A friction welding parameter optimization method based on temperature and pressure

Abstract

This invention discloses a method for optimizing friction welding parameters based on temperature and pressure. The method uses a controlled variable approach to optimize friction welding parameters. Other process variables are fixed, while the target friction welding parameters are adjusted. An evaluation method is used to assess the welded products with different parameters, outputting a pass / fail result. Finally, the optimal set of parameters is selected from the pass parameters as the optimized welding parameters. The core advantage of this method is that it allows for simultaneous detection and prediction of the plate material and preset parameters before actual welding, promptly identifying parameter combinations that do not meet the optimization standards, thereby preventing welding defects in advance. This achieves a shift from "post-production detection" to "pre-production prevention," effectively avoiding welding defects caused by improper parameters and significantly reducing production costs and quality risks.

Description

Technical Field

[0001] This invention relates to the field of friction stir welding technology, and in particular to a method for optimizing friction stir welding parameters based on temperature and pressure. Background Technology

[0002] In friction stir welding (FSW), material bonding relies on the plastic flow and densification behavior resulting from the combined effects of heat input generated by the rotating stirring head and axial pressure. Unlike the "heat input-dominated" mechanism of traditional welding, FSW is essentially a solid-state plastic forming process combining temperature and pressure. The temperature at the stirring head determines the degree of material softening and flowability, while the axial pressure of the stirring head regulates the material's flow path and densification under these temperature conditions. Both excessively low and excessively high temperatures and pressures will lead to a decrease in weld quality.

[0003] Current friction stir welding methods rely on manual observation of the weld and non-destructive testing after welding to determine weld quality. They do not obtain optimized welding parameters before welding and lack preventative measures. This leads to pressure and temperature imbalances caused by defects during welding, resulting in increased scrap rates. Therefore, a friction stir welding parameter optimization method based on temperature and pressure is needed to address these issues. Summary of the Invention

[0004] In view of the above-mentioned defects or deficiencies in the prior art, it is desirable to provide a method for optimizing friction welding parameters based on temperature and pressure.

[0005] This invention provides a method for optimizing friction welding parameters based on temperature and pressure, comprising:

[0006] S100, benchmark test: Two flawless plates are welded together to obtain the average temperature of the stirring head during the welding process. and axial pressure average and temperature fluctuation threshold and pressure fluctuation threshold ;

[0007] S200. Select a friction welding parameter that affects the welding quality, conduct a controlled variable experiment, and obtain experimental data. The experimental data includes the corresponding friction welding parameter, as well as the temperature curve and axial pressure curve at the stirring head during the welding process.

[0008] S300. The test data are evaluated using evaluation methods, and the evaluation results include qualified and unqualified.

[0009] For S400, if the statistical evaluation results are qualified, the corresponding maximum value of the friction welding parameters will be used as the welding optimization parameters.

[0010] According to the technical solution provided in the embodiments of this application, the evaluation method specifically includes the following steps:

[0011] S310. Data preprocessing: Remove interference data from the temperature curve and axial pressure curve to obtain test temperature data and test pressure data.

[0012] S320. If the test temperature data and test pressure data satisfy the threshold inequality set, proceed to the next step; otherwise, output "unqualified".

[0013] S330. Observe whether there are surface defects on the weld surface. If there are, it is judged as unqualified; otherwise, it is judged as qualified.

[0014] According to the technical solution provided in the embodiments of this application, the set of threshold inequalities in step S320 is as follows:

[0015] in, It is the temperature fluctuation threshold. It is the pressure fluctuation threshold. and These are the maximum and minimum test temperatures. and These are the maximum and minimum test pressure values. It is the average test temperature. It is the average test pressure.

[0016] According to the technical solution provided in the embodiments of this application, in step S310, the interference data includes initial data and ending data. The initial data includes temperature data and pressure data between the start of welding and the stable stage of welding, and the ending data includes temperature data and pressure data between the end of welding and the stable stage of welding.

[0017] According to the technical solution provided in the embodiments of this application, the surface defects include micropores, fractures, holes, rough peeling, grooves, and uneven flash.

[0018] According to the technical solution provided in the embodiments of this application, in step S200, N pairs of test plates are selected to perform misalignment processing and test plate welding respectively, and N sets of misalignment test data are obtained. The misalignment processing is to select one of the test plates in each pair and process a misalignment groove on its edge to be welded. The distance of the misalignment groove along the moving direction of the stirring head is recorded as the misalignment amount. The misalignment amounts of the N pairs of test plates are in an arithmetic sequence.

[0019] According to the technical solution provided in the embodiments of this application, in step S200, M pairs of test plates are selected to perform gap processing and test plate welding respectively, and M sets of gap test data are obtained. Gap processing involves selecting one of the test plates in each pair and processing a gap space at its edge to be welded. The distance of the gap space along the moving direction of the stirring head is recorded as the gap amount. The gap amounts of the M pairs of test plates are in an arithmetic sequence.

[0020] According to the technical solution provided in the embodiments of this application, the evaluation method can also be used for the quality inspection of finished products of friction stir welding.

[0021] Compared with the prior art, the beneficial effects of the present invention are:

[0022] This invention uses a controlled variable method to optimize friction welding parameters. Other process variables are fixed, while the target friction welding parameters are adjusted. An evaluation method is used to assess the welded products with different parameters, outputting a pass / fail result. Finally, the optimal set of parameters is selected from the pass parameters as the welding optimization parameters. The core advantage of this method is that it allows for simultaneous detection and prediction of the plate material and preset parameters before actual welding, promptly identifying parameter combinations that do not meet the optimization standards, thereby preventing welding defects in advance. This achieves a shift from "post-production detection" to "pre-production prevention," effectively avoiding welding defects caused by improper parameters and significantly reducing production costs and quality risks.

[0023] Furthermore, the evaluation method is based on the temperature and axial pressure at the stirring head, extracting temperature and axial pressure fluctuations during the welding process. If the temperature or axial pressure fluctuation exceeds a threshold, it indicates that the axial pressure and temperature field in the welding zone has become unstable, the internal stress distribution of the weld is unbalanced, and there is a high risk of defect formation. This method achieves rapid and accurate judgment of weld formation quality by integrating dual key information of temperature and pressure. It can not only efficiently identify the causes of defects, but also provide accurate and quantitative evaluation basis for the process effects of different friction welding parameters, thereby supporting the selection of welding parameters with optimal comprehensive performance and significantly improving the scientificity and reliability of process window selection.

[0024] It should be understood that the description in the Summary of the Invention is not intended to limit the key or essential features of the embodiments of the present invention, nor is it intended to restrict the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0025] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0026] Figure 1A flowchart illustrating the steps of a friction welding parameter optimization method based on temperature and pressure, provided in an embodiment of this application;

[0027] Figure 2 Temperature curves for optimizing the misalignment amount provided in the embodiments of this application;

[0028] Figure 3 An axial pressure curve for optimizing the misalignment amount provided in the embodiments of this application;

[0029] Figure 4 Temperature curves for optimizing the gap amount provided in the embodiments of this application;

[0030] Figure 5 An axial pressure curve for optimizing the clearance amount provided in the embodiments of this application;

[0031] Figure 6 A weld surface diagram with optimized misalignment provided for embodiments of this application;

[0032] Figure 7 A weld surface diagram with optimized gap amount provided for embodiments of this application. Detailed Implementation

[0033] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0034] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0035] Example 1

[0036] Please refer to Figures 1 to 7 The present invention provides a method for optimizing friction welding parameters based on temperature and pressure, comprising:

[0037] S100, benchmark test: Two flawless plates are welded together to obtain the average temperature of the stirring head during the welding process. and axial pressure average and temperature fluctuation threshold and pressure fluctuation threshold Among these, it is necessary to obtain the maximum and average temperatures during the pretreatment experiment. The difference between them, and the minimum and average temperatures. The difference between the values ​​is compared, and the absolute value of the difference is selected as the temperature fluctuation threshold. That is, temperature fluctuation threshold If the value is positive, then similarly, the pressure fluctuation threshold can be obtained. .

[0038] S200. Select a friction welding parameter that affects the welding quality, conduct a controlled variable experiment, and obtain experimental data. The experimental data includes the corresponding friction welding parameter, as well as the temperature curve and axial pressure curve at the stirring head during the welding process. The controlled variable method refers to keeping other process parameters constant while manually adjusting the corresponding friction welding parameter.

[0039] S300. The test data are evaluated using evaluation methods, and the evaluation results include qualified and unqualified.

[0040] For S400, if the statistical evaluation results are qualified, the corresponding maximum value of the friction welding parameters will be used as the welding optimization parameters.

[0041] This invention uses a controlled variable method to optimize friction welding parameters. Other process variables are fixed, while the target friction welding parameters are adjusted. An evaluation method is used to assess the welded products with different parameters, outputting a pass / fail result. Finally, the optimal set of parameters is selected from the pass parameters as the welding optimization parameters. The core advantage of this method is that it allows for simultaneous detection and prediction of the plate material and preset parameters before actual welding, promptly identifying parameter combinations that do not meet the optimization standards, thereby preventing welding defects in advance. This achieves a shift from "post-production detection" to "pre-production prevention," effectively avoiding welding defects caused by improper parameters and significantly reducing production costs and quality risks.

[0042] In some embodiments, the evaluation method specifically includes the following steps:

[0043] S310. Data preprocessing: Remove interference data from the temperature curve and axial pressure curve to obtain test temperature data and test pressure data.

[0044] In some embodiments, in step S310, the interference data includes initial data and ending data, the initial data including temperature and pressure data between the start of welding and the stable welding stage, and the ending data including temperature and pressure data between the end of welding and the stable welding stage.

[0045] For reference Figures 2 to 5Before welding, both the temperature and axial pressure curves showed a significant and rapid increase. This portion did not indicate stable welding and therefore cannot be used as reference data; it should be discarded as interfering data. Similarly, during the final stages of welding, both the temperature and axial pressure curves showed a significant and rapid decrease. This portion also did not involve welding and should not be used as reference data; it should also be discarded as interfering data. Ultimately, the time of the welding stabilization phase was retained as the experimental temperature and pressure data. In actual operation, the welding stabilization phase can be directly observed from the curves.

[0046] S320. If the test temperature data and test pressure data satisfy the threshold inequality set, proceed to the next step; otherwise, output "unqualified".

[0047] The threshold inequalities in step S320 are as follows:

[0048] in, It is the temperature fluctuation threshold. It is the pressure fluctuation threshold. and These are the maximum and minimum test temperatures. and These are the maximum and minimum test pressure values. It is the average test temperature. It is the average test pressure.

[0049] S330. Observe whether there are surface defects on the weld surface. If there are, it is judged as unqualified; otherwise, it is judged as qualified.

[0050] In some embodiments, the surface defects include micropores, fractures, holes, rough peeling, grooves, and uneven flash.

[0051] The evaluation method is based on the temperature and axial pressure at the stirring head. It extracts temperature and axial pressure fluctuations during the welding process. If the fluctuation range exceeds a threshold, it indicates that the axial pressure and temperature field in the welding zone has become unstable, the internal stress distribution of the weld is unbalanced, and there is a high risk of defect formation. This method achieves rapid and accurate judgment of weld formation quality by combining and comparing temperature and pressure information.

[0052] In some embodiments, the evaluation method can also be used for the quality inspection of finished products from friction stir welding. By recording the temperature and axial pressure curves at the stirring head during the friction stir welding process, and determining whether the temperature or pressure fluctuation range exceeds the corresponding fluctuation threshold, it is possible to understand whether there are stress defects caused by stress distribution imbalance inside the weld. Combined with observation of the weld surface, this achieves the purpose of quality inspection without requiring additional testing, thus improving quality inspection efficiency.

[0053] Example 2

[0054] Based on Example 1, an optimization of the misalignment amount is provided for a sheet material based on 6061:

[0055] S100, benchmark test: Two flawless plates are welded together to obtain the average temperature of the stirring head during the welding process. and axial pressure average and temperature fluctuation threshold and pressure fluctuation threshold ;

[0056] Among them, the average temperature The temperature fluctuation threshold is 490℃. 100℃; Average axial pressure The temperature fluctuation threshold is 3.0 kN. It is 0.3 kN.

[0057] S200. Select 4 pairs of test plates and perform misalignment processing and test plate welding respectively to obtain 4 sets of misalignment test data. The misalignment test data includes the misalignment amount, as well as the temperature curve and axial pressure curve at the stirring head during the welding process.

[0058] Among them, the misalignment of the four pairs of test plates was 0.1 mm, 0.3 mm, 0.5 mm, and 0.7 mm, respectively, and the corresponding temperature curves were as follows: Figure 2 The corresponding axial pressure curve is Figure 3 ;

[0059] In some embodiments, the evaluation method specifically includes the following steps:

[0060] S310. Data preprocessing: Remove interference data from the temperature curve and axial pressure curve to obtain test temperature data and test pressure data.

[0061] The full name of the temperature curve is the temperature-time curve, and the full name of the axial pressure curve is the axial pressure-application curve. Figure 2 During the test, the temperature data should be retained for 22-35 seconds. Figure 3 During the test, the data should be retained for 22-50 seconds.

[0062] S320. If the test temperature data and test pressure data satisfy the threshold inequality set, proceed to the next step; otherwise, output "unqualified".

[0063] The threshold inequalities in step S320 are as follows:

[0064] in, It is the temperature fluctuation threshold. It is the pressure fluctuation threshold. and These are the maximum and minimum test temperatures. and These are the maximum and minimum test pressure values. It is the average test temperature. This is the average test pressure. See Table 1 for detailed data. Evaluation results of the optimization of the misalignment parameter.

[0065] S330. Observe whether there are surface defects on the weld surface. If there are, it is judged as unqualified; otherwise, it is judged as qualified.

[0066] The weld surface can be referenced. Figure 6 It is evident that the weld surface with a misalignment of 0.1mm has relatively small burrs, the weld surface with a misalignment of 0.3mm has acceptable quality, the weld surface with a misalignment of 0.5mm shows a difference between the weld surface with and without misalignment, and the weld surface with a misalignment of 0.7mm has obvious porosity defects. The judgment results are detailed in Table 1. Evaluation results of misalignment parameter optimization.

[0067] Table 1. Evaluation results of the optimization of the misalignment parameter

[0068] The test temperature range is: The temperature fluctuation during the test is ( ~ The test pressure range is The test pressure fluctuation is ( ~ ).

[0069] As can be seen from Table 1, the evaluation results are all qualified when the misalignment is 0.1mm, 0.3mm and 0.5mm. Therefore, the maximum value of 0.5mm is selected as the optimization parameter for misalignment. That is to say, when the misalignment is less than 0.5mm, the weld surface is well formed, the temperature distribution is uniform, the pressure curve is stable and the welding process is in a stable state. When the misalignment increases, the material constraint weakens, the plasticized metal overflows and the load-bearing capacity decreases, resulting in increased temperature fluctuations, reduced pressure, and inducing surface defects and uneven flash morphology.

[0070] Example 3

[0071] Based on Example 1, an optimization of the gap amount is provided for a sheet material based on 6061:

[0072] S100, benchmark test: Two flawless plates are welded together to obtain the average temperature of the stirring head during the welding process. and axial pressure average and temperature fluctuation threshold and pressure fluctuation threshold ;

[0073] Among them, the average temperature The temperature fluctuation threshold is 490℃. 100℃; Average axial pressure The temperature fluctuation threshold is 3.0 kN. 0.3KN

[0074] S200. Select 5 pairs of test plates and perform gap processing and test plate welding respectively to obtain 5 sets of gap test data. The gap test data includes the gap amount, as well as the temperature curve and axial pressure curve of the stirring head during the welding process.

[0075] The gaps between the five pairs of test plates were 0.2 mm, 0.4 mm, 0.6 mm, 0.8 mm, and 1.0 mm, respectively; the corresponding temperature curves were... Figure 4 The corresponding axial pressure curve is Figure 5 .

[0076] In some embodiments, the evaluation method specifically includes the following steps:

[0077] S310. Data preprocessing: Remove interference data from the temperature curve and axial pressure curve to obtain test temperature data and test pressure data.

[0078] The full name of the temperature curve is the temperature-time curve, and the full name of the axial pressure curve is the axial pressure-application curve. Figure 4 During the test, temperature data should be retained for 22-50 seconds. Figure 5 During the test, the data should be retained for 22-50 seconds.

[0079] S320. If the test temperature data and test pressure data satisfy the threshold inequality set, proceed to the next step; otherwise, output "unqualified".

[0080] The threshold inequalities in step S320 are as follows:

[0081] in, It is the temperature fluctuation threshold. It is the pressure fluctuation threshold. and These are the maximum and minimum test temperatures. and These are the maximum and minimum test pressure values. It is the average test temperature. This is the average test pressure. See Table 2 for detailed data. Evaluation results of the gap parameter optimization.

[0082] S330. Observe the weld surface for surface defects. If defects are found, the weld is deemed unqualified; otherwise, it is deemed qualified. The weld surface can be referenced. Figure 7 It is evident that the weld surface with a gap of 0.2 mm has acceptable surface quality and no defects. The weld surface with a gap of 0.4 mm also has acceptable surface quality. The weld surface with a gap of 0.6 mm shows that the burrs at the gap are significantly reduced, indicating minor defects. The weld surface with a gap of 0.8 mm shows a significant difference in surface roughness between the gap and the area without gap, and cracks are present. The weld surface with a gap of 1.0 mm shows an even more significant difference in surface quality, and welding defects such as holes appear at the gap. The evaluation results are detailed in Table 2. Evaluation results of gap parameter optimization.

[0083] Table 2. Evaluation results of gap parameter optimization

[0084] In Table 2, the test temperature range is... The temperature fluctuation during the test is ( ~ The test pressure range is The test pressure fluctuation is ( ~ As can be seen from Table 2, although the test temperature fluctuation conforms to the threshold inequality, the test pressure fluctuation corresponding to gaps of 0.4 mm, 0.6 mm and 0.8 mm does not conform to the threshold inequality. Therefore, the latter three have internal stress imbalance during welding, and there are also surface defects at the weld. The final evaluation result is unqualified.

[0085] As can be seen from Table 2, the evaluation results are qualified when the gap is 0.2 mm and 0.4 mm. Therefore, the maximum value of 0.4 mm is selected as the gap optimization parameter. That is to say, when the gap is less than 0.6 mm, the weld surface is well formed, the temperature distribution is uniform, the pressure curve is stable, and the welding process is in a stable state. When the gap increases, the material constraint weakens, the plasticized metal overflows and the load-bearing capacity decreases, resulting in increased temperature fluctuations, reduced pressure, and the induction of surface defects and uneven flash morphology.

[0086] In the description of this specification, the terms "connection," "installation," and "fixing," etc., should be interpreted broadly. For example, "connection" can be a fixed connection, a detachable connection, or an integral connection; it can be a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0087] In the description of this specification, the terms "one embodiment," "some embodiments," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0088] The above are merely preferred embodiments of this application and are not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for optimizing friction welding parameters based on temperature and pressure, characterized in that, include: S100, benchmark test: Two flawless plates are welded together to obtain the average temperature of the stirring head during the welding process. and axial pressure average and temperature fluctuation threshold and pressure fluctuation threshold ; S200. Select a friction welding parameter that affects the welding quality, conduct a controlled variable experiment, and obtain experimental data. The experimental data includes the corresponding friction welding parameter, as well as the temperature curve and axial pressure curve at the stirring head during the welding process. S300. The test data are evaluated using evaluation methods, and the evaluation results include qualified and unqualified. For S400, if the statistical evaluation results are qualified, the corresponding maximum value of the friction welding parameters will be used as the welding optimization parameters.

2. The method for optimizing friction welding parameters based on temperature and pressure according to claim 1, characterized in that, The evaluation method specifically includes the following steps: S310. Data preprocessing: Remove interference data from the temperature curve and axial pressure curve to obtain test temperature data and test pressure data. S320. If the test temperature data and test pressure data satisfy the threshold inequality set, proceed to the next step; otherwise, output "unqualified". S330. Observe whether there are surface defects on the weld surface. If there are, it is judged as unqualified; otherwise, it is judged as qualified.

3. The method for optimizing friction welding parameters based on temperature and pressure according to claim 2, characterized in that, The threshold inequalities in step S320 are as follows: ； in, It is the temperature fluctuation threshold. It is the pressure fluctuation threshold. and These are the maximum and minimum test temperatures. and These are the maximum and minimum test pressure values. It is the average test temperature. It is the average test pressure.

4. The method for optimizing friction welding parameters based on temperature and pressure according to claim 2, characterized in that, In step S310, the interference data includes initial data and ending data. The initial data includes temperature and pressure data between the start of welding and the stable welding stage, and the ending data includes temperature and pressure data between the end of welding and the stable welding stage.

5. The method for optimizing friction welding parameters based on temperature and pressure according to claim 2, characterized in that, The surface defects include micropores, fractures, holes, rough peeling, grooves, and uneven flash.

6. The method for optimizing friction welding parameters based on temperature and pressure according to claim 2, characterized in that, In step S200, N pairs of test plates are selected for misalignment processing and welding, respectively, to obtain N sets of misalignment test data. The misalignment processing involves selecting one of the test plates in each pair and processing a misalignment groove on its edge to be welded. The distance of the misalignment groove along the direction of movement of the stirring head is recorded as the misalignment amount. The misalignment amounts of the N pairs of test plates form an arithmetic sequence.

7. The method for optimizing friction welding parameters based on temperature and pressure according to claim 2, characterized in that, In step S200, M pairs of test plates are selected for gap processing and welding, respectively, to obtain M sets of gap test data. Gap processing involves selecting one of the test plates in each pair and processing a gap space at its edge to be welded. The distance of the gap space along the direction of movement of the stirring head is recorded as the gap amount. The gap amounts of the M pairs of test plates form an arithmetic sequence.

8. The method for optimizing friction welding parameters based on temperature and pressure according to claim 2, characterized in that, The evaluation method can also be used for the quality inspection of finished products from friction stir welding.

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