An x80 pipeline electric arc additive manufacturing method and system based on a thermal-force coupling regulation mechanism

Abstract

The present application relates to a kind of X80 pipeline electric arc additive manufacturing method and system based on thermal-mechanical coupling regulation mechanism, the method includes: first, the continuous cooling transformation curve of X80 steel is determined to determine phase transition temperature interval;Thermal-mechanical coupling finite element model containing mobile heat source and mobile force source is constructed and calibrated;On this basis, process prediction model is established, and the mapping relationship between process parameter and temperature field, stress field is obtained;Optimization algorithm is used in simulation space to carry out reverse optimization, and the optimal thermal-mechanical coupling process parameter combination is obtained;Finally, in actual repair process, deposit layer temperature is monitored in real time by infrared temperature measurement, when temperature falls into phase transition temperature interval and approaches optimal impact intervention temperature, drive with welding ultrasonic impact device to execute mechanical impact.The present application can significantly reduce residual tensile stress and improve the degree of grain refinement.

Description

Technical Field

[0001] This invention relates to the field of additive manufacturing and remanufacturing technology of materials, specifically to an X80 pipe arc additive manufacturing method and system based on a thermo-mechanical coupling control mechanism. Background Technology

[0002] X80 pipeline steel is the mainstream material for modern long-distance high-pressure oil and gas pipelines. When using electric arc additive manufacturing (WAAM) technology to repair pipeline defects in the field, the repair area is prone to residual tensile stress close to the material's yield strength due to localized non-uniform rapid heating and cooling cycles, and the grain structure is coarse, which seriously affects the service safety of the pipeline.

[0003] In existing technologies, post-weld ultrasonic impact treatment (UIT) is typically used to eliminate residual stress. The conventional practice is to perform the impact after the weld has completely cooled to room temperature.

[0004] However, this "room temperature impact" method has significant limitations: on the one hand, cold metals have high deformation resistance, resulting in a shallow plastic deformation layer induced by impact; on the other hand, it misses the optimal window for utilizing the material's high-temperature phase transformation properties. Although existing research indicates that transformation-induced plasticity (TRIP) can more effectively eliminate stress, the phase transformation temperature window for X80 steel is extremely narrow and dynamically changes with the cooling rate, making it unsuitable for stress elimination using TRIP. Furthermore, current process parameter designs largely rely on manual trial and error, lacking quantitative control methods for complex thermo-mechanical coupling fields, and thus failing to accurately capture the optimal impact timing.

[0005] Therefore, there is an urgent need for an intelligent manufacturing method and system that can accurately predict and control the thermo-mechanical coupling field in real time. Summary of the Invention

[0006] To address the aforementioned problems, the purpose of this invention is to provide a method and system for arc additive manufacturing of X80 pipes based on a thermo-mechanical coupling control mechanism. This method overcomes the problems of high residual stress, coarse grains, and blind timing in traditional impact processes in the existing arc additive manufacturing of X80 pipe repair layers. To achieve the above objectives, the present invention adopts the following technical solution: This invention provides a method for arc additive manufacturing of X80 pipes based on a thermo-mechanical coupling control mechanism, comprising the following steps: S1. Phase transformation characteristic calibration: Conduct thermal simulation tests on X80 pipeline steel to obtain the continuous cooling transformation curve under preset cooling conditions, and determine the phase transformation temperature range Ar3~Ar1 for the transformation of X80 pipeline steel from austenite to ferrite or bainite. S2. Thermo-mechanical coupling model calibration: Establish a thermo-mechanical coupling finite element model containing a moving heat source and a moving force source; calibrate the key parameters of the finite element model using the molten pool morphology characteristics and mechanical response data obtained from physical experiments, and generate a training sample set containing process parameters and thermo-mechanical field evolution data based on the calibrated model. S3. Generation of process prediction model: Based on the training sample set, a process prediction model is constructed to guide physical manufacturing, and the mapping relationship between physical process parameters and the temperature field, residual stress field and grain characteristics of the deposition layer is established. S4. Parameter reverse optimization: Based on the mapping relationship, a multi-objective function containing residual stress index and grain size index is constructed, and within the preset process parameter search window, the optimization algorithm is used to call the process prediction model to perform reverse optimization to obtain the optimal thermo-mechanical coupling process parameter combination that satisfies the preset optimization objective. The optimal thermo-mechanical coupling process parameter combination includes at least the impact intervention temperature. S5. Temperature closed-loop control execution: Based on the optimal combination of thermo-mechanical coupling process parameters that meets the preset optimization target, the optimal process parameters are converted into control commands and sent to the additive manufacturing system. During the repair process, the temperature data of the deposited layer is collected in real time. When the monitored temperature T falls into the phase transition temperature range Ar3 to Ar1 and is within the allowable deviation range set based on the optimal impact intervention temperature, the ultrasonic impact device is triggered to perform mechanical impact action.

[0007] The X80 pipe arc additive manufacturing method based on thermo-mechanical coupling control mechanism, preferably, in step S2: The mobile heat source adopts a double ellipsoidal mobile heat source model. By matching the simulated cross-sectional morphology of the molten pool with the actual macroscopic contour of the molten pool, the front and rear half-axis, depth parameters and thermal efficiency are calibrated. The moving force source is equivalently set by applying a pulsating pressure boundary condition that moves with the welding path in the finite element model. The amplitude of the pulsating pressure is calculated by the mapping relationship determined by the impact amplitude-pressure calibration test.

[0008] The X80 pipe arc additive manufacturing method based on thermo-mechanical coupling control mechanism, preferably, in step S3: The process prediction model is a nonlinear regression model trained on a sample set, and the nonlinear regression model includes at least one of artificial neural networks, support vector regression, or random forest regression. The input parameters of the mapping relationship include at least welding current, welding speed, ultrasonic impact amplitude and impact intervention temperature, and the output parameters include at least residual stress peak value and grain refinement index.

[0009] The X80 pipe arc additive manufacturing method based on thermo-mechanical coupling control mechanism, preferably, in step S4: The multi-objective function is constructed by normalizing the predicted residual stress and grain size values ​​and then weighting them according to a preset weight. The optimization algorithm adopts a hybrid optimization strategy that combines a global search algorithm and a local search algorithm. The global search algorithm is used to determine the initial solution of the parameters, and the local search algorithm is used to accurately optimize within the neighborhood of the initial solution. The process parameter search window is used to limit the range of values ​​for welding current, welding speed, ultrasonic impact amplitude, and impact intervention temperature.

[0010] The X80 pipe arc additive manufacturing method based on thermo-mechanical coupling control mechanism, preferably, in step S5: In the physical execution phase of step S5, the optimal impact intervention temperature is limited to the phase transition temperature range Ar3 to Ar1 determined in step S1. The real-time acquisition of deposition layer temperature data is achieved using a non-contact infrared thermometer installed behind the welding torch in the direction of travel, with a sampling frequency of not less than 20Hz. The ultrasonic impact device for welding is installed at the end of the robotic arm, maintaining a predetermined spatial distance from the welding torch and moving synchronously along the welding path.

[0011] This invention also provides an X80 pipe arc additive manufacturing system based on a thermo-mechanical coupling control mechanism, comprising: The additive manufacturing unit includes a welding power source, a wire feeding mechanism, and a multi-axis robotic arm for performing material deposition. The ultrasonic impact unit includes an ultrasonic generator and a welding impact head installed at the end of the multi-axis robotic arm, for applying high-frequency mechanical energy; The sensing unit includes a non-contact infrared thermometer for real-time feedback of the surface temperature of the deposited layer; The main control unit is communicatively connected to the additive manufacturing unit, the ultrasonic impact unit, and the sensing unit. The main control unit is equipped with control logic to receive the temperature signal from the sensing unit and compare it with the optimal impact intervention temperature and phase change temperature range determined by the X80 pipeline arc additive manufacturing method based on the thermo-mechanical coupling control mechanism. When the temperature meets the triggering condition, the main control unit outputs a control signal to drive the ultrasonic impact unit to work.

[0012] The present invention has the following advantages due to the adoption of the above technical solutions: (1) Significant synergistic control effect: By locking the impact time in the phase transition range and utilizing the superposition effect of phase transition volume expansion and mechanical impact, residual tensile stress can be eliminated more thoroughly and grains can be refined compared with conventional room temperature impact. (2) Intelligent design: By replacing manual trial and error with “simulation + prediction model + reverse optimization”, the problem of difficult parameter determination under complex thermo-mechanical coupling field is solved; (3) High precision of closed-loop control: The mapping path from the "virtual model optimal solution" to the "physical device control command" is clearly defined, and the influence of environmental heat dissipation fluctuations is eliminated through real-time temperature feedback. Attached Figure Description

[0013] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts. In the drawings: Figure 1 This is a flowchart of the X80 pipe electric arc additive manufacturing method based on the thermo-mechanical coupling control mechanism of the present invention; Figure 2 The diagram shows the mesh generation and molten pool morphology calibration, where (a) is a schematic diagram of the mesh generation of the weld zone and the substrate zone, and (b) is a schematic diagram comparing the experimentally measured molten pool profile with the simulated molten pool profile. Figure 3 A schematic diagram of the system hardware architecture and control logic; Figure 4 The figures show a comparison of residual stress and grain size between the embodiments of the present invention and the prior art, wherein (a) is a comparison of residual stress and (b) is a comparison of grain size. Detailed Implementation

[0014] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the invention and to fully convey the scope of the invention to those skilled in the art.

[0015] like Figure 1 As shown, this invention provides a method for arc additive manufacturing of X80 pipes based on a thermo-mechanical coupling control mechanism, comprising the following steps: S1. Phase transformation characteristic calibration: Conduct thermal simulation tests on X80 pipeline steel to obtain the continuous cooling transformation curve under preset cooling conditions, and determine the phase transformation temperature range Ar3~Ar1 for the transformation of X80 pipeline steel from austenite to ferrite or bainite. S2. Thermo-mechanical coupling model calibration: Establish a thermo-mechanical coupling finite element model containing a moving heat source and a moving force source; calibrate the key parameters of the finite element model using the molten pool morphology characteristics and mechanical response data obtained from physical experiments, and generate a training sample set containing process parameters and thermo-mechanical field evolution data based on the calibrated model. S3. Generation of process prediction model: Based on the training sample set, a process prediction model is constructed to guide physical manufacturing, and the mapping relationship between physical process parameters and the temperature field, residual stress field and grain characteristics of the deposition layer is established. S4. Parameter reverse optimization: Based on the mapping relationship, a multi-objective function containing residual stress index and grain size index is constructed, and within the preset process parameter search window, the optimization algorithm is used to call the process prediction model to perform reverse optimization to obtain the optimal thermo-mechanical coupling process parameter combination that satisfies the preset optimization objective. The optimal thermo-mechanical coupling process parameter combination includes at least the impact intervention temperature. S5. Temperature closed-loop control execution: Based on the optimal combination of thermo-mechanical coupling process parameters that meets the preset optimization target, the optimal process parameters are converted into control commands and sent to the additive manufacturing system. During the repair process, the temperature data of the deposited layer is collected in real time. When the monitored temperature T falls into the phase transition temperature range Ar3 to Ar1 and is within the allowable deviation range set based on the optimal impact intervention temperature, the ultrasonic impact device is triggered to perform mechanical impact action.

[0016] In the above embodiments, preferably, in step S2: The mobile heat source adopts a double ellipsoidal mobile heat source model. By matching the simulated cross-sectional morphology of the molten pool with the actual macroscopic contour of the molten pool, the front and rear half-axis, depth parameters and thermal efficiency are calibrated. The moving force source is equivalently set by applying a pulsating pressure boundary condition that moves with the welding path in the finite element model. The amplitude of the pulsating pressure is calculated by the mapping relationship determined by the impact amplitude-pressure calibration test.

[0017] In the above embodiments, preferably, in step S3: The process prediction model is a nonlinear regression model trained on a sample set, and the nonlinear regression model includes at least one of artificial neural networks, support vector regression, or random forest regression. The input parameters of the mapping relationship include at least welding current, welding speed, ultrasonic impact amplitude and impact intervention temperature, and the output parameters include at least residual stress peak value and grain refinement index.

[0018] In the above embodiments, preferably, in step S4: The multi-objective function is constructed by normalizing the predicted residual stress and grain size values ​​and then weighting them according to a preset weight. The optimization algorithm adopts a hybrid optimization strategy that combines a global search algorithm and a local search algorithm. The global search algorithm is used to determine the initial solution of the parameters, and the local search algorithm is used to accurately optimize within the neighborhood of the initial solution. The process parameter search window is used to limit the range of values ​​for welding current, welding speed, ultrasonic impact amplitude, and impact intervention temperature.

[0019] In the above embodiments, preferably, in step S5: In the physical execution phase of step S5, the optimal impact intervention temperature is limited to the phase transition temperature range Ar3 to Ar1 determined in step S1. The real-time acquisition of deposition layer temperature data is achieved using a non-contact infrared thermometer installed behind the welding torch in the direction of travel, with a sampling frequency of not less than 20Hz. The ultrasonic impact device for welding is installed at the end of the robotic arm, maintaining a predetermined spatial distance from the welding torch and moving synchronously along the welding path.

[0020] This invention also provides an X80 pipe arc additive manufacturing system based on a thermo-mechanical coupling control mechanism, comprising: The additive manufacturing unit includes a welding power source, a wire feeding mechanism, and a multi-axis robotic arm for performing material deposition. The ultrasonic impact unit includes an ultrasonic generator and a welding impact head installed at the end of the multi-axis robotic arm, for applying high-frequency mechanical energy; The sensing unit includes a non-contact infrared thermometer for real-time feedback of the surface temperature of the deposited layer; The main control unit is communicatively connected to the additive manufacturing unit, the ultrasonic impact unit, and the sensing unit. The main control unit is equipped with control logic to receive the temperature signal from the sensing unit and compare it with the optimal impact intervention temperature and phase change temperature range determined by the X80 pipeline arc additive manufacturing method based on the thermo-mechanical coupling control mechanism. When the temperature meets the triggering condition, the main control unit outputs a control signal to drive the ultrasonic impact unit to work.

[0021] Example 1: The following example illustrates the phase transformation window repair of the V-groove defect on the outer wall of an X80 pipe during welding impact repair.

[0022] Step S1: Phase transition characteristic calibration X80 pipeline steel base material samples were selected, and their continuous cooling transformation curves (CCT curves) were determined using a Gleeble thermal simulation testing machine. At the estimated welding cooling rate (15℃ / s) in this embodiment, the onset temperature Ar3 for the austenite-to-ferrite or bainite transformation was determined to be approximately 580℃, and the termination temperature Ar1 was approximately 420℃. The phase transformation temperature range was determined to be 580–420℃.

[0023] Step S2: Thermo-Mechanical Coupled Model Calibration A three-dimensional finite element model of the repair area was established in ABAQUS software, with the mesh in the molten pool region refined to 0.5 mm. A Goldak double ellipsoidal heat source was used, and the thermal efficiency η=0.85 and shape parameters were calibrated through a single-pass welding experiment.

[0024] The mesh division of the weld zone and the substrate zone is as follows: Figure 2 As shown in (a), a comparison is made between the experimentally measured molten pool profile and the simulation-calculated molten pool profile. Figure 2 As shown in (b).

[0025] A mechanical calibration bench was built, and a force sensor was used to measure the impact force of the impact head under different amplitudes. An amplitude-pressure mapping relationship was established and applied to the model.

[0026] Based on the calibrated model, 80 simulation experiments were designed using the Latin hypercube sampling method. The process parameters were set as follows: welding current 140–180 A, scanning speed 300–600 mm / min, impact amplitude 20–60 μm, and optimal impact intervention temperature 400–700℃. The simulations were run and residual stress and grain size data were extracted to form a process-response sample set.

[0027] Steps S3 and S4: Generation of process prediction model and reverse optimization of parameters The aforementioned 80 sets of data were used to train a nonlinear process prediction model, such as a BP neural network (backpropagation neural network) process prediction model. A multi-objective optimization function was constructed, with optimization objectives including minimizing residual stress and minimizing grain size.

[0028] A genetic algorithm (e.g., NSGA-II) was used to optimize the prediction model. A set of optimal process parameters was calculated: welding current 165 A, scanning speed 420 mm / min, impact amplitude 45 μm, and optimal impact intervention temperature 520℃. Upon verification, 520℃ falls within the 580–420℃ range determined in step S1, indicating the parameters are valid.

[0029] Step S5: As Figure 3 As shown, temperature closed-loop control is executed. System hardware configuration: For example, it uses a KUKA welding robot, an ultrasonic impact gun for welding, a Micro-Epsilon infrared thermometer (sampling rate 50 Hz), and a Siemens PLC.

[0030] The control logic is as follows: The PLC receives the optimal parameter instruction. During the welding process, an infrared thermometer monitors the temperature T of the welded area in real time. When 420℃≤T≤580℃, the PLC outputs an analog signal to turn on the ultrasonic generator, with the amplitude set to 45 μm. When T<420℃ or T>580℃, the impact stops.

[0031] like Figure 4 As shown, the results of the efficacy verification indicate that the residual stress on the surface of the repaired layer after repair was -185 MPa (compressive stress), while the conventional repair without the method of this invention was +120 MPa (tensile stress). EBSD analysis showed that the average grain size was 8.2 μm, which is significantly better than the 14.5 μm of the conventional process.

[0032] Furthermore, it should be noted that this method is also applicable to the repair of walls with different thicknesses or defect shapes. For example, for the repair of internal corrosion pits, simply adjust the geometry of the finite element model in step S2, regenerate the sample set, and train the process prediction model to obtain the optimal parameters suitable for the specific working condition. This demonstrates the universality advantage of this invention based on "model-driven" principles, overcoming the limitation of traditional empirical formulas being "specific to a specific machine."

[0033] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for arc additive manufacturing of X80 pipes based on a thermo-mechanical coupling control mechanism, characterized in that, Includes the following steps: S1. Phase transformation characteristic calibration: Conduct thermal simulation tests on X80 pipeline steel to obtain the continuous cooling transformation curve under preset cooling conditions, and determine the phase transformation temperature range Ar3~Ar1 for the transformation of X80 pipeline steel from austenite to ferrite or bainite. S2. Thermo-mechanical coupling model calibration: Establish a thermo-mechanical coupling finite element model containing a moving heat source and a moving force source; calibrate the key parameters of the finite element model using the molten pool morphology characteristics and mechanical response data obtained from physical experiments, and generate a training sample set containing process parameters and thermo-mechanical field evolution data based on the calibrated model. S3. Generation of process prediction model: Based on the training sample set, a process prediction model is constructed to guide physical manufacturing, and the mapping relationship between physical process parameters and the temperature field, residual stress field and grain characteristics of the deposition layer is established. S4. Parameter reverse optimization: Based on the mapping relationship, a multi-objective function containing residual stress index and grain size index is constructed, and within the preset process parameter search window, the optimization algorithm is used to call the process prediction model to perform reverse optimization to obtain the optimal thermo-mechanical coupling process parameter combination that satisfies the preset optimization objective. The optimal thermo-mechanical coupling process parameter combination includes at least the impact intervention temperature. S5. Temperature closed-loop control execution: Based on the optimal combination of thermo-mechanical coupling process parameters that meets the preset optimization target, the optimal process parameters are converted into control commands and sent to the additive manufacturing system. During the repair process, the temperature data of the deposited layer is collected in real time. When the monitored temperature T falls into the phase transition temperature range Ar3 to Ar1 and is within the allowable deviation range set based on the optimal impact intervention temperature, the ultrasonic impact device is triggered to perform mechanical impact action.

2. The X80 pipe arc additive manufacturing method based on thermo-mechanical coupling control mechanism according to claim 1, characterized in that, In S2: The mobile heat source adopts a double ellipsoidal mobile heat source model. By matching the simulated cross-sectional morphology of the molten pool with the actual macroscopic contour of the molten pool, the front and rear half-axis, depth parameters and thermal efficiency are calibrated. The moving force source is equivalently set by applying a pulsating pressure boundary condition that moves with the welding path in the finite element model. The amplitude of the pulsating pressure is calculated by the mapping relationship determined by the impact amplitude-pressure calibration test.

3. The X80 pipe arc additive manufacturing method based on thermo-mechanical coupling control mechanism according to claim 1, characterized in that, In S3: The process prediction model is a nonlinear regression model trained on a sample set, and the nonlinear regression model includes at least one of artificial neural networks, support vector regression, or random forest regression. The input parameters of the mapping relationship include at least welding current, welding speed, ultrasonic impact amplitude and impact intervention temperature, and the output parameters include at least residual stress peak value and grain refinement index.

4. The X80 pipe arc additive manufacturing method based on thermo-mechanical coupling control mechanism according to claim 1, characterized in that, In S4: The multi-objective function is constructed by normalizing the predicted residual stress and grain size values ​​and then weighting them according to a preset weight. The optimization algorithm adopts a hybrid optimization strategy that combines a global search algorithm and a local search algorithm. The global search algorithm is used to determine the initial solution of the parameters, and the local search algorithm is used to accurately optimize within the neighborhood of the initial solution. The process parameter search window is used to limit the range of values ​​for welding current, welding speed, ultrasonic impact amplitude, and impact intervention temperature.

5. The X80 pipe arc additive manufacturing method based on thermo-mechanical coupling control mechanism according to claim 1, characterized in that, In S5: In the physical execution phase of step S5, the optimal impact intervention temperature is limited to the phase transition temperature range Ar3 to Ar1 determined in step S1. The real-time acquisition of deposition layer temperature data is achieved using a non-contact infrared thermometer installed behind the welding torch in the direction of travel, with a sampling frequency of not less than 20Hz. The ultrasonic impact device for welding is installed at the end of the robotic arm, maintaining a predetermined spatial distance from the welding torch and moving synchronously along the welding path.

6. An arc additive manufacturing system for implementing the X80 pipe arc additive manufacturing method based on the thermo-mechanical coupling control mechanism as described in any one of claims 1-5, characterized in that, include: The additive manufacturing unit includes a welding power source, a wire feeding mechanism, and a multi-axis robotic arm for performing material deposition. The ultrasonic impact unit includes an ultrasonic generator and a welding impact head installed at the end of the multi-axis robotic arm, for applying high-frequency mechanical energy; The sensing unit includes a non-contact infrared thermometer for real-time feedback of the surface temperature of the deposited layer; The main control unit is communicatively connected to the additive manufacturing unit, the ultrasonic impact unit, and the sensing unit. The main control unit is equipped with control logic to receive the temperature signal from the sensing unit and compare it with the optimal impact intervention temperature and phase change temperature range determined by the X80 pipeline arc additive manufacturing method based on the thermo-mechanical coupling control mechanism. When the temperature meets the triggering condition, the main control unit outputs a control signal to drive the ultrasonic impact unit to work.

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