A Simulation and Prediction Method for Temperature Field of Laser Powder Bed Additive Workpiece Based on Temperature Monitoring

Abstract

This invention discloses a method for simulating and predicting the temperature field of laser powder bed additive manufacturing workpieces based on temperature monitoring. First, existing experimental testing methods are used to test the temperature-related parameters in the boundary conditions and material properties involved in the temperature field simulation of the laser powder bed additive manufacturing process, and a temperature-parameter correlation model is established. Then, the obtained temperature-parameter correlation model is corrected by comparing the simulated temperature field data and the tested temperature field data. Finally, an accurate temperature-parameter correlation model is obtained, and a software package is integrated to establish an accurate temperature field prediction method. This method achieves accurate prediction of the temperature distribution and evolution across the entire (surface + interior) of the laser powder bed additive manufacturing workpiece, solving the problem of inaccurate temperature field simulation caused by inaccurate parameters. Furthermore, this simulation and prediction method is applicable to the temperature field prediction of workpieces with different shapes, avoiding the problem of non-portability of temperature measurement kits in experimental testing of temperature fields for workpieces with different shapes.

Description

Technical Field

[0001] This invention relates to the field of laser additive manufacturing technology, and specifically to a method for simulating and predicting the temperature field of laser powder bed additive workpieces based on temperature monitoring. Background Technology

[0002] Traditional metal workpiece manufacturing often employs subtractive manufacturing processes, primarily including drilling, milling, grinding, wire cutting, and electrical discharge machining. Compared to subtractive manufacturing, additive manufacturing has unique manufacturing principles. On the one hand, it can achieve energy conservation and emission reduction during production; on the other hand, it holds the promise of significant technological breakthroughs in the manufacturing sector and promotes high-quality development. However, currently, laser powder bed additive manufacturing of metal workpieces still faces some common problems, mainly falling into two categories: internal defects and residual stress. In laser powder bed additive manufacturing, metal workpieces are formed by the melting, condensation, and stacking of metal powder. Internal defects such as gas retention or pores caused by incomplete melting can occur, affecting the mechanical properties of the metal workpiece, such as strength and toughness. Furthermore, additive manufacturing of metal workpieces typically involves rapid heating and cooling processes. For metal workpieces, thermal expansion and contraction, microstructural phase transformations, and interfacial mismatches all generate internal stress. Under rapid cooling, this internal stress is difficult to release fully in a very short time, and the residual stress significantly impacts the service performance of the metal workpiece, such as corrosion resistance and fatigue resistance. In fact, when the residual stress reaches a certain level, it can directly cause the metal workpiece to crack. Comparatively, in laser powder bed additive manufacturing of metal workpieces, the problem of residual stress is more prevalent and has a more profound impact on the product, urgently requiring a solution.

[0003] In laser powder bed additive manufacturing, the residual stress inside the metal workpiece mainly includes phase transformation stress and thermal stress, both of which are closely related to its temperature change history. Therefore, to effectively solve the problem of residual stress in additively manufactured metal workpieces, it is essential to obtain the temperature distribution and evolution data of the workpiece. Especially after the metal workpiece is printed, it slowly cools to room temperature in a powder-encapsulated environment, which can take several hours or even more than ten hours. This process is equivalent to uncontrolled heat treatment, making its temperature evolution data even more crucial. However, after being printed layer by layer, the metal workpiece is embedded in the powder material, making it difficult to directly measure the overall temperature of the metal workpiece using temperature measuring instruments. Therefore, accurately obtaining the temperature distribution and temperature evolution data of the metal workpiece is a major challenge in laser powder bed additive manufacturing technology.

[0004] The existing solutions to this problem are as follows:

[0005] 1. Testing with temperature measuring instruments such as infrared thermal imagers: These non-contact temperature measuring instruments can measure the temperature of the workpiece surface and the temperature distribution and evolution process of the workpiece surface during laser scanning, but they cannot measure the temperature of the part embedded in the powder.

[0006] 2. Testing via thermal sensors, etc.: These contact temperature measuring instruments can measure the temperature of the workpiece part embedded in the powder, but they can only measure the temperature of the workpiece surface. Moreover, 3D printing is a continuous process, and under normal circumstances, sensors cannot be placed while printing.

[0007] 3. Predicting the internal temperature distribution and evolution of a workpiece during laser powder bed additive manufacturing through simulation: However, parameters related to material properties and boundary conditions are usually temperature-dependent, and it is often difficult to accurately set these temperature-dependent parameters. Therefore, the accuracy of the data obtained from simulation cannot be guaranteed. For example, the thermal conductivity of a material is related to its own temperature, but the correlation coefficient is uncertain; the absorption coefficient of the powder to the laser is also empirical. Summary of the Invention

[0008] To address the common problem of inaccurate temperature field distribution and variation simulations caused by inaccurate parameters in current simulation and prediction methods, this invention provides a temperature field simulation and prediction method for laser powder bed additive workpieces based on temperature monitoring. This method achieves accurate prediction of the temperature distribution and evolution across the entire (surface + interior) of the laser powder bed additive workpiece.

[0009] To achieve the above objectives, the present invention adopts the following technical solution:

[0010] This invention provides a method for simulating and predicting the temperature field of laser powder bed additive manufacturing workpieces based on temperature monitoring, comprising:

[0011] S1. Conduct experimental tests at different temperatures on the temperature-related parameters of the boundary conditions and 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.

[0012] S2. An additive manufacturing process test platform is built to obtain temperature field test data of the additive manufacturing process. The temperature field test data of the additive manufacturing process is divided into two parts: one part is the temperature field test data of the workpiece additive surface, and the other part is the temperature field test data of the workpiece additive interior. Both of them include temperature distribution data and temperature change data.

[0013] S3. Establish an additive manufacturing process simulation model, input the preliminary temperature-parameter correlation model for simulation, and obtain temperature field simulation data of the additive manufacturing process.

[0014] S4. Compare the temperature field simulation data of the additive manufacturing process with the temperature field test data of the additive manufacturing process. If the comparison conditions are not met, correct the temperature-parameter correlation model to obtain an accurate temperature-parameter correlation model.

[0015] 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 manufacturing process based on the constructed prediction model.

[0016] The above technical solution is adopted:

[0017] The general steps for predicting the temperature field of a laser powder bed additive manufacturing workpiece are as follows: First, geometric modeling and meshing are performed using temperature field simulation software. Then, a solver is selected, and boundary conditions, initial conditions, and material properties are set. Finally, the temperature field of the laser powder bed additive manufacturing process is solved and predicted. To achieve accurate prediction of the temperature field of the laser powder bed additive manufacturing workpiece, the key is to accurately assign values ​​to the parameters related to material properties and boundary conditions. Regarding the determination of these parameters, in addition to direct experimental measurement, this invention also uses process simulation data and process test data for calibration and determination, which is a unique innovation of this invention.

[0018] For laser powder bed additive manufacturing, key material properties include density, thermal conductivity, specific heat capacity, and latent heat of phase transition. The materials primarily consist of workpiece material, substrate material, and powder material, all with essentially the same elemental composition. However, due to their particle morphology, powder materials exhibit significantly different density, thermal conductivity, and specific heat capacity compared to bulk materials like the workpiece and substrate. Specifically, the thermal conductivity of bulk and powdered metals can be measured using an electrothermal steady-state method. The specific heat capacity and latent heat of phase transition of bulk and powdered metals are tested using differential scanning calorimetry. The material density at different temperatures is determined by mass-to-volume ratio.

[0019] For laser powder bed additive manufacturing, the boundary conditions mainly involve parameters such as laser source model parameters, thermal convection / radiation coefficients of the exposed additive surface, thermal convection coefficients of the substrate bottom and sides, and thermal resistance of the powder-bulk interface. Specifically, the thermal convection / radiation coefficients of the exposed additive surface and the thermal convection coefficients of the substrate bottom and sides can be measured using an electrothermal steady-state method. Since surface convection and radiation occur simultaneously, the radiation coefficient is first measured in a vacuum environment using an electrothermal steady-state method. Then, the total surface thermal convection and thermal radiation are measured in a nitrogen environment, and the thermal convection coefficient is calculated by subtraction. The total thermal resistance is tested using a protective hot plate method, and the powder thermal resistance is obtained by subtracting it. Combined with the previously measured thermal conductivity of the metal powder, the powder thermal resistance can be calculated. Furthermore, the laser powder bed additive manufacturing process involves a shallow molten pool, and the main parameters of the laser source include laser power, laser spot radius, and the material's absorption rate of the laser. Specifically, the absorption rate of the metal powder to the laser is tested using an integrating sphere spectrometer.

[0020] One of the core aspects of this invention is to calibrate a series of temperature-parameter correlation models obtained from experiments by comparing process simulation data with process test data. There are two main reasons why temperature-parameter correlation models need calibration. First, heat loss occurs during the testing process, causing certain errors in the test results. Examples include the steady-state method for measuring thermal conductivity, thermal radiation, and thermal convection using electric heating; the protective hot plate method for measuring thermal resistance at the powder-bulk interface; and the differential scanning calorimeter method for measuring specific heat capacity and latent heat of phase change. The test temperature can generally reach the temperature of the laser additive manufacturing process, and the obtained temperature-parameter correlation model can meet the requirements of the simulated temperature. However, due to the existence of heat loss during testing, the test results for these parameters will be too high. Second, there are temperature limitations in the testing; the test data is only applicable to a certain temperature range, and for temperature-correlated parameters, it cannot fully meet the requirements of the simulated temperature. For example, the experimental test temperature range for measuring laser absorptivity using an integrating sphere spectrometer is 50-150℃, depending on the coating material on the inner wall of the integrating sphere.

[0021] Therefore, temperature-related parameters, including thermal conductivity, specific heat capacity, heat convection, heat radiation, powder-bulk interface thermal resistance, and laser absorptivity, need to be corrected. Based on the two categories of parameter correction, a primary correction (coarse adjustment) is required for the laser absorptivity, followed by a secondary correction (fine adjustment) for thermal conductivity, specific heat capacity, heat convection, heat radiation, and powder-bulk interface thermal resistance. The reasons for this primary and secondary correction are twofold: First, the laser input is a heat source, and heat source parameters directly affect the total heat input; therefore, even small fluctuations in the laser absorptivity parameter can significantly impact the total heat input. Second, the experimental test temperature for the laser absorptivity parameter is much lower than the laser additive manufacturing melt temperature, only 50-150℃, resulting in a large deviation between the measured and true values. In contrast, the experimental test conditions for other parameters can reach several hundred to 1000 degrees Celsius, which are closer to the actual temperatures of the laser additive manufacturing process, leading to more accurate measured values. Therefore, a primary correction for the laser absorptivity is necessary before performing secondary corrections for the other parameters.

[0022] Furthermore, in step S2, the temperature field test data of the workpiece additive surface is obtained by measuring with 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 and data is collected during the laser head movement printing process; the internal temperature field test data of the workpiece additive is obtained in real time by an array of temperature sensors arranged inside the test workpiece.

[0023] Furthermore, the entire additive manufacturing process in step S2 is divided into two printing stages. The first printing stage is the temperature preparation stage, in which a test workpiece is printed first, and an array of temperature sensors is arranged inside the test workpiece. A special substrate and a molding cylinder piston kit are used. After installation, the second printing stage continues 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 using an ultra-high-speed infrared thermal imager and an array of temperature sensors.

[0024] Furthermore, the first printed test workpiece has an array of inner holes inside, and the temperature sensors in the array temperature sensor group are installed in the array inner holes. The connecting wires of the temperature sensors in the array temperature sensor group pass through the special substrate and the molding cylinder piston kit, and are connected to the external data acquisition equipment.

[0025] Furthermore, the temperature sensor in the array-type temperature sensor group is a type B high-temperature thermocouple with a diameter of less than 2 mm. The thermocouple wire material of the type B high-temperature thermocouple is platinum-rhodium 30-platinum-rhodium 6, and the sheath is made of high-temperature alloy, molybdenum metal, or platinum-rhodium sheath.

[0026] Furthermore, in step S3, the simulation model is modeled based on the first printed test workpiece, and the simulation conditions are consistent with the test conditions in step S2.

[0027] Furthermore, there are two comparison conditions in step S4: a primary correction condition and a secondary correction condition. The primary correction condition involves selecting simulated and tested temperature distribution data at the same time within three different time periods, finding all temperature data corresponding to the test location in the temperature simulation data, including the surface temperature and internal temperature of the test workpiece, and comparing the simulated temperature at the same location with the process test temperature, with the data difference not exceeding 10%. The secondary correction condition is that the difference between the simulated temperature gradient and the test temperature gradient of the workpiece does not exceed 5%.

[0028] Furthermore, during the first calibration, the laser source model and the temperature-laser absorptivity correlation model are corrected; during the second calibration, the temperature-parameter correlation models for thermal conductivity, specific heat capacity, heat convection, heat radiation, and powder-bulk interface thermal resistance parameters are slightly corrected.

[0029] Furthermore, a calibration is first performed, coupling all the preliminary temperature-parameter correlation models obtained from the experiments into the simulation model, determining the laser source model, changing only the temperature-laser absorptivity correlation model, and performing multiple simulation corrections until the simulated temperature field data and the tested temperature field data meet the calibration conditions.

[0030] Then, a second correction is performed. In the simulation model, the temperature-laser absorptivity correlation parameter is fixed, and other temperature-parameter correlation models are changed. Multiple simulation corrections are performed until the simulated temperature field data and the tested temperature field data meet the second correction conditions.

[0031] Specifically, during the first calibration, the temperature-parameter correlation models for thermal conductivity, specific heat capacity, thermal convection, thermal radiation, and powder-bulk interface thermal resistance are based on experimentally obtained data models. The steps of the first calibration are as follows: 1) Build a process testing platform; 2) Conduct process tests and record temperature distribution and changes; 3) Establish a simulation model based on the process testing model; 4) Input the corresponding temperature-parameter correlation model for simulation; 5) Compare the simulated temperature distribution and changes with the temperature distribution and changes obtained from the process tests; 6) Compare the data. If the conditions are met, stop the simulation; if the conditions are not met, change the temperature-laser absorptivity correlation model for simulation until the conditions are met and a good temperature-laser absorptivity correlation model is obtained, thus completing the first calibration of the parameter model.

[0032] To obtain both simulated and tested temperature field data for additive manufacturing during a calibration process, the first step is to test the workpiece temperature during laser powder bed additive manufacturing, including temperature distribution and changes. Workpiece temperature comprises two parts: the temperature of the additive surface and the internal temperature. The additive surface temperature is measured using an ultra-high-speed infrared thermal imager. The internal temperature is measured using an array of high-temperature sensors. The process involves five steps: First, the process test model is designed based on the arrangement of the array of high-temperature sensors, ensuring the model's internal structure perfectly matches the sensor array's installation. Second, the process test model is printed using powder bed additive manufacturing. Third, the process test model is separated and replaced with a matching substrate. This involves creating openings in the substrate corresponding to the process test model area for high-temperature sensor wiring. Fourth, the process test model is reassembled on the replaced substrate at the corresponding locations, and the array of high-temperature sensors is installed on it. Fifth, powder is spread, printing is performed, and the internal temperature distribution and changes of the workpiece are recorded during the printing process to obtain the additive manufacturing temperature field test data.

[0033] Next, a simulation of the laser powder bed additive manufacturing process was conducted. The temperature-parameter correlation model obtained from the experiment was reprogrammed and embedded into the simulation model settings. The simulation was then converged, debugged, and completed to obtain the temperature field simulation data of the additive manufacturing process.

[0034] Finally, taking into account both the additive surface temperature and internal temperature of the workpiece, and through data comparison and feedback, a calibration was completed after multiple corrections to the temperature-laser absorptivity correlation model. The criterion for determining the completion of a calibration was to select simulated and tested temperature distribution data at the same time within three different time periods, find all temperature data corresponding to the test location in the simulated temperature distribution data, including the surface temperature and internal temperature of the test workpiece, and compare the simulated temperature at the same location with the process test temperature; the data difference should not exceed 10%.

[0035] After the first calibration is completed, a second calibration is performed. This involves fixing the temperature-laser absorptivity correlation model and adjusting and calibrating other temperature-parameter correlation models. It's important to note that the more test temperature points and the more detailed the comparison, the more accurate the final calibrated temperature-parameter correlation model will be. The basic steps for the second calibration are the same as for the first. It primarily addresses experimental testing errors caused by heat loss, performing second calibration on temperature-parameter correlation models for thermal conductivity, specific heat capacity, heat convection, heat radiation, and powder-bulk interface thermal resistance. Here, the criterion for successful second calibration is that the simulated temperature gradient of the workpiece does not exceed 5% of the test temperature gradient.

[0036] Furthermore, for different types of additive manufacturing powder materials, it is necessary to perform the same temperature-parameter correlation model calibration procedure, establish a temperature-parameter correlation model database for different materials, and accurately predict the workpiece temperature field of different materials under different process conditions.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] (1) The present invention adopts a full-domain temperature testing system to obtain high-precision surface and internal temperature test data of the workpiece 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 type B platinum-rhodium thermocouples (diameter <2 mm, temperature resistance 1600℃) implanted into the pre-set channel of the workpiece to realize synchronous acquisition of multiple temperature points inside the workpiece (such as 64 measuring points).

[0039] (2) When the workpiece temperature is tested in the laser powder bed additive manufacturing process, the present invention adopts staged printing, first printing a test workpiece with holes → implanting a high temperature sensor → second powder spreading and printing to achieve in-situ monitoring.

[0040] (3) The parameter correction of the present invention adopts a two-stage mechanism. First correction: the temperature-laser absorptivity correlation model is corrected first, and the laser source parameters are iteratively optimized by comparing the simulated and measured temperature distribution (error ≤ 10%). Second correction: the temperature-laser absorptivity correlation model is fixed, and parameters such as thermal conductivity / specific heat capacity / thermal convection / thermal radiation / interface thermal resistance are corrected to make the temperature gradient error between the simulation and the measured ≤ 5%.

[0041] (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 method for predicting the temperature field of laser powder bed additive workpieces. In the later stage, the temperature data of different structure workpieces in the laser powder bed additive process can be obtained through simulation, which is simple and efficient.

[0042] (5) The method for predicting the temperature field of laser powder bed additive manufacturing workpieces is not limited by materials. It can establish a database of temperature-parameter correlation models for different materials to meet the requirements for predicting the temperature field of laser powder bed additive manufacturing workpieces of different materials.

[0043] (6) The laser powder bed additive workpiece temperature field prediction method can be applied to the temperature field prediction of all additive workpieces of different shapes of the same material, avoiding the problem of temperature measurement kits being unportable due to changes in the shape of the additive workpiece and the problem of special-shaped additive workpieces being untestable in temperature field experimental testing.

[0044] (7) The laser powder bed additive workpiece temperature field prediction method can truly realize theoretical simulation to guide production practice. The process parameters can be adjusted by the temperature data obtained through simulation, effectively avoiding thermal stress cracking and other problems that occur in the additive manufacturing process, greatly improving the level of laser powder bed additive manufacturing, and helping the additive manufacturing industry to develop efficiently. Attached Figure Description

[0045] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0046] Figure 1 This is a schematic flowchart of the laser powder bed additive workpiece temperature field simulation and prediction method based on temperature monitoring in this invention.

[0047] Figure 2 This is a schematic diagram of the temperature testing platform for the laser powder bed additive manufacturing process in this invention;

[0048] Figure 3 This is a schematic diagram of the structure of the special substrate in this invention;

[0049] Figure 4 This is a schematic diagram of the side and bottom structure of the workpiece used for temperature testing in the laser powder bed additive manufacturing process of this invention;

[0050] Figure 5 This is a schematic diagram of the structure of the molding cylinder piston assembly in this invention;

[0051] The specific reference numerals in the attached figures are as follows:

[0052] 1. Sealed outer cover, 2. Powder supply cylinder, 3. Forming cylinder, 4. Powder return cylinder, 5. Laser head, 6. Ultra-high speed infrared thermal imager, 7. Metal powder, 8. Special substrate, 8-1. Bottom surface of substrate, 8-2. Bottom groove of substrate, 8-3. Threaded through hole of substrate, 9. Test workpiece, 9-1. Inner hole, 10. Array temperature sensor group, 10-1. Temperature sensor measuring point, 10-2. Temperature sensor connecting wire, 11. Forming cylinder piston kit, 11-1. Piston kit through hole, 12. Powder scraper. Detailed Implementation

[0053] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0054] The embodiments of the present invention disclose a method for simulating and predicting the temperature field of laser powder bed additive workpieces based on temperature monitoring, which is generally based on... Figure 1 The process diagram is used to implement the creation.

[0055] Step (1): Using existing experimental testing methods, experimental tests are conducted at different temperatures on the temperature-related parameters of the boundary conditions and material properties involved in the temperature field simulation of the laser powder bed additive manufacturing process. The specific temperature-related parameters and experimental testing methods are as follows:

[0056] For laser powder bed additive manufacturing, key material properties include density, thermal conductivity, specific heat capacity, and latent heat of phase transition. The materials primarily consist of workpiece material, substrate material, and powder material, all with essentially the same elemental composition. However, due to their particle morphology, powder materials exhibit significantly different density, thermal conductivity, and specific heat capacity compared to bulk materials like the workpiece and substrate. Specifically, the thermal conductivity of bulk and powdered metals can be measured using an electrothermal steady-state method. The specific heat capacity and latent heat of phase transition of bulk and powdered metals are tested using differential scanning calorimetry. The material density at different temperatures is determined by mass-to-volume ratio.

[0057] For laser powder bed additive manufacturing, the boundary conditions mainly involve parameters such as laser source model parameters, thermal convection / radiation coefficients of the exposed additive surface, thermal convection coefficients of the substrate bottom and sides, and thermal resistance of the powder-bulk interface. Specifically, the thermal convection / radiation coefficients of the exposed additive surface and the thermal convection coefficients of the substrate bottom and sides can be measured using an electrothermal steady-state method. Since surface convection and radiation occur simultaneously, the radiation coefficient is first measured in a vacuum environment using an electrothermal steady-state method. Then, the total surface thermal convection and thermal radiation are measured in a nitrogen environment, and the thermal convection coefficient is calculated by subtraction. The total thermal resistance is tested using a protective hot plate method, and the powder thermal resistance is obtained by subtracting it. Combined with the previously measured thermal conductivity of the metal powder, the powder thermal resistance can be calculated. Furthermore, the laser powder bed additive manufacturing process involves a shallow molten pool, and the main parameters of the laser source include laser power, laser spot radius, and the material's absorption rate of the laser. Specifically, the absorption rate of the metal powder to the laser is tested using an integrating sphere spectrometer.

[0058] Based on the experimental test results, temperature-parameter correlation models were established. These temperature-parameter correlation models include temperature-laser absorptivity correlation model, temperature-powder (bulk) thermal conductivity correlation model, temperature-powder (bulk) specific heat capacity correlation model, temperature-bulk surface thermal convection correlation model, temperature-bulk surface thermal radiation correlation model, and temperature-powder / bulk interface thermal resistance correlation model.

[0059] Step (2) calibrates the obtained temperature-parameter correlation model. Depending on the parameters, the calibration is divided into primary calibration and secondary calibration. Primary calibration includes the laser source model and the temperature-laser absorption rate correlation model, while the rest are secondary calibrations.

[0060] Step (3) 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 the currently popular commercial simulation software) to construct an accurate temperature field prediction method, and predict the temperature field of the laser powder bed additive manufacturing process based on the constructed temperature field prediction method.

[0061] It should be noted that in step (2) temperature-parameter correlation model correction, the additive manufacturing process temperature field test is performed first to obtain the additive manufacturing process temperature field test data, the additive manufacturing process temperature field simulation is performed to obtain the additive manufacturing process temperature field simulation data, and finally the temperature-parameter correlation model is corrected by comparing the additive manufacturing process temperature field test data and the additive manufacturing process temperature field simulation data.

[0062] In the temperature field test during the additive manufacturing 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 manufacturing equipment, the temperature field on the surface of the test workpiece 9 can be monitored in real time and data can be collected during the laser head 5's printing process; the other part is the internal temperature field test of the test workpiece 9, which is performed in real time by arranging an array of temperature sensors 10 on the test workpiece 9, such as... Figure 2 As shown, the laser powder bed additive manufacturing equipment includes a sealed outer cover 1, a powder supply cylinder 2, a forming cylinder 3 and a powder return cylinder 4 located at the bottom of the sealed outer cover 1, and a laser head 5 located inside the sealed outer cover 1.

[0063] In a specific implementation case, the structural schematic diagram of test workpiece 9 is as follows: Figure 4 As shown, the dimensions are 240 * 240 * 240 mm. The test workpiece 9 has arrayed inner holes 9-1, each 230 mm deep, 10 mm in diameter, and 50 mm apart at the center. These inner holes 9-1 are used to arrange the array of temperature sensor groups 10. The more arrayed 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, resulting in a denser arrangement of temperature sensor measuring points 10-1 within the test workpiece 9, more temperature data points acquired, and higher temperature test resolution. In this implementation example, there are 16 arrayed inner holes 9-1, with 4 temperature sensor measuring points 10-1 evenly arranged from top to bottom in each inner hole 9-1, for a total of 64 temperature sensor measuring points arranged within the test workpiece 9. It should be noted that the arrangement and number of temperature sensor measuring points 10-1 are not unique and can be adjusted according to testing requirements.

[0064] Based on the structural dimensions of the test workpiece 9, the dedicated substrate 8 is processed. For example... Figure 3As shown, the dimensions of the dedicated substrate 8 are 600 * 600 * 100 mm. A 240 * 240 mm area is defined at the center of the dedicated substrate 8, matching the dimensions of the test workpiece 9. Within this area, an array of threaded through-holes 8-3 are machined. The center of each 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 threaded through-hole 8-3 is 10 mm. Next, a groove is milled on the bottom surface of the dedicated substrate 8 in this area. The dimensions of the bottom groove 8-2 on the bottom surface 8-1 are 240 * 240 * 40 mm.

[0065] Based on the structural dimensions of the dedicated substrate 8, a piston assembly through hole 11-1 is formed on the molding cylinder piston assembly 11. For example... Figure 5 As shown, the piston assembly through hole 11-1 has a diameter of 50mm and is located below the bottom groove 8-2 area of ​​the substrate. It is used for arranging the temperature sensor connection line 10-2 and connecting it to an external data acquisition device.

[0066] The entire additive manufacturing printing test process is divided into two stages. The first stage prints the test workpiece 9 and arranges the array temperature sensor group 10 inside the test workpiece 9. A special substrate 8 and a molding cylinder piston kit 11 are used. After installation, the second stage of printing continues on the test workpiece 9 printed in the first stage. During the second stage of printing, 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.

[0067] The specific process is as follows:

[0068] Test workpiece 9 is first formed by laser powder bed additive manufacturing. After forming, test workpiece 9 is separated from the conventional substrate and then fixed directly above the bottom groove area of ​​the dedicated substrate 8. The array temperature sensor group 10 passes through the threaded through hole 8-3 of the substrate and is inserted into the inner hole 9-1 of the test workpiece 9. The length of the temperature sensor group is 280mm, and each temperature sensor group has 4 temperature sensor measuring points 10-1. The diameter of the temperature sensor group does not exceed the diameter of the threaded through hole 8-3 of the substrate or the diameter of the inner hole 9-1 of the test workpiece 9. After all the temperature sensor groups are inserted, a hollow threaded nail is screwed upward from the bottom groove of the dedicated substrate 8 into the threaded through hole 8-3 of the substrate to lock the temperature sensor group. The thread length of the hollow threaded nail is 12mm, and after tightening, it can hold the temperature sensor group to prevent it from loosening. The temperature sensor connecting wire 10-2 passes through the center hole of the hollow threaded nail. All temperature sensor connection wires 10-2 of all temperature sensor groups converge in the groove at the bottom of the dedicated substrate 8, pass through the piston assembly through hole 11-1, and connect to the external data acquisition device.

[0069] After the test workpiece 9, temperature sensor group, ultra-high-speed infrared thermal imager 6, and special substrate 8 are installed and fixed, pre-powder is applied so that the metal powder 7 is flush with the upper surface of the test workpiece 9. Metal powder 7 is then applied to the surface of the test workpiece 9 using a powder scraper 12 for layer-by-layer scanning and printing, with each layer of metal powder 7 having a thickness of 30μm. There are no specific restrictions on the laser scanning path; this implementation uses unidirectional scanning, with adjacent layers scanning perpendicular to each other, a scanning range of a complete 240*240mm area, a scanning interval of 0.08mm, a scanning speed of 800mm / s, and a laser power of 200W. Under these determined process parameters, two different scanning tests are performed: a single-layer scan, where there is no interlayer temperature accumulation; and a multi-layer scan, with more than 100 layers, where interlayer temperature accumulation occurs. During both scanning tests, the ultra-high-speed infrared thermal imager 6 and the array temperature sensor group 10 record the surface temperature and internal test point temperature of the test workpiece 9 at different times.

[0070] Specifically, the structure and dimensions of the test workpiece 9 are not specific; it can be of any shape and size. However, the workpiece model used in the simulation process must be identical to the test workpiece 9 used in the process test. Simultaneously, the array of temperature sensor groups 10 can be evenly distributed according to the structure and dimensions of the test workpiece 9. The temperature sensors use type B high-temperature thermocouples, with a maximum temperature measurement capability of 1600℃. The thermocouple wire material is platinum-rhodium 30-platinum-rhodium 6, and the sheath is made of 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.

[0071] After completing the above process tests, the next step is to simulate the temperature field of the additive manufacturing process. In this 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. Second, the material properties obtained through experimental testing, the temperature-parameter correlation model involved in the boundary conditions, and the laser source model are coupled into the simulation solver through programming. Then, the solver settings are configured, including mesh generation and time step. Next, the single-layer scanning and multiple-scanning processes of additive manufacturing are simulated according to the process parameters of the process test. Finally, post-processing is performed to obtain the temperature field simulation data of the additive manufacturing process.

[0072] In the specific implementation process, since the laser powder bed additive manufacturing process involves a shallow molten pool, a Gaussian surface heat source model is selected for the laser source model. First, temperature test data and simulated data from single-layer and multiple scans in laser powder bed additive manufacturing are compared. If the comparison fails, feedback is sent to the simulation end, and the temperature-laser absorptivity correlation model is corrected and re-simulated. This process is repeated multiple times until the temperature-laser absorptivity correlation model has undergone one calibration. The criteria for one calibration are as follows: Simulated and tested temperature distribution data are selected at the same time in three different time periods (initial, middle, and final stages). All temperature data corresponding to the test location are found in the simulated temperature distribution data, including the surface and internal temperatures of the test workpiece 9. The simulated temperature at the same location is compared with the process test temperature; the data difference should not exceed 10%. Then, the temperature-laser absorptivity correlation model is fixed, and the simulated data is compared with the test data. If the comparison fails, feedback is sent to the simulation end, and other temperature-parameter correlation models (see...) are then corrected. Figure 1 The simulation is then corrected and repeated multiple times until the secondary calibration of other temperature-parameter correlation models is completed. The secondary calibration standard is as follows: the simulated temperature gradient of the workpiece is basically consistent with the test temperature gradient, and the temperature gradient difference does not exceed 5%.

[0073] Finally, after calibrating the temperature-parameter correlation model, the simulation program is integrated with the model to form a method for simulating and predicting the temperature field of laser powder bed additive manufacturing workpieces based on temperature monitoring. The same temperature-parameter correlation model calibration program is required for different printing materials, and the parameter process package can be continuously expanded. This patented method for simulating and predicting the temperature field of laser powder bed additive manufacturing workpieces can accurately predict the workpiece temperature field of different materials under different process conditions.

[0074] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A temperature monitoring based simulation prediction method for temperature field of a workpiece in laser powder bed additive manufacturing, characterized in that, Comprise: S1, the temperature related parameters in the boundary conditions and material properties involved in the temperature field simulation of laser powder bed additive manufacturing process are tested at different temperatures, and a preliminary temperature-parameter correlation model is established according to the test results, including: laser absorption, thermal conductivity, specific heat capacity, heat convection, heat radiation, powder-block interface thermal resistance; S2, build a test platform for additive process, obtain additive manufacturing process temperature field test data, which is divided into two parts, one is the workpiece additive surface temperature field test data, the other is the workpiece additive internal temperature field test data, which includes temperature distribution data and temperature change data; S3, establish an additive process simulation model, input the preliminary temperature-parameter correlation model for simulation, and obtain additive manufacturing process temperature field simulation data; S4, compare the additive manufacturing process temperature field simulation data with the additive manufacturing process temperature field test data, and correct the preliminary temperature-parameter correlation model when the comparison condition is not met, and obtain the accurate temperature-parameter correlation model; S5, obtain the accurate temperature-parameter correlation model, integrate the program package to build the temperature field prediction model, and predict the temperature field of laser powder bed additive workpiece process according to the built prediction model; There are two comparison conditions in step S4, which are first correction condition and second correction condition; The first 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 test workpiece surface temperature and internal temperature, compare the simulation temperature with the process test temperature at the same position, and the data difference is not more than 10%; The second correction condition is that the temperature gradient of the workpiece simulation and the test temperature gradient difference is not more than 5%; When the first correction is made, the laser light source model and the temperature-laser absorption rate correlation model are corrected; When the second correction is made, the temperature-parameter correlation model of thermal conductivity, specific heat capacity, heat convection, heat radiation and powder-block interface thermal resistance is slightly corrected; First, the first correction is made, all the temperature-parameter correlation models obtained by experiment are coupled into the simulation model, the laser light source model is determined, only the temperature-laser absorption rate correlation model is changed, and multiple simulation corrections are made until the simulated temperature field data and the tested temperature field data meet the first correction condition; Then, the second correction is made, the temperature-laser absorption rate correlation parameter is fixed in the simulation model, the other temperature-parameter correlation models are changed, and multiple simulation corrections are made until the simulated temperature field data and the tested temperature field data meet the second correction condition.

2. The temperature monitoring based laser powder bed additive workpiece temperature field simulation prediction method according to claim 1, characterized in that, In step S2, the workpiece additive surface temperature field test data is measured by a super high-speed infrared thermal imager, which is installed inside the laser powder bed additive equipment to monitor and test the temperature field of the workpiece surface in real time during the laser head movement printing process and collect data; The workpiece additive internal temperature field test data is obtained by real-time detection of the array type temperature sensor group arranged inside the test workpiece.

3. The temperature monitoring based laser powder bed addition workpiece temperature field simulation prediction method of claim 2, wherein, The whole additive manufacturing process in step S2 is divided into two segments of printing, the first segment of printing is a temperature measurement preparation stage, a test workpiece is printed first, and an array type temperature sensor group is arranged inside the test workpiece, a special substrate and a forming cylinder piston sleeve set are used, after installation, the second segment of printing is continued on the test workpiece of the first segment of printing; the second segment of printing is a temperature measurement stage, the temperature field of the laser powder bed additive manufacturing process is monitored in real time through a super high speed infrared thermal imager and an array type temperature sensor group.

4. The temperature monitoring based laser powder bed addition workpiece temperature field simulation prediction method according to claim 3, characterized in that, The test workpiece of the first segment of printing is formed with an array of internal holes, the temperature sensors in the array type temperature sensor group are correspondingly installed into the array of internal holes, the connecting lines of the temperature sensors in the array type temperature sensor group pass through the special substrate and the forming cylinder piston sleeve set, and are connected to an external data acquisition device.

5. The temperature monitoring based laser powder bed addition workpiece temperature field simulation prediction method of claim 4, wherein, The temperature sensors in the array type temperature sensor group are B type high temperature thermocouples, the diameter of which is less than 2mm, the thermocouple wire material of the B type high temperature thermocouple is platinum rhodium 30-platinum rhodium 6, and the sleeve adopts a high temperature alloy, molybdenum metal or platinum rhodium sleeve.

6. The temperature monitoring based laser powder bed additive workpiece temperature field simulation prediction method according to any one of claims 3-5, characterized in that, In step S3, a simulation model is modeled according to the test workpiece of the first segment of printing, and the simulation conditions are consistent with the test conditions in step S2.

7. The temperature monitoring based laser powder bed addition workpiece temperature field simulation prediction method of claim 1, wherein, For different printing materials, the same flow temperature-parameter correlation model correction program is carried out, a temperature-parameter correlation model database of different materials is established to predict the workpiece temperature field of different materials under different process conditions.

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