Laser powder bed additive workpiece temperature field simulation and prediction method based on temperature monitoring

Abstract

The invention discloses a laser powder bed additive material workpiece temperature field simulation and prediction method based on temperature monitoring, which comprises the following steps of: firstly, testing boundary conditions related to temperature field simulation in a laser powder bed additive material manufacturing process and temperature-related parameters in material attributes by adopting an existing experimental test method; a temperature-parameter correlation model is established; then, through comparison of temperature field simulation data and temperature field test data, the obtained temperature-parameter correlation model is corrected; and finally, obtaining an accurate temperature-parameter correlation model, and integrating a program package to establish an accurate temperature field prediction method. According to the method, precise prediction of global (surface + internal) temperature distribution and evolution of the laser powder bed additive material workpiece is realized, the problem of temperature field simulation misalignment caused by inaccurate parameters generally existing at present is solved, and the simulation prediction method is suitable for temperature field prediction of workpieces in different shapes and has a wide application prospect. And the problem that the temperature measurement suite cannot be transplanted in the temperature field experiment test of workpieces with different shapes is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of laser additive manufacturing, and in particular to a method for simulating and predicting the temperature field of a laser powder bed additive workpiece based on temperature monitoring. Background Art

[0002] In the traditional manufacturing process of metal workpieces, subtractive processes are often used, mainly including drilling, milling, grinding, wire cutting, electric spark machining, etc. Compared with traditional subtractive manufacturing, additive manufacturing has its own unique manufacturing principle. On the one hand, it can achieve energy conservation and emission reduction in the production process. On the other hand, it is expected to bring major technological breakthroughs to the manufacturing industry and promote high-quality development of the manufacturing industry. However, for now, laser powder bed additive manufacturing of metal workpieces still faces some common problems, mainly the following two types of problems: one is the internal defect problem of the product, and the other is the residual stress problem of the product. In the laser powder bed additive manufacturing process, the metal workpiece is formed by melting and condensing metal powder and stacking it. Gas stagnation or holes caused by insufficient melting will appear inside. These defects have a certain impact on the mechanical properties of the metal workpiece, such as strength and toughness. Furthermore, additive manufacturing of metal workpieces typically involves rapid heating and cooling. Thermal expansion and contraction, microstructural phase transitions, and interface mismatches all generate internal stresses in metal workpieces. Under the effects of rapid cooling, these stresses are difficult to fully release in a very short period of time. The residual stress can significantly impact the workpiece's service performance, such as corrosion resistance and fatigue resistance. Furthermore, when residual stress reaches a certain level, it can directly cause cracking in the metal workpiece. Relatively speaking, residual stress is a more common problem in laser powder bed additive manufacturing of metal workpieces, with a more profound impact on the product, and urgently needs to be addressed.

[0003] During the laser powder bed additive manufacturing process, the residual stress inside the metal workpiece mainly includes phase change stress, thermal stress, etc., which are closely related to its temperature change history. Therefore, in order to effectively solve the residual stress problem of additively manufactured metal workpieces, it is necessary to first obtain the temperature distribution and evolution data of the workpiece. In particular, after the metal workpiece is printed, it slowly cools to room temperature in a powder-encapsulated environment, which takes several hours or even more than ten hours. This process is equivalent to an uncontrolled heat treatment, and its temperature evolution data is even more important. However, after the metal workpiece is printed layer by layer, it will be buried in the powder raw material, so it is difficult to directly measure the overall temperature of the metal workpiece using a temperature measuring instrument. Therefore, accurately obtaining the temperature distribution and temperature evolution data of the metal workpiece is a major problem in laser powder bed additive manufacturing technology.

[0004] The existing solutions to this problem are: 1. Testing with infrared thermal imagers and other temperature measuring instruments: This type of non-contact temperature measuring instrument can measure the surface temperature of the workpiece, as well as the temperature distribution and evolution of the workpiece surface during laser scanning, but it cannot measure the temperature of the workpiece buried inside the powder.

[0005] 2. Testing through thermal sensors, etc.: This type of contact temperature measuring instrument can measure the temperature of the workpiece part buried in the powder, but it can only measure the temperature of the workpiece surface. In addition, 3D printing is a continuous process, and under normal circumstances, sensors cannot be arranged while printing.

[0006] 3. Using simulation to predict the temperature distribution and evolution within a workpiece during laser powder bed additive manufacturing: However, parameters such as material properties and boundary conditions are typically temperature-dependent, making it difficult to precisely set these temperature-dependent parameters. Therefore, the accuracy of the data obtained from simulations cannot be guaranteed. For example, the thermal conductivity of a material is related to its own temperature, but the correlation coefficient is uncertain; the powder's absorption coefficient for laser light is also empirical. Summary of the Invention

[0007] In order to solve the problem of inaccurate simulation of temperature field distribution and changes caused by inaccurate parameters, which is common in current simulation prediction methods, the present invention provides a temperature field simulation prediction method for laser powder bed additive workpieces based on temperature monitoring, which realizes the accurate prediction of the temperature distribution and evolution of the entire domain (surface + interior) of laser powder bed additive workpieces.

[0008] In order to achieve the above object, the present invention adopts the following technical solutions: The present invention provides a method for simulating and predicting the temperature field of a laser powder bed additive workpiece based on temperature monitoring, comprising: S1. Conduct experimental tests at different temperatures on the boundary conditions and temperature-related parameters in material properties involved in the temperature field simulation of the laser powder bed additive manufacturing process, and establish a preliminary temperature-parameter correlation model based on the test results. S2. Build a test platform for the additive manufacturing process to obtain temperature field test data for the additive manufacturing process. The temperature field test data for the additive manufacturing process is divided into two parts: one part is the temperature field test data for the surface of the workpiece additive material, and the other part is the temperature field test data for the internal part of the workpiece additive material, both of which include temperature distribution data and temperature change data; S3. Establishing an additive manufacturing process simulation model, inputting a preliminary temperature-parameter correlation model for simulation, and obtaining the additive manufacturing process temperature field simulation data; S4. Comparing the temperature field simulation data of the additive manufacturing process with the temperature field test data of the additive manufacturing process, and when the comparison conditions are not met, correcting the temperature-parameter correlation model to obtain an accurate temperature-parameter correlation model; S5. Obtain an accurate temperature-parameter correlation model, integrate the program package to build a temperature field prediction model, and predict the temperature field of the laser powder bed additive workpiece process based on the constructed prediction model.

[0009] Using the above technical solution: The general steps for predicting the temperature field of a laser powder bed additive workpiece are: first, perform geometric modeling and meshing based on temperature field simulation software, then select a solver and set boundary conditions, initial conditions, and material properties, and finally solve and predict the temperature field of the laser powder bed additive process. In order to accurately predict the temperature field of a laser powder bed additive workpiece, the key is to accurately assign values ​​to parameters related to material properties and boundary conditions. Regarding the determination of parameters, in addition to direct measurement through experiments, the present invention also uses process simulation data and process test data for calibration and determination, which is also the unique innovation of the present invention.

[0010] For the laser powder bed additive manufacturing process, parameters related to material properties mainly include density, thermal conductivity, specific heat capacity, and latent heat of phase change. Materials mainly include workpiece material, substrate material, and powder material, and the elemental composition of the three is basically the same. Due to its particle morphology, the density, thermal conductivity, specific heat capacity and other parameters of powder materials differ greatly from those of bulk materials such as workpieces and substrates. Specifically, the thermal conductivity of bulk and powdered metals can be measured using the electric heating steady-state method. The specific heat capacity of bulk and powdered metals and the latent heat of phase change of the corresponding materials are tested using a differential scanning calorimeter. The density of the material at different temperatures is measured using the mass-to-volume ratio.

[0011] For the laser powder bed additive manufacturing process, the parameters involved in the boundary conditions primarily include the laser source model parameters, the thermal convection / radiation coefficients of the exposed surface of the additive material, the thermal convection coefficients of the substrate bottom and sides, and the thermal resistance of the powder-bulk interface. Specifically, the thermal convection / radiation coefficients of the exposed surface of the additive material and the thermal convection coefficients of the substrate bottom and sides can be measured using the steady-state electric heating method. Because surface convection and radiation occur simultaneously, the emissivity coefficient is first measured using the steady-state electric heating method in a vacuum environment. The total surface thermal convection and radiation are then measured in a nitrogen environment, and the thermal convection coefficient is calculated by subtraction. The total thermal resistance is measured using the guarded hot plate method, and the powder-bulk interface thermal resistance is then subtracted from the powder thermal resistance. The powder thermal resistance can then be calculated by combining this with the previously measured thermal conductivity of the metal powder. Furthermore, the laser powder bed additive manufacturing process involves a shallow melt pool. Key laser source parameters include laser power, laser spot radius, and the material's laser absorptivity. Specifically, the laser absorptivity of the metal powder is measured using an integrating sphere spectrometer.

[0012] One of the core points of the present invention is to calibrate a series of temperature-parameter correlation models obtained from the experiment by comparing process simulation data with process test data. There are two main reasons why the temperature-parameter correlation model needs to be calibrated. First, there is heat loss in the test process, which causes certain errors in the test results. For example, the electric heating steady-state method for measuring thermal conductivity, thermal radiation, and thermal convection, the protected hot plate method for measuring the thermal resistance of the powder-bulk interface, and the differential scanning calorimeter measurement of specific heat capacity and latent heat of phase change. The test temperature can basically reach the temperature of the laser additive process, and the obtained temperature-parameter correlation model can meet the requirements of the simulation temperature. However, due to the existence of test heat loss, the test results of such parameters will be biased. Second, there is a temperature limit for the test, and the test data is only applicable to a certain temperature range. For temperature-related parameters, it cannot fully meet the requirements of the simulation temperature. For example, the integrating sphere spectrometer measurement of laser absorptivity has an experimental test temperature range of 50-150°C, which depends on the coating material of the inner wall of the integrating sphere.

[0013] Therefore, temperature-dependent parameters, including thermal conductivity, specific heat capacity, convection, radiation, powder-bulk interface thermal resistance, and laser absorptivity, require calibration. According to the two categories of parameter calibration, laser absorptivity requires a primary calibration (coarse calibration). Based on this primary calibration, secondary calibrations (fine calibration) are then performed on thermal conductivity, specific heat capacity, convection, radiation, and powder-bulk interface thermal resistance. The reasons for the separation of primary and secondary calibration parameters are: first, the laser input is a heat source, and the heat source parameters directly affect the total heat input. Therefore, small fluctuations in the laser absorptivity parameter can have a significant impact on the total heat input. Second, the experimental test temperature for the laser absorptivity parameter is much lower than the laser additive melt pool temperature, only 50-150°C. Therefore, the measured values ​​of the laser absorptivity parameter deviate significantly from the true values. However, the experimental test conditions for other parameters can reach hundreds to 1,000°C, which is closer to the actual temperature of the laser additive process, and the measured values ​​of these parameters are closer to the true values. Therefore, the laser absorptivity needs to be calibrated first, followed by secondary calibrations for the other parameters.

[0014] Furthermore, in step S2, the temperature field test data of the additive surface of the workpiece is measured by an ultra-high-speed infrared thermal imager. By installing an ultra-high-speed infrared thermal imager inside the laser powder bed additive equipment, the temperature field of the test workpiece surface is monitored in real time during the laser head movement printing process and data is collected; the temperature field test data of the internal additive temperature field of the workpiece is obtained by real-time detection by an array temperature sensor group arranged inside the test workpiece.

[0015] Furthermore, the entire additive manufacturing process in step S2 is divided into two printing stages. The first printing stage is the temperature measurement preparation stage, in which a test workpiece is printed first, and an array temperature sensor group is arranged inside the test workpiece. A special substrate and a forming cylinder piston kit are used. After installation, the second printing stage is continued on the test workpiece printed in the first stage; the second printing stage is the temperature testing stage, in which the temperature field of the laser powder bed additive manufacturing process is monitored in real time by an ultra-high-speed infrared thermal imager and an array temperature sensor group.

[0016] Furthermore, an array of inner holes is formed inside the test workpiece printed in the first section, and the temperature sensors in the array temperature sensor group are installed correspondingly in the array inner holes. The connecting wires of the temperature sensors in the array temperature sensor group pass through the dedicated substrate and the forming cylinder piston kit and are connected to the external data acquisition equipment.

[0017] Furthermore, the temperature sensor in the array temperature sensor group is a B-type high-temperature thermocouple with a diameter of less than 2 mm. The thermocouple wire material of the B-type high-temperature thermocouple is platinum-rhodium 30-platinum-rhodium 6, and the sleeve is made of high-temperature alloy, molybdenum metal or platinum-rhodium sleeve.

[0018] Furthermore, in step S3, a simulation model is built based on the test workpiece printed in the first section, and the simulation conditions are consistent with the test conditions in step S2.

[0019] Furthermore, there are two comparison conditions in step S4, namely a primary correction condition and a secondary correction condition; the primary correction condition is to select the simulation and test temperature distribution data at the same time in three different time periods, find all temperature data corresponding to the test position in the temperature simulation data, including the surface temperature and internal temperature of the test workpiece, and compare the simulation temperature and process test temperature at the same position. The data difference does not exceed 10%; the secondary correction condition is that the simulation temperature gradient of the workpiece and the test temperature gradient do not differ by more than 5%.

[0020] Furthermore, during the first correction, the laser light source model and the temperature-laser absorptivity correlation model are corrected; during the second correction, the temperature-parameter correlation model of thermal conductivity, specific heat capacity, thermal convection, thermal radiation, and powder-bulk interface thermal resistance parameters is slightly corrected.

[0021] Furthermore, a correction is first performed to couple the preliminary temperature-parameter correlation model obtained from all experiments into the simulation model, determine the laser light source model, and only change the temperature-laser absorptivity correlation model. Multiple simulation corrections are performed until the simulated temperature field data and the tested temperature field data meet the correction conditions. Then perform a secondary correction, fix the temperature-laser absorptivity correlation parameters in the simulation model, change other temperature-parameter correlation models, and perform multiple simulation corrections until the simulated temperature field data and the tested temperature field data meet the secondary correction conditions.

[0022] Specifically, during the primary calibration, experimentally derived data models are used for temperature-parameter correlation models for thermal conductivity, specific heat capacity, thermal convection, thermal radiation, and powder-bulk interface thermal resistance. The primary calibration steps are as follows: 1) Build a process test platform; 2) Conduct process testing and record temperature distribution and changes; 3) Build a simulation model based on the process test model; 4) Input the corresponding temperature-parameter correlation model for simulation; 5) Compare the simulated temperature distribution and changes with those obtained from the process test; 6) Compare the data. If the conditions are met, the simulation is terminated; if not, the temperature-laser absorptivity correlation model is modified and simulation is continued until the conditions are met and a good temperature-laser absorptivity correlation model is obtained, completing the primary calibration of the parameter model.

[0023] To obtain both simulated and measured temperature field data during the additive manufacturing process during a calibration, the first step is to complete workpiece temperature testing during the laser powder bed additive manufacturing process, including temperature distribution and temperature variation. Workpiece temperature consists of two components: the workpiece surface temperature and the workpiece internal temperature. The surface temperature is measured using an ultra-high-speed infrared thermal imager. The internal temperature is measured using an array of high-temperature sensors. In the first step, a process test model is designed based on the layout of the array of high-temperature sensors, with the emphasis on ensuring that the model's internal structure fully complies with the installation requirements. In the second step, the process test model is printed using powder bed additive manufacturing. In the third step, the process test model is separated and a matching substrate is replaced. The substrate corresponding to the process test model requires openings in the areas corresponding to the process test model for high-temperature sensor wiring. In the fourth step, the process test model is reassembled on the replaced substrate, and the array of high-temperature sensors is installed on the process test model. In the fifth step, powder coating is completed, printing is performed, and the temperature distribution and variations within the workpiece are recorded during the printing process to obtain test data for the additive manufacturing process temperature field.

[0024] Secondly, a simulation of the laser powder bed additive manufacturing process is carried out. The temperature-parameter correlation model obtained from the experiment is embedded into the simulation model setting through reprogramming. The simulation is converged and debugged, and the temperature field simulation data of the additive manufacturing process is obtained.

[0025] Finally, a calibration is completed by comprehensively considering both the workpiece's surface temperature and internal temperature, through data comparison and feedback, and after multiple revisions to the temperature-laser absorptivity correlation model. The criterion for determining a calibration is to select simulated and tested temperature distribution data from the same moment in three different time periods. Within the simulated temperature distribution data, all temperature data corresponding to the test location, including both the surface and internal temperatures of the test workpiece, are found. The simulated and tested temperatures at the same location are then compared to ensure the data differ by no more than 10%.

[0026] After the completion of the primary calibration, a secondary calibration is performed to fix the temperature-laser absorptivity correlation model, and adjust and calibrate other temperature-parameter correlation models. It should be noted that the more temperature points are tested and the finer the comparison, the more accurate the final calibrated temperature-parameter correlation model will be. For the secondary calibration, the basic steps are the same as the primary calibration. Mainly targeting the experimental test errors caused by heat loss, secondary calibration is performed on temperature-parameter correlation models such as thermal conductivity, specific heat capacity, thermal convection, thermal radiation, and powder-bulk interface thermal resistance. Here, the judgment condition for the completion of the secondary calibration is that the simulated temperature gradient of the workpiece and the test temperature gradient do not exceed 5%.

[0027] Furthermore, for different types of AM powder materials, a temperature-parameter correlation model calibration procedure of the same process is required to establish a temperature-parameter correlation model database for different materials and accurately predict the workpiece temperature field of different materials under different process conditions.

[0028] Compared with the prior art, the present invention has the following beneficial effects: (1) The present invention adopts a full-range temperature testing system to obtain high-precision surface and internal temperature test data of the workpiece during the additive manufacturing process. Surface monitoring uses an ultra-high-speed infrared thermal imager to capture the temperature of the molten pool and heat-affected zone in real time. Internal monitoring uses an array of B-type platinum-rhodium thermocouples (diameter <2 mm, temperature resistance 1600°C) implanted in the pre-set channels of the workpiece to achieve synchronous temperature acquisition at multiple points inside the workpiece (such as 64 measuring points).

[0029] (2) When the present invention performs workpiece temperature testing during the laser powder bed additive manufacturing process, a phased printing method is used. First, a test workpiece with a hole is printed. Then, a high-temperature sensor is implanted. Then, powder is applied for the second time and printing is continued to achieve in-situ monitoring.

[0030] (3) The parameter correction of the present invention adopts a two-stage mechanism. The first correction is to prioritize the correction of the temperature-laser absorptivity correlation model, and iteratively optimize the laser light source parameters by comparing the simulated and measured temperature distributions (error ≤ 10%). The second correction is to fix the temperature-laser absorptivity correlation model and correct the thermal conductivity / specific heat capacity / thermal convection / thermal radiation / interface thermal resistance and other parameters to make the error between the simulated and measured temperature gradients ≤ 5%.

[0031] (4) Based on the temperature monitoring data of the laser powder bed additive manufacturing process, the temperature-parameter correlation model is corrected and embedded into the existing temperature field simulation software to form a complete and effective laser powder bed additive workpiece temperature field prediction method. Later, the temperature data of workpieces with different structures in the laser powder bed additive process can be obtained through simulation, which is simple and efficient.

[0032] (5) The temperature field prediction method for laser powder bed additive manufacturing workpieces is not restricted by materials. A temperature-parameter correlation model database for different materials can be established to meet the temperature field prediction of laser powder bed additive manufacturing workpieces of different materials.

[0033] (6) The temperature field prediction method for laser powder bed additive workpieces can be applied to the temperature field prediction of all additive workpieces of different shapes made of the same material, avoiding the problem faced by temperature field experimental testing that the temperature measurement kit cannot be transplanted due to the change in the shape of the additive workpiece and the problem that additive workpieces with special shapes cannot be experimentally tested.

[0034] (7) The temperature field prediction method of laser powder bed additive workpieces can truly realize the theoretical simulation to guide production practice. The temperature data obtained through simulation can be used to adjust the process parameters, effectively avoiding thermal stress cracking problems that occur during the additive manufacturing process, greatly improving the level of laser powder bed additive manufacturing, and helping the efficient development of the additive manufacturing industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0036] Figure 1 Schematic diagram of the process of the temperature field simulation and prediction method of the laser powder bed additive workpiece based on temperature monitoring in the present invention; Figure 2 Schematic diagram of the temperature testing platform for the laser powder bed additive process in the present invention; Figure 3 Schematic diagram of the structure of the dedicated substrate in the present invention; Figure 4 Schematic diagram of the side and bottom structures of a workpiece for temperature testing during the laser powder bed additive process of the present invention; Figure 5 It is a structural schematic diagram of the forming cylinder piston kit in the present invention; Among them, the specific drawings are marked as follows: Sealed outer cover 1, powder supply cylinder 2, forming cylinder 3, powder return cylinder 4, laser head 5, ultra-high-speed infrared thermal imager 6, metal powder 7, special substrate 8, substrate bottom surface 8-1, substrate bottom groove 8-2, substrate threaded through hole 8-3, test workpiece 9, inner hole 9-1, array temperature sensor group 10, temperature sensor measuring point 10-1, temperature sensor connecting line 10-2, forming cylinder piston kit 11, piston kit through hole 11-1, powder scraper 12. DETAILED DESCRIPTION

[0037] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0038] The embodiment of the present invention discloses a method for simulating and predicting the temperature field of a laser powder bed additive workpiece based on temperature monitoring. Figure 1 The process diagram is used to implement the creation.

[0039] Step (1) uses existing experimental test methods to conduct experimental tests at different temperatures on the boundary conditions and temperature-related parameters in the material properties involved in the temperature field simulation of the laser powder bed additive manufacturing process. The corresponding temperature-related parameters and experimental test methods are as follows: For the laser powder bed additive manufacturing process, parameters related to material properties mainly include density, thermal conductivity, specific heat capacity, and latent heat of phase change. Materials mainly include workpiece material, substrate material, and powder material, and the elemental composition of the three is basically the same. Due to its particle morphology, the density, thermal conductivity, specific heat capacity and other parameters of powder materials differ greatly from those of bulk materials such as workpieces and substrates. Specifically, the thermal conductivity of bulk and powdered metals can be measured using the electric heating steady-state method. The specific heat capacity of bulk and powdered metals and the latent heat of phase change of the corresponding materials are tested using a differential scanning calorimeter. The density of the material at different temperatures is measured using the mass-to-volume ratio.

[0040] For the laser powder bed additive manufacturing process, the parameters involved in the boundary conditions primarily include the laser source model parameters, the thermal convection / radiation coefficients of the exposed surface of the additive material, the thermal convection coefficients of the substrate bottom and sides, and the thermal resistance of the powder-bulk interface. Specifically, the thermal convection / radiation coefficients of the exposed surface of the additive material and the thermal convection coefficients of the substrate bottom and sides can be measured using the steady-state electric heating method. Because surface convection and radiation occur simultaneously, the emissivity coefficient is first measured using the steady-state electric heating method in a vacuum environment. The total surface thermal convection and radiation are then measured in a nitrogen environment, and the thermal convection coefficient is calculated by subtraction. The total thermal resistance is measured using the guarded hot plate method, and the powder-bulk interface thermal resistance is then subtracted from the powder thermal resistance. The powder thermal resistance can then be calculated by combining this with the previously measured thermal conductivity of the metal powder. Furthermore, the laser powder bed additive manufacturing process involves a shallow melt pool. Key laser source parameters include laser power, laser spot radius, and the material's laser absorptivity. Specifically, the laser absorptivity of the metal powder is measured using an integrating sphere spectrometer.

[0041] Based on the experimental test results, temperature-parameter correlation models are established. These temperature-related parameter models include temperature-laser absorptivity correlation model, temperature-powder (block) thermal conductivity correlation model, temperature-powder (block) specific heat capacity correlation model, temperature-block surface thermal convection correlation model, temperature-block surface thermal radiation correlation model, and temperature-powder / block interface thermal resistance correlation model.

[0042] Step (2) calibrates the obtained temperature-parameter correlation model. According to the different parameters, the calibration is divided into primary calibration and secondary calibration. The primary calibration includes the laser light source model and the temperature-laser absorptivity correlation model, and the rest are secondary calibrations.

[0043] Step (3) is to obtain an accurate temperature-parameter correlation model, integrate the program package (the temperature field simulation program used includes but is not limited to the open source simulation software Openfoam, or use the currently popular purchased commercial simulation software) to construct an accurate temperature field prediction method, and perform temperature field prediction of the laser powder bed additive workpiece process based on the constructed temperature field prediction method.

[0044] It should be noted that in the temperature-parameter correlation model correction process of step (2), the additive process temperature field test is first performed to obtain the additive manufacturing process temperature field test data, and then the additive process temperature field simulation is performed to obtain the additive manufacturing process temperature field simulation data. Finally, the temperature-parameter correlation model is corrected by comparing the additive process temperature field test data with the additive process temperature field simulation data.

[0045] In the temperature field test of the additive process, the test is divided into two parts: one part is the surface temperature field test of the test workpiece 9. By installing an ultra-high-speed infrared thermal imager 6 inside the laser powder bed additive equipment, the temperature field on the surface of the test workpiece 9 can be monitored in real time during the printing process of the laser head 5 and data can be collected; the other part is the temperature field test inside the test workpiece 9. By arranging an array temperature sensor group 10 on the test workpiece 9, real-time detection is performed. Figure 2 As shown, the laser powder bed additive equipment includes a sealed outer cover 1, a powder supply cylinder 2, a forming cylinder 3 and a powder return cylinder 4 are provided at the bottom of the sealed outer cover 1, and a laser head 5 is provided inside the sealed outer cover 1.

[0046] In a specific implementation case, the structural diagram of the test workpiece 9 is as follows Figure 4 As shown, the length, width and height dimensions are 240 * 240 * 240 mm. An array of inner holes 9-1 are opened in the test workpiece 9, and the depth of the inner holes 9-1 is 230 mm, the diameter is 10 mm, and the center spacing is 50 mm. The inner holes 9-1 are used for arranging the array temperature sensor group 10. The more the number of array inner holes 9-1 inside the test workpiece 9, the more temperature sensor measuring points 10-1 are set in each inner hole 9-1, the denser the arrangement of the temperature sensor measuring points 10-1 in the test workpiece 9 will be, the more temperature data points will be obtained, and the higher the temperature test resolution will be. In this embodiment, the number of array inner holes 9-1 is 16, and each inner hole 9-1 has 4 temperature sensor measuring points 10-1 evenly arranged from top to bottom, and a total of 64 temperature sensors are arranged in the test workpiece 9. It should be noted that the arrangement position and number of the temperature sensor measuring points 10-1 are not unique and can be adjusted according to test requirements.

[0047] According to the structural dimensions of the test workpiece 9, the dedicated substrate 8 is processed. Figure 3 As shown, the length, width and height dimensions of the dedicated substrate 8 are 600 * 600 * 100 mm. In the center of the dedicated substrate 8, an area of ​​240 * 240 mm is demarcated to match the length and width dimensions of the test workpiece 9. In this area, an array of substrate threaded through holes 8-3 are machined. The center of each substrate threaded through hole 8-3 is collinear with the center of the inner hole 9-1 of the test workpiece 9. The diameter of the substrate threaded through hole 8-3 is 10 mm. Next, the bottom surface of the dedicated substrate 8 in this area is milled. The length, width and height dimensions of the substrate bottom groove 8-2 of the substrate bottom surface 8-1 are 240 * 240 * 40 mm.

[0048] According to the structural dimensions of the dedicated substrate 8, a piston kit through hole 11-1 is provided on the forming cylinder piston kit 11. Figure 5 As shown, the piston kit through hole 11-1 has a diameter of 50 mm and is located below the groove 8-2 area at the bottom of the substrate. It is used for arranging the temperature sensor connecting line 10-2 and connecting to the external data acquisition equipment.

[0049] The entire additive manufacturing printing test process is divided into two sections. The first section prints a test workpiece 9, and an array temperature sensor group 10 is arranged inside the test workpiece 9. A special substrate 8 and a forming cylinder piston kit 11 are used. After installation, the second section of printing is continued on the test workpiece 9 printed in the first section. During the second printing process, the temperature field of the laser powder bed additive manufacturing process is monitored in real time by the ultra-high-speed infrared thermal imager 6 and the array temperature sensor group 10.

[0050] The specific process is as follows: The test workpiece 9 is first printed and formed using laser powder bed additive manufacturing. After forming, the test workpiece 9 is separated from the conventional substrate and fixed directly above the bottom slot area of ​​the dedicated substrate 8. The array temperature sensor group 10 is inserted through the threaded through-hole 8-3 of the substrate and implanted into the inner hole 9-1 of the test workpiece 9. The temperature sensor group is 280 mm long, and each temperature sensor group is equipped with four temperature sensor points 10-1. The thickness of the temperature sensor group does not exceed the diameter of the threaded through-hole 8-3 of the substrate or the inner hole 9-1 of the test workpiece 9. After all temperature sensor groups are implanted, a hollow screw is screwed upward from the bottom slot of the dedicated substrate 8 into the threaded through-hole 8-3 of the substrate to lock the temperature sensor group. The threaded section of the hollow screw is 12 mm long and, when tightened, supports the temperature sensor group, preventing it from loosening. The temperature sensor connection cable 10-2 is passed through the center hole of the hollow screw. The temperature sensor connection lines 10 - 2 of all temperature sensor groups are gathered in the bottom groove of the dedicated base plate 8 , pass through the piston kit through hole 11 - 1 and are connected to the external data acquisition equipment.

[0051] After the test workpiece 9, temperature sensor assembly, ultra-high-speed infrared thermal imager 6, and dedicated substrate 8 are installed and secured, a pre-powdering process is performed, ensuring that the metal powder 7 is flush with the upper surface of the test workpiece 9. A powder scraper 12 is used to apply metal powder 7 layer by layer, scanning and printing the metal powder 7 onto the surface of the test workpiece 9. Each layer of metal powder 7 is 30 μm thick. There are no specific restrictions on the laser scanning path. This embodiment uses unidirectional scanning, with adjacent layers scanned perpendicularly. The scanning range covers a complete 240 x 240 mm area, with a scan pitch of 0.08 mm, a scan speed of 800 mm / s, and a laser power of 200 W. Under the above-specified process parameters, two different scanning tests are conducted: a single-layer scan to test for the absence of interlayer temperature accumulation; and a multi-layer scan with over 100 layers to test for the presence of interlayer temperature accumulation. During each of these two scanning tests, the ultra-high-speed infrared thermal imager 6 and array temperature sensor assembly 10 are used to record the surface temperature and internal test point temperatures of the test workpiece 9 at different times.

[0052] Specifically, the structure and size of the test workpiece 9 are not specific and can be of any shape and size. However, the workpiece model used in the simulation process must be the same as the test workpiece 9 in the process test. At the same time, the arrangement of the array temperature sensor group 10 can be evenly distributed according to the structure and size of the test workpiece 9. The temperature sensor adopts a type B high-temperature thermocouple, and the maximum temperature measurement capability of the thermocouple must reach 1600°C. The thermocouple wire material is platinum rhodium 30-platinum rhodium 6, and the sheath adopts a high-temperature alloy, molybdenum metal or platinum rhodium sheath. The diameter of the high-temperature thermocouple is less than 2mm. Multiple temperature sensors can form a temperature sensor group, and the number of temperature sensors in a temperature sensor group corresponds to the number of temperature test points in an inner hole 9-1.

[0053] After completing the above process tests, the next step is to simulate the temperature field of the additive manufacturing process. In a specific implementation case, the open source simulation software Openfoam is used to simulate the temperature field of the laser powder bed additive manufacturing process. First, a simulation model is established based on the structural dimensions of the test workpiece 9. Secondly, the material properties obtained through experimental testing, the temperature-parameter correlation model involved in the boundary conditions, and the laser light source model are coupled to the simulation solver through programming. After that, the solver settings are performed, including meshing, time step, etc. Then, the single-layer scanning and multi-scan processes of additive manufacturing are simulated according to the process parameters of the process test. Finally, post-processing is performed to obtain the simulated temperature field data of the additive manufacturing process.

[0054] During the specific implementation, since the laser powder bed additive manufacturing process is a shallow molten pool, the laser light source model selects the Gaussian surface heat source model. First, the temperature test data and simulation data of single-layer scanning and multiple scanning of laser powder bed additive manufacturing are compared. If the comparison is unqualified, it will be fed back to the simulation end, and the temperature-laser absorption rate correlation model will be corrected and simulated again. The above steps are repeated many times until the temperature-laser absorption rate correlation model is corrected once. The standard for one correction is as follows: select the simulation and test temperature distribution data at the same time in three different time periods (initial, middle, and final sections), find all temperature data corresponding to the test position in the temperature distribution simulation data, including the surface temperature and internal temperature of the test workpiece 9, compare the simulation temperature and process test temperature at the same position, and the data difference does not exceed 10%. Then, fix the temperature-laser absorption rate correlation model, and then compare the simulation data with the test data. If the comparison is unqualified, it will be fed back to the simulation end, and other temperature-parameter correlation models (see Figure 1 ) to make corrections and then simulate again. Repeat these steps multiple times until the secondary calibration of other temperature-parameter correlation models is complete. The secondary calibration standard is as follows: the simulated temperature gradient of the workpiece is basically consistent with the tested temperature gradient, with the temperature gradient difference not exceeding 5%.

[0055] Finally, after the temperature-parameter correlation model is calibrated, the simulation program is integrated with the temperature-parameter correlation model to form a temperature monitoring-based simulation and prediction method for laser powder bed additive workpiece temperature fields. The same temperature-parameter correlation model calibration procedure is required for different printing materials, and parameter process packages can be continuously expanded. The laser powder bed additive workpiece temperature field simulation and prediction method invented in this patent can accurately predict the workpiece temperature field of different materials under different process conditions.

[0056] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for simulating and predicting the temperature field of a laser powder bed additive workpiece based on temperature monitoring, characterized in that: include: S1. Conduct experimental tests at different temperatures on the boundary conditions and temperature-related parameters of material properties involved in the temperature field simulation of the laser powder bed additive manufacturing process. Build a preliminary temperature-parameter correlation model based on the test results. The parameters include: laser absorptivity, thermal conductivity, specific heat capacity, thermal convection, thermal radiation, and powder-bulk interface thermal resistance. S2. Build a test platform for the additive manufacturing process to obtain temperature field test data for the additive manufacturing process. The temperature field test data for the additive manufacturing process is divided into two parts: one part is the temperature field test data for the surface of the workpiece additive material, and the other part is the temperature field test data for the internal part of the workpiece additive material, both of which include temperature distribution data and temperature change data; S3. Establishing an additive manufacturing process simulation model, inputting a preliminary temperature-parameter correlation model for simulation, and obtaining the additive manufacturing process temperature field simulation data; S4. Comparing the temperature field simulation data of the additive manufacturing process with the temperature field test data of the additive manufacturing process, and when the comparison conditions are not met, correcting the preliminary temperature-parameter correlation model to obtain an accurate temperature-parameter correlation model; S5. Obtain an accurate temperature-parameter correlation model, integrate the program package to build a temperature field prediction model, and predict the temperature field of the laser powder bed additive workpiece process based on the constructed prediction model.

2. The temperature field simulation and prediction method for laser powder bed additive workpiece based on temperature monitoring according to claim 1 is characterized in that: In step S2, the temperature field test data of the additive workpiece surface is measured by an ultra-high-speed infrared thermal imager. By installing an ultra-high-speed infrared thermal imager inside the laser powder bed additive equipment, the temperature field of the test workpiece surface is monitored in real time during the laser head movement printing process and data is collected; the temperature field test data of the additive workpiece interior is obtained by real-time detection by an array temperature sensor group arranged inside the test workpiece.

3. The temperature field simulation and prediction method for laser powder bed additive workpiece based on temperature monitoring according to claim 2 is characterized in that: The entire additive manufacturing process in step S2 is divided into two printing stages. The first printing stage is the temperature measurement preparation stage. First, a test workpiece is printed, and an array temperature sensor group is arranged inside the test workpiece. A special substrate and forming cylinder piston kit are used. After installation, the second printing stage is continued on the test workpiece printed in the first stage; the second printing stage is the temperature testing stage. The temperature field of the laser powder bed additive manufacturing process is monitored in real time by an ultra-high-speed infrared thermal imager and an array temperature sensor group.

4. The method for simulating and predicting the temperature field of a laser powder bed additive workpiece based on temperature monitoring according to claim 3 is characterized in that: An array of inner holes is formed inside the test workpiece printed in the first section, and the temperature sensors in the array temperature sensor group are installed correspondingly in the array inner holes. The connecting wires of the temperature sensors in the array temperature sensor group pass through the dedicated substrate and the forming cylinder piston kit and are connected to the external data acquisition equipment.

5. The method for simulating and predicting the temperature field of a laser powder bed additive workpiece based on temperature monitoring according to claim 4 is characterized in that: The temperature sensor in the array temperature sensor group is a B-type high-temperature thermocouple with a diameter of less than 2 mm. The thermocouple wire material of the B-type high-temperature thermocouple is platinum-rhodium 30-platinum-rhodium 6, and the sleeve is made of high-temperature alloy, molybdenum metal or platinum-rhodium sleeve.

6. The method for simulating and predicting the temperature field of a laser powder bed additive workpiece based on temperature monitoring according to any one of claims 3 to 5, characterized in that: In step S3, a simulation model is built based on the test workpiece printed in the first section, and the simulation conditions are consistent with the test conditions in step S2.

7. The method for simulating and predicting the temperature field of a laser powder bed additive workpiece based on temperature monitoring according to claim 6, characterized in that: There are two comparison conditions in step S4, namely the primary correction condition and the secondary correction condition; the primary correction condition is to select the simulation and test temperature distribution data at the same time in three different time periods, find all the temperature data corresponding to the test position in the temperature distribution simulation data, including the surface temperature and internal temperature of the test workpiece, and compare the simulation temperature and process test temperature at the same position. The data difference does not exceed 10%; the secondary correction condition is that the difference between the simulation temperature gradient of the workpiece and the test temperature gradient does not exceed 5%.

8. The method for simulating and predicting the temperature field of a laser powder bed additive workpiece based on temperature monitoring according to claim 7, characterized in that: During the first correction, the laser light source model and the temperature-laser absorptivity correlation model are corrected; during the second correction, the temperature-parameter correlation model of thermal conductivity, specific heat capacity, thermal convection, thermal radiation, and powder-bulk interface thermal resistance parameters is slightly corrected.

9. The method for simulating and predicting the temperature field of a laser powder bed additive workpiece based on temperature monitoring according to claim 8, characterized in that: First, a calibration is performed. All the temperature-parameter correlation models obtained from the experiments are coupled to the simulation model. The laser light source model is determined. Only the temperature-laser absorptivity correlation model is changed. Multiple simulation corrections are performed until the simulated temperature field data and the tested temperature field data meet the calibration conditions. Then perform a secondary correction, fix the temperature-laser absorptivity correlation parameters in the simulation model, change other temperature-parameter correlation models, and perform multiple simulation corrections until the simulated temperature field data and the tested temperature field data meet the secondary correction conditions.

10. The temperature field simulation and prediction method for laser powder bed additive workpiece based on temperature monitoring according to claim 1, characterized in that: For different printing materials, the temperature-parameter correlation model calibration procedure of the same process is carried out to establish a temperature-parameter correlation model database for different materials to predict the workpiece temperature field of different materials under different process conditions.

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