding. A certain concentration of ammonia is pre-filled in the heating space to detect the release of volatile components of the PVC jacket material during heating.

[0112] During the heating stage, the manufacturer uses high-precision camera equipment to take fixed images of the samples in the heating space. The camera equipment captures the changes in color, glossiness and surface roughness of each sample at different temperatures. The acquired image data is analyzed through image processing to extract key indicators such as color difference, glossiness and surface roughness. Then, the heating performance index at each temperature is calculated based on these data, and finally the comprehensive heating performance index of each sample is calculated through a comprehensive formula. After calculation, the comprehensive heating performance index of this batch of samples is 0.87.

[0113] Next, the samples are further heated to a molten state, and the manufacturer uses a camera to record the expansion speed, flow uniformity, number and area of ​​bubbles, crack conditions and color changes of the samples during the melting process. These image data are processed to obtain detailed characteristics of each sample in the melting stage, such as flow uniformity, bubble area variance and crack characteristic index. Through these characteristic data collected at different time points, the comprehensive melting performance index of this batch of samples is calculated to be 0.91.

[0114] PVC material is a polymer with no obvious melting point and gradually softens during heating. The melting of PVC material usually occurs at 160℃ to 180℃. In this range, the molecular chain of PVC begins to move and gradually becomes a flowable molten state, which is suitable for processes such as extrusion and injection molding. PVC will undergo thermal decomposition at high temperatures, especially the dehydrochlorination reaction, which is a signature reaction of PVC thermal decomposition. The thermal decomposition of PVC usually starts at 200℃ to 220℃, so 160℃ to 180℃ is the processing temperature range of PVC material. Ammonia is a colorless gas with a strong pungent odor and has certain chemical stability. At normal temperature and pressure, ammonia is relatively stable and will not decompose spontaneously. When the temperature reaches above 400℃, the stability of ammonia decreases and decomposition occurs, so ammonia can still maintain stability at around 180℃. In the detection of PVC materials, ammonia reacts with hydrogen chloride to form ammonium chloride precipitation. This reaction can be used to detect the release of hydrogen chloride gas. After the temperature rises to 180°C, it is kept constant. While the sample is heated, the ammonia in the closed heating space reacts with the hydrogen chloride gas that may be released by the sample to generate ammonium chloride precipitation. The closed heating space is filled with ammonia gas with a concentration of 5% to 10%. The heating is stopped after the last time node of the melting stage. After cooling, the manufacturer uses a high-resolution camera to shoot these reaction products and detects the area of ​​ammonium chloride precipitation through image processing technology. Some data are shown in Table 3. Based on the area of ​​the white pixel points, the manufacturer calculated that the volatility index of this batch of samples is 0.19.

[0115] Table 3. White spot area

[0116] Sample No. <![CDATA[White spot area (cm 2 )]]> <![CDATA[Total image area (cm 2 )]]> Sample 11 2.24 100.00 Sample 12 2.02 100.00 Sample 13 2.34 100.00 Sample 14 3.17 100.00 Sample 15 3.98 100.00 Sample 16 2.20 100.00 Sample 17 2.45 100.00 Sample 18 3.05 100.00 Sample 19 2.31 100.00 Sample 20 3.12 100.00

[0117] After the samples were cooled, the manufacturer cleaned, air-dried and weighed them, and the final weight of the samples was measured. The thermogravimetric analysis index of the samples was calculated to be 0.91 based on the initial weight of the samples. The index indicates the weight change of the samples after heating treatment, reflecting the loss of the material during the heating process.

[0118] Finally, the manufacturer processed all the test data comprehensively and obtained a comprehensive performance index of 0.90 for this batch of samples through weighted calculation. This index covers the performance of the samples in multiple dimensions such as heating, melting, volatilization and thermogravimetric analysis. Based on this comprehensive performance index, the manufacturer found that the samples of this batch of granular PVC sheathing materials did not meet the quality requirements for further processing, and the manufacturer may need to readjust the production method of granular PVC sheathing materials.

[0119] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for detecting the performance of PVC sheathing materials based on image processing, characterized in that: include: Step S1: obtaining J portions of granular PVC sheath material samples of the same weight, and placing them in J closed heating spaces respectively; Step S2: acquiring and processing an image of the sample during the heating stage to obtain first data, wherein the first data includes the color, glossiness and surface roughness of the sheath material during the heating process, and calculating the comprehensive heating performance index of the sample according to the first data; Step S3: acquiring and processing an image of the sample in the melting stage to obtain second data, wherein the second data includes the expansion speed of the sheath material, flow uniformity, the number and area of ​​bubbles, cracks, and the color of the sheath material during the melting process; and calculating a comprehensive melting performance index based on the second data; Step S4: filling the closed heating space with ammonia gas, and judging the release of hydrogen chloride gas by detecting whether ammonium chloride precipitation is generated; acquiring an ammonium chloride image and processing it using image processing technology to obtain a volatility index; Step S5: obtaining and processing the cooled sample to obtain a thermogravimetric analysis index; Step S6: Obtain and process the data from step S2 to step S5 to obtain a comprehensive performance index for measuring the performance stability of the sample.

2. The method for detecting the performance of PVC sheathing material based on image processing according to claim 1 is characterized in that: The enclosed heating space comprises: The sample is laid flat in the closed heating space, and J closed heating spaces are respectively set with J different inclination angles, and the inclination angles belong to [R1, R2], where R1 represents the first angle threshold and R2 represents the second angle threshold; the different inclination angles are used to simulate different positions and directions of the PVC sheath material during the processing.

3. The method for detecting the performance of PVC sheathing material based on image processing according to claim 1, characterized in that: The comprehensive heating performance index includes: Acquire K fixed-angle images of the sample at fixed temperature values ​​during the heating stage and perform image processing to obtain first data of the sample, wherein the first data includes the color, glossiness and surface roughness of the sheath material during the heating process; calculate the heating performance index according to the first data; J×K heating performance indexes are obtained and processed to obtain the comprehensive heating performance index. The calculation formula of the comprehensive heating performance index is: Wherein, W1 represents the comprehensive heating performance index, J represents the number of the closed heating spaces, K represents the number of images taken at fixed temperature intervals, represents the weight coefficient of the heating performance index of the jth enclosed heating space at the kth temperature, Represents the heating performance index of the jth enclosed heating space at the kth temperature.

4. The method for detecting the performance of PVC sheathing material based on image processing according to claim 1, characterized in that: The second data includes: Acquire an image of the sample at the melting stage and obtain the boundary of the sample by image processing technology; The flow uniformity is calculated according to the expansion speed of the boundary of the sample, and the calculation formula of the flow uniformity is: Among them, σ j represents the flow uniformity of the sample in the jth closed heating space, H represents the number of sampling points, and v h represents the expansion speed of the hth sampling position, Represents the average expansion speed.

5. The method for detecting the performance of PVC sheathing material based on image processing according to claim 1 is characterized in that: The second data also includes: Acquire an image of the sample at the melting stage and perform binarization processing on the image, detect bubbles using connected domain analysis, and calculate the bubble area variance according to the number and area of ​​the bubbles; The crack is identified through edge detection and morphological operations, and the crack characteristic index is calculated; the calculation formula of the crack characteristic index is: Among them, LW j represents the crack characteristic index of the sample in the jth closed heating space, β1 represents the length weight coefficient, P represents the total number of cracks, L p represents the length of the pth crack, β2 represents the width weight coefficient, S p represents the width of the pth crack.

6. The method for detecting the performance of PVC sheathing material based on image processing according to claim 1, characterized in that: The images of the sample in the melting stage are acquired at N different time nodes and processed to obtain the second data; and the melting performance index of the sample at the nth time node is obtained by weighted fusion according to the flow uniformity, bubble area variance, crack characteristic index and melting chromatic aberration in the second data; and the comprehensive melting performance index W2 of the sample in the melting stage is obtained by weighted fusion according to the weights of different time nodes.

7. The method for detecting the performance of PVC sheathing material based on image processing according to claim 1, characterized in that: The volatility index includes: Ammonia gas of a preset concentration is filled in the closed heating space, and the release of hydrogen chloride gas is determined by detecting ammonium chloride precipitation; an image of the ammonium chloride precipitation taken by a high-resolution camera is obtained and grayscaled and denoised to obtain a first image; the first image is processed to identify white pixels in the first image; the area of ​​the ammonium chloride precipitation is obtained according to the number of the white pixels, and the proportion of the ammonium chloride precipitation in the jth first image is obtained according to the area. The volatility index W3 is calculated based on the ratio.

8. The method for detecting the performance of PVC sheathing material based on image processing according to claim 1, characterized in that: The thermogravimetric analysis index includes: The cooled sample is obtained and cleaned, air-dried and weighed to obtain a weight of the cooled sample G2. The weight of the sample is obtained and the thermogravimetric analysis index is calculated. The calculation formula of the thermogravimetric analysis index is: Wherein, W4 represents the thermogravimetric analysis index, and G1 represents the weight of the sample.

9. The method for detecting the performance of PVC sheathing material based on image processing according to claim 1, characterized in that: The comprehensive performance index includes: The comprehensive heating performance index, the comprehensive melting performance index, the volatility index and the thermogravimetric analysis index are obtained and processed to obtain the comprehensive performance index. The calculation formula of the comprehensive performance index is: W=λ1·W1+λ2·W2+λ3·(1-W3)+λ4·W4; Wherein, W represents the comprehensive performance index, λ1 represents the weight coefficient of the comprehensive heating performance index, λ2 represents the weight coefficient of the comprehensive melting performance index, λ3 represents the weight coefficient of the volatility index, and λ4 represents the weight coefficient of the thermogravimetric analysis index.

10. A PVC sheath material performance detection system based on image processing, characterized in that: include: An experimental setting module is used to obtain J portions of granular PVC sheathing materials of the same weight and place them in J closed heating spaces respectively; A heating performance index module is used to obtain and process the image of the granular PVC sheath material during the heating stage to obtain first data, wherein the first data includes the color, glossiness and surface roughness of the sheath material during the heating process, and calculate a comprehensive heating performance index based on the first data; A melting performance index module is used to obtain and process the image of the granular PVC sheathing material in the melting stage to obtain second data, wherein the second data includes the expansion speed of the sheathing material, flow uniformity, the number and area of ​​bubbles, cracks and the color of the sheathing material during the melting process; and calculate the comprehensive melting performance index according to the second data; The volatile gas detection module is used to fill the closed heating space with ammonia gas, and indirectly judge the release of hydrogen chloride gas by detecting whether ammonium chloride precipitation is generated; obtain the ammonium chloride image and process it using image processing technology to obtain a volatility index; Thermogravimetric analysis module, used to obtain and process the cooled PVC sheath material to obtain thermogravimetric analysis index; The comprehensive performance index module obtains and processes the data of the heating performance index module, the melting performance index module, the volatile gas detection module and the thermogravimetric analysis module to obtain a comprehensive performance index for measuring the performance stability of the sample.

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