Dynamic demonstration method and system for implementing defect repair by using friction stir welding process

Abstract

The invention relates to the technical field of metal defect repair, and discloses a dynamic demonstration method for implementing defect repair by using a friction stir welding process, which comprises the following steps: defect identification and three-dimensional modeling; repairing parameter matching and path planning; initializing dynamic simulation demonstration; the repairing process is dynamically visualized; performing interactive adjustment and secondary demonstration; and outputting and recording a repair scheme. The invention further provides a dynamic demonstration system for implementing defect repair by using the friction stir welding process. The defect elimination mechanism is visually displayed through three-dimensional dynamic demonstration, an operator is helped to quickly understand the influence of parameters on the repair effect, real-time parameter adjustment and simulation verification are supported, repeated test welding is avoided, material waste is reduced, and the repair efficiency is improved. The multi-physics field coupling algorithm reduces simulation result and actual repair deviation, and provides reliable guidance for field operation. And AR superposition, data tracing and multi-material adaptation are supported, and the method is suitable for high-precision welding defect repair in the fields of aerospace, automobile manufacturing and the like.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of metal defect repair, in particular to a dynamic demonstration method for defect repair by using friction stir welding process, and relates to a dynamic demonstration system for defect repair by using friction stir welding process. BACKGROUND

[0002] Friction stir welding (FSW) is widely used in the welding of light metals such as aluminum alloy and magnesium alloy, and in the process of defect repair. Some unqualified metal components may have defects such as pores, cracks, and incomplete welding. Some metal components used in the process may have defects such as cracks and wear. In order to reduce the production and use cost of these components, defect repair can be performed by using friction stir welding technology.

[0003] Traditional repair relies on the experience of operators, and the defect position, the movement trajectory of the stir head and the repair effect are difficult to visualize, so it is difficult for beginners to quickly master the skills. There is a lack of dynamic demonstration tools, and students cannot intuitively understand the defect elimination mechanism (such as the extrusion and closure process of plastic flow on pores), and the training period is long. Existing simulation software mainly focuses on static result output, and cannot dynamically simulate the heat-force-material interaction behavior in the repair process, which has a large deviation from the actual repair. SUMMARY

[0004] In view of the above problems existing in the prior art dynamic demonstration method for defect repair by using friction stir welding process, the present application is proposed.

[0005] The purpose of the present application is to provide a dynamic demonstration method for defect repair by using friction stir welding process, which is to realize the visualization of friction stir welding defect repair, the efficiency of parameter optimization and the intuitiveness of teaching and training through three-dimensional modeling, multi-physical field simulation and interactive adjustment.

[0006] To solve the above technical problems, the present application provides the following technical scheme: The dynamic demonstration method for defect repair by using friction stir welding process comprises the following steps: S1. Defect identification and three-dimensional modeling: scan the welding defect area of the repaired workpiece by industrial CT, obtain the type, size and spatial coordinates of the defect, and construct a three-dimensional model of the defect area based on the scanning data; S2. Repair parameter matching and path planning: call the preset parameter database according to the three-dimensional model of the defect, match the key parameters of friction stir welding repair, the rotation speed of the stir head is 500-3000 rpm, the welding pressure is 1-5 kN, and the welding speed is 50-500 mm / min, and plan the repair path of the stir head based on the defect profile, including the feeding direction, the residence time and the number of round trips; S3. Dynamic simulation demonstration initialization: import the three-dimensional model of the defect, repair parameters and path into the dynamic demonstration system, set the demonstration parameters, time scaling ratio 1:1-1:10, view angle adjustable range 0°-360°, generate the initial frame of the dynamic simulation of the repair process; S4. Dynamic visualization of the repair process: based on multi-physical field simulation algorithm, including heat conduction, plastic flow, stress distribution model, real-time calculation of the temperature field of the stirring zone in the repair process, temperature interval 600-1200℃, material plastic deformation and defect closure state, dynamic demonstration of the stirring head motion trajectory, defect gradual elimination process and microstructure change of the repair area through three-dimensional rendering technology; S5. Interactive adjustment and secondary demonstration: support users to adjust the repair parameters through the interactive terminal, the system updates the simulation results in real time and demonstrates the adjusted repair process again until the optimal repair scheme is generated; S6. Repair scheme output and record: package the optimal repair parameters, path and dynamic demonstration video, and store them in the database simultaneously to form a traceable defect repair case.

[0007] As a preferred scheme of the dynamic demonstration method for defect repair by using the friction stir welding process, in the step S1, the defect recognition further includes automatically classifying the defect types through a machine learning algorithm, and the classification accuracy is ≥95%; the resolution of the three-dimensional model is ≤0.01mm, which ensures the accurate restoration of the defect details, and the workpiece surface is scanned through a laser contour sensor simultaneously to capture the defect morphology such as surface depression and undercut.

[0008] As a preferred scheme of the dynamic demonstration method for defect repair by using the friction stir welding process, in the step S1, the three-dimensional modeling further includes fusing CT and laser data to construct a complete three-dimensional model containing internal and surface defects, the model error is ≤0.02mm, and the micro-crack tip with a curvature radius ≤0.05mm is modeled finely; the defect types are classified through a deep learning model trained based on 10000+ defect samples, the classification accuracy is ≥98%, and key parameters such as crack propagation direction and pore density are labeled.

[0009] As a preferred scheme of the dynamic demonstration method for defect repair by using the friction stir welding process, in the step S2, the parameter database comprises the repair parameter reference values corresponding to the different materials such as aluminum alloy / magnesium alloy / copper alloy and the plate thickness of 1-20 mm, the parameters are dynamically corrected by the deviation coefficient of the defect size of 0.8-1.2 and the reference value, the deviation coefficient is equal to the maximum defect size / typical defect size, the correction coefficient is calculated based on the three-dimensional model of the defect, when the defect depth is greater than 1 / 3 of the plate thickness, the pressure is increased by 10-20 %; when the crack length is greater than 5 mm, the path is increased by 2-3 times of reciprocating scanning; the movement path of the stir head is planned: the "center coverage + edge transition" strategy is adopted, the spiral line with the pitch of 0.5-2 m is fed in the defect area, the straight line transition with the transition length of greater than or equal to 2 mm is adopted in the edge area, and the smooth connection between the repair area and the base material is ensured.

[0010] As a preferred scheme of the dynamic demonstration method for defect repair by using the friction stir welding process, in the step S3, the physical field display is further included: the temperature field can be displayed alone / overlapped, the color temperature range is 200-1200 ℃, the stress field is 0-500 MPa, and the material flow velocity field is 0-50 mm / s. Meanwhile, the workpiece base material attributes and the stir head parameters are loaded to generate the initial frame of the demonstration.

[0011] As a preferred scheme of the dynamic demonstration method for defect repair by using the friction stir welding process, in the step S4, the multi-physical field simulation algorithm further comprises a thermal field: the center temperature of the red stirring area is increased to 900 ℃ with time, the temperature of the yellow heat affected zone is 600-800 ℃, and the heat diffusion is dynamically displayed through color gradient; Plastic flow: the particle tracking technology is adopted to demonstrate the flow trajectory of the material along the rotation direction of the stir head, and the process that the pores are extruded and filled is directly displayed; Defect state: the defect volume change is updated every 0.5 seconds until the complete closure.

[0012] As a preferred scheme of the dynamic demonstration method for defect repair by using the friction stir welding process, in the step S5, the interactive adjustment supports two modes: The parameter fine adjustment user adjusts the rotating speed from 1900 rpm to 2000 rpm through the slider of the interactive terminal, and the system immediately recalculates the heat input and the material flow velocity; Linkage adjustment: after the adjustment, the system generates a new dynamic demonstration frame within 3 seconds, the defect closure time is shortened by 15 %, but the heat affected zone range is expanded by 5 %, which helps the user to weigh the parameter influence.

[0013] As a preferred scheme of the dynamic demonstration method for defect repair by using the friction stir welding process, in the step S5, the output file includes: a repair parameter table containing rotation speed, pressure, path coordinates; a dynamic demonstration video containing a temperature field, a defect change time axis; and simulated strength data of the repaired joint based on stress simulation, all of which are associated with the defect ID and stored in a database for subsequent tracing.

[0014] The dynamic demonstration system for defect repair by using the friction stir welding process comprises: A defect detection module comprising an industrial CT scanner and a defect recognition unit, used for obtaining three-dimensional data of the welding defects and classifying the defects; A parameter database storing baseline values and correction algorithms of the friction stir welding repair parameters corresponding to different materials, plate thicknesses and defect types; A path planning unit generating a repair path of the friction stir welding head based on the three-dimensional model of the defect, including coordinate points, feed speed and turning information; A multi-physical field simulation unit, which is internally provided with a heat conduction model, a plastic flow model and a stress distribution model, and is used for calculating dynamic physical quantities in the repair process; A dynamic rendering module, which uses GPU accelerated rendering technology to convert the simulation data into a visual three-dimensional dynamic demonstration video with a frame rate of ≥30 fps; An interactive terminal comprising a touch screen and a parameter adjustment slider, which supports real-time adjustment of the repair parameters by the user and triggers a secondary demonstration; A data storage and output module, used for storing the defect model, the repair scheme and the demonstration video, and supporting PDF / MP4 format output.

[0015] As a preferred scheme of the dynamic demonstration system for defect repair by using the friction stir welding process, in the step S5, the output file includes: a repair parameter table containing rotation speed, pressure, path coordinates; a dynamic demonstration video containing a temperature field, a defect change time axis; and simulated strength data of the repaired joint based on stress simulation, all of which are associated with the defect ID and stored in a database for subsequent tracing. The defect detection module further comprises a laser profile sensor, which is used to supplement the scanning of the defect surface morphology, and after fusion with the industrial CT data, the surface roughness representation error of the three-dimensional model is ≤0.5 μm; The multi-physical field simulation unit is connected with the actual welding equipment through a PLC interface, can read the current and voltage data of the real welding process, is used for calibrating the simulation model, and makes the simulated temperature field have a deviation of ≤50℃ from the actual measurement; The dynamic rendering module supports AR augmented reality function, can superimpose the dynamic demonstration picture with the real-time image of the workpiece to be repaired, has an alignment error of ≤1 mm, and is convenient for the operator to compare the simulated and actual repair positions.

[0016] The beneficial effects of the present application: through three-dimensional dynamic demonstration, the defect elimination mechanism is intuitively displayed, the operation personnel are helped to quickly understand the influence of parameters on the repair effect, real-time parameter adjustment and simulation verification are supported, multiple trial weldings are avoided, material waste is reduced, and repair efficiency is improved; the multi-physics field coupling algorithm reduces the deviation between simulation results and actual repair, provides reliable guidance for on-site operation; AR superposition, data tracing and multi-material adaptation are supported, and the present application is suitable for high-precision welding defect repair in the fields of aerospace and automobile manufacturing. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor. Among them: Figure 1 It is a defect data acquisition process schematic diagram of the present application.

[0018] Figure 2 It is a multi-physics field dynamic simulation and visualization process schematic diagram of the present application.

[0019] Figure 3 It is a parameter matching and path planning process schematic diagram of the present application.

[0020] Figure 4 It is a parameter optimization and scheme output process schematic diagram of the present application. DETAILED DESCRIPTION

[0021] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings of the specification.

[0022] In the following description, many specific details are set forth in order to provide a thorough understanding of the present application, but the present application can also be implemented in other ways different from the description, and those skilled in the art can make similar generalizations without departing from the connotation of the present application, therefore the present application is not limited by the specific embodiments disclosed below.

[0023] Secondly, the "one embodiment" or "embodiment" referred to herein means that the specific features, structures or characteristics can be included in at least one implementation of the present application. "In one embodiment" appearing in different places in the specification does not mean the same embodiment, nor is it an independent or alternative embodiment that excludes other embodiments.

[0024] Thirdly, the application is described in detail in combination with the schematic diagram. In the detailed description of the embodiments of the application, the cross-sectional view of the device structure is locally enlarged without the general proportion for the convenience of illustration, and the schematic diagram is only an example which should not limit the scope of protection of the application herein. In addition, the three-dimensional spatial dimensions including length, width and depth should be included in the actual manufacture. Embodiment 1

[0025] Reference Figures 1-4 For the first embodiment of the application, a dynamic demonstration method for implementing defect repair by using a friction stir welding process is provided, comprising, The dynamic demonstration method for implementing defect repair by using a friction stir welding process comprises the following steps: S1. Defect identification and three-dimensional modeling: the welding defect area of the workpiece to be repaired is scanned by industrial CT to obtain the type (porosity / crack / undercut), size (length / depth / area) and spatial coordinates of the defect, and a three-dimensional model of the defect area is constructed based on the scanning data; S2. Repair parameter matching and path planning: according to the three-dimensional model of the defect, the preset parameter database is called to match the key parameters of the friction stir welding repair, the rotation speed of the stir head is 500-3000 rpm, the welding pressure is 1-5 kN, and the welding speed is 50-500 mm / min, and the repair path of the stir head is planned based on the defect profile, including the feeding direction, the residence time, and the number of round trips; S3. Dynamic simulation demonstration initialization: the three-dimensional model of the defect, the repair parameters and the path are imported into the dynamic demonstration system, the demonstration parameters are set, the time scaling ratio is 1:1-1:10, the adjustable range of the viewing angle is 0°-360°, and the initial frame of the dynamic simulation of the repair process is generated; S4. Dynamic visualization of the repair process: based on the multi-physical field simulation algorithm, including the heat conduction, plastic flow and stress distribution model, the temperature field of the stirring zone in the repair process is calculated in real time, the temperature interval is 600-1200℃, the material plastic deformation and the defect closure state, the movement trajectory of the stir head, the gradual elimination process of the defect and the microstructure change of the repair area are dynamically demonstrated by three-dimensional rendering technology; S5. Interactive adjustment and secondary demonstration: the user can adjust the repair parameters (such as rotation speed ±100 rpm, pressure ±0.5 kN) through the interactive terminal, the system updates the simulation results in real time and demonstrates the adjusted repair process again until the optimal repair scheme is generated; S6. Repair scheme output and record: the optimal repair parameters, path and dynamic demonstration video are packaged and output, and are stored in the database at the same time to form a traceable defect repair case.

[0026] Wherein: S1. Defect identification and three-dimensional modeling: Take the repair of a weld defect in an aero-engine case (6061 aluminum alloy, 8 mm thick) as an example: Industrial CT scanning found that there were 3 pores (1-2 mm in diameter) and 1 micro-crack (3 mm long and 2 mm deep) inside; The laser profile sensor detected a 0.3 mm depression on the corresponding surface; The system fused the data to build a three-dimensional model, automatically classified it as a "pore + surface depression + micro-crack" composite defect, and labeled the feature that the crack extended along the 45° direction.

[0027] S2. Repair parameter matching and path planning: The intelligent parameter library calls the 6061 aluminum alloy composite defect reference parameters: speed 1500 rpm, pressure 3 kN, speed 200 mm / min; Because the crack depth reaches 1 / 4 of the plate thickness, the system automatically corrects the pressure to 3.3 kN; because there is a surface depression, the path adds 1 surface finishing scan (speed reduced to 150 mm / min); The path planning is "central spiral covering the pore area (pitch 1 mm) → reciprocating scanning the crack area (2 times) → edge straight line finishing the surface depression", ensuring that the repair area diameter is ≥ 2 times the maximum size of the defect.

[0028] S3. Dynamic simulation demonstration initialization: Import the three-dimensional model, parameters and path into the system, set the time scaling ratio to 1:5 (i.e. the actual 10-second repair process is 2 seconds in the demonstration), the default viewing angle is 45° along the welding direction, and the side view (observe the depth direction repair) or cross-sectional view (observe the internal defect closure) can be switched to by mouse dragging.

[0029] S4. Dynamic visualization of the repair process: During the demonstration, the operator can observe: Temperature field: the temperature at the center of the stirring zone rises from room temperature to 950°C (red) within 10 seconds, the heat-affected zone (HAZ) is yellow (600-800°C), and it dynamically expands as the stirring head moves; Material flow: tracking particles show that plastic material forms a spiral flow along the rotation direction of the stirring head, and the pores are gradually squeezed to the edge and filled (the pores are completely closed at 15 seconds); Stress field: stress concentration (350 MPa) appears at the edge of the repair area, and gradually decreases to 150 MPa (matching the parent material) during the cooling process.

[0030] S5. Interactive adjustment and second demonstration: If the operator finds that the heat-affected zone is too large (more than 5 mm as required), he can reduce the speed from 1500 rpm to 1300 rpm through the interactive terminal: System real-time demonstration: heat input reduced by 12%, peak temperature dropped to 880℃, heat-affected zone width reduced from 6mm to 4.5mm; Synchronous display side effects: defect closure time extended from 15 seconds to 18 seconds, efficiency and heat-affected zone need to be balanced; Finally, the compromise of 1400rpm and 3.2kN pressure was chosen, which met the requirements of heat-affected zone and ensured the repair efficiency.

[0031] S6. Repair scheme output and record: Output files include: ① Repair parameter table (including speed, pressure, path coordinates); ② Dynamic demonstration video (MP4 format, including temperature field, defect change timeline); ③ Simulated strength data of repaired joint (based on stress simulation), all data associated with defect ID, stored in database for subsequent traceability.

[0032] As a preferred scheme of the dynamic demonstration method for defect repair by using friction stir welding process, in step S1, the defect recognition further includes automatically classifying the defect type by a machine learning algorithm, and the classification accuracy is ≥95%; the resolution of the three-dimensional model is ≤0.01mm, which ensures the accurate restoration of defect details (such as micro-crack tips), and the workpiece surface is scanned by a laser profile sensor (accuracy 0.1μm) at the same time to capture the defect morphology such as surface indentation and undercut.

[0033] Specifically, in step S1, the three-dimensional modeling further includes fusing CT and laser data to construct a complete three-dimensional model containing internal and surface defects, the model error is ≤0.02mm, and the micro-crack tip (curvature radius ≤0.05mm) is modeled in detail; the defect type is automatically classified by the trained deep learning model (based on 10000+ defect samples), and the classification accuracy is ≥98%, and the key parameters (such as crack propagation direction, pore density) are labeled.

[0034] Further, in step S2, the parameter database contains the repair parameter benchmark values corresponding to different materials (aluminum alloy / magnesium alloy / copper alloy) and plate thickness (1-20mm), the parameters are dynamically corrected by the deviation coefficient (0.8-1.2) of the defect size and the benchmark value, the deviation coefficient = defect maximum size / typical defect size, at the same time, the correction coefficient is calculated based on the three-dimensional model of the defect, when the defect depth > 1 / 3 of the plate thickness, the pressure increases by 10-20%; when the crack length > 5mm, the path increases by 2-3 times of reciprocating scanning; the motion path of the stir head is planned: the "center coverage + edge transition" strategy is adopted, the spiral feed (pitch 0.5-2mm) is used in the defect area, and the straight line transition (transition length ≥2mm) is used in the edge area, which ensures the smooth connection of the repair area and the base material.

[0035] Further, in step S3, the physical field display is also included: temperature field can be displayed alone / overlaid, color temperature range 200-1200℃, stress field (0-500MPa), material flow rate field (0-50mm / s); At the same time, the workpiece base material properties (such as thermal conductivity, yield strength) and the stirrer head parameters (material H13 steel, diameter 5-15mm, shaft shoulder diameter 8-20mm) are loaded to generate the initial frame for demonstration.

[0036] Preferably, in step S4, the multi-physical field simulation algorithm further includes a thermal field: the temperature at the center of the stirring zone rises to 900℃ (red) over time, and the temperature in the heat affected zone (HAZ) is 600-800℃ (yellow), and the thermal diffusion is dynamically displayed through color gradient; Plastic flow: particle tracking technology is used to demonstrate the flow trajectory of the material along the rotation direction of the stirrer head, and the process of air holes being extruded and filled is intuitively displayed; Defect state: the defect volume change (such as the air hole being reduced from 2mm³ to 0) is updated every 0.5 seconds until it is completely closed.

[0037] It should be noted that in step S5, interactive adjustment supports two modes: Parameter fine-tuning (single parameter independent adjustment), the user adjusts the speed from 1900rpm to 2000rpm through the slider of the interactive terminal, and the system immediately recalculates the heat input (increased by 10%) and the material flow rate (increased by 20%); Linkage adjustment (speed and welding speed are synchronously increased / decreased in proportion 1:50), after adjustment, the system generates new dynamic demonstration frames in 3 seconds, showing that the defect closure time is shortened by 15%, but the heat affected zone range is expanded by 5%, helping the user to weigh the parameter influence.

[0038] Preferably, in step S5, the output file includes: repair parameter table (including speed, pressure, path coordinates); dynamic demonstration video (MP4 format, including temperature field, defect change timeline); simulated strength data of the repaired joint (based on stress simulation), all data are associated to the defect ID and stored in the database for subsequent tracing.

[0039] In summary, through three-dimensional dynamic demonstration, the defect elimination mechanism is intuitively displayed, helping the operator to quickly understand the influence of parameters on the repair effect, supporting real-time adjustment and simulation verification of parameters, avoiding multiple trial welding, reducing material waste by 60%, and improving repair efficiency by 30%. The multi-physical field coupling algorithm ensures that the simulation result deviates from the actual repair by ≤10%, providing reliable guidance for on-site operation. Supporting AR superposition, data tracing and multi-material adaptation, it is suitable for high-precision welding defect repair in the fields of aerospace, automobile manufacturing, etc. Embodiment 2

[0040] Reference Figures 1-4For the second embodiment of the invention, which is different from the first embodiment, a dynamic demonstration system for implementing defect repair using the friction stir welding process is provided, comprising: A defect detection module, including an industrial CT scanner and a defect recognition unit, is used to obtain three-dimensional data of the welding defects and classify them; it is composed of an industrial CT scanner (scanning range 500x500x300mm), a laser profile sensor (scanning rate 1000 lines / s), and a defect classification module, which is used to obtain multi-dimensional data of the defects and automatically classify them; A parameter database stores the friction stir welding repair parameter benchmark values and correction algorithms corresponding to different materials, plate thicknesses, and defect types; it stores 2000+ sets of actual repair case parameters, including a material-plate thickness-defect type correlation model, supporting automatic parameter correction based on defect characteristics (correction error ≤5%); A path planning unit generates a repair path for the stir head based on the three-dimensional model of the defect (including coordinate points, feed speed, and turning information); it uses AI algorithms to optimize the stir head motion trajectory, supports custom path types (spiral line, reciprocating line, and composite curve), and outputs coordinate points (accuracy ±0.01mm); A multi-physics field simulation unit, which has built-in heat conduction, plastic flow, and stress distribution models, is used to calculate dynamic physical quantities during the repair process; it integrates a heat conduction model (Fourier equation), a plastic flow model (Johnson-Cook constitutive equation), and a stress calculation model (finite element method), with a simulation error ≤8%; A dynamic rendering module uses GPU accelerated rendering technology to convert simulation data into visual three-dimensional dynamic demonstration videos with a frame rate ≥30fps; it uses UnrealEngine for GPU accelerated rendering, supports real-time visualization of physical fields, perspective interaction, and key frame labeling, and has a screen resolution ≥1920x1080; An interactive terminal, which includes a touch screen and a parameter adjustment slider, supports real-time adjustment of repair parameters and triggering of secondary demonstration; it includes a 15.6-inch touch screen, a parameter adjustment knob, and a VR interface, supports touch / knob parameter adjustment, and allows immersive observation of repair details in VR mode (field of view 110°); A data storage and output module is used to store defect models, repair schemes, and demonstration videos, and supports PDF / MP4 format output. It supports communication with welding equipment (such as friction stir welding robots) and detection equipment (such as ultrasonic flaw detectors) to realize parameter issuance and repair effect closed-loop verification, and the data interface module supports offline / online working modes: offline mode outputs standard format files (PDF / G code); online mode dynamically adjusts repair parameters through real-time interaction with welding equipment via industrial Ethernet (communication rate 100Mbps).

[0041] Preferably, the defect detection module also includes a laser profile sensor for supplementing the scanning of the defect surface topography, and after fusion with the industrial CT data, the surface roughness representation error of the three-dimensional model is ≤0.5 μm; The multi-physics field simulation unit is connected with the actual welding equipment through a PLC interface, and can read the current and voltage data of the real welding process, which is used to calibrate the simulation model (so that the simulated temperature field has a deviation of ≤50℃ from the actual measurement). The dynamic rendering module supports AR augmented reality function, which can superimpose (alignment error ≤1mm) the dynamic demonstration picture with the real-time image of the workpiece to be repaired, so as to facilitate the operator to compare the simulated and actual repair positions. The dynamic rendering platform supports the comparison function before and after defect repair, which can superimpose the defect model before repair and the simulated organization (such as grain size, misplacement distribution) after repair, and mark the mechanical property prediction value (tensile strength, elongation).

[0042] In summary, the industrial CT and laser profile sensor work together to solve the limitations of single detection method: CT is good at internal defects, and laser is good at surface topography. After fusion, the recognition accuracy of composite defects is improved to 98%, which provides a reliable basis for subsequent parameter matching.

[0043] Based on the repair data construction in the aerospace field for 10 years, including extreme working condition cases (such as magnesium alloy repair in-10℃ low temperature environment), the parameters can be automatically corrected (such as increasing the speed by 8% at low temperature to ensure heat input), to ensure that the simulation results are close to the actual situation on site.

[0044] The "thermal-force-material" multi-field coupling algorithm is adopted, considering the friction heat generation between the stirring head and the workpiece (friction coefficient 0.3-0.5), material plastic flow (strain rate 10-100s⁻¹) and cooling shrinkage stress, and the simulation error is ≤8% (compared with the actual temperature measurement of welding).

[0045] VR glasses are supported, and the operator can "be in" the repair scene to intuitively observe the relative position of the stirring head and the defect; the data interface module can directly output G code to the friction stir welding robot, realizing seamless connection of "demonstration scheme-robot execution", and reducing the on-site debugging time.

[0046] The application aims at the core pain points of "insufficient visualization, difficult parameter optimization, and disconnection between simulation and reality" in the friction stir welding defect repair process. Through multi-dimensional technical innovation, the dynamic demonstration and precise optimization of the repair process are realized. The technical key points and creativity include: multi-source fusion defect three-dimensional modeling technology, which creatively fuses industrial CT (internal defect detection) and laser profile sensor (surface defect detection) data, constructs a three-dimensional model containing "internal + surface" complete defects, and finely models the micro-crack tip, realizes automatic identification of defect types and intelligent labeling of key parameters through a deep learning model, solving the problem of incomplete and low-precision identification of composite defects by traditional single detection method; dynamic adaptive parameter matching and path planning strategy, which constructs a multi-material parameter database, dynamically corrects the repair parameters through "defect size-benchmark value deviation coefficient", and innovatively adopts the "center spiral coverage + edge straight line transition" path planning strategy to ensure smooth connection between the repair area and the base material. This strategy breaks through the limitations of traditional fixed parameter repair and realizes precise matching of parameters and defect characteristics; multi-physical field real-time coupling simulation and dynamic visualization technology, which constructs a multi-field coupling algorithm based on heat conduction, plastic flow and stress distribution model, calculates the temperature field of the stirring zone, material flow trajectory and defect volume change in real time, and realizes dynamic visualization through GPU accelerated rendering. Creatively, the abstract physical process is converted into a dynamic picture that can be directly perceived, solving the problem that traditional static simulation cannot show the heat-force-material interaction behavior; interactive parameter optimization and AR augmented reality fusion support two modes of "parameter fine-tuning" and "linkage adjustment", and a new dynamic demonstration frame is generated within 3 seconds after adjustment, quantitatively showing the influence of parameter changes on the thermal influence zone and defect closure time, assisting users in weighing optimization; combined with AR technology, the demonstration picture is superimposed with the real-time image of the workpiece, realizing intuitive comparison between "simulation and reality". This interactive mechanism breaks through the bottleneck of traditional parameter optimization relying on experience and trial and error, improving optimization efficiency and on-site guidance accuracy; full-process data closed loop and equipment cooperation, the system is connected with the actual welding equipment through the PLC interface, reads the real welding current and voltage data to calibrate the simulation model, and outputs G code to directly drive the welding robot, realizing the data closed loop of "defect detection-simulation optimization-actual execution-effect verification". Creatively, it breaks down the barrier between "simulation demonstration" and "on-site execution", solving the problem of disconnection between traditional simulation and actual operation.

[0047] Compared with the defect repair technology based on the traditional friction stir welding technology, the technical progress of the present application mainly embodies in the following dimensions: the repair efficiency and material utilization rate are greatly improved, the traditional scheme repair relies on "trial and error method", single defect parameter debugging needs multiple trial welding, and the material waste is more, the present application reduces the number of trial welding through real-time simulation optimization of parameters, reduces the material waste rate, improves the repair efficiency, and reduces the production cost; the training cycle and the operation threshold are significantly reduced, the traditional scheme repair relies on the experience of the operator, the training cycle of the novice needs 3-6 months, the present application directly shows the defect elimination mechanism through dynamic visual demonstration, cooperates with AR immersive observation, shortens the training cycle of the novice to 1-2 weeks, and reduces the technical threshold; the deviation between simulation and actual repair is significantly reduced, the deviation between the simulation results of the traditional simulation software and the actual is often >20%, the present application can control the simulation error to ≤8% through the multi-field coupling algorithm and the real-time data calibration of the equipment, provides reliable theoretical guidance for the on-site operation, and reduces the repair failure caused by unreasonable parameters; the adaptability and scene expansion are comprehensively enhanced, the traditional equipment only supports fixed material and defect type repair, and needs to be disassembled for replacement, the present application supports multi-material adaptation, realizes dynamic correction through the parameter database and path customization, can meet the high-precision repair requirements in the fields of aviation, automobile manufacturing and the like, and the scene expansion is improved; the digitization and intelligent level are greatly improved, the traditional repair process has low digitization degree, relies on manual recording and experience inheritance, the present application realizes traceability of the repair case through the defect ID correlation of the whole process data, supports cooperation with the welding robot and the detection equipment to form a digital closed loop, and promotes the welding repair from "experience driven" to "data driven".

[0048] It should be noted that the constructions and arrangements shown in the various exemplary embodiments described herein are illustrative only. Although only a few embodiments have been described in detail in this disclosure, those skilled in the art who review this disclosure will readily appreciate that many modifications are possible (e.g., variations in sizes, dimensions, structures, shapes and proportions of the various elements, values of parameters, mounting arrangements, use of materials, colors, orientations, etc.) without materially departing from the novel teachings and advantages of the subject matter described in the instant disclosure. For example, elements shown as integrally formed can be constructed of multiple parts or elements, the position of elements can be reversed or otherwise varied, and the nature or number of elements can be altered or varied. The order or sequence of any process or method steps can be varied or re-sequenced without materially affecting the operation of the subject matter. Any reference to claim interpretation includes claim interpretation in both the affirmative and negative form. Any "means plus function" clauses are intended to cover the structures described herein as performing the recited functionality and not only structural equivalents but also equivalent structures. Other substitutions, modifications, changes, and omissions can be made in the design, operating conditions, and arrangement of the exemplary embodiments without departing from the scope of the present disclosure. Accordingly, while the best mode embodiments have been described herein, those skilled in the art will appreciate that various modifications, changes, alterations, deviations, variations, adaptations, and / or alterations can be made in the design, operating conditions, and arrangements of the embodiments disclosed herein without departing from the essential scope of the present disclosure as set forth in the appended claims.

Claims

1. A dynamic demonstration method for defect repair using friction stir welding, characterized in that, Includes the following steps: S1. Defect Identification and 3D Modeling: By scanning the welding defect area of ​​the workpiece to be repaired with industrial CT, the type, size and spatial coordinates of the defect are obtained, and a 3D model of the defect area is constructed based on the scan data; S2. Repair Parameter Matching and Path Planning: Based on the 3D defect model, the preset parameter database is called to match the key parameters of friction stir welding repair, and the repair path of the stirring head is planned based on the defect contour; S3. Dynamic simulation demonstration initialization: Import the defect 3D model, repair parameters and path into the dynamic demonstration system, set the demonstration parameters, time scaling ratio 1:1-1:10, and adjustable viewing angle range 0°-360°, and generate the initial frame of the dynamic simulation of the repair process. S4. Dynamic visualization of the repair process: Based on multiphysics simulation algorithm, the temperature field of the stirring zone, material plastic deformation and defect closure state are calculated in real time during the repair process. The movement trajectory of the stirring head, the gradual elimination process of defects and the microstructure changes of the repair area are dynamically demonstrated through three-dimensional rendering technology. S5. Interactive Adjustment and Secondary Demonstration: Users can adjust the repair parameters through the interactive terminal. The system updates the simulation results in real time and re-demonstrates the adjusted repair process until the optimal repair solution is generated. S6. Repair Solution Output and Recording: Package the optimal repair parameters, paths, and dynamic demonstration videos, and store them synchronously in the database to form a traceable defect repair case.

2. The dynamic demonstration method for defect repair using friction stir welding as described in claim 1, characterized in that, In step S1, defect identification also includes automatically classifying defect types using machine learning algorithms, with a classification accuracy of ≥95%; the resolution of the three-dimensional model is ≤0.01mm, and the workpiece surface is scanned simultaneously using a laser contour sensor to capture the morphology of surface depressions and undercut defects.

3. The dynamic demonstration method for defect repair using friction stir welding as described in claim 2, characterized in that, In step S1, the three-dimensional modeling also includes fusing CT and laser data to construct a complete three-dimensional model containing internal and surface defects with a model error ≤0.02mm, and performing fine modeling on the microcrack tips with a curvature radius ≤0.05mm; classifying defect types using a deep learning model trained on 10,000+ defect samples with a classification accuracy ≥98%, and labeling key crack parameters.

4. The dynamic demonstration method for defect repair using friction stir welding as described in claim 3, characterized in that, In step S2, the parameter database contains repair parameter benchmark values ​​corresponding to different materials and plate thicknesses of 1-20mm. The parameters are dynamically corrected by a deviation coefficient of 0.8-1.2 between the defect size and the benchmark value. The deviation coefficient = maximum defect size / typical defect size. At the same time, the correction coefficient is calculated based on the three-dimensional defect model. When the defect depth is greater than 1 / 3 of the plate thickness, the pressure is increased by 10-20%; when the crack length is greater than 5mm, the path is increased by 2-3 reciprocating scans. The movement path of the stirring head is planned: the "center coverage + edge transition" strategy is adopted. The defect area adopts a spiral feed with a pitch of 0.5-2m, and the edge area adopts a straight transition with a transition length of ≥2mm.

5. The dynamic demonstration method for defect repair using friction stir welding as described in claim 4, characterized in that, Step S3 also includes physical field display: temperature field can be displayed individually or superimposed, color temperature range 200-1200℃, stress field 0-500MPa, and material flow velocity field 0-50mm / s; Simultaneously load the workpiece base material properties and stirring head parameters to generate the initial demonstration frame.

6. The dynamic demonstration method for defect repair using friction stir welding as described in claim 5, characterized in that, In step S4, the multiphysics simulation algorithm also includes a thermal field: the temperature at the center of the red stirring zone rises to 900℃ over time, and the temperature of the yellow heat-affected zone is 600-800℃, dynamically displaying heat diffusion through color gradient. Plastic Flow: Using particle tracking technology, the flow trajectory of the material along the direction of rotation of the stirring head is demonstrated, visually showing the process of pores being squeezed and filled; Defect status: The defect volume change is updated every 0.5 seconds until it is completely closed.

7. The dynamic demonstration method for defect repair using friction stir welding as described in claim 6, characterized in that, In step S5, the interactive adjustment supports two modes: For parameter fine-tuning, users can adjust the rotation speed from 1900 rpm to 2000 rpm using the slider on the interactive terminal, and the system will immediately recalculate the heat input and material flow rate. The system is adjusted in tandem, and after the adjustment, a new dynamic demonstration frame is generated within 3 seconds, showing that the defect closure time is shortened by 15%, but the heat-affected zone is expanded by 5%, helping users to weigh the impact of parameters.

8. The dynamic demonstration method for defect repair using friction stir welding as described in claim 7, characterized in that, In step S5, the output files include: a repair parameter table, including rotational speed, pressure, and path coordinates; a dynamic demonstration video, including temperature field and defect change time axis; and simulated strength data of the repaired joint based on stress simulation. All data are associated with the defect ID and stored in the database for subsequent traceability.

9. A dynamic demonstration system for defect repair using friction stir welding, characterized in that, include: The defect detection module, including an industrial CT scanner and a defect recognition unit, is used to acquire and classify three-dimensional data of welding defects. The parameter database stores the baseline values ​​and correction algorithms for friction stir welding repair parameters corresponding to different materials, plate thicknesses, and defect types. The path planning unit generates a repair path for the stirring head based on the 3D defect model, including coordinate points, feed speed, and steering information. The multiphysics simulation unit has built-in models for heat conduction, plastic flow, and stress distribution, which are used to calculate dynamic physical quantities during the repair process. The dynamic rendering module uses GPU-accelerated rendering technology to transform simulation data into a visual 3D dynamic demonstration video with a frame rate of ≥30fps. The interactive terminal includes a touch screen and parameter adjustment slider, which allows users to adjust and repair parameters in real time and trigger a secondary demonstration. The data storage and output module is used to store defect models, repair solutions, and demonstration videos, and supports PDF / MP4 format output.

10. The dynamic demonstration system for defect repair using friction stir welding as described in claim 9, characterized in that, The defect detection module also includes a laser contour sensor to supplement the scanning of the defect surface morphology. After being fused with industrial CT data, the surface roughness characterization error of the three-dimensional model is ≤0.5μm. The multiphysics simulation unit is connected to the actual welding equipment via a PLC interface, and can read the current and voltage data of the actual welding process for calibrating the simulation model, so that the deviation between the simulated temperature field and the actual measurement is ≤50℃. The dynamic rendering module supports AR augmented reality functionality, which can overlay dynamic demonstration images with real-time images of the workpiece to be repaired, with an alignment error of ≤1mm.

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