CN111581862B

CN111581862B - Equivalent test method for mechanical property of welding joint microcell

Info

Publication number: CN111581862B
Application number: CN202010311158.6A
Authority: CN
Inventors: 何洪; 李落星; 徐从昌
Original assignee: Hunan University
Current assignee: Hunan University
Priority date: 2020-04-20
Filing date: 2020-04-20
Publication date: 2022-04-08
Anticipated expiration: 2040-04-20
Also published as: CN111581862A

Abstract

The invention relates to an equivalent test method for mechanical properties of a welding joint microcell, which comprises the steps of providing a flat welding sample, and establishing a corresponding relation database of tensile mechanical property data and a welding thermal cycle curve and/or the highest temperature; taking a sample to be tested which is made of the same material as the welding sample, welding the sample to be tested according to target welding process parameters, monitoring the temperature change condition of a target micro-area on the sample to be tested, and obtaining a welding thermal cycle curve and/or the highest temperature of the target micro-area; and comparing the welding thermal cycle curve and/or the highest temperature with the corresponding relation database to obtain the mechanical property of the target micro-area. The method has high efficiency, accuracy and reliability in predicting the mechanical property of the welding joint microcell.

Description

Equivalent test method for mechanical property of welding joint microcell

Technical Field

The invention relates to an equivalent test method for mechanical property of a welding joint microcell, belonging to the technical field of performance test of metal material connection manufacturing.

Background

Welding is an extremely important process for realizing material connection, and permanent connection methods are realized by forming intermolecular bonding between materials to be connected through heating or pressurizing, or simultaneously heating and pressurizing, and the like, and the welding methods include fusion welding, pressure welding, brazing and the like. Melting welding requires heating the material to be welded and the filler material (also called "welding wire") to a temperature above the melting point to form a liquid molten pool, and then solidifying to obtain a weld; pressure welding is generally a process of making a material to be welded reach a semi-solid state or a melting point under the simultaneous action of heating heat and pressure, and then cooling to obtain a weld joint; in the brazing process, the required heat is low, and only the process of melting the filling material and then infiltrating the surface of the material to be welded to obtain the weld seam is needed. Regardless of the type of welding method, a certain amount of heat needs to be input during the welding process, so that not only the filling material but also the connecting part of the materials to be welded can be heated. Often, such a heating process and a cooling process after welding are regarded as a case where the joining fine regions of the materials to be joined are subjected to a heat treatment. The effect of this welding heat may alter the microstructure and the mechanical properties of the weld zone of the materials to be joined, i.e. the properties of the material in the vicinity of the joint will change from their original properties after welding, often referred to as the heat influence.

The metal material is most industrially used, and the strength of the material is mainly derived from grain boundary strengthening, precipitation phase strengthening, and dislocation strengthening (also called work hardening), and the size of crystal grains (the total area of grain boundaries per unit volume), the density of dislocations (the length of dislocations per unit volume), the size and number density of precipitation phases (the shape and size of particles and the number of precipitation phase particles per unit volume). At present, the most important connection mode of the metal structural parts is welding. However, most ferrous metals (e.g., pure iron, carbon steel, cast iron, etc.) and non-ferrous metals (aluminum alloys, copper alloys, titanium alloys, etc.) have some sensitivity to the heat of welding. After the heat input of the welding, if the temperature of the materials after being heated is increased to be higher than the forming temperature of the strengthening elements, the grains, the phase types, the precipitated phases and the dislocation forms at the periphery of the welding seam are influenced to a certain extent, for example, the heating can promote the increase of the quantity of the precipitated phases, but the overhigh temperature can cause the growth of the size of the precipitated phases and the change of the phases, for example, the dislocation density is reduced after the temperature is increased, for example, the grain size is further increased after the temperature is increased to be higher than the recrystallization temperature, and for example, the steel materials can form more pearlite strengthening after being heated to the normalizing temperature.

In order to describe the influence of welding heat on the performance of a welding material, the heat input mode of the welding material needs to be clearly described, and the welding heat cycle description is commonly used and defined as the process of the temperature of a certain point on a welding seam and a base material nearby the welding seam changing along with time and is represented by a welding heat cycle curve. Including maximum temperature and heating time. When welding metal materials, different areas of the weld joint are subjected to different thermal cycles and thus different thermal influences are obtained, as shown in fig. 1. Thus, the different fine areas of the weld joint vary differently when heated. Taking low carbon steel as an example, as shown in fig. 2, the position closer to the weld joint is, the highest temperature of the thermal cycle is higher, and even the phase transformation region of the crystal grains can be reached, as shown in position 3 in fig. 2, the temperature reaches the austenitizing region, and after the welding is finished, the normalizing treatment is equivalently carried out, so that the region is called a normalizing region, more normalizing pearlite phases can be formed, and the mechanical property of the normalizing pearlite phases can be improved. Taking 6082 aluminum alloy as an example, as shown in fig. 3, the position in the figure is the distance from the center of the weld joint as a reference point. As shown in the figure, when the distance is less than a certain distance, the state of the precipitated phase in the 6082 aluminum alloy is significantly changed after the temperature exceeds 150 ℃, for example, when the temperature exceeds 480 ℃, the precipitated phase is dissolved, and after the subsequent cooling process, the chemical element forms a solid solution state, and the strengthening effect is not as strong as that of the original precipitated phase strengthening state; when the temperature exceeds 235 ℃, the size of a precipitated phase is obviously coarsened, the strengthening effect is obviously reduced, and the mechanical property is reduced. The different regions of the weld joint are affected by the heat after welding (i.e., the resulting properties differ) due to the different thermal cycles experienced by the different regions.

As can be seen from the above, the performance of the welded joint may be enhanced or weakened in different areas after welding. Therefore, when the welding part is applied in engineering, the mechanical property of each microscopic region of the welding joint is required to be known so as to predict the position of the mechanical property weak area of the welding structure or the failure position in the service process of the joint. In addition, when the finite element simulation strength of the structural member is checked and calculated, the mechanical property parameters of the micro-areas, such as stress-curve and other data, are also needed to be known.

Through experimental tests, such as tensile tests of the welded joint (the test method corresponds to the national standard GB-T2651), parameters such as the integral strength, the elongation after fracture and the like of the welded joint can be obtained. However, the obtained performance indexes still cannot completely or finely characterize the mechanical properties of the welded joint, and the mechanical properties (yield strength, tensile strength, elongation, and the like) of each microscopic region are also required. The general method is to slice each microscopic region parallel to the section of the welding seam to prepare a tensile sample for tensile experiment. However, when the thickness of the welded material is thin, the width of the obtained welded tensile sample is insufficient, and the requirement of the specified size of the sample of GB-T2651 cannot be met, so that similar mechanical property characterization cannot be carried out.

At present, some methods for testing local mechanical properties of a welded joint are reported in the prior literature, such as: micro-shear test, micro-tensile test, micro-stamping test, artificial neural network prediction, Gleeble thermal cycle loading simulation and the like [1-5 ]. Although the micro-shear test and micro-tensile test methods are feasible in view of the small extent of the weld joint in each area, the preparation of the sample pieces is time consuming and costly. The micro-stamping test needs to obtain a force-displacement curve first and then carry out finite element reverse iteration, and the measurement precision is influenced because friction exists between a pressure head and a measured material and estimation is difficult. The local mechanical property of the welding joint is tested by adopting an artificial neural network prediction method, the reliability of the prediction result can be ensured only if enough sample data exists, and the method has huge workload. The Gleeble thermal cycle simulation method is characterized in that the performance change of the alloy is explored by simulating the thermal cycle characteristics in the welding process, the test cost of the method is high, and the real situation of a thermal influence area in actual welding is difficult to reflect. In addition, the micro-sample testing method is adopted, and because the sample size is small, the testing precision is sensitive to the size, the result is inaccurate, and the error is large.

Reference documents:

[1] liu Jing an, Wang Yuan Liang, Sun hong, micro-area performance research of the aluminum alloy section for the vehicle welding joint [ J ] light alloy processing technology (1):41-43.

[2]Lavan D A.Microtensile properties of weld metal[J].Experimental Techniques，1999，23(3):31-34.

[3] The partial mechanical property analysis of the welded joint of aluminum alloy for cars [ J ] welding bulletin, 2009, 30(1).

[4] The method is characterized by comprising the following steps of stone polder, fan Ding, Chenjianhong, welding joint mechanical property prediction based on a neural network method [ J ]. welding science, 2004, 25(2):73-76.

[5]Wen Liu，Fenggui Lu，Renjie Yang，et al.Gle-eble simulation of the HAZ in Inconel 617welding[J].Journal of Materials Processing Technology，2015，225:221-228.

Disclosure of Invention

Aiming at the defects of the prior art, the invention provides an equivalent test method for the mechanical property of the welding joint micro-area, so as to efficiently, accurately and reliably predict the mechanical property of the welding joint micro-area.

In order to solve the technical problems, the technical scheme of the invention is as follows:

an equivalent test method for mechanical properties of a welding joint microcell comprises the following steps:

s1, providing a flat welding sample, wherein the welding sample is provided with at least 1 straight edge; taking the position of the straight line edge as a starting point, and dividing the welding sample into micro areas L in sequence along the direction which is vertical to the straight line edge and parallel to the welding sample₁Micro-area L₂Micro-area L₃… …, micro-region L_n；

Wherein n is a positive integer; the thickness of the single micro-area is not more than 2 mm;

s2, applying a welding seam on the welding sample along the straight line edge, and simultaneously respectively monitoring the temperature change condition of each micro-area to obtain the welding thermal cycle curve and/or the highest temperature of each micro-area;

or applying a welding seam on the welding sample along the straight line edge, and carrying out finite element simulation on the temperature field of the welding sample under the same condition to obtain a welding thermal cycle curve and/or the highest temperature of each micro-area;

s3, cutting the welding sample processed by the S2 according to the division condition of each micro-area to obtain a tensile sample l₁Tensile specimen l₂Tensile specimen l₃… … tensile specimen l_n；

S4, detecting the tensile mechanical property of each tensile sample obtained in the step S3 to obtain the mechanical property data of each tensile sample, and establishing a corresponding relation database of the tensile mechanical property data and a welding thermal cycle curve and/or the highest temperature;

s5, taking a to-be-detected sample with the same material as the welding sample, welding according to target welding process parameters, and simultaneously monitoring the temperature change condition of a target micro-area on the to-be-detected sample to obtain a welding thermal cycle curve and/or the highest temperature of the target micro-area;

or, taking a sample to be tested which is the same in material as the welding sample, and carrying out temperature field finite element simulation on the sample to be tested under target welding process parameters to obtain a welding thermal cycle curve and/or the highest temperature of a target micro-area;

and S6, comparing the welding thermal cycle curve and/or the highest temperature obtained in the step S5 with the corresponding relation database obtained in the step S4, and obtaining the tensile mechanical property of the target micro-area.

In the present invention, the maximum temperature is understood to be the maximum value of the temperature on the corresponding welding thermal cycle curve. The database can be further simplified by carrying out correspondence and comparison on the highest temperature.

The thickness of an individual micro-region refers to the dimension of the micro-region in a direction perpendicular to the straight side and parallel to the weld specimen.

Optionally, the welding sample is sequentially divided into micro-areas L along a direction perpendicular to the straight line edge and parallel to the welding sample by using the position of the midpoint of the right-angle edge as a starting point₁Micro-area L₂Micro-area L₃… …, micro-region L_n；

Further, in S1, n is equal to or greater than 3 and equal to or less than 20.

Further, in S1, the thickness t of the welding specimen is 8 to 12 mm.

Further, in S1, the thickness of the single micro-region is 0.8 to 1.2 mm.

Further, in S2 and/or S5, optionally, the temperature change condition of the corresponding micro-area is monitored by a contact temperature measurement means or a non-contact temperature measurement means, and further optionally, the temperature change condition of the corresponding micro-area is monitored by a thermocouple; further optionally, the temperature change condition of the corresponding micro-area is monitored by an infrared temperature measuring device.

Further, in S5, the test sample to be tested includes at least one of an angle joint, a butt joint, and a lap joint.

Further, in S4, the tensile mechanical property includes at least one of tensile strength, yield strength, elongation after fracture, and strain hardening index.

Further, the sample is one of aluminum alloy, copper alloy, titanium alloy, pure iron, carbon steel and cast iron.

Optionally, the aluminum alloy is 6000 series aluminum alloy, and further is 6082 aluminum alloy or 6061 aluminum alloy.

By the equivalent test method, on one hand, the property characterization of the micro-area of the thin-wall welding joint can be effectively carried out, and on the other hand, the welding temperature cycle curve or the highest temperature of the welding joint corresponds to the welding temperature cycle curve or the highest temperature of each area of the target joint, so that the mechanical property of the target position of the target joint is obtained, complex sample preparation is not needed, and the test process is simplified; therefore, the method has high efficiency, accuracy and reliability in predicting the mechanical property of the welding joint microcell.

The equivalent test method can predict the performance of each micro-area of the welding joint after welding by certain welding parameters, thereby continuously optimizing or selecting proper welding parameters.

Drawings

FIG. 1 is a thermal cycling profile of a weld joint.

FIG. 2 is a heat affected zone diagram of different fine areas of a low carbon steel welded joint.

FIG. 3 is a graph showing the temperature change at different positions of a joint during the welding of aluminum alloy.

Fig. 4 is a schematic sectional structure of a welding sample.

Fig. 5 is a schematic view of a state in which a welding specimen is clamped.

FIG. 6 is a finite element network model of a welded specimen.

FIG. 7 is a comparison graph of weld pool morphology and cross-sectional dimensions after weld formation obtained from simulation (left) and experiment (right).

Fig. 8 is a comparison graph of temperature change curves of a1 point, a2 point and A3 point simulation and experiment.

Fig. 9 is a temperature field distribution cloud chart obtained by simulation when each micro-area of the sample plate is in the highest temperature state in the welding process.

FIG. 10(a) is a schematic diagram of a stratified sampling situation; fig. 10(b) is a top view of a single tensile specimen.

Fig. 11 is a graph of engineering stress versus strain for each equivalent tensile specimen.

Fig. 12 is a schematic view of a temperature measurement point of a sample to be measured.

Fig. 13 is a comparison graph of temperature change curves of test points of simulation and experiment.

Fig. 14 is a temperature field profile of a sample to be measured.

Fig. 15 is a drawing of a tensile test specimen prepared from a test specimen.

Fig. 16 is a simulation mesh model of a sample to be measured.

FIG. 17 is a force-displacement curve of tensile finite element simulation results and test results for a test specimen to be tested.

In fig. 5, 1-clamp, 2-weld specimen.

Detailed Description

The following description describes alternative embodiments of the invention to teach one of ordinary skill in the art how to make and use the invention. Some conventional aspects have been simplified or omitted for the purpose of teaching the present invention. Those skilled in the art will appreciate that variations or substitutions from these embodiments will fall within the scope of the invention. Those skilled in the art will appreciate that the features described below can be combined in various ways to form multiple variations of the invention.

Example 1

In this embodiment, the equivalent test method for the mechanical property of the welding joint micro-area includes the following steps:

s1, providing a flat welding sample, wherein the welding sample is provided with at least 1 straight edge; taking the position of the right-angle edge as a starting point, and dividing the welding sample into micro-areas L in sequence along the direction which is perpendicular to the straight line edge and parallel to the welding sample₁Micro-area L₂Micro-area L₃… …, micro-region L_nSee, fig. 4;

wherein n is a positive integer; the thickness of each micro-area is 0.95 mm;

Among them, the flat plate-like welded sample (i.e., equivalent welded joint): 10mm in thickness and 300mm in length, and a 6061 aluminum plate with a heat treatment state of T6, 342MPa in tensile strength, 323MPa in yield strength and 9% in elongation after fracture.

The reliability of the equivalent test method of this embodiment is further verified as follows:

the welding wire uses ER5356 aluminum alloy welding wire with the diameter of 1.2mm, and the chemical compositions of the aluminum plate and the welding wire are shown in Table 1. The welding equipment adopts an OTC double-pulse MIG welding machine (the model is DP400), the welding current is 150A, the welding speed is 60cm/min, the dry elongation of the welding wire is 15mm, the protective gas is argon with the concentration of 99.999 percent, and the gas flow is 20L/min. Before welding, the aluminum plate is scrubbed clean by acetone, and then the surface to be welded is cleaned by a steel wire brush until the metallic luster is exposed. The welding position is the aluminum plate side, and the welding direction is test plate length direction, and in order to prevent aluminum plate welding from taking place the shake, both ends compress tightly with clamping device, and the tight state of clamp is shown in fig. 5.

In order to obtain the temperature distribution of different positions of the aluminum plate in the welding process, a row of temperature measuring points are arranged at the position 140mm away from the arc starting position on one side of the sample plate, a K-type thermocouple is used for collecting temperature data in the welding process in real time, the distances from the temperature measuring points to the upper surface of the sample plate are respectively 5mm, 10mm and 15mm, and are respectively marked as A1, A2 and A3, and the arrangement scheme of the temperature measuring points is shown in FIG. 5.

Chemical composition of welding wire (mass fraction%) of surface 16061-T6 aluminum alloy and ER5356

Temperature field finite element simulation:

the difference of the performance of the micro-area of the welding joint is mainly caused by the difference of heat input quantity during welding, so that the temperature change of each area needs to be accurately represented. In order to carry out temperature field and performance prediction analysis, the invention adopts a finite element analysis method to carry out simulation analysis on the temperatures of different positions in a welding sample, and carries out calibration on the temperature curve measured by the experiment, thereby establishing the corresponding relation between the heated temperature and the performance of the heat affected micro-area.

And establishing a three-dimensional finite element model and carrying out grid division by adopting a welding analysis platform of Simufact software according to the actual sizes of the aluminum plate and the tool. The model mesh adopts three-dimensional 8-node units, the minimum size of the welding sample and the welding seam mesh is divided into 1mm multiplied by 2mm for considering both the calculation efficiency and the simulation precision, the total number of the nodes is 125612, the total number of the units is 105735, and the finite element simulation mesh model is shown in fig. 6. 6061 aluminum alloy is selected as material parameters, 45 steel is selected as a clamping device material, physical parameters carried in a Simufact software material library are selected, and meanwhile, technological parameters adopted by the welding experiment are input.

The filling of welding wires adopts the simulation of a living and dead unit and a double-ellipsoid mobile heat source model^[6-7]. According to the existing research^[8]The heat transfer among the aluminum plate to be welded, the tool and the ambient air is simplified into convection heat transfer and radiation heat transfer, and the expression is as follows:

in the formula: h is_convIs the convective heat transfer coefficient; t is the instantaneous temperature of the surface of the welding part; t is₀Is at room temperature; ε is the emissivity coefficient; sigma is the Stefan Boltzmann constant, T₀Taking room temperature 293K, h_convTaking 400W/K.m²σ is 5.68 × 10^-8J/K⁴·m²S, ε is 0.08^[9-10]。

Reference documents:

[6] zhengzhegtai, numerical simulation research and application of the welding process of large thick-wall structures [ D ]. tianjin university, 2007.

[7]AKBARI MOUSAVI S A A，MIRESMAEILI R.Experimental and numerical analyses of residual stress distributions in TIG welding process for 304L stainless steel[J].Journal of Materials Processing Technology，2008，208:383-394.

[8]Shanmugam N S，Buvanashekaran G，Sankaranarayanasamy K，et al.A transient finite element simulation of the temperature field and bead profile of T-joint laser welds[J].International Journal of Modelling and Simulation，2010，30(1):108-122.

[9] Zhang Jian Qiang, Zhang Guo Chi Yan, Zhao Hai Yan, et al. aluminum alloy sheet welding stress three-dimensional finite element simulation [ J ]. welding academic newspaper, 2007, 28(6):5-9.

[10]BIKASS S，ANDERSSON B，PILIPENKO A，LANGTANGEN H P.Simulation of initial cooling rate effect on the extrudate distortion in the aluminum extrusion process[J].Applied Thermal Engineering，2012，40:326-336.

FIG. 7 is a comparison graph of weld pool morphology and cross-sectional dimensions after weld formation obtained through simulation and experiment. The measured actual welding seam cladding height is 4.7mm, the welding seam cladding height of the simulation result is 4.8mm, the simulation error is 2.1%, and the welding seam cladding height is within an acceptable range. The experimental cladding height is small, which is probably because the welding seam becomes fish scale in the experiment and the selection of the section of the molten pool has certain error. Fig. 8 is a comparison graph of temperature change curves of a1 point, a2 point and A3 point simulation and experiment. The initial temperature (within 10 s) curve obtained by the experiment has certain fluctuation, which is caused by the unstable voltage in the arc starting stage. When the A1 is taken as an analysis object, the elapsed time of the experiment and the simulation is 12.5s and 12.3s from the room temperature to the peak temperature, and the elapsed time of the experiment and the simulation is 26.5s and 25.5s from the peak temperature to 200 ℃, respectively, it can be seen that the temperature measuring points are subjected to the rapid heating and cooling processes, and the temperature gradient of the measuring point far away from the welding seam position is far smaller than that of the welding seam near (such as the A3 point). The comparative analysis of the simulation result and the experimental measurement result shows that the parameters (peak temperature, heating speed and cooling speed) of the simulation temperature curve are better consistent with the related parameters in the experiment (the error is less than 4.6 percent), which indicates that the simulation result is reliable and the simulation of the temperature field of each micro-area of the welding joint is realized.

Fig. 9 is a cloud diagram of the profile of the temperature field of the sample plate cross section when the welding process obtained by simulation is stable. It can be seen that the temperature on the cross section is in a horizontal state (in a contour line distribution rule), the highest temperature close to the position of the welding line reaches 633 ℃, and the highest temperature is the middle temperature of the welding line; along the direction far away from the weld joint fusion line, the temperature is gradually reduced, and the temperature distribution difference of the same layer on the section is very small, so that the equivalent sample of the heat affected zone can be obtained by slicing and preparing the sample perpendicular to the surface of the sample plate, and the feasibility of the joint scheme is effectively proved. Therefore, the temperature field distribution of the welding sample can be determined through finite element simulation no matter when the temperature field distribution of the welding sample is determined or the sample to be measured is predicted.

And (3) testing mechanical properties:

in order to obtain the mechanical properties of the heat affected micro-area material under different thermal cycles, an equivalent tensile sample is obtained by adopting a layered sample preparation mode and a quasi-static tensile test is carried out. As shown in fig. 10, the welded test panels were sliced in layers by wire cutting in a direction away from the weld line, and the sample preparation was as shown in fig. 10 (a). Samples prepared from the weld line were numbered in order from 1. The dimensions of the tensile specimens were designed according to the standard ASTM E8M-09, the detailed dimensions are shown in FIG. 10(b), and the dimensions of the tensile specimens and the sampling pattern are shown in FIG. 10. Mechanical testing of tensile specimens was carried out on an Instron 3369 universal tester at a tensile speed of 2 mm/min.

Fig. 11 is a result of a quasi-static tensile test of the obtained tensile sample. From the engineering stress-strain curves of the samples, the tensile strength of each sample in the direction away from the weld seam shows a tendency of decreasing and then increasing, wherein the elongation after fracture of the sample 2 and the sample 3 in the heat-affected micro-zone tensile sample is 23.35% and 23.49% respectively, which are obviously higher than those of other heat-affected micro-zone samples, and the samples have better plastic deformation capability at the positions of the sample 2 and the sample 3. The tensile strength of sample 5 was the lowest of 201MPa, the strength coefficient thereof was about 58.6% of that of the base material, the tensile strengths of sample 16 and sample 17 were 340MPa and 343MPa, respectively, and the elongation after fracture was 9.29% and 9.06%, respectively, and it was concluded that the mechanical properties of the material were no longer affected by the heat of welding from the position of sample 16. From this result, it is understood that the heat-affected zone is in the range of the samples included in samples 1 to 15, and the width is about 17 mm.

Combining the temperature simulation result and the tensile experiment test result, obtaining the temperature change range of each tensile sample, and establishing the corresponding relation between the highest temperature range and the area range of each sample by considering the linear cutting processing error (the diameter of the linear cutting wire is 0.2mm) and the actual thickness of the tensile sample, as shown in table 2.

TABLE 2 temperature and range of area in which the samples are exposed

Experiment verification and application:

through the test, the corresponding relation between the temperature change of the micro-area of the equivalent welded joint and the heating temperature of the micro-area and the mechanical property (stress-strain curve) shown in the figure 1 is accurately obtained. The method has the further potential engineering application value that the temperature distribution rule of the sample to be tested is obtained through simulation, the sample to be tested is equivalent to a multilayer material, a finite element simulation fine model is established according to the corresponding relation between each layered temperature range and the mechanical property (constitutive relation), the mechanical property of the sample to be tested is finely analyzed and predicted, and compared with the traditional CAE simulation modeling analysis method, the method is more accurate in calculation and prediction precision.

The reliability of a fine model established for equivalently using a welding sample as multiple materials on the performance prediction of the sample to be tested is verified. And carrying out butt welding on 6061-T6 aluminum plates which are produced by extruding the same batch of aluminum rods and have the thickness of 3mm and the size of 300mm x 150mm, and carrying out simulation calibration on the temperature field of the sample to be tested. And then according to the temperature field distribution condition of the sample to be tested in the simulation, enabling the finite element model of the sample to be tested to be equivalent to a multilayer material composition, endowing the heat affected micro-area material attribute corresponding to the temperature in the table 2 in the tensile simulation finite element model of the sample to be tested, and evaluating the accuracy of the established equivalent method through comparing the quasi-static tensile test result of the sample to be tested with the simulation result.

Distribution of temperature fields of samples to be tested (butt joints):

while welding the butt-jointed test plates, temperature acquisition was carried out on points B1, B2 and B3 at positions 5mm, 10mm and 15mm from the center of the weld by using K-type thermocouples (see FIG. 12). The temperature variation curve of the temperature simulation and experiment temperature measuring points of the butt joint is shown in figure 13, and the cross-section molten pool morphology and the simulated temperature distribution cloud chart are shown in figure 14. As can be seen from the comparison of the temperature simulation and the experimental curve and the molten pool morphology, the temperature simulation and the experimental curve (error < 2.63%) have good consistency of the molten pool morphology, and thus, the following results can be obtained: the temperature field simulation of the butt joint can reflect the temperature distribution condition of real welding.

The butt joint tensile sample was prepared according to the international standard GT/B2651-2008, and the tensile sample size is shown in FIG. 15. The thickness of the tensile specimen was 0.95 mm.

The maximum temperature conditions of the temperature variation curves of the simulation/experiment at points B1, B2 and B3 in fig. 12 are shown in table 3.

TABLE 3 simulation/experiment table of maximum temperature conditions of temperature variation curves

As can be seen from Table 3, the highest temperature in the temperature curves of the simulation and the experiment is very close, and the simulation highest temperature and the experiment highest temperature can be used for prediction. The applicant compares the data in table 2 with the experimental maximum temperature to obtain predicted values of the mechanical properties of the tensile samples corresponding to the points measured at B1, B2 and B3, which are shown in table 4.

Predicted values of mechanical properties of tensile specimens corresponding to point measurement points of tables 4B 1, B2 and B3

Table 5 shows the measured values of the mechanical properties of the tensile specimens corresponding to the points B1, B2, and B3. Comparing table 4 and table 5, it can be known that the predicted value of the mechanical property obtained by the method of the present application is very close to the measured value, which shows that the equivalent test method of the present invention has strong reliability and can accurately predict the mechanical property of the target area.

Measured values of mechanical properties of tensile specimens corresponding to the point measurement in tables 5B 1, B2, and B3

And establishing a finite element simulation model of the tensile experiment according to the actual size of the quasi-static tensile sample. The simulation model adopts 8-node grid units, the total number of nodes of the whole model is 19374, and the total number of grids is 14668. The material in the simulation is MAT24 material card, and considering the simulation precision and the calculation efficiency, the mesh size near the welding seam (in the range of 3.8 mm-19 mm from the welding seam center) is divided into 0.5mm multiplied by 1mm, and the mesh size at the position far from the welding seam is divided into 1mm multiplied by 1 mm. Dividing the temperature field cloud chart obtained by simulation by combining the temperature in the table 3, dividing the grids of the butt joint at 555-633 ℃ into a Z1 area, giving the mechanical property to the sample 1, dividing the grids at 505-555 ℃ into a Z2 area, giving the mechanical property to the sample 2, and so on, and finally dividing the area lower than the temperature range of the sample 16 into a parent metal area, wherein the divided simulation model is shown in fig. 16. After the grid model is partitioned, the measured material performance of each region is endowed to the finite element model, then all degrees of freedom of the fixed end are restrained, a speed load of 2mm/min is applied to the loading end, and the grid unit is subjected to simulation in a full integral unit mode.

FIG. 17 is a force-displacement curve of the tensile finite element simulation results and test results for a butt joint. In the initial stage, the tensile sample is in the elastic deformation stage, the force changes linearly along with the displacement, the plastic deformation of the tensile sample begins to dominate along with the increase of the displacement, the change of the force and the displacement gradually increases in a nonlinear manner, and finally fracture failure occurs. The peak force of simulation and experiment is 7182N and 7172N respectively, the simulation error is 0.1%, the displacement of the simulation and experiment when the fracture fails is 3.3mm and 3.5mm respectively, and the simulation error is 5.7%. The variation trend of force along with displacement, the magnitude of peak force and the displacement corresponding to fracture failure in the simulation and experiment results of the butt joint can be seen, the consistency of the simulation and experiment results is good, the method for performing finite element simulation prediction on welding joints equivalent to various materials is feasible, the prediction precision can meet engineering requirements, and the method also has a certain reference value for strength simulation prediction of other joint forms.

In conclusion, the equivalent test method has strong reliability, can effectively predict the mechanical properties of different areas of the welding joint, and has engineering application value.

The foregoing examples are set forth to illustrate the present invention more clearly and are not to be construed as limiting the scope of the invention, which is defined in the appended claims to which the invention pertains, as modified in all equivalent forms, by those skilled in the art after reading the present invention.

Claims

1. An equivalent test method for mechanical properties of a welding joint microcell is characterized by comprising the following steps:

s1, providing a flat welding sample, wherein the welding sample is provided with at least 1 straight edge; taking the position of the straight line edge as a starting point, and dividing the welding sample into micro areas in sequence along the direction which is perpendicular to the straight line edge and parallel to the welding sampleL ₁Micro-areaL ₂Micro-areaL ₃… …, micro-areaL _n；

s3, cutting the welding sample processed by the S2 according to the division condition of each micro-area to obtain a tensile samplel ₁Tensile specimenl ₂Tensile specimenl ₃… … tensile test specimenl _n；

2. The equivalent test method according to claim 1, wherein in S1, n is 3. ltoreq. n.ltoreq.20.

3. The equivalent test method according to claim 1, wherein in S1, the thickness of the welding specimen is 8-12 mm.

4. The equivalent test method according to claim 1, wherein in S1, the thickness of the single micro domains is 0.8-1.2 mm.

5. The equivalent test method as set forth in claim 1, wherein the temperature change of the corresponding micro-zone is monitored by a thermocouple in S2 and/or S5.

6. The equivalent test method according to claim 1, wherein in S5, the test specimen to be tested includes at least one of a corner joint, a butt joint, and a lap joint.

7. The equivalent test method as claimed in claim 1, wherein in S4, the tensile mechanical properties include at least one of tensile strength, yield strength, elongation after break, strain hardening index.

8. The equivalent test method according to any one of claims 1 to 7, wherein the test piece is one of an aluminum alloy, a copper alloy, a titanium alloy, pure iron, carbon steel, cast iron.