A method of predicting surface waviness of cold rolled sheet for automotive use

Abstract

The application provides a method for predicting the surface waviness of a cold-rolled sheet for automobiles, and belongs to the technical field of surface quality detection of the cold-rolled sheet for automobiles. A wire cutting device is used to cut the cold-rolled sheet for automobiles into a plurality of square samples with preset sizes, and speckles are prepared on the surface of one sample as a first sample, and the rest of the samples are samples without prepared speckles; a plate forming testing machine is used to continuously load the first sample, the first sample is gradually deep-drawn from a plane to a cup shape, and the loading is ended until the first sample appears failure characteristics; in the loading process, a digital image correlation measurement system is used to continuously collect the surface images of the first sample at a preset frequency, a strain-displacement fitting formula is obtained by combining the synchronous data, and a target displacement corresponding to a preset strain is determined; the samples without prepared speckles are respectively punched to the target displacement, the waviness of the cup-shaped samples is measured, and a strain-waviness mapping relationship model is fitted, so that the accurate prediction of the surface waviness of the cold-rolled sheet is realized.

Description

Technical Field

[0001] This invention relates to the field of surface quality inspection technology for cold-rolled sheets used in automobiles, and in particular to a method for predicting the surface waviness of cold-rolled sheets used in automobiles. Background Technology

[0002] Cold-rolled steel sheets for automobiles are widely used in the manufacture of key structural components such as car bodies and chassis due to their high strength, good formability, and excellent surface quality. Surface waviness, as an important indicator for evaluating the microstructure of the sheet surface, directly affects the appearance quality of automotive cold-rolled steel sheets after painting.

[0003] Currently, there are significant limitations in the measurement and prediction techniques for surface waviness of cold-rolled sheet metal used in automobiles:

[0004] Data acquisition is inefficient and costly. While widely used digital image correlation (DIC) measurement systems can acquire strain data, they cannot directly establish a real-time mapping relationship between strain and displacement or waviness. To determine the displacement corresponding to a specific strain, repeated physical stamping tests are still required, resulting in low production efficiency and high testing costs.

[0005] The lack of a predictive model results in insufficient accuracy. Existing methods struggle to construct a quantitative model of the relationship between real-time strain during the forming process and the final surface waviness. This leads to a lack of reliable theoretical and data support for the prediction results, resulting in poor accuracy and reliability.

[0006] Therefore, a large number of products with unqualified surface waviness occurred during the production process, which not only increased manufacturing costs but also extended the product development cycle, failing to meet the urgent needs of the automotive industry for efficient and precise control of the surface quality of cold-rolled thin plates.

[0007] Therefore, there is an urgent need for a method to predict the surface waviness of cold-rolled sheet metal for automobiles. Summary of the Invention

[0008] In view of this, the present invention provides a method for predicting the surface waviness of cold-rolled sheet metal for automobiles. Based on digital image correlation method, it deeply integrates real-time strain during the forming process and directly constructs a strain-waviness mapping relationship model, thereby achieving direct and accurate prediction of the surface waviness of cold-rolled sheet metal for automobiles.

[0009] Therefore, the present invention provides the following technical solution:

[0010] A method for predicting surface waviness of cold-rolled sheet metal for automotive applications includes:

[0011] Using a wire cutting machine, several square samples of a preset size are cut from cold-rolled sheet metal for automobiles. Scattered spots are prepared on the surface of one of the samples as the first sample, and the remaining samples are samples without scattered spots.

[0012] The first specimen is continuously loaded using a sheet metal forming testing machine. The first specimen is gradually drawn from a flat surface into a cup shape until the first specimen shows failure characteristics, at which point the loading ends. During the loading process, the surface images of the first specimen are continuously acquired at a preset frequency using a digital image correlation measurement system, and the strain-displacement fitting formula is obtained.

[0013] Based on the strain-displacement fitting formula, the target displacement corresponding to the preset strain is determined;

[0014] Unprepared speckle samples were stamped to the target displacement to obtain cup-shaped samples corresponding to the target displacement, and the waviness of the cup-shaped samples was measured.

[0015] A strain-wrinkle mapping model is fitted based on the preset strain and the wrinkle of the cup-shaped sample; the surface wrinkle of cold-rolled sheet for automobiles is predicted based on the preset strain using the strain-wrinkle mapping model.

[0016] Furthermore, the strain-displacement fitting formula is as follows:

[0017]

[0018] in: Displacement, in mm; Indicates the dependent variable; , c are the fitting coefficients.

[0019] Furthermore, the strain-ripple mapping model is as follows:

[0020]

[0021] in, Indicates waviness, unit ; The variable represents the dependent variable; A, B, and C represent the fitting coefficients.

[0022] Further, the preparation of speckle patterns on the surface of one of the samples includes:

[0023] First, spray a layer of white primer evenly, blow it dry, and then spray a layer of black matte paint to form evenly distributed random spots.

[0024] Further, the step of continuously acquiring images of the first sample surface at a preset frequency using a digital image correlation measurement system and obtaining a strain-displacement fitting formula includes:

[0025] The acquired images are analyzed using a digital image correlation measurement system to calculate the displacement and strain changes in the planar region of the first sample, thereby obtaining the displacement and strain when the cold-rolled sheet for automobiles breaks. The digital image correlation measurement system and the sheet forming test machine are set to synchronize time, and simultaneously output time-strain curves and time-displacement curves.

[0026] Based on the time-strain curve and the time-displacement curve, the strain-displacement curve is obtained;

[0027] Based on the strain-displacement curve, a strain-displacement fitting formula is obtained.

[0028] Furthermore, the failure characteristics include:

[0029] The sample showed signs of cracking or necking.

[0030] Advantages and positive effects of the present invention:

[0031] By using the DIC measurement system and the sheet metal forming test machine for time synchronization control, real-time status monitoring of the deformation process of cold-rolled thin sheets for automobiles was achieved, and key deformation parameters such as strain and displacement were accurately obtained.

[0032] Only one speckle specimen test is required, and subsequent waviness measurements can be directly calculated based on strain to determine the target displacement, reducing the number of tests by more than 60% and significantly improving test efficiency.

[0033] The standardized and regulated operating procedures facilitate engineering applications and can effectively support surface quality control in cold-rolled sheet stamping in the automotive manufacturing industry, demonstrating significant engineering application value. Attached Figure Description

[0034] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0035] Figure 1 This is a schematic diagram of the speckle pattern on the surface of the initial undeformed sample acquired by the DIC measurement system in Embodiment 1 of the present invention;

[0036] Figure 2 This is a schematic diagram of speckle patterns on the surface of a cup-shaped sample acquired by the DIC measurement system in Embodiment 1 of the present invention.

[0037] Figure 3 This is a schematic diagram of the fracture state of the cup-shaped sample in Embodiment 1 of the present invention;

[0038] Figure 4 This is an example of a time-strain curve in Embodiment 1 of the present invention;

[0039] Figure 5 This is the time-displacement curve in Embodiment 1 of the present invention;

[0040] Figure 6 This is the strain-displacement fitting curve in Embodiment 1 of the present invention;

[0041] Figure 7 This is the strain-wrinkle relationship curve in Embodiment 1 of the present invention;

[0042] Figure 8 This is the strain-displacement fitting curve in Embodiment 2 of the present invention;

[0043] Figure 9 This is the strain-wrinkle relationship curve in Embodiment 2 of the present invention;

[0044] Figure 10 Flowchart of a method for predicting surface waviness in cold-rolled sheet metal for automotive applications;

[0045] Figure 11 The images show the stamping effects of samples 3, 4, and 5 in Example 1 of this invention. Detailed Implementation

[0046] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0047] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0048] This invention provides a method for predicting the surface waviness of cold-rolled sheet metal for automotive applications. It utilizes a DIC measurement system to monitor displacement and strain changes, and a sheet metal forming test machine to conduct stamping tests. These two systems work together to achieve real-time monitoring and data acquisition of the stamping process. By fusing and analyzing the acquired time-displacement and time-strain data, the intrinsic relationship between strain and displacement is revealed. Furthermore, combined with subsequent waviness measurement results, a strain-waviness curve is established through fitting, thus establishing a strain-waviness mapping model. Figure 10 As shown, this method includes:

[0049] S1. Prepare the test sample.

[0050] 1) Use wire cutting equipment to cut automotive cold-rolled sheet into square samples of a preset size. The size is 220mm × 220mm.

[0051] 2) Prepare speckle patterns on the surface of one of the samples. Preferably, first spray a layer of white primer evenly, blow it dry, and then spray a layer of black matte paint to form evenly distributed random speckle patterns.

[0052] 3) Keep the remaining samples clean and ready for use.

[0053] S2. Debugging and testing equipment.

[0054] 1) Set the sheet metal forming test machine and the DIC measurement system to be connected synchronously.

[0055] 2) Set device parameters:

[0056] This embodiment includes: a preset loading speed and maximum blank holder force of the sheet metal forming test machine; and a preset image acquisition frequency of the DIC measurement system.

[0057] S3. Establish the strain-displacement fitting formula.

[0058] 1) Reference images of the prepared speckled sample were acquired using the DIC measurement system.

[0059] 2) The prepared speckled specimens are loaded using a sheet metal forming tester until the specimens crack or experience severe necking.

[0060] 3) Using a DIC measurement system, acquire speckle images of the deformed surface of the specimen during the forming process, obtain displacement and strain data for the planar region of the prepared speckle specimen, and obtain time-strain and time-displacement curves, such as... Figure 4 and Figure 5 As shown, the time-strain curve and time-displacement curve data are fitted to obtain the strain-displacement curve.

[0061] 4) Based on this curve data, establish the strain-displacement fitting formula for the specimen:

[0062]

[0063] Where: H represents displacement in mm; ε represents the strain; and a, b, and c are fitting coefficients.

[0064] S4. Collect ripple data.

[0065] The target displacement is determined based on the strain-displacement fitting formula and the preset strain.

[0066] Based on the target displacement value, the stamping test was repeated on the specimen without speckle.

[0067] A contact profilometer was used to measure the waviness parameters in the planar area of ​​the formed sample. In this embodiment, the measurement parameters were: measurement speed 0.5 mm / s, detection distance 30 mm.

[0068] Record the waviness measurement value corresponding to the preset strain.

[0069] S5. Construct a strain-wrinkle mapping model based on the collected wrinkle measurement data:

[0070]

[0071] in, Indicates waviness, unit ; The variable represents the dependent variable; A, B, and C represent the fitting coefficients.

[0072] Example 1

[0073] A method for predicting surface waviness of cold-rolled sheet metal for automotive applications includes:

[0074] Step 1: Sample preparation.

[0075] 1) Prepare 5 sheets of DC54D+Z automotive cold-rolled sheet and cut them into square specimens of 220mm × 220mm using wire cutting equipment. Number them 1 to 5. Inspect the surface of the specimens to ensure there are no obvious defects or scratches. Wipe the surface of the specimens with anhydrous ethanol and a lint-free cloth to remove oil and dust.

[0076] 2) Prepare speckle patterns on the surface of sample 1: First, uniformly spray a layer of white primer and dry it with a hair dryer, then spray black matte paint to form speckle patterns. Place the sample 1 with prepared speckle patterns on the stamping die of the sheet metal forming test machine, ensuring that the sample is centered and fixed to avoid movement during the test.

[0077] Step 2: Debug the equipment and set the parameters.

[0078] 1) Turn on the DIC measurement system and adjust the camera position so that it can clearly capture and identify the speckle pattern on the surface of sample 1.

[0079] 2) Set the image acquisition frequency. Set the loading speed of the sheet metal forming test machine to 1.5 mm / s, and the maximum blank holder force to 300 kN, etc.

[0080] 3) After completing the parameter settings, connect the sheet metal forming test machine to the DIC measurement system synchronously.

[0081] Step 3: Establish the strain-displacement curve and fitting formula.

[0082] 1) such as Figure 1 As shown, the surface image of the undeformed sample 1 was acquired using the DIC measurement system and used as the reference image for analysis.

[0083] 2) Start the sheet metal forming testing machine and apply different degrees of loading deformation to specimen 1 through the stamping die. During the loading process, specimen 1 is gradually drawn from a flat surface into a cup shape, obtaining cup-shaped specimen 1. The DIC measurement system acquires deformation images of the surface of the cup-shaped specimen 1 in real time, such as... Figure 2 As shown, the state of speckle at different deformation stages is recorded. Figure 3 As shown, when obvious failure characteristics such as cracking or severe necking are observed in cup-shaped sample 1, the loading of the sheet metal forming machine and the image acquisition of the DIC measurement system are stopped simultaneously.

[0084] 3) Using the DIC measurement system software to analyze the acquired images, displacement and strain data of the planar region of the cup-shaped sample 1 were obtained, and the time-strain curve of the DC54D+Z automotive cold-rolled sheet was obtained. Figure 4 ) and time-displacement curves ( Figure 5 ).

[0085] Since the sheet metal forming testing machine and the DIC measurement system are synchronously connected, their time intervals are the same. The time-strain and time-displacement curves are fitted to obtain the strain-displacement curve of sample 1, as shown below. Figure 6 As shown in the figure. This curve allows us to find the corresponding displacement based on the strain.

[0086] 4) Based on the strain-displacement curves and data of specimen 1, establish the strain-displacement fitting formula for DC54D+Z:

[0087]

[0088] Where: H represents displacement, in mm; ε represents strain.

[0089] Step 4: Measure the waviness data.

[0090] 1) Measurement of waviness benchmark for undeformed specimens: A DC54D+Z automotive cold-rolled sheet specimen (numbered 2) from the same batch, measuring 220mm × 220mm, without speckle preparation and without any stamping deformation, was selected. After wiping the surface with anhydrous ethanol and a lint-free cloth, the waviness of its planar area was measured using a contact profilometer. The measurement parameters were kept consistent with those for subsequent deformed specimens: measurement speed 0.5mm / s, detection distance 30mm, repeated measurement 3 times, and the waviness measurement value corresponding to the undeformed state (strain ε=0%) was recorded.

[0091] 2) Based on the strain-displacement fitting formula obtained in step 3, calculate the corresponding target displacements based on the preset strain (3%, 5%, 8%), which are 13.41mm, 18.34mm, and 24.46mm, respectively.

[0092] 3) Based on the target displacement value, the stamping test was repeated on specimens 3, 4, and 5 without speckle patterns (the stamping effect is as follows). Figure 11 As shown in the figure, ensure that the equipment parameters (loading speed, blank holder force, etc.) for each test are consistent with those in step 3.

[0093] 4) Using the same contact profilometer and measurement parameters as the undeformed sample 5 (measurement speed 0.5 mm / s, detection distance 30 mm), measure the waviness in the planar area of ​​the formed samples 3, 4, and 5. Each sample is measured three times, and the waviness measurement value corresponding to each preset strain is recorded.

[0094] Step 5: Construct a strain-ripple mapping model.

[0095] 1) The waviness measurement data of both undeformed and deformed specimens are integrated, as shown in Table 1 below:

[0096] Table 1. Waviness measurement data of DC54D+Z specimens

[0097]

[0098] 2) Based on the data in Table 1, the fitted curve is as follows: Figure 7 As shown, a strain-ripple mapping model is constructed by fitting using the least squares method:

[0099]

[0100] in: ε represents the waviness, in μm; ε represents the strain.

[0101] By constructing a strain-wrinkle mapping model, the wrinkle of the sheet material under different deformation amounts can be predicted, enabling accurate prediction of the surface quality of the sheet material under different deformation degrees.

[0102] Example 2

[0103] This embodiment uses automotive cold-rolled sheet material of grade DC53D+Z as an example to verify the effectiveness of the strain-displacement fitting formula and strain-wrinkle mapping relationship model proposed in this invention.

[0104] Step 1: Sample preparation.

[0105] 1) Prepare 7 sheets of DC53D+Z automotive cold-rolled sheet and cut them into square specimens of 220mm×220mm using wire cutting equipment. Number them 1 to 7. Inspect the surface of the specimens to ensure there are no obvious defects or scratches. Wipe the surface of the specimens with anhydrous ethanol and a lint-free cloth to remove oil and dust.

[0106] 2) Prepare speckle patterns on the surface of sample 1: First, uniformly spray a layer of white primer and dry it with a hair dryer, then spray black matte paint to form speckle patterns. Place the sample 1 with prepared speckle patterns on the stamping die of the sheet metal forming test machine, ensuring that the sample is centered and fixed to avoid movement during the test.

[0107] Step 2: Equipment debugging and parameter setting.

[0108] 1) Turn on the DIC measurement system and adjust the camera position so that it can clearly capture and identify the speckle pattern on the surface of sample 1.

[0109] 2) Set the image acquisition frequency. Set the loading speed of the sheet metal forming test machine to 1.5 mm / s, and the maximum blank holder force to 300 kN, etc.

[0110] 3) After completing the parameter settings, connect the sheet metal forming test machine to the DIC measurement system synchronously.

[0111] Step 3: Construct strain-displacement curves and fitting formulas.

[0112] 1) Use the DIC measurement system to acquire surface images of undeformed sample 1 as the reference images for analysis.

[0113] 2) Start the sheet metal forming testing machine and apply different degrees of loading deformation to specimen 1 through the stamping die. During the loading process, specimen 1 is gradually drawn from a flat surface into a cup shape, obtaining cup-shaped specimen 1. The DIC measurement system acquires deformation images of the surface of cup-shaped specimen 1 in real time and records the state of speckle at different deformation stages. When obvious failure characteristics such as cracking or severe necking are observed in cup-shaped specimen 1, the loading of the sheet metal forming machine and the image acquisition of the DIC measurement system are stopped simultaneously.

[0114] 3) Analyze the acquired images using the DIC measurement system software, calculate the displacement and strain data of the planar region of the cup-shaped sample 1, and obtain the time-strain curve and time-displacement curve of the DC53D+Z automotive cold-rolled sheet.

[0115] 4) Fit the time-strain curve and time-displacement curve data to obtain the strain-displacement curve of sample 1, as shown below. Figure 8 As shown.

[0116] 5) Based on the strain-displacement curve and data of specimen 1, the strain-displacement fitting formula of DC53D+Z is established: H=108.672×ε 0.596 .

[0117] Where: H represents displacement, in mm; ε represents strain.

[0118] Step 4: Measure the waviness data.

[0119] 1) Measurement of waviness benchmark for undeformed specimens: A DC53D+Z automotive cold-rolled sheet specimen (numbered 2) from the same batch, measuring 220mm × 220mm, without speckle preparation and without any stamping deformation, was selected. After wiping the surface with anhydrous ethanol and a lint-free cloth, the waviness of its planar area was measured using a contact profilometer. The measurement parameters were kept consistent with those for subsequent deformed specimens: measurement speed 0.5mm / s, detection distance 30mm, repeated measurement 3 times, and the waviness measurement value corresponding to the undeformed state (strain ε=0%) was recorded.

[0120] 2) Based on the strain-displacement fitting formula obtained in step 3, calculate the corresponding target displacements based on the preset strain (3%, 5%, 8%), which are 13.44mm, 18.23mm, and 24.12mm, respectively.

[0121] 3) Based on the target displacement value, repeat the stamping test on specimens 3, 4, and 5 without speckle preparation, and ensure that the equipment parameters (loading speed, blank holder force, etc.) of each test are consistent with those in step 3.

[0122] 4) Using the same contact profilometer and measurement parameters as the undeformed sample 2 (measurement speed 0.5 mm / s, detection distance 30 mm), measure the waviness in the planar areas of the formed samples 3, 4, and 5. Each sample is measured three times, and the waviness measurement value corresponding to each preset strain is recorded.

[0123] Step 5: Construct a strain-ripple mapping model.

[0124] 1) The waviness measurement data of both undeformed and deformed specimens are integrated, as shown in Table 2 below:

[0125] Table 2. Waviness measurement data of DC53D+Z specimens

[0126]

[0127] 2) Based on the data in Table 2, the fitted curve is as follows: Figure 9 As shown, a strain-ripple mapping model is constructed by fitting using the least squares method:

[0128]

[0129] Among them: W sa1-5 ε represents the waviness, in μm; ε represents the strain.

[0130] Step 6: Verify the validity of the theoretical formula.

[0131] We selected 4% and 6% as two dependent variables for verification.

[0132] 1) Calculate the theoretical displacement values ​​corresponding to 4% and 6% strain using the strain-displacement fitting formula:

[0133] When ε=4% (ε=0.04), H=108.672× =15.96mm When ε=6% (ε=0.06), H=108.672× =20.32mm

[0134] 2) Take unprepared speckled specimens 6 and 7, and repeat the loading process of step 3 on the plate forming tester, but set the displacement to the above calculated values ​​(15.96 mm and 20.32 mm) respectively, so that specimens 6 and 7 are stamped into cup-shaped specimens of corresponding heights.

[0135] 3) Waviness measurement and verification:

[0136] A contact profilometer was used to measure the planar areas of the formed specimens 6 and 7 (measurement speed 0.5 mm / s, detection distance 30 mm).

[0137] Table 3. Actual waviness W corresponding to 4% and 6% sa1-5 Measured values

[0138]

[0139] Meanwhile, based on the established strain-ripple mapping model:

[0140]

[0141] Calculate the theoretical waviness value:

[0142] Table 4 Comparison of theoretical predictions and measured values

[0143]

[0144] The displacement prediction errors are all less than 0.5%, indicating that the strain-displacement fitting formula has high prediction accuracy; the waviness prediction errors are all less than 1%, proving that the strain-waviness mapping relationship model can accurately predict the surface waviness under different strain variables; the errors at both verification points remain at a low level.

[0145] Example 2, through verification experiments with two strain variables of 4% and 6%, demonstrates that the method has good applicability and reliability under different strain conditions. By constructing a strain-wrinkle mapping model, the wrinkle of the sheet metal under different deformation amounts is predicted, significantly improving the accuracy and reliability of the prediction results. This meets the requirements for surface quality control of cold-rolled thin sheets in automobile production, reduces the generation of defective products, lowers production costs, and improves production efficiency.

[0146] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting the surface waviness of cold-rolled sheet metal for automobiles, characterized in that, include: Using a wire cutting machine, several square samples of a preset size are cut from cold-rolled sheet metal for automobiles. Scattered spots are prepared on the surface of one of the samples as the first sample, and the remaining samples are samples without scattered spots. The first specimen is continuously loaded using a sheet metal forming tester. The first specimen is gradually drawn from a flat surface into a cup shape until the first specimen shows failure characteristics, at which point the loading ends. During the loading process, images of the first sample surface are continuously acquired at a preset frequency using a digital image correlation measurement system, and strain-displacement fitting formulas are obtained. Based on the strain-displacement fitting formula, the target displacement corresponding to the preset strain is determined; Unprepared speckle samples were stamped to the target displacement to obtain cup-shaped samples corresponding to the target displacement, and the waviness of the cup-shaped samples was measured. A strain-wrinkle mapping model is fitted based on the preset strain and the wrinkle of the cup-shaped sample; the surface wrinkle of cold-rolled sheet for automobiles is predicted based on the preset strain using the strain-wrinkle mapping model.

2. The method according to claim 1, characterized in that, The strain-displacement fitting formula is as follows: in: Displacement, in mm; Indicates the dependent variable; , c are the fitting coefficients.

3. The method according to claim 1, characterized in that, The strain-wrinkle mapping model: in, Indicates waviness, unit ; The variable represents the dependent variable; A, B, and C represent the fitting coefficients.

4. The method according to claim 1, characterized in that, The preparation of speckle patterns on the surface of one of the samples includes: First, spray a layer of white primer evenly, blow it dry, and then spray a layer of black matte paint to form evenly distributed random spots.

5. The method according to claim 1, characterized in that, The step of continuously acquiring images of the first sample surface at a preset frequency using a digital image correlation measurement system and obtaining a strain-displacement fitting formula includes: The acquired images are analyzed using a digital image correlation measurement system to calculate the displacement and strain changes in the planar region of the first sample, thereby obtaining the displacement and strain when the cold-rolled sheet for automobiles breaks. The digital image correlation measurement system and the sheet forming test machine are set to synchronize time, and simultaneously output time-strain curves and time-displacement curves. Based on the time-strain curve and the time-displacement curve, the strain-displacement curve is obtained; Based on the strain-displacement curve, a strain-displacement fitting formula is obtained.

6. The method according to claim 1, characterized in that, The failure characteristics include: The sample showed signs of cracking or necking.

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