A sample pretreatment and test integration method for a plate charpy impact experiment

Abstract

This invention proposes an integrated method for sample pretreatment and testing in the Charpy impact test of sheet metal, comprising the following steps: S1: Cutting the thin sheet metal to be tested into rectangular blanks, removing burrs and cleaning surface oil stains; S2: Using an adaptive stacking algorithm, automatically calculating the required number of stacking layers and the optimal stacking order based on the input nominal thickness and thickness tolerance of a single sheet metal, maximizing the total thickness of the stacked sheets; S3: Sequentially placing the blanks with the calculated number of layers into an automatic alignment device, achieving complete alignment of the center lines and end faces of each layer of blanks through laser positioning and a precision clamping mechanism. This invention achieves interlayer metallurgical bonding through dynamic thermal simulation pressing technology, with a bonding strength greater than or equal to the strength of the parent material. During impact across the entire temperature range, the sample exhibits no interlayer separation, resulting in a higher test success rate compared to traditional methods and solving the problem of low-temperature delamination of thin sheet metal samples.

Description

Technical Field

[0001] This invention relates to the field of material mechanical property testing technology, and in particular to an integrated method for sample pretreatment and testing of plate Charpy impact test. Background Technology

[0002] The Charpy impact test is the most commonly used standard test method for determining the ductile-brittle transition temperature of metallic materials and evaluating their low-temperature toughness and impact resistance. It is widely used in key fields such as aerospace, shipbuilding, pressure vessels, nuclear power engineering, and the automotive industry. However, for thin sheets with a thickness of <2.5 mm, it is impossible to directly fabricate standard-sized monolithic specimens. Current technology generally employs a stacking method, where multiple layers of thin sheets are stacked to a total thickness of approximately 10 mm, mechanically fixed by binding with stainless steel wire, wrapping with high-temperature resistant tape, or bonding with epoxy resin, and then a V-notch is fabricated for impact testing. This method has the following technical drawbacks: Samples are prone to scattering during low-temperature impact: binding straps, tapes, and adhesives undergo severe embrittlement failure at low temperatures, causing lamination and fragmentation during impact, rendering test results completely invalid. This problem is particularly pronounced at liquid nitrogen temperatures, resulting in insufficient success rates for traditional methods and failing to meet the requirements for evaluating the performance of low-temperature materials.

[0003] The notch alignment accuracy is extremely poor: manual stacking cannot guarantee that the center lines and end faces of each layer of board are completely aligned. After notch processing, the notch depth deviation between layers can reach more than 0.2mm, far exceeding the standard allowable deviation. The increase in notch depth will reduce the impact energy test results, leading to increased dispersion of impact energy test results using traditional methods. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to overcome the defects of the existing technology. The present invention proposes an integrated method for sample pretreatment and testing of plate Charpy impact test.

[0005] To solve the aforementioned technical problems, the technical solution adopted by the present invention is as follows: An integrated method for sample pretreatment and testing in Charpy impact testing of sheet metal includes the following steps: S1: Cut the thin sheet material to be tested into a rectangular blank, remove burrs and clean the surface oil; S2: Using an adaptive stacking algorithm, based on the nominal thickness of the input single sheet material... and thickness tolerance Automatically calculate the required number of stacking layers And the optimal stacking order to maximize the total thickness of the stack. ; S3: Place the blanks with the calculated number of layers into the automatic alignment device in sequence, and achieve complete alignment of the center line and end face of each layer of blanks through laser positioning and precision clamping mechanism; S4: The aligned stacked blanks are sent into a dynamic hot simulation pressing device and hot-pressed under a protective atmosphere to achieve interlayer metallurgical bonding and form an integral stacked blank. S5: Transfer the pressed laminated blanks to a CNC milling machine to machine a V-shaped notch; S6: The notched sample is sent into the modular multi-temperature zone pretreatment chamber and kept at a constant temperature according to the preset test temperature. The internal temperature of the sample is monitored in real time by an implanted micro thermocouple. S7: After the sample reaches the set temperature and is kept at that temperature for a sufficient time, the sample is transferred to the impact testing machine station through a vacuum isolation transfer system. S8: The impact testing machine automatically completes the centering, clamping and impact of the specimen, and collects data of the entire impact process through a high-frequency force sensor and a high-speed camera. S9: After the impact is completed, the fracture sample is transferred to a 3D laser scanning system, and the fiber cross-sectional area, lateral expansion and crack length are automatically calculated by deep learning algorithm; S10: Automatically integrates data from the entire process to generate a test report that meets standards.

[0006] Preferably, the adaptive stacking algorithm described in step S2 is calculated using the following formula: S21: Theoretical number of layers calculation formula: in, The theoretical number of layers, The nominal thickness is for a single sheet of material. This is the rounding function; S22: Determining the set of candidate layers: S23: Formula for calculating total thickness deviation: in, for Total thickness deviation of the stacked layers For the first The actual measured thickness of the blank; S24: Optimal layer selection: S25: Determining the optimal stacking combination: Using a 0-1 knapsack dynamic programming algorithm, select from all available blanks. To minimize the total thickness deviation, the state transition equation is: in, In the past Select several sheets of raw material with a total thickness of Minimum deviation at time, For the first The thickness of the blank.

[0007] Preferably, the dynamic thermal simulation pressing process described in step S4 satisfies the following conditions: The heating rate is 5℃ / s to 15℃ / s, and the cooling rate is 10℃ / s to 30℃ / s; The protective atmosphere during the pressing process is argon, with an oxygen content of [missing information]. ; For austenitic stainless steel: pressing temperature 950℃~1050℃, pressure 50MPa~80MPa, holding time 5min~10min; For aluminum alloys: pressing temperature 450℃~500℃, pressure 30MPa~50MPa, holding time 3min~8min; For titanium alloys: pressing temperature 850℃~900℃, pressure 60MPa~90MPa, holding time 8min~15min.

[0008] Preferably, the automatic alignment device described in step S3 includes a high-precision marble worktable, two sets of vertically arranged laser displacement sensors, a four-axis precision clamping robot, and an end-face positioning stop; wherein, the flatness of the marble worktable is... Laser displacement sensor measurement accuracy Four-axis precision clamping robot with high repeatability verticality of end face positioning block .

[0009] Preferably, the multi-temperature zone intelligent preprocessing described in step S6 calculates the heat preservation time using the lumped parameter method: in, Total insulation time, To preset the basic insulation time, The density of the sample, The specific heat capacity at constant pressure of the sample. The volume of the sample is 1. The convective heat transfer coefficient is... The surface area of ​​the sample. To set the temperature, The initial temperature of the sample. The target temperature for the sample. ; When the implanted miniature thermocouple detects that the sample center temperature reaches Then, continue to keep warm. time.

[0010] Preferably, the vacuum-isolated transfer system described in step S7 includes a vacuum-sealed channel, a thermostatic clamping robot, and an infrared temperature sensor at the impact station; wherein, the vacuum degree of the vacuum-sealed channel is ≤ The temperature deviation between the gripping part of the thermostatic clamping robot and the sample temperature is ≤ The infrared temperature sensor at the impact station confirms the surface temperature of the sample 0.1 seconds before impact. If the temperature deviation exceeds ±2℃, it automatically returns to the pretreatment chamber for reheating.

[0011] Preferably, during the automatic impact and data acquisition process described in step S8, the force-time data acquired by the high-frequency force sensor is converted into a force-displacement curve using the following formula: in, For instantaneous impact force, For force sensor calibration coefficients, The sensor output voltage, The instantaneous velocity of the pendulum, The initial velocity of the pendulum before impact. The total mass of the pendulum system, This represents the pendulum displacement.

[0012] Preferably, in the automatic fracture analysis described in step S9, the fiber cross-sectional area ratio is calculated using the following formula: in, For fiber cross-sectional area, The area of ​​the fiber region on the fracture surface. This represents the total area of ​​the fracture surface; and The 3D point cloud data of the fracture obtained by the 3D laser scanning system is automatically identified and calculated using the U-Net deep learning semantic segmentation algorithm.

[0013] Preferably, in the data processing and report generation process described in step S10, consortium blockchain technology is used to encrypt and store the data throughout the entire process. Each sample generates a unique SHA-256 hash identifier, which includes sample preparation parameters, pretreatment temperature and time curves, impact process data, and fracture analysis results, ensuring that the data is tamper-proof and traceable.

[0014] An integrated system for sample pretreatment and testing in a Charpy impact test of sheet metal is provided for performing the aforementioned integrated method for sample pretreatment and testing in a Charpy impact test of sheet metal. The system specifically includes the following functional units: The sheet metal blank preparation unit includes a CNC shearing machine, a deburring workstation, an ultrasonic cleaner, and a compressed air dryer, which are connected in sequence. Adaptive stacking computing and control unit: including a high-precision micrometer, an industrial computer and a stacking robot, with the industrial computer having a built-in adaptive stacking algorithm; Automatic alignment and positioning unit: includes a high-precision marble worktable, two sets of vertically arranged laser displacement sensors, a four-axis precision clamping robot, and an end face positioning block; Dynamic thermal simulation pressing unit: includes a vacuum chamber, induction heating system, hydraulic pressurization system and atmosphere control system; Standard notch machining unit: includes a five-axis CNC milling machine, carbide forming milling cutters, and an online laser inspection probe; Modular multi-temperature zone pretreatment unit: includes at least 6 independently temperature-controlled pretreatment chambers, an embedded micro thermocouple array, and a temperature control system; Vacuum-isolated automatic transfer unit: includes a vacuum-sealed channel, a thermostatic clamping manipulator, a linear motor drive mechanism, and an infrared temperature sensor for the impact station; Automatic impact and data acquisition unit: including pendulum impact testing machine, 1MHz high-frequency force sensor, 10000fps high-speed camera and sample centering mechanism; Automatic fracture analysis unit: includes a 3D laser scanning system, an industrial computer, and a U-Net deep learning semantic segmentation model; Central control and data processing unit: includes industrial PLC controller, touch screen, data server and blockchain encryption module, and is electrically connected to all system units.

[0015] Compared with the prior art, the beneficial effects of the present invention are: This invention achieves interlayer metallurgical bonding through dynamic thermal simulation pressing technology, with a bonding strength greater than or equal to the strength of the parent material. The sample does not exhibit interlayer separation under impact across the entire temperature range, improving the test success rate compared to traditional methods and solving the problem of low-temperature delamination in thin plate samples.

[0016] This invention employs an automatic alignment device with laser positioning and precision clamping, achieving an alignment error of ≤0.01mm for the center lines of each layer and a notch depth deviation of ≤0.02mm for each layer after processing, meeting standard requirements. The dispersion of impact energy test results is reduced compared to traditional methods, and the notch alignment accuracy is improved.

[0017] This invention is compatible with various thin metal sheets with a thickness of 0.5mm to 2.5mm, and supports continuous testing at multiple temperature points within the range of -196℃ to 300℃, meeting the material performance testing needs of different industries. Attached Figure Description

[0018] The disclosure of this invention is illustrated with reference to the accompanying drawings. It should be understood that the drawings are for illustrative purposes only and are not intended to limit the scope of protection of this invention. In the drawings, the same reference numerals are used to refer to the same parts. Wherein: Figure 1 This is a flowchart of the integrated sample pretreatment and testing method for Charpy impact test of sheet metal according to the present invention. Figure 2 This invention provides the framework for an integrated system for sample pretreatment and testing in the Charpy impact test of sheet metal. Figure 1 ; Figure 3 This invention provides the framework for an integrated system for sample pretreatment and testing in the Charpy impact test of sheet metal. Figure 2 . Detailed Implementation

[0019] It is readily understood that, based on the technical solution of this invention, those skilled in the art can propose various interchangeable structural methods and implementations without altering the essential spirit of the invention. Therefore, the following detailed embodiments and accompanying drawings are merely illustrative examples of the technical solution of this invention and should not be considered as the entirety of the invention or as limitations or restrictions on the technical solution of this invention.

[0020] Specific embodiments of the present invention are described below with reference to the accompanying drawings.

[0021] Please see Figures 1-3 This invention proposes an integrated system for sample pretreatment and testing of sheet metal in Charpy impact testing. All units of the system are mounted on the same rigid frame, connected via an industrial Ethernet bus, and uniformly scheduled by a central control unit.

[0022] The system specifically includes the following 10 functional units: The sheet metal blank preparation unit includes a CNC shearing machine, a deburring workstation, an ultrasonic cleaner, and a compressed air dryer, which are connected in sequence. Adaptive stacking computing and control unit: including a high-precision micrometer, an industrial computer and a stacking robot, with the industrial computer having a built-in adaptive stacking algorithm; Automatic alignment and positioning unit: includes a high-precision marble worktable, two sets of vertically arranged laser displacement sensors, a four-axis precision clamping robot, and an end face positioning block; Dynamic thermal simulation pressing unit: includes a vacuum chamber, induction heating system, hydraulic pressurization system and atmosphere control system; Standard notch machining unit: includes a five-axis CNC milling machine, carbide forming milling cutters, and an online laser inspection probe; Modular multi-temperature zone pretreatment unit: includes at least 6 independently temperature-controlled pretreatment chambers, an embedded micro thermocouple array, and a temperature control system; Vacuum-isolated automatic transfer unit: includes a vacuum-sealed channel, a thermostatic clamping manipulator, a linear motor drive mechanism, and an infrared temperature sensor for the impact station; Automatic impact and data acquisition unit: including pendulum impact testing machine, 1MHz high-frequency force sensor, 10000fps high-speed camera and sample centering mechanism; Automatic fracture analysis unit: includes a 3D laser scanning system, an industrial computer, and a U-Net deep learning semantic segmentation model; Central control and data processing unit: includes industrial PLC controller, touch screen, data server and blockchain encryption module, and is electrically connected to all the above units.

[0023] An integrated method for sample pretreatment and testing of plate materials using the above system includes the following steps: S1: Plate blank preparation: This is performed by the plate blank preparation unit. The thin plate to be tested is cut into rectangular blanks of 55mm±0.1mm×12mm±0.1mm by a CNC shearing machine. The edge burrs are removed with 800# sandpaper, and then the surface oil is removed by ultrasonic cleaning with acetone for 10 minutes. After being dried with compressed air, it is transported to the adaptive stacking calculation and control unit. S2: Adaptive Stacking Calculation: Performed by the adaptive stacking calculation and control unit, a high-precision micrometer automatically measures the actual thickness of each blank. The industrial computer, through the adaptive stacking algorithm, automatically calculates the required number of stacking layers n and the optimal stacking combination based on the input nominal thickness t and thickness tolerance Δt of a single sheet, so that the total thickness of the stacked sheets H=10mm±0.02mm. The stacking robot transports the selected blanks to the automatic alignment and positioning unit. S3: Automatic Alignment and Positioning: Performed by the automatic alignment and positioning unit, the blanks with the calculated number of layers are placed sequentially on the marble worktable. Two sets of vertically arranged laser displacement sensors measure the position of each layer of blanks in real time. The four-axis precision clamping robot adjusts each blank one by one to achieve complete alignment of the center line and end face of each layer of blanks with an alignment error of ≤0.01mm. After being clamped by a special high-temperature resistant fixture, it is transported to the dynamic thermal simulation pressing unit. S4: Dynamic thermal simulation pressing: Performed by the dynamic thermal simulation pressing unit, the aligned stacked blanks are sent into the vacuum chamber and evacuated to 1×10⁻⁶. -2Pa, high-purity argon gas is introduced to atmospheric pressure, and hot pressing is carried out under a protective atmosphere to achieve interlayer metallurgical bonding, forming an integral stacked blank, which is then cooled to room temperature and transported to the standard notch processing unit; S5: Standard notch machining: Performed by the standard notch machining unit, the pressed laminated blanks are clamped on the worktable of a five-axis CNC milling machine, and V-shaped notches conforming to GB / T229-2020 are machined using carbide forming milling cutters. The online laser detection probe automatically detects the notch size. If it is not qualified, it is automatically re-machined. After it is qualified, it is transported to the modular multi-temperature zone pre-processing unit. S6: Multi-temperature zone intelligent pretreatment: Performed by a modular multi-temperature zone pretreatment unit, the sample with the notched part is sent into the pretreatment chamber at the specified temperature and kept at a constant temperature according to the preset test temperature. The implanted micro thermocouple monitors the internal center temperature of the sample in real time. After the set temperature is reached and the temperature is kept for a sufficient time, a signal is sent to the central control unit. S7: Vacuum-isolated automatic transfer: Performed by the vacuum-isolated automatic transfer unit. When the transfer command is received from the central control unit, the thermostatic clamping robot takes out the sample from the pretreatment chamber and transfers it to the impact testing machine station within ≤1 second through the vacuum sealing channel. The infrared temperature sensor at the impact station confirms the surface temperature of the sample 0.1 seconds before impact. If the temperature deviation exceeds ±2℃, it will automatically return to the pretreatment chamber for reheating. S8: Automatic Impact and Data Acquisition: Performed by the automatic impact and data acquisition unit, the specimen centering mechanism automatically completes the specimen centering (centering accuracy ≤0.025mm), the pendulum impact testing machine automatically releases the pendulum to complete the impact, and a 1MHz high-frequency force sensor and a 10000fps high-speed camera synchronously acquire force-time data and high-speed images of the entire impact process, and transmit the data to the central control unit. S9: Automatic Fracture Analysis: Performed by the automatic fracture analysis unit, after the impact is completed, the transfer robot transfers the fracture sample to the 3D laser scanning system, scans and obtains the three-dimensional point cloud data of the fracture, and automatically calculates the fiber cross-sectional area, lateral expansion and crack length through the U-Net deep learning semantic segmentation algorithm. The results are transmitted to the central control unit. S10: Data Processing and Report Generation: Executed by the central control and data processing unit, it automatically integrates data from the entire process and generates test reports that comply with GB / T229-2020, ASTM E23 and ISO148 standards. It uses blockchain technology to encrypt and store the data throughout the process, and generates a unique SHA-256 hash identifier for each sample to ensure that the data is tamper-proof and traceable.

[0024] Overall operation process of integrated system The integrated system of this invention adopts a three-level control architecture of central control - unit execution - result feedback, and the overall operation flow is as follows: 1. The operator inputs the type of material to be tested, nominal thickness, thickness tolerance, and test temperature point on the touch screen of the central control unit; 2. The system automatically generates test tasks and schedules the sheet metal blank preparation unit to start cutting the blank; 3. Each unit automatically performs the corresponding operation in the order of steps S1 to S10 above, and all process parameters are uploaded to the central control unit in real time; 4. After all samples have been tested, the system automatically generates a test report and stores it on the data server, while also generating a blockchain hash identifier; 5. Operators can use the touchscreen to query test results, export reports, or trace historical data.

[0025] The adaptive stacking algorithm formula definition of this invention 1. Theoretical number of layers calculation formula: in, The theoretical number of layers (take a positive integer). The nominal thickness of a single sheet of material (unit: mm). This is the rounding function; 2. Determining the set of candidate layers: 3. Formula for calculating total thickness deviation: in, for Total thickness deviation of the stacked layers (unit: mm). For the first The actual measured thickness of the blank (unit: mm, accuracy ±0.001 mm). 4. Optimal layer selection: 5. Determining the optimal stacking combination: Using a 0-1 knapsack dynamic programming algorithm, select from all available blanks. To minimize the total thickness deviation, the state transition equation is: in, In the past Select several sheets of raw material with a total thickness of Minimum deviation at time, For the first The thickness of the blank, with boundary conditions as follows: .

[0026] Preparation Example Preparation Example 1: Preparation of 1mm thick 304 austenitic stainless steel laminate sample This preparation example uses 304 austenitic stainless steel sheet with a thickness of 1.00 mm ± 0.02 mm (base material tensile strength 610 MPa). Standard Charpy V-notch impact specimens are prepared according to the method of this invention. The specific steps are as follows: 1. Preparation of sheet blanks: Cut the sheet into 55mm×12mm rectangular blanks using a CNC shearing machine, a total of 120 sheets. Remove the edge burrs with 800# sandpaper, then ultrasonically clean with acetone for 10 minutes to remove surface oil stains, and dry with compressed air for later use.

[0027] 2. Adaptive stacking computation: Enter the nominal thickness of a single sheet of material. Thickness tolerance ; Calculate the theoretical number of layers according to formula (1) layer; The candidate layer set is determined according to formula (2). ; The actual thickness of 120 blanks was measured using a micrometer with an accuracy of ±0.001mm, and the range was 0.98mm to 1.02mm. The minimum total thickness deviation for each candidate layer is calculated using formulas (3) and (4): The minimum deviation is 0.15mm (total thickness 9.85mm), which does not meet the requirements. The minimum deviation is 0.08mm (total thickness 9.92mm), which does not meet the requirements. Minimum deviation 0.00mm (total thickness 10.00mm), meets requirements. The minimum deviation is 0.07mm (total thickness 10.07mm), which does not meet the requirements. The minimum deviation is 0.16mm (total thickness 10.16mm), which does not meet the requirements. Optimal number of layers ; The optimal stacking combination determined by the dynamic programming algorithm (Formula 5) is: 1.02mm, 0.99mm, 1.01mm, 0.98mm, 1.00mm, 1.00mm, 1.02mm, 0.99mm, 1.01mm, 0.98mm; Total thickness calculation: 1.02+0.99+1.01+0.98+1.00+1.00+1.02+0.99+1.01+0.98=10.00mm, deviation 0mm.

[0028] 3. Automatic alignment and positioning: Place 10 blank sheets one by one onto the marble worktable of the automatic alignment device; Two sets of laser displacement sensors measure the lateral and longitudinal positions of each blank, respectively; The four-axis clamping robot adjusts each sheet individually to ensure that the centerline deviation of each layer is ≤0.008mm and the end face deviation is ≤0.005mm. Clamp the stacked blanks with a special high-temperature resistant fixture to keep them aligned.

[0029] 4. Dynamic thermal simulation of pressing: The clamped stacked blanks are fed into the vacuum chamber of the dynamic thermal simulation pressing device, and the vacuum is evacuated to 1×10⁻⁶. -2 Pa, fill with high-purity argon gas to atmospheric pressure; Heat to 1000℃ at a rate of 10℃ / s and hold for 2 minutes to ensure uniform temperature. Apply a pressure of 60 MPa and maintain the temperature and pressure for 8 minutes; Cool to room temperature at a rate of 20℃ / s and remove the stacked blanks.

[0030] 5. Standard notch machining: Transfer the stacked blanks to a five-axis CNC milling machine and use a carbide forming milling cutter to machine a V-shaped notch that meets the requirements of GB / T 229-2020 "Charpy Impact Test Method for Metallic Materials". The notch depth is 2.00mm±0.02mm, the root radius is 0.25mm±0.01mm, and the perpendicularity deviation between the notch centerline and the longitudinal axis of the specimen is ≤0.02mm.

[0031] 6. Repeat the above steps to prepare a total of 10 parallel samples, numbered S1-1 to S1-10.

[0032] Preparation Example 2: Preparation of 1.5mm thick 6061-T6 aluminum alloy laminate sample This preparation example uses 6061-T6 aluminum alloy sheet with a thickness of 1.50mm ± 0.03mm (base material tensile strength 310MPa). Standard Charpy V-notch impact specimens are prepared according to the method of this invention. The specific steps are as follows: 1. Preparation of sheet blanks: Cut into rectangular blanks of 55mm×12mm, a total of 80 sheets, clean and dry for later use.

[0033] 2. Adaptive stacking computation: enter , ; Theoretical number of layers layer; The actual thickness measured ranges from 1.47mm to 1.53mm. Optimal number of layers The optimal combination has a total thickness of 9.99 mm and a deviation of -0.01 mm.

[0034] 3. Automatic alignment and positioning: Centerline deviation of each layer ≤ 0.009mm, end face deviation ≤ 0.006mm.

[0035] 4. Dynamic thermal simulation pressing: pressing temperature 480℃, pressure 40MPa, heat and pressure holding for 5min, protective atmosphere argon.

[0036] 5. Standard notch processing: Same as preparation example 1.

[0037] 6. A total of 10 parallel samples were prepared, numbered S2-1 to S2-10.

[0038] Preparation Example 3: Preparation of 2mm thick TC4 titanium alloy laminate sample This preparation example uses TC4 titanium alloy sheet with a thickness of 2.00 mm ± 0.02 mm (base material tensile strength 930 MPa) to prepare a standard Charpy V-notch impact specimen according to the method of this invention. The specific steps are as follows: 1. Preparation of sheet blanks: Cut into rectangular blanks of 55mm×12mm, a total of 60 sheets, clean and dry for later use.

[0039] 2. Adaptive stacking computation: enter , ; Theoretical number of layers layer; The actual thickness measured ranges from 1.98mm to 2.02mm; Optimal number of layers The optimal total thickness is 10.01 mm, with a deviation of +0.01 mm.

[0040] 3. Automatic alignment and positioning: centerline deviation of each layer ≤ 0.007mm, end face deviation ≤ 0.005mm.

[0041] 4. Dynamic thermal simulation pressing: pressing temperature 880℃, pressure 75MPa, heat and pressure holding for 12min, protective atmosphere argon.

[0042] 5. Standard notch processing: Same as preparation example 1.

[0043] 6. A total of 10 parallel samples were prepared, numbered S3-1 to S3-10.

[0044] Comparative Example Comparative Example 1: Preparation of 304 stainless steel laminate samples by conventional binding method The same 1mm thick 304 stainless steel sheet as in Preparation Example 1 was used to prepare the sample according to the traditional binding method: 1. Cut into 100 rectangular blanks of 55mm×12mm each, clean and dry them for later use.

[0045] 2. Ten layers are manually stacked, with a total thickness of approximately 10mm.

[0046] 3. Tightly bind the wire with φ0.5mm stainless steel wire 3 times at a distance of 10mm from both ends, tighten it and cut off the excess wire.

[0047] 4. Process the V-shaped notch, using the same process parameters as in Preparation Example 1.

[0048] 5. A total of 10 parallel samples were prepared, numbered D1-1 to D1-10.

[0049] Comparative Example 2: Preparation of 6061 aluminum alloy laminated samples by epoxy resin bonding method Using the same 1.5 mm thick 6061-T6 aluminum alloy sheet as in Preparation Example 2, samples were prepared by epoxy resin bonding method: 1. Cut into 80 rectangular blanks of 55mm×12mm each, clean and dry them for later use.

[0050] 2. Manually stack 7 layers, and evenly apply E-51 epoxy resin adhesive between each layer, with an adhesive layer thickness of approximately 0.05mm.

[0051] 3. Clamp with a fixture and cure at room temperature for 24 hours, then cure at 80°C for 4 hours.

[0052] 4. Process the V-shaped notch, using the same process parameters as in Preparation Example 1.

[0053] 5. A total of 10 parallel samples were prepared, numbered D2-1 to D2-10.

[0054] Comparative Example 3: Preparation of TC4 titanium alloy laminated samples by conventional binding method The same 2mm thick TC4 titanium alloy sheet as in Preparation Example 3 was used to prepare the sample according to the traditional binding method: 1. Cut into 60 rectangular blanks of 55mm×12mm each, clean and dry them for later use.

[0055] 2. Five layers are manually stacked, with a total thickness of approximately 10mm.

[0056] 3. Tightly bind the ends with φ0.5mm titanium alloy wire for 3 turns at a distance of 10mm from both ends.

[0057] 4. Process the V-shaped notch, using the same process parameters as in Preparation Example 1.

[0058] 5. A total of 10 parallel samples were prepared, numbered D3-1 to D3-10.

[0059] Example Using the integrated testing system of this invention, Charpy impact tests were conducted on the samples of Preparation Examples 1-3 and Comparative Examples 1-3 at different temperatures: -196℃ (liquid nitrogen), -100℃ (dry ice + alcohol), 0℃ (ice-water mixture), 25℃ (room temperature), and 100℃ (oil bath). Three parallel samples were tested at each temperature point, and the test results are shown in the table below: Table 1 Comparison of Impact Test Results of 304 Stainless Steel Samples Table 2 Comparison of Impact Test Results of 6061 Aluminum Alloy Samples Table 3 Comparison of Impact Test Results of TC4 Titanium Alloy Samples Test Result Analysis: 1. Comparison of low-temperature sample integrity rate: The samples prepared by the method of this invention have a 100% integrity rate in the entire temperature range of -196℃ to 100℃, with no interlayer separation phenomenon; while the traditional method causes all samples to disperse at -196℃, making the test completely ineffective, and the integrity rate is only 20% to 30% at -100℃. As the temperature increases, the integrity rate gradually increases, but at room temperature, 20% to 30% of the samples still experience interlayer separation.

[0060] 2. Impact Energy Comparison: At the same temperature, the impact energy measured by the method of this invention is significantly higher than that of the traditional method. For example, at -100℃, the impact energy of the 304 stainless steel sample increased from 85.7J using the traditional method to 142.3J, an increase of (142.3-85.7) / 85.7×100%≈66%; the impact energy of the 6061 aluminum alloy sample increased from 12.3J to 25.6J, an increase of (25.6-12.3) / 12.3×100%≈108%; and the impact energy of the TC4 titanium alloy sample increased from 23.6J to 41.7J, an increase of (41.7-23.6) / 23.6×100%≈77%. This is because the interlayer friction and separation in the traditional method consume a large amount of energy, resulting in the measured impact energy being much lower than the true value of the material.

[0061] 3. Comparison of test result dispersion: The impact energy dispersion measured by the method of this invention is ≤5%, indicating good data consistency; while the dispersion of traditional methods is as high as 18% to 36%, with large data fluctuations, failing to accurately reflect the true performance of the material. This is mainly due to the poor alignment accuracy of manual stacking in traditional methods, resulting in inconsistent notch depths in each layer.

[0062] Example 2: Comparison of Notch Alignment Accuracy The notches of the samples prepared in Example 1 and Comparative Example 1 were three-dimensionally scanned using a Keyence VK-X200 laser confocal microscope (measurement accuracy ±0.001 mm). The depth deviation of each notch layer was measured, and the results are shown in the table below: Table 4 Comparison of Notch Depth Deviation The average maximum deviation of this invention is: (0.018 + 0.016 + 0.019) / 3 = 0.0177 ≈ 0.018 mm The average depth deviation of this invention is: (0.009 + 0.008 + 0.010) / 3 = 0.009 mm The average maximum deviation using the traditional method is: (0.215 + 0.198 + 0.231) / 3 = 0.2147 ≈ 0.215 mm The average depth deviation using the traditional method is: (0.102 + 0.095 + 0.113) / 3 = 0.1033 ≈ 0.103 mm Analysis of test results: The average deviation of the notch depth of each layer of the method of the present invention is only 0.009 mm, which is much smaller than the 0.103 mm of the traditional method. It fully meets the requirement of GB / T229-2020 for notch depth deviation ≤0.05 mm, while the deviation of the traditional method is more than twice the standard requirement. This is the main reason for the large dispersion of the test results of the traditional method.

[0063] Example 3: Interlayer Bond Strength Test The interlaminar tensile bond strength of the laminated specimens prepared in Example 1 and Comparative Example 1 was tested using an Instron 5982 universal testing machine. The specimen size was 10 mm × 10 mm × 10 mm, and the tensile rate was 1 mm / min. The results are as follows: The sample prepared by the method of the present invention has an interlaminar bond strength of 586 MPa, which is approximately 96% of the strength of the parent material (610 MPa) (586 / 610×100%). The fracture locations are all located inside the parent material, and there is no interlaminar separation phenomenon. The specimens prepared by the traditional binding method have an interlaminar bond strength of 32 MPa, which is only 32 / 610×100%≈5% of the strength of the parent material, and the fracture location is located at the interlaminar interface.

[0064] Test results analysis: This proves that the dynamic thermal simulation pressing technology of the present invention can achieve interlayer metallurgical bonding, and the bonding strength is higher than that of traditional mechanical bonding methods, fundamentally solving the problem of sample separation during low-temperature impact.

[0065] The technical scope of this invention is not limited to the content described above. Those skilled in the art can make various modifications and variations to the above embodiments without departing from the technical concept of this invention, and all such modifications and variations should fall within the protection scope of this invention.

Claims

1. An integrated method for sample pretreatment and testing in Charpy impact testing of sheet metal, characterized in that, Includes the following steps: S1: Cut the thin sheet material to be tested into a rectangular blank, remove burrs and clean the surface oil; S2: Using an adaptive stacking algorithm, based on the nominal thickness of the input single sheet material... and thickness tolerance Automatically calculate the required number of stacking layers And the optimal stacking order to maximize the total thickness of the stack. ; S3: Place the blanks with the calculated number of layers into the automatic alignment device in sequence, and achieve complete alignment of the center line and end face of each layer of blanks through laser positioning and precision clamping mechanism; S4: The aligned stacked blanks are sent into a dynamic hot simulation pressing device and hot-pressed under a protective atmosphere to achieve interlayer metallurgical bonding and form an integral stacked blank. S5: Transfer the pressed laminated blanks to a CNC milling machine to machine a V-shaped notch; S6: The notched sample is sent into the modular multi-temperature zone pretreatment chamber and kept at a constant temperature according to the preset test temperature. The internal temperature of the sample is monitored in real time by an implanted micro thermocouple. S7: After the sample reaches the set temperature and is kept at that temperature for a sufficient time, the sample is transferred to the impact testing machine station through a vacuum isolation transfer system. S8: The impact testing machine automatically completes the centering, clamping and impact of the specimen, and collects data of the entire impact process through a high-frequency force sensor and a high-speed camera. S9: After the impact is completed, the fracture sample is transferred to a 3D laser scanning system, and the fiber cross-sectional area, lateral expansion and crack length are automatically calculated by deep learning algorithm; S10: Automatically integrates data from the entire process to generate a test report that meets standards.

2. The integrated method for sample pretreatment and testing in the Charpy impact test of sheet metal according to claim 1, characterized in that, The adaptive stacking algorithm described in step S2 is calculated using the following formula: S21: Theoretical number of layers calculation formula: in, The theoretical number of layers, The nominal thickness is for a single sheet of material. This is the rounding function; S22: Determining the set of candidate layers: S23: Formula for calculating total thickness deviation: in, for Total thickness deviation of the stacked layers For the first The actual measured thickness of the blank; S24: Optimal layer selection: S25: Determining the optimal stacking combination: Using a 0-1 knapsack dynamic programming algorithm, select from all available blanks. To minimize the total thickness deviation, the state transition equation is: in, In the past Select several sheets of raw material with a total thickness of Minimum deviation at time, For the first The thickness of the blank.

3. The integrated method for sample pretreatment and testing in the Charpy impact test of sheet metal according to claim 1, characterized in that, The dynamic thermal simulation pressing process described in step S4 meets the following conditions: The heating rate is 5℃ / s to 15℃ / s, and the cooling rate is 10℃ / s to 30℃ / s; The protective atmosphere during the pressing process is argon, with an oxygen content of [missing information]. ; For austenitic stainless steel: pressing temperature 950℃~1050℃, pressure 50MPa~80MPa, holding time 5min~10min; For aluminum alloys: pressing temperature 450℃~500℃, pressure 30MPa~50MPa, holding time 3min~8min; For titanium alloys: pressing temperature 850℃~900℃, pressure 60MPa~90MPa, holding time 8min~15min.

4. The integrated method for sample pretreatment and testing in the Charpy impact test of sheet metal according to claim 1, characterized in that, The automatic alignment device described in step S3 includes a high-precision marble worktable, two sets of vertically arranged laser displacement sensors, a four-axis precision clamping robot, and an end-face positioning stop; wherein, the flatness of the marble worktable is... Laser displacement sensor measurement accuracy Four-axis precision clamping robot with high repeatability verticality of end face positioning block .

5. The integrated method for sample pretreatment and testing in the Charpy impact test of sheet metal according to claim 1, characterized in that, The multi-temperature zone intelligent preprocessing described in step S6 calculates the heat preservation time using the lumped parameter method: in, Total insulation time, To preset the basic insulation time, The density of the sample, The specific heat capacity at constant pressure of the sample. The volume of the sample is 1. The convective heat transfer coefficient is... The surface area of ​​the sample. To set the temperature, The initial temperature of the sample. The target temperature for the sample. ; When the implanted miniature thermocouple detects that the sample center temperature reaches Then, continue to keep warm. time.

6. The integrated method for sample pretreatment and testing of sheet metal in Charpy impact testing according to claim 1, characterized in that, The vacuum-isolated transfer system described in step S7 includes a vacuum-sealed channel, a thermostatic clamping robot, and an infrared temperature sensor at the impact station; wherein, the vacuum degree of the vacuum-sealed channel is ≤ The temperature deviation between the gripping part of the thermostatic clamping robot and the sample temperature is ≤ The infrared temperature sensor at the impact station confirms the surface temperature of the sample 0.1 seconds before impact. If the temperature deviation exceeds ±2℃, it automatically returns to the pretreatment chamber for reheating.

7. The integrated method for sample pretreatment and testing in the Charpy impact test of sheet metal according to claim 1, characterized in that, During the automatic impact and data acquisition process described in step S8, the force-time data acquired by the high-frequency force sensor is converted into a force-displacement curve using the following formula: in, For instantaneous impact force, For force sensor calibration coefficients, The sensor output voltage, The instantaneous velocity of the pendulum, The initial velocity of the pendulum before impact. The total mass of the pendulum system is... This represents the pendulum displacement.

8. The integrated method for sample pretreatment and testing in the Charpy impact test of sheet metal according to claim 1, characterized in that, In the automatic fracture analysis described in step S9, the fiber cross-sectional area ratio is calculated using the following formula: in, For fiber cross-sectional area, The area of ​​the fiber region on the fracture surface. This represents the total area of ​​the fracture surface; and The 3D point cloud data of the fracture obtained by the 3D laser scanning system is automatically identified and calculated using the U-Net deep learning semantic segmentation algorithm.

9. The integrated method for sample pretreatment and testing in the Charpy impact test of sheet metal according to claim 1, characterized in that, In the data processing and report generation process described in step S10, consortium blockchain technology is used to encrypt and store the data throughout the entire process. Each sample generates a unique SHA-256 hash identifier, which includes sample preparation parameters, pretreatment temperature and time curves, impact process data, and fracture analysis results, ensuring that the data is tamper-proof and traceable.

10. An integrated system for sample pretreatment and testing in Charpy impact testing of sheet metal, characterized in that, The integrated method for specimen pretreatment and testing of plate material according to any one of claims 1-9, the system specifically includes the following functional units: The sheet metal blank preparation unit includes a CNC shearing machine, a deburring workstation, an ultrasonic cleaner, and a compressed air dryer, which are connected in sequence. Adaptive stacking computing and control unit: including a high-precision micrometer, an industrial computer and a stacking robot, with the industrial computer having a built-in adaptive stacking algorithm; Automatic alignment and positioning unit: includes a high-precision marble worktable, two sets of vertically arranged laser displacement sensors, a four-axis precision clamping robot, and an end face positioning block; Dynamic thermal simulation pressing unit: includes a vacuum chamber, induction heating system, hydraulic pressurization system and atmosphere control system; Standard notch machining unit: includes a five-axis CNC milling machine, carbide forming milling cutters, and an online laser inspection probe; Modular multi-temperature zone pretreatment unit: includes at least 6 independently temperature-controlled pretreatment chambers, an embedded micro thermocouple array, and a temperature control system; Vacuum-isolated automatic transfer unit: includes a vacuum-sealed channel, a thermostatic clamping manipulator, a linear motor drive mechanism, and an infrared temperature sensor for the impact station; Automatic impact and data acquisition unit: including pendulum impact testing machine, 1MHz high-frequency force sensor, 10000fps high-speed camera and sample centering mechanism; Automatic fracture analysis unit: includes a 3D laser scanning system, an industrial computer, and a U-Net deep learning semantic segmentation model; Central control and data processing unit: includes industrial PLC controller, touch screen, data server and blockchain encryption module, and is electrically connected to all system units.

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