A Laser Additive Manufacturing Life Prediction Method Based on Coaxial Damage Detection

Abstract

The present invention discloses a method for predicting the life of laser additive manufacturing based on coaxial damage detection. This method uses a coaxial damage detection device to detect defects during the additive process, a life prediction system to predict the life of the additive component, and a real-time adjustment system to control the additive process. The coaxial device can obtain information on pores, lack of fusion, and crack defects in the formed deposition layer; the life prediction system can perform three-dimensional imaging of defects and finite element analysis to predict the life of the additive component; the real-time adjustment system can adjust the process parameters of the laser additive process in real time. This method realizes the prediction of the life of the additive component during the laser additive manufacturing process and reduces the possibility of defects in the additive component by changing the process parameters of laser power, scanning speed, and feeding speed in real time, thereby extending the life of the additive component.

Description

Technical Field

[0001] The present invention relates to the field of laser additive manufacturing, and specifically, to a method for predicting the service life of laser additive manufacturing. Background Art

[0002] As a new rapid prototyping technology, additive manufacturing technology has the advantages of high efficiency, low cost, strong designability, and high automation compared with traditional equal-material and subtractive technologies, and has broad prospects in the field of manufacturing precision and complex parts. Laser is widely used as a heat source in additive manufacturing technology due to its advantages of concentrated heat source energy, good forming effect, wide range of materials used, and no need for a vacuum environment.

[0003] However, during the laser additive manufacturing process, the manufactured parts often have defects such as pores, poor fusion, and cracks, and these defects have a crucial impact on the service life of the components. In terms of pore defect detection, it is usually necessary to use methods such as metallographic observation, ultrasonic testing, X-ray testing, or industrial CT testing after the test piece is processed. Most of these post-testing methods need to be carried out after processing and manufacturing. If pore defects are detected, these test pieces need to be scrapped, which often wastes a large amount of resources. The prediction of the service life of additive parts is usually carried out after the additive parts are manufactured, and it is impossible to predict the service life during the laser additive process. Moreover, the current research on service life prediction focuses on the traditional manufacturing field, and it has high engineering application value in the additive manufacturing field. Using computer simulation to generate defects during the laser additive process and predict the service life as well as real-time regulation of the additive process is a better way to apply to engineering practice.

[0004] Currently, there is a lack of a method for predicting the service life of additive parts during the additive manufacturing process and controlling the process to improve the service life. Summary of the Invention

[0005] Aiming at the deficiencies of the prior art, the present invention provides a method for predicting the service life of laser additive manufacturing based on coaxial damage detection. This method reconstructs three-dimensional defects according to the defect information of the additive part transmitted by the coaxial damage detection device during the laser additive manufacturing process, and predicts the defect distribution of the overall additive part under the current process parameters based on the artificial neural network algorithm combined with factors such as the defect generation position, heat dissipation conditions, heat input, clamping conditions, and material properties. By setting initial conditions and boundary conditions, including temperature, pressure, and humidity conditions, the service environment of the additive part in actual application is simulated, and finite element analysis calculations are carried out to predict the service life of the additive part and change process parameters such as laser power, scanning speed, and feeding speed by real-time transmitting electrical signals to the laser, control cabinet, and feeder to reduce the possibility of defects in the additive part and extend the service life of the additive part.

[0006] To achieve the above object, a laser additive manufacturing life prediction method based on coaxial damage detection mentioned in the present invention is specifically as follows:

[0007] (1) Import the additive part model and material into the life prediction system, and the life prediction system obtains the physical property parameters of the used material according to the database.

[0008] (2) During the laser additive manufacturing process, use coaxial equipment to obtain the information of pores, unmelted, and crack defects in the formed deposition layer.

[0009] (3) The life prediction system receives the defect information and maps it inside the additive part model, predicts the internal defects of the overall additive part, and simulates and analyzes the life of the additive part in the service environment by changing the initial conditions and boundary conditions.

[0010] (4) The real-time adjustment system obtains the information transmitted by the life prediction system and adjusts the process parameters of the laser additive manufacturing process to adjust the heat input, reduce the pores, unmelted, and crack defects, and improve the life of the additive part.

[0011] Preferably, the additive part model described in step (1) is to construct a geometric model in the modeling software according to the actual structural dimensions of the additive part, and perform mesh division on the additive area, heat affected zone, and substrate area in the additive part geometric model in the mesh division software using a sparse-dense transition mesh division method to ensure the calculation efficiency and accuracy.

[0012] Preferably, the material database in step (1) obtains material parameters through experimental measurement, literature collection, and data input. The database contains material parameters such as density, Young's modulus, coefficient of thermal expansion, Poisson's ratio, thermal conductivity, specific heat capacity, and yield strength. The types include metal materials, ceramic materials, composite materials, and gradient materials. Search for the corresponding material physical property parameters from the database according to the input material type and transmit them to the life prediction system.

[0013] Preferably, the coaxial damage detection device described in step (2) includes an X-ray emission probe and a detector. The X-ray light source of the coaxial damage detection device is a point light source. When the X-ray encounters a defect, its intensity will change. The detector obtains the intensity of the X-ray penetrating when the point light source irradiates at different positions and the position coordinates of the projection of the internal defects of the additive part, and obtains the size and three-dimensional position information of the defects through the algebraic reconstruction method, and transmits it to the life prediction system as a digital signal.

[0014] Preferably, the life prediction system described in step (3) is centered on the artificial neural network algorithm and trained based on a large amount of experimental data. It comprehensively analyzes factors such as the location of defect generation, heat dissipation conditions, heat input, clamping conditions, and material properties to predict the possibility of defects in the additive part. The life prediction system can display images in the imported finite element model according to the digital signals transmitted by the coaxial damage detection device, simulate the existence of defects in the additive part by removing the meshes in the corresponding areas, and predict the defect distribution of the overall additive part under the current process parameters. By setting the initial conditions of temperature, pressure, and humidity, it simulates the service environment of the additive part in actual applications. By setting the heat input and force boundary conditions, it simulates the actual service process of the additive part, conducts finite element analysis to calculate the material failure time under the action of pores and cracks, thereby predicting the life of the additive part, and outputs the life information to the display and transmits it to the real-time adjustment system in the form of digital signals.

[0015] Preferably, the real-time adjustment system described in step (4) receives the information from the life prediction system, judges whether the life of the additive part meets the required life for actual service under the current process parameters. If so, it maintains the current laser output power, laser head scanning speed, and feeding speed of the feeding device. If not, it judges whether the heat input is too large or too small at this time, and transmits electrical signals to the laser, control cabinet, and feeding device. The laser, control cabinet, and feeding device receive the electrical signals and correspondingly change the parameters of laser power, scanning speed, and feeding speed by 5% each time, conduct process adjustment, reduce defects, and extend the life of the additive part. Then, the coaxial damage detection system conducts damage detection, and the life prediction system conducts life prediction to judge whether the requirements are met under the current process parameters.

[0016] Advantages of the present invention:

[0017] (1) The present invention realizes the detection and three-dimensional reconstruction of pores and crack defects in the additive part during the laser additive manufacturing process.

[0018] (2) The present invention can predict the life of the overall additive part in the service environment during the laser additive manufacturing process.

[0019] (3) The present invention can conduct process regulation during the laser additive manufacturing process, reduce the possibility of defects in the additive part by changing the process parameters of laser power, scanning speed, and feeding speed in real time, and extend the life of the additive part. Description of the Drawings

[0020] Figure 1 It is a schematic flow chart of the coaxial damage detection of defects in the life of laser additive manufacturing.

[0021] Figure 2 It is a schematic flow chart of the life prediction system for laser additive manufacturing.

[0022] Figure 3 It is a schematic flow diagram of a real-time adjustment system for laser additive manufacturing. Specific implementation manners

[0023] The following specifically describes a method for predicting the service life of laser additive manufacturing based on coaxial damage detection according to the present invention in conjunction with the accompanying drawings.

[0024] The working process of the method of the present invention is as Figures 1 - 3 shown.

[0025] Figure 1 It is a schematic flow diagram of coaxial damage detection defects in the service life of laser additive manufacturing.

[0026] Step 1: Establish a geometric model of the laser additive part. The specific steps include constructing a geometric model in finite element modeling software according to the actual structural dimensions of the additive part.

[0027] Step 2: Establish a mesh model. Adopt a mesh division method with a transition from sparse to dense for the additive area, heat affected zone and substrate zone in the geometric model of the additive part in finite element software for mesh division to ensure the efficiency and accuracy of calculation.

[0028] Step 3: Import the mesh model into the service life prediction system, and input the type of material used in the service life prediction system. Search the database for corresponding material density, Young's modulus, coefficient of thermal expansion, Poisson's ratio, thermal conductivity, specific heat capacity, yield strength material parameters and assign them to the finite element model.

[0029] Step 4: The laser head focuses as required. At the same time, set the laser power, scanning speed, and feeding speed, turn on the coaxial damage detection device, and link the device with the service life prediction system to ensure successful data transmission.

[0030] Step 5: Perform additive manufacturing processing. The coaxial damage detection device moves along with the laser head and detects the damage of the formed deposition layer. The detector obtains the X-ray intensity penetrating when irradiated by X-ray point sources at different positions and the position coordinates of the projection of internal defects of the additive part. The size and three-dimensional position information of the defects are obtained through the algebraic reconstruction method and transmitted to the service life prediction system as digital signals.

[0031] Figure 2 It is a schematic flow diagram of the service life prediction system for laser additive manufacturing.

[0032] Step 6: The service life prediction system can perform image display in the imported finite element model according to the digital signals transmitted by the coaxial damage detection device, and perform defect reconstruction by eliminating the grids in the corresponding areas.

[0033] Step 7: The life prediction system takes the artificial neural network algorithm as the core to comprehensively analyze the defect generation location, heat dissipation conditions, heat input, clamping conditions, and material property factors, predicts the defect distribution information of the overall additive part under the current process parameters, and eliminates the meshes in the corresponding areas.

[0034] Step 8: Input the temperature, pressure, and humidity information of the additive part in the actual service environment into the life prediction system for initial condition setting. Set the temperature and pressure boundary conditions according to the actual service situation, and perform finite element analysis to calculate the material failure time under the action of pores and cracks, so as to predict the life of the additive part. Output the life information to the display and transmit it to the real-time adjustment system in the form of digital signals.

[0035] Figure 3 It is a schematic flow diagram of the real-time adjustment system for laser additive manufacturing.

[0036] Step 9: The real-time adjustment system receives the information from the life prediction system, judges whether the life of the additive part meets the required life in actual service under the current process parameters. If so, keep the current laser output power, laser head scanning speed, and feeding device feeding speed; if not, judge whether the heat input is too large or too small at this time, and transmit electrical signals to the laser, control cabinet, and feeding device. The laser, control cabinet, and feeding device receive the electrical signals and correspondingly change the laser power, scanning speed, and feeding speed parameters. Each change degree is 1% of the current parameter, perform process adjustment, reduce defects, and extend the life of the additive part.

[0037] Step 10: The coaxial damage detection system conducts damage detection, and the life prediction system conducts life prediction to judge whether the requirements are met under the current process parameters. If not, continuously correct the process parameters.

[0038] The above is only the preferred implementation mode of the present invention. It should be noted that for those of ordinary skill in the art of this technology, several improvements can be made without departing from the principle of the present invention, and these improvements should also be regarded as the protection scope of the present invention.

Claims

1. A laser additive manufacturing life prediction method based on coaxial damage detection, characterized in that This method uses a coaxial damage detection device and a life prediction system to predict the life of an additive part, and a real-time adjustment system to control the additive process; The coaxial damage detection device can obtain the information of pores, unmelted, and crack defects in the formed deposition layer, including an X-ray emission probe and a detector. The X-ray light source of the coaxial damage detection device is a point light source. When the X-ray encounters a defect, its intensity will change. The detector obtains the intensity of the X-ray penetrating when the point light source irradiates at different positions and the position coordinates of the internal defect projection of the additive part. The size and three-dimensional position information of the defect are obtained through the algebraic reconstruction technique and transmitted to the life prediction system as a digital signal; The life prediction system can perform three-dimensional imaging of defects and finite element analysis of the life of the additive part. It is trained based on a large amount of experimental data with an artificial neural network algorithm as the core. It comprehensively analyzes factors such as the defect generation position, heat dissipation conditions, heat input, clamping conditions, and material properties to predict the possibility of defects in the additive part. The life prediction system can display the image in the imported finite element model according to the digital signal transmitted by the coaxial damage detection device, simulate the existence of defects in the additive part by removing the mesh in the corresponding area, and predict the defect distribution of the overall additive part under the current process parameters. By setting the initial conditions of temperature, pressure, and humidity, the service environment of the additive part in actual application is simulated. By setting the heat input and force boundary conditions, the real service process of the additive part is simulated, and finite element analysis is carried out to calculate the material failure time under the action of pores and cracks, so as to predict the life of the additive part. The life information is output to the display and transmitted to the real-time adjustment system in the form of a digital signal; The real-time adjustment system can adjust the process parameters of the laser additive process in real time. It receives the information of the life prediction system, judges whether the life of the additive part meets the required life for actual service under the current process parameters. If so, it maintains the current laser output power, laser head scanning speed, and feeding device feeding speed. If not, it judges whether the heat input is too large or too small at this time, and transmits an electrical signal to the laser, control cabinet, and feeding device. The laser, control cabinet, and feeding device receive the electrical signal and correspondingly change the laser power, scanning speed, and feeding speed parameters by 5% each time to perform process adjustment, reduce defects, and extend the life of the additive part. Then, the coaxial damage detection system performs damage detection, and the life prediction system performs life prediction to judge whether it meets the requirements under the current process parameters; The steps are as follows: (1) Import the digital model and material of the additive part into the life prediction system, and the life prediction system obtains the physical property parameters of the used material according to the database; (2) Use the coaxial damage detection device to obtain the information of pores, unmelted, and crack defects in the formed deposition layer during the laser additive manufacturing process; (3) The life prediction system receives the defect information and maps it inside the additive part model, predicts the internal defects of the overall additive part, and simulates and analyzes the life of the additive part in the service environment by changing the initial conditions and boundary conditions; (4) The real-time adjustment system obtains the information transmitted by the life prediction system and adjusts the current process parameters in the laser additive manufacturing process to achieve suitable process parameters, thereby adjusting the heat input, reducing porosity, lack of fusion, and crack defects, and improving the life of the additive component.

2. The laser additive manufacturing life prediction method based on coaxial damage detection according to claim 1, characterized in that The additive component model is constructed by building a geometric model in modeling software according to the actual structural dimensions of the additive component, and the additive area, heat affected zone, and substrate area in the additive component geometric model are meshed in the meshing software using a dense-to-sparse transition meshing method to ensure the efficiency and accuracy of the calculation.

3. A laser additive manufacturing life prediction method based on coaxial damage detection according to claim 1, characterized in that, The material database obtains material parameters through experimental measurements, literature collection, and data input. The database contains material parameters such as density, Young's modulus, coefficient of thermal expansion, Poisson's ratio, thermal conductivity, specific heat capacity, and yield strength. The types include metal materials, ceramic materials, composite materials, and gradient materials. According to the input material type, the corresponding material physical property parameters are searched from the database and transmitted to the life prediction system.

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