Plastic stress whitening evaluation method
By performing tensile tests and image processing on plastic samples, a normalized curve of grayscale image is constructed, which solves the problem of the inability to assess the whitening of plastic stress in existing technologies and achieves accurate assessment of the whitening state of plastic.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-18
- Publication Date
- 2026-04-14
AI Technical Summary
The lack of effective methods in the existing technology to assess the stress whitening phenomenon in plastics makes it impossible to accurately determine the usage status and limitations of plastics.
The whitening state of plastic samples was evaluated by performing tensile tests on the samples and capturing continuous video images under a specific light source using a camera. The images were then converted into grayscale images, the average grayscale value of each pixel was calculated, and a normalized curve was constructed.
It enables effective assessment of the critical deformation and degree of whitening in plastics, with high accuracy and low error rate, and is applicable to a variety of plastic materials.
Smart Images
Figure CN121856009A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of polymer materials technology, specifically to a method for evaluating stress whitening in plastics. Background Technology
[0002] When plastics are subjected to external stress, they undergo deformation. As the deformation increases, the product often appears to turn white, a phenomenon known as "stress whitening." Stress whitening not only affects the appearance of the product but also shortens its lifespan and may even pose a risk of breakage in the whitened areas. However, current technology lacks effective research on assessing stress whitening or its critical state in plastic products. Simple visual inspection or deduction based on external stress is insufficient to determine the extent of stress whitening and to effectively evaluate the plastic's usage status and limitations. Summary of the Invention
[0003] Based on the shortcomings of existing technologies, the purpose of this invention is to provide a method for evaluating stress whitening of plastics. This method involves performing a tensile test on the sample and constructing the relationship between sample deformation and whitening state through specific data acquisition and processing methods. This allows for the effective evaluation of the critical whitening deformation and the degree of whitening deformation of the sample.
[0004] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A method for evaluating stress whitening in plastics includes the following steps: (1) The plastic sample to be tested is subjected to a tensile test until the plastic sample breaks. At the same time, a camera is used to continuously capture video images of the plastic sample under the illumination of a light source. The shooting frequency during image acquisition is ≥30FPS. The straight-line distance between the light source and the plastic sample to be tested is 0.8~1.2m. (2) Convert the acquired image into a grayscale image, and calculate the average grayscale value of the pixels in the image. Construct a curve using the average grayscale value of the pixels in the grayscale image of the plastic sample under different deformations and perform normalization processing to obtain a normalized curve; the grayscale image has a gray level ≥ 256. (3) The stress whitening of the plastic sample under test is evaluated by normalization curve.
[0005] To effectively assess stress-induced whitening in plastics and predict their critical whitening state, usage status under stress, or limitations, this invention does not consider the specific external stress. Instead, it establishes a relationship between the deformation of the sample during stretching and the whitening state. After confirming the correspondence between the whitening condition and grayscale values of the actual product, those skilled in the art can intuitively infer whether whitening has occurred based on the actual deformation of the sample (e.g., constructing a normalized curve for a product and inferring the risk of whitening under a certain deformation based on the actual whitening condition and grayscale values). Alternatively, it can infer the critical deformation before whitening by corresponding grayscale values to the degree of whitening (e.g., assessing the possible range of critical deformation of the product under whitening conditions corresponding to the critical grayscale value based on the relationship of the normalized curve). To ensure the effectiveness of the assessment, this invention... In the proposed design, a camera is used to continuously capture images of the plastic sample during the tensile test. The captured images are then directly converted to grayscale, and the average grayscale values of the image pixels are used to construct a deformation curve. During this process, the image capture frequency must reach 30 FPS or higher to ensure the accuracy of the appearance images captured during continuous deformation. Furthermore, the distance between the light source and the sample cannot be too large or too small; otherwise, the image will not effectively represent the differences in appearance under different degrees of whitening, making it impossible to obtain accurate data for curve construction during subsequent grayscale image conversion. On the other hand, after the image is converted to grayscale, the grayscale values of the pixels can effectively represent the whitening appearance of the sample under different deformations. If the grayscale levels are insufficient during image conversion, the accuracy of subsequent grayscale average value calculation will decrease, thereby increasing the evaluation error of the final curve.
[0006] Preferably, in step (1), the plastic sample to be tested includes at least one of polypropylene, polyethylene, ABS (acrylonitrile-butadiene-styrene terpolymer), PC / ABS alloy, PA6, and PA66.
[0007] More preferably, the plastic sample to be tested includes at least one of polypropylene, polyethylene, PA6, and PA66.
[0008] The technical solution of this invention is based on the similarity between the whitening phenomenon of plastics when subjected to external force and the deformation when subjected to external stretching, and therefore can be applied to various types of plastics, with a wide evaluation range.
[0009] More preferably, the plastic sample to be tested is a black sample.
[0010] Compared to other color systems, black samples exhibit higher accuracy in pixel grayscale values when converted to grayscale mode after image capture, which is more conducive to quantifying sample whitening and subsequent curve construction and evaluation. However, this is not the only approach; those skilled in the art can apply the solution described in this invention to samples of other color systems such as red, blue, and yellow.
[0011] Specifically, the test sample described in this invention only needs to be able to confirm the whitening of the product by means of the naked eye (that is, the product will have a visible difference from its intrinsic color in the whitening state). At this time, the gray value of the sample when whitening can be confirmed by testing. For example, in common knowledge in the art, the gray value of PA66 series products and PA6 precipitate products in the whitening state is >1.7. Those skilled in the art can apply the evaluation method described in this invention to various specific products based on this common knowledge. Alternatively, before applying the evaluation method described in this invention, data on the gray value of the corresponding resin system product in the whitening state can be collected in advance and a database can be constructed. Based on the database data, the range of gray values under the effective whitening state can be confirmed. At the same time, the critical deformation of the product can be confirmed based on the database or common knowledge, and the gray value corresponding to the product when the whitening state is visible to the naked eye can be estimated. There are no specific limitations on this.
[0012] Preferably, in step (1), the tensile test of the plastic sample to be tested is carried out in accordance with ISO527-1-2012, and the tensile rate is 45~55mm / min, specifically 50mm / min.
[0013] More preferably, the tensile test is performed using a Zwick-Roell electronic universal testing machine.
[0014] Preferably, in step (1), when the camera is acquiring continuous video images, the pixel area of the image is 300×240, and the white area of the plastic sample to be tested is set in the central area of the image.
[0015] Preferably, step (1) includes acquiring images of the plastic sample to be tested under at least 6 different deformations, wherein the at least 6 deformations are uniformly distributed between 0% and the maximum deformation.
[0016] In addition to the data from the initial sample, the more consecutive images acquired, the higher the accuracy of the subsequent constructed and normalized curves used to evaluate the sample state. Furthermore, under the premise of ensuring the validity of the data processing results, subsequent data processing operations using 6 consecutive images are more efficient.
[0017] Preferably, in step (1), the light source includes any one of low color temperature, medium color temperature, and high color temperature light sources.
[0018] In the technical solution of this invention, there are no special restrictions on the choice of light source during shooting. Specifically, a low color temperature light source with a color temperature of less than 3000K, a medium color temperature light source with a color temperature of 3000~6000K, or a high color temperature light source with a color temperature higher than 6000K can be used, as long as it does not affect the acquisition of the final data.
[0019] Preferably, the deformation of the plastic sample under test during the tensile test is ≥0.
[0020] More preferably, the deformation of the plastic sample under test during the tensile test is 0-60%.
[0021] More specifically, the deformation of the plastic sample under test during the tensile test is a range of one or any two of 0%, 10%, 20%, 30%, 40%, 50%, and 60%.
[0022] The deformation of the plastic sample to be tested during tensile testing is not subject to any special requirements, nor is the deformation during image acquisition. As long as the deformation does not exceed the limit deformation of the plastic sample to be tested (i.e., the maximum deformation before fracture), those skilled in the art can select an appropriate deformation for image acquisition based on the specific conditions of the sample.
[0023] Preferably, in step (2), the grayscale image is in 8-bit format.
[0024] Preferably, in step (2), the normalization process is as follows: the average gray value A0 of the pixels in the grayscale mode image of the plastic sample under test when the deformation is 0 is taken as the reference point, and the average gray value A1 of the pixels in the grayscale mode image of the plastic sample under test when the deformation is 0 is calculated using the formula Ax=A1 / A0. The normalized gray value and the deformation of the plastic sample under test are used to construct a curve and fit it.
[0025] The beneficial effect of this invention is that it provides a method for evaluating stress whitening of plastics. The method involves performing a tensile test on the sample and constructing the relationship between the sample deformation and the whitening state through specific data acquisition and processing methods. This allows for the effective evaluation of the critical whitening deformation and the degree of whitening deformation of the sample. Attached Figure Description
[0026] Figure 1 These are images of the plastic sample to be tested described in Example 1 of the present invention at different deformation amounts of 0-60%; Figure 2 The images shown are grayscale images converted from photos of the plastic sample to be tested at different deformation rates (0-60%) as described in Example 1 of this invention. Figure 3 This is a statistical chart showing the average grayscale value of pixels in the grayscale image of the plastic sample to be tested as described in Embodiment 1 of the present invention. Figure 4 The images shown are: (left) a grayscale image of the plastic sample to be tested obtained after testing and processing, showing the average grayscale value of each pixel versus the sample deformation curve; and (right) a normalized fitted grayscale average value versus sample deformation curve after normalization.
[0027] Figure 5 The image shows the plastic sample to be tested as described in Comparative Example 1 of this invention.
[0028] Figure 6 The image shows the plastic sample to be tested as described in Comparative Example 2 of this invention. Detailed Implementation
[0029] To better illustrate the purpose, technical solution, and advantages of this invention, the invention will be further described below with reference to specific embodiments and comparative examples. The purpose of this description is to provide a detailed understanding of the invention, not to limit its scope. All other embodiments obtained by those skilled in the art without inventive effort are within the protection scope of this invention. Unless otherwise specified, the experimental reagents and instruments involved in the implementation of this invention are commonly used reagents and instruments.
[0030] Example 1 An embodiment of the plastic stress whitening assessment method of the present invention includes the following steps: (1) A black plastic sample of PA66-C112 manufactured by KINGFA, with dimensions of ISO 527 1A, was subjected to a tensile test according to ISO527-1-2012 until the sample broke. The test was conducted using a Zwick-Roell electronic universal testing machine (model: BT2-FR020TEW-A50(20KN)) at a tensile rate of 50 mm / min. Simultaneously, a Hikvision industrial camera (model: MV-CH050-10UC) was used to continuously capture video images of the sample at a distance of 1 m from the sample, illuminated by a 6000K color temperature light source. The image capture frequency was 30 FPS. During image acquisition, the whitish area of the sample was located in the center of the image. The pixel area of the image was 300×240. The camera position was fixed, and images were captured at deformation values of 0%, 10%, 20%, 30%, 40%, 50%, and 60%. Figure 1 As shown, the sample turns whiter and the degree of whitening increases with increasing deformation. (2) Convert the acquired image into a grayscale image, such as Figure 2As shown, the grayscale image has 256 gray levels and is in 8-bit format; simultaneously, the average grayscale value of each pixel in the image is calculated, such as... Figure 3 As shown, curves are constructed using the average grayscale values of pixels in grayscale images of the plastic sample under different deformations, and then normalized to obtain the normalized curves, as shown below. Figure 4 As shown, the specific steps are as follows: The average grayscale value A0 of the pixels in the grayscale image of the plastic sample under test when the deformation is 0 is used as the baseline. The average grayscale value A1 of the pixels in the grayscale image of the plastic sample under other deformation conditions is calculated using the formula Ax = A1 / A0. A curve is constructed and fitted using the normalized grayscale value and the corresponding deformation of the plastic sample under test, resulting in the fitting formula y = 0.9798e. 1.8553x R 2 =0.9989.
[0031] (3) The stress whitening of the plastic sample to be tested was evaluated by normalization curve. According to common knowledge, the gray value of PA66 products when whitening is >1.7. Based on this common knowledge, a total of 20 commercially available PA66 products other than the sample to be tested were subjected to high-frequency sampling (one point was recorded for 1% deformation) tensile tests. The critical whitening of the product was judged by visual inspection. The gray value at the critical whitening was collected to establish a database. The results showed that the gray value was ≥1.72, indicating that the common knowledge corresponds to the measured value. Based on the fitted curve obtained from this application, and substituting the database critical value of 1.72, it can be seen that the critical deformation of the product is 30.3%. Subsequently, a traditional high-frequency sampling (recording one point for every 1% deformation) tensile test was conducted on the PA66 product. The results showed that the grayscale value of the product reached 1.72 when the deformation was 28%, and the product showed critical whitening. That is, the error rate of the deformation result evaluated by the curve obtained in this embodiment is 8.3%, which does not exceed the critical 15% of the industry evaluation standard, indicating that the evaluation result has high validity.
[0032] Example 2 An embodiment of the plastic stress whitening assessment method of the present invention differs from Embodiment 1 only in that, during the data acquisition, images are collected at deformation values of 0%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, and 60%, ultimately yielding the fitting formula y = 0.9863e 1.8467x R 2 =0.9994, while the normalized curve prediction obtained by the scheme in this embodiment, when the gray value is 1.72 and the deformation is 30.1%, has an error rate of 7.6%, indicating that the evaluation result is effective.
[0033] Example 3 This invention provides an embodiment of the plastic stress whitening assessment method, differing from Embodiment 1 only in that the test plastic sample is PA6-C111 produced by KINGFA. The PA6 sample is black. Based on common knowledge, the grayscale value of PA6 products at the point of whitening is >1.7. Using this knowledge, 20 commercially available PA6 products (excluding the test sample) were subjected to high-frequency point sampling (recording one point per 1% deformation) tensile tests. The critical whitening point was determined visually, and the grayscale values at this point were collected to establish a database. The results show that the grayscale values are all ≥1.74, indicating that the common knowledge corresponds to the measured values. During the evaluation, the fitted formula y = 0.9367e was obtained. 1.6829x R 2 =0.9889. According to the normalized curve prediction obtained in this embodiment, when the gray value is 1.74, the deformation is 36.8%. Subsequently, the PA6 product was subjected to a traditional high-frequency sampling (recording one point for 1% deformation) tensile test. The results showed that the gray value of the product reached 1.74 when the deformation was 34%, and the product showed critical whitening. That is, the error rate of the deformation result evaluated by the curve obtained in this embodiment is 8.2%, which does not exceed 15%, and the result is valid.
[0034] Example 4 One embodiment of the plastic stress whitening assessment method of the present invention differs from Embodiment 1 only in that the light source is a 4000K color temperature light source, resulting in the fitting formula y = 0.9706e 1.7992x R 2 =0.9975. When evaluating the results, the normalized curve prediction obtained by this embodiment has an error rate of 13.6% when the gray value is 1.72 and the deformation is 31.8%, which does not exceed 15%, so the result is valid.
[0035] Example 5 One embodiment of the plastic stress whitening assessment method of the present invention differs from Embodiment 1 only in that the light source is a 2000K color temperature light source, resulting in the fitting formula y = 0.9691e 1.8099x R 2 =0.9971. When evaluating, the normalized curve prediction obtained in this embodiment, with a gray value of 1.72 and a deformation of 31.7%, has an error rate of 13.2%, which does not exceed 15%, and the result is valid.
[0036] Comparative Example 1 A method for assessing stress whitening in plastics, differing from Example 1 only in that the light source is positioned at a straight-line distance of 0.5m from the plastic sample under test. During the assessment, if... Figure 5As shown, the excessive concentration of the light source and the reflection of light by the spline cause both the initial local "whitening" and the "whitening" as the light source moves during the stretching process. This results in many overlapping values when the acquired image is converted to grayscale mode, making it impossible to construct a curve to evaluate the stress whitening.
[0037] Comparative Example 2 A method for assessing stress whitening in plastics, differing from Example 1 only in that the light source is positioned at a straight-line distance of 1.5m from the plastic sample under test. During the assessment, if... Figure 6 As shown, the acquired images cannot identify the changes in the whiteness of the sample surface during the stretching process, resulting in many overlapping values when converting the acquired images to grayscale mode, making it impossible to construct a curve to evaluate stress whitening.
[0038] Comparative Example 3 A method for evaluating stress whitening in plastics, differing from Example 3 only in that the image acquisition frequency is 20 FPS, yielding the fitting formula y = 0.9308e 1.4983x R 2 =0.9819. When conducting the evaluation, the normalized curve prediction obtained by this scheme, when the gray value is 1.74, has a deformation of 41.7% and an error rate of 22.8%, which exceeds 15%. The evaluation result has too large an error and the evaluation is invalid.
[0039] Comparative Example 4 A method for evaluating stress whitening in plastics, differing from Example 1 only in that the grayscale image has a grayscale level of 128 and an 8-bit format, yielding the fitting formula y = 0.9618e 1.6613x R 2 =0.9945; When conducting the evaluation, the normalized curve prediction obtained by this scheme, when the gray value is 1.72 and the deformation is 35.0%, has an error rate of 24.9%, which exceeds 15%. The evaluation result has too large an error and the evaluation is invalid.
[0040] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the scope of protection of the present invention. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the essence and scope of the technical solutions of the present invention.
Claims
1. A method for evaluating stress whitening in plastics, characterized in that, Includes the following steps: (1) The plastic sample to be tested is subjected to a tensile test until the plastic sample breaks. At the same time, a camera is used to continuously capture video images of the plastic sample under the illumination of a light source. The shooting frequency during image acquisition is ≥30FPS. The straight-line distance between the light source and the plastic sample to be tested is 0.8~1.2m. (2) Convert the acquired image into a grayscale image, and calculate the average grayscale value of the pixels in the image. Construct a curve using the average grayscale value of the pixels in the grayscale image of the plastic sample under different deformations and perform normalization processing to obtain a normalized curve; the grayscale image has a gray level ≥ 256. (3) The stress whitening of the plastic sample under test is evaluated by normalization curve.
2. The method for evaluating stress whitening in plastics as described in claim 1, characterized in that, The plastic sample to be tested was a black sample.
3. The method for evaluating stress whitening of plastics as described in claim 1, characterized in that, In step (1), the tensile test of the plastic sample to be tested is carried out in accordance with ISO 527-1-2012, and the tensile rate is 45~55mm / min.
4. The method for evaluating stress whitening of plastics as described in claim 1, characterized in that, In step (1), when the camera is acquiring continuous video images, the pixel area of the image is 300×240, and the white area of the plastic sample to be tested is set in the central area of the image.
5. The method for evaluating stress whitening in plastics as described in claim 1, characterized in that, Step (1) includes acquiring images of the plastic sample under at least 6 different deformations, wherein the at least 6 deformations are uniformly distributed between 0% and the maximum deformation.
6. The method for evaluating stress whitening of plastics as described in claim 1, characterized in that, The deformation of the plastic sample under test during the tensile test is ≥0; preferably, the deformation of the plastic sample under test during the tensile test is 0~60%.
7. The method for evaluating stress whitening in plastics as described in claim 1, characterized in that, In step (2), the grayscale image is in 8-bit format.
8. The method for evaluating stress whitening of plastics as described in claim 1, characterized in that, In step (2), the normalization process is as follows: the average gray value A0 of the pixels in the grayscale mode image of the plastic sample under test when the deformation is 0 is taken as the reference point, and the average gray value A1 of the pixels in the grayscale mode image of the plastic sample under test when the deformation is 0 is calculated using the formula Ax=A1 / A0. The normalized gray value and the deformation of the plastic sample under test are used to construct a curve and fit it.