CN115625894A - Laser additive manufacturing life prediction method based on coaxial damage detection - Google Patents
Laser additive manufacturing life prediction method based on coaxial damage detection Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
- B29C—SHAPING OR JOINING OF PLASTICS; SHAPING OF MATERIAL IN A PLASTIC STATE, NOT OTHERWISE PROVIDED FOR; AFTER-TREATMENT OF THE SHAPED PRODUCTS, e.g. REPAIRING
- B29C64/00—Additive manufacturing, i.e. manufacturing of three-dimensional [3D] objects by additive deposition, additive agglomeration or additive layering, e.g. by 3D printing, stereolithography or selective laser sintering
- B29C64/30—Auxiliary operations or equipment
- B29C64/386—Data acquisition or data processing for additive manufacturing
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
- B29C—SHAPING OR JOINING OF PLASTICS; SHAPING OF MATERIAL IN A PLASTIC STATE, NOT OTHERWISE PROVIDED FOR; AFTER-TREATMENT OF THE SHAPED PRODUCTS, e.g. REPAIRING
- B29C64/00—Additive manufacturing, i.e. manufacturing of three-dimensional [3D] objects by additive deposition, additive agglomeration or additive layering, e.g. by 3D printing, stereolithography or selective laser sintering
- B29C64/10—Processes of additive manufacturing
- B29C64/106—Processes of additive manufacturing using only liquids or viscous materials, e.g. depositing a continuous bead of viscous material
- B29C64/124—Processes of additive manufacturing using only liquids or viscous materials, e.g. depositing a continuous bead of viscous material using layers of liquid which are selectively solidified
- B29C64/129—Processes of additive manufacturing using only liquids or viscous materials, e.g. depositing a continuous bead of viscous material using layers of liquid which are selectively solidified characterised by the energy source therefor, e.g. by global irradiation combined with a mask
- B29C64/135—Processes of additive manufacturing using only liquids or viscous materials, e.g. depositing a continuous bead of viscous material using layers of liquid which are selectively solidified characterised by the energy source therefor, e.g. by global irradiation combined with a mask the energy source being concentrated, e.g. scanning lasers or focused light sources
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
- B29C—SHAPING OR JOINING OF PLASTICS; SHAPING OF MATERIAL IN A PLASTIC STATE, NOT OTHERWISE PROVIDED FOR; AFTER-TREATMENT OF THE SHAPED PRODUCTS, e.g. REPAIRING
- B29C64/00—Additive manufacturing, i.e. manufacturing of three-dimensional [3D] objects by additive deposition, additive agglomeration or additive layering, e.g. by 3D printing, stereolithography or selective laser sintering
- B29C64/10—Processes of additive manufacturing
- B29C64/141—Processes of additive manufacturing using only solid materials
- B29C64/153—Processes of additive manufacturing using only solid materials using layers of powder being selectively joined, e.g. by selective laser sintering or melting
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
- B29C—SHAPING OR JOINING OF PLASTICS; SHAPING OF MATERIAL IN A PLASTIC STATE, NOT OTHERWISE PROVIDED FOR; AFTER-TREATMENT OF THE SHAPED PRODUCTS, e.g. REPAIRING
- B29C64/00—Additive manufacturing, i.e. manufacturing of three-dimensional [3D] objects by additive deposition, additive agglomeration or additive layering, e.g. by 3D printing, stereolithography or selective laser sintering
- B29C64/30—Auxiliary operations or equipment
- B29C64/386—Data acquisition or data processing for additive manufacturing
- B29C64/393—Data acquisition or data processing for additive manufacturing for controlling or regulating additive manufacturing processes
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B33—ADDITIVE MANUFACTURING TECHNOLOGY
- B33Y—ADDITIVE MANUFACTURING, i.e. MANUFACTURING OF THREE-DIMENSIONAL [3-D] OBJECTS BY ADDITIVE DEPOSITION, ADDITIVE AGGLOMERATION OR ADDITIVE LAYERING, e.g. BY 3-D PRINTING, STEREOLITHOGRAPHY OR SELECTIVE LASER SINTERING
- B33Y10/00—Processes of additive manufacturing
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B33—ADDITIVE MANUFACTURING TECHNOLOGY
- B33Y—ADDITIVE MANUFACTURING, i.e. MANUFACTURING OF THREE-DIMENSIONAL [3-D] OBJECTS BY ADDITIVE DEPOSITION, ADDITIVE AGGLOMERATION OR ADDITIVE LAYERING, e.g. BY 3-D PRINTING, STEREOLITHOGRAPHY OR SELECTIVE LASER SINTERING
- B33Y50/00—Data acquisition or data processing for additive manufacturing
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B33—ADDITIVE MANUFACTURING TECHNOLOGY
- B33Y—ADDITIVE MANUFACTURING, i.e. MANUFACTURING OF THREE-DIMENSIONAL [3-D] OBJECTS BY ADDITIVE DEPOSITION, ADDITIVE AGGLOMERATION OR ADDITIVE LAYERING, e.g. BY 3-D PRINTING, STEREOLITHOGRAPHY OR SELECTIVE LASER SINTERING
- B33Y50/00—Data acquisition or data processing for additive manufacturing
- B33Y50/02—Data acquisition or data processing for additive manufacturing for controlling or regulating additive manufacturing processes
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P10/00—Technologies related to metal processing
- Y02P10/25—Process efficiency
Abstract
The invention discloses a laser additive manufacturing service life prediction method based on coaxial damage detection. The coaxial equipment can acquire the defect information of air holes, unmelted air holes and cracks in the formed deposition layer; the service life prediction system can perform defect three-dimensional imaging and finite element analysis on the service life of the additive part; the real-time adjusting system can adjust the technological parameters of the laser material adding process in real time. The method realizes the prediction of the service life of the additive material in the laser additive manufacturing process, reduces the possibility of the occurrence of the defects of the additive material by changing the process parameters of laser power, scanning speed and feeding speed in real time, and prolongs the service life of the additive material.
Description
Technical Field
The invention relates to the field of laser additive manufacturing, in particular to a method for predicting the service life of laser additive manufacturing.
Background
As a new rapid prototyping technology, the additive manufacturing technology has the advantages of high efficiency, low cost, strong designability, high automation and the like compared with the traditional equal-material and material-reducing technology, and has wide prospects in the field of manufacturing of precise and complex parts. The laser has the advantages of concentrated heat source energy, good forming effect, wide material use, no need of vacuum environment and the like, and is widely applied to the additive manufacturing technology as a heat source.
However, in the laser additive manufacturing process, the manufactured parts often have defects such as air holes, poor fusion and cracks, and the defects have a crucial influence on the service life of the parts. In the aspect of air hole defect detection, a test piece is generally required to be processed and then subjected to metallographic observation, ultrasonic detection, X-ray detection or industrial CT detection, most of the posterior detection methods are required to be processed and manufactured, if the air hole defect is detected, the test piece needs to be scrapped, and a large amount of resources are wasted. The service life of the additive material part is generally predicted after the additive material part is manufactured, the service life cannot be predicted in the laser additive material process, and at present, research on service life prediction is concentrated in the traditional manufacturing field and has high engineering application value in the additive material manufacturing field. The method is a good way for being applied to engineering practice by simulating the generation of defects in the laser material increase process by using a computer, predicting the service life and regulating and controlling the material increase process in real time.
Methods for conducting life prediction and conducting process control on an additive part in an additive manufacturing process to improve the life are absent at present.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a laser additive manufacturing service life prediction method based on coaxial damage detection. The method comprises the steps of carrying out three-dimensional defect reconstruction according to the defect information of the additive piece transmitted by coaxial damage detection equipment in the laser additive manufacturing process, predicting the defect distribution of the integral additive piece under the current process parameters based on an artificial neural network algorithm in combination with defect generation positions, heat dissipation conditions, heat input, clamping conditions and material attribute factors, simulating the service environment of the additive piece in practical application by setting initial conditions and boundary conditions including temperature, pressure and humidity conditions, carrying out finite element analysis and calculation to predict the service life of the additive piece, and changing the process parameters of laser power, scanning speed and feeding speed by transmitting electric signals to a laser, a control cabinet and a feeder in real time to reduce the possibility of the occurrence of the defects of the additive piece, so that the service life of the additive piece is prolonged.
In order to achieve the above object, the present invention provides a method for predicting a laser additive manufacturing lifetime based on coaxial damage detection, which includes:
(1) Introducing the additive part model and the material into a life prediction system, and acquiring physical performance parameters of the used material by the life prediction system according to a database;
(2) Using coaxial equipment to obtain the defect information of air holes, unmelted and cracks in the formed deposition layer in the laser additive manufacturing process;
(3) The service life prediction system receives the defect information and maps the defect information into the additive part model to predict the internal defects of the overall additive part, and the service life of the additive part in the service environment is simulated and analyzed by changing the initial conditions and the boundary conditions;
(4) The real-time adjusting system obtains the information transmitted by the service life predicting system and adjusts the technological parameters in the laser additive manufacturing process so as to adjust the heat input, reduce the defects of air holes, unmelted air holes and cracks and prolong the service life of the additive parts.
Preferably, the additive part model in the step (1) is a geometric model built in modeling software according to the actual structure size of the additive part, and the additive area, the heat affected area and the substrate area in the geometric model of the additive part are subjected to meshing in meshing software in a density transition meshing manner, so as to ensure the efficiency and accuracy of calculation.
Preferably, the material database in step (1) is obtained through experimental measurement, literature collection and data input to obtain material parameters, the database comprises density, young modulus, thermal expansion coefficient, poisson ratio, thermal conductivity, specific heat capacity and yield strength material parameters, the types of the material parameters comprise metal materials, ceramic materials, composite materials and gradient materials, and corresponding material physical property parameters are searched from the database according to the input material types and are transmitted to the life prediction system.
Preferably, the coaxial damage detection device in step (2) includes an X-ray emission probe and a detector, the X-ray source of the coaxial damage detection device is a point light source, when the X-ray encounters a defect, the intensity of the X-ray changes, the detector obtains the intensity of the X-ray penetrating the point light source at different positions and the position coordinates of the projection of the defect inside the additive part, the size and the three-dimensional position information of the defect are obtained by an algebraic iteration method, and the size and the three-dimensional position information are transmitted to the life prediction system as digital signals.
Preferably, the life prediction system in the step (3) is trained based on a large amount of experimental data with an artificial neural network algorithm as a core, and comprehensively analyzes the defect generation position, the heat dissipation condition, the heat input, the clamping condition and the material attribute factor to predict the occurrence possibility of the defects of the additive part, the life prediction system can display images in an introduced finite element model according to digital signals transmitted by the coaxial damage detection equipment, simulate the existence of the defects in the additive part by removing grids in corresponding areas, predict the defect distribution of the integral additive part under the current process parameters, simulate the service environment of the additive part in practical application by setting initial conditions of temperature, pressure and humidity, simulate the real service process of the additive part by setting the heat input and force boundary conditions, predict the life of the additive part by carrying out finite element analysis and calculation on the failure time of the material under the action of air holes and cracks, output life information to a display and transmit the life information to the real-time adjustment system in the form of digital signals.
Preferably, the real-time adjusting system in the step (4) receives information of the life predicting system, judges whether the life of the additive part meets the life required by actual service under the current process parameters, if so, keeps the current laser output power, laser head scanning speed and feeding speed of a feeding device, if not, judges whether the thermal input is too large or too small at the moment, transmits electric signals to the laser, a control cabinet and the feeding device, the laser, the control cabinet and the feeding device receive the electric signals and correspondingly change the laser power, scanning speed and feeding speed parameters, 5% of the current parameters are changed every time, process adjustment is carried out, defects are reduced, the life of the additive part is prolonged, then the coaxial damage detecting system carries out damage detection, and the life predicting system carries out life prediction to judge whether the life meets the requirements under the current process parameters.
The invention has the beneficial effects that:
(1) The invention realizes the detection and three-dimensional reconstruction of the air holes and the crack defects of the additive part in the laser additive manufacturing process.
(2) The method can predict the service life of the integral additive part in the service environment in the laser additive manufacturing process.
(3) The method can regulate and control the process in the laser additive manufacturing process, reduce the possibility of generating the defects of the additive part by changing the technological parameters of the laser power, the scanning speed and the feeding speed in real time, and prolong the service life of the additive part.
Drawings
Fig. 1 is a schematic flow diagram of laser additive manufacturing lifetime coaxial damage detection defects.
Fig. 2 is a schematic flow diagram of a laser additive manufacturing life prediction system.
Fig. 3 is a schematic flow diagram of a laser additive manufacturing real-time tuning system.
Detailed Description
The following describes a laser additive manufacturing lifetime prediction method based on coaxial damage detection in detail with reference to the accompanying drawings.
The working flow of the method of the invention is shown in figures 1-3.
FIG. 1 is a schematic flow diagram of laser additive manufacturing lifetime co-axial damage defect detection.
Step 1, establishing a geometric model of the laser additive part, wherein the geometric model is established in finite element modeling software according to the actual structure size of the additive part;
step 2, establishing a grid model, and performing grid division on an additive area, a heat affected area and a substrate area in the geometric model of the additive in finite element software in a density transition grid division mode to ensure the efficiency and the accuracy of calculation;
step 3, importing the grid model into a life prediction system, inputting the type of the used material into the life prediction system, searching corresponding material density, young modulus, thermal expansion coefficient, poisson ratio, thermal conductivity, specific heat capacity and yield strength material parameters in a database, and endowing the corresponding material density, young modulus, thermal expansion coefficient, poisson ratio, thermal conductivity, specific heat capacity and yield strength material parameters to a finite element model;
step 4, the laser head focuses according to the requirement, sets the laser power, the scanning speed and the feeding speed, opens the coaxial damage detection equipment, and links the equipment with the service life prediction system to ensure the successful transmission of data;
and 5, additive manufacturing processing is carried out, the coaxial damage detection device moves along with the laser head and carries out damage detection on the formed deposition layer, the detector obtains the intensity of X rays penetrating when the X-ray point light sources at different positions irradiate and the position coordinates of the projection of the internal defect of the additive part, the size and the three-dimensional position information of the defect are obtained through an algebraic iteration method, and the defect size and the three-dimensional position information are transmitted to a service life prediction system through digital signals.
Fig. 2 is a schematic flow diagram of a laser additive manufacturing life prediction system.
Step 6, the service life prediction system can display images in the imported finite element model according to the digital signals transmitted by the coaxial damage detection equipment, and defect reconstruction is carried out by eliminating the form of corresponding area grids;
step 7, comprehensively analyzing and predicting the defect generation position, the heat dissipation condition, the heat input, the clamping condition and the material attribute factor of the defect by the life prediction system by taking an artificial neural network algorithm as a core, and eliminating grids of corresponding areas, wherein the defect distribution information of the overall additive part under the current process parameters is obtained;
and 8, inputting temperature, pressure and humidity information of the additive part in a real service environment in a service life prediction system to set initial conditions, setting temperature and pressure boundary conditions according to the real service condition, performing finite element analysis to calculate the failure time of the material under the action of the air holes and the cracks so as to predict the service life of the additive part, outputting the service life information to a display and transmitting the service life information to a real-time adjusting system in a digital signal mode.
Fig. 3 is a schematic flow diagram of a laser additive manufacturing real-time tuning system.
Step 9, the real-time adjusting system receives the service life predicting system information, judges whether the service life of the additive part meets the service life required by actual service under the current process parameters, and if so, maintains the current laser output power, laser head scanning speed and feeding speed of a feeding device; if the heat input is not enough, judging whether the heat input is too large or too small at the moment, transmitting electric signals to the laser, the control cabinet and the feeding device, receiving the electric signals by the laser, the control cabinet and the feeding device, correspondingly changing the parameters of laser power, scanning speed and feeding speed, wherein the change degree is 1% of the current parameters each time, adjusting the process, reducing the defects and prolonging the service life of the material adding piece;
and step 10, carrying out damage detection by a coaxial damage detection system, carrying out life prediction by a life prediction system to judge whether the current process parameters meet requirements, and continuously carrying out process parameter correction if the current process parameters do not meet the requirements.
The foregoing is only a preferred embodiment of this invention and it should be noted that modifications can be made by those skilled in the art without departing from the principle of the invention and these modifications should also be considered as the protection scope of the invention.
Claims (3)
1. A laser additive manufacturing life prediction method based on coaxial damage detection is characterized in that the method uses coaxial damage detection equipment and a life prediction system to predict the life of an additive part, and uses a real-time adjusting system to control an additive process;
the coaxial damage detection device can acquire the defect information of pores, unmelted defects and cracks in a formed deposition layer and comprises an X-ray emission probe and a detector, wherein an X-ray light source of the coaxial damage detection device is a point light source, when an X-ray encounters a defect, the intensity of the X-ray is changed, the detector acquires the intensity of the X-ray penetrating when the point light source at different positions irradiates and the position coordinates of the projection of the defect in the additive part, the size and the three-dimensional position information of the defect are acquired through an algebraic iteration method, and the information is transmitted to a service life prediction system through a digital signal;
the service life prediction system can perform defect three-dimensional imaging and finite element analysis on the service life of the additive part, train according to a large amount of experimental data by taking an artificial neural network algorithm as a core, comprehensively analyze defect generation positions, heat dissipation conditions, heat input, clamping conditions and material attribute factors to predict the possibility of occurrence of the defects of the additive part, display images in an introduced finite element model according to digital signals transmitted by coaxial damage detection equipment, simulate the existence of the defects in the additive part by removing grids in corresponding areas, predict the defect distribution of the integral additive part under the current technological parameters, simulate the service environment of the additive part in practical application by setting initial conditions of temperature, pressure and humidity, simulate the real service process of the additive part by setting conditions of heat input and force boundaries, perform finite element analysis to calculate the failure time of materials under the action of air holes and cracks so as to predict the service life of the additive part, output life information to a display and transmit the life information to a real-time adjustment system in the form of digital signals;
the real-time adjusting system can adjust the technological parameters of the laser material adding process in real time, receive the information of the service life predicting system, judge whether the service life of the material adding piece meets the service life required by actual service under the current technological parameters, if so, keep the current laser output power, laser head scanning speed and feeding speed of a feeding device, if not, judge whether the heat input is too large or too small at the moment, and transmit electric signals to the laser, a control cabinet and the feeding device, the laser, the control cabinet and the feeding device receive the electric signals and correspondingly change the parameters of the laser power, the scanning speed and the feeding speed, 5 percent of the current parameters are changed every time, process adjustment is carried out, defects are reduced, the service life of the material adding piece is prolonged, then the coaxial damage detecting system carries out damage detection, and the service life predicting system carries out service life prediction to judge whether the requirements are met under the current technological parameters;
the method comprises the following steps:
(1) Introducing the additive digital model and the material into a life prediction system, and acquiring physical performance parameters of the used material by the life prediction system according to a database;
(2) Acquiring defect information of air holes, unmelted air holes and cracks in a formed deposition layer by using coaxial damage detection equipment in the laser additive manufacturing process;
(3) The service life prediction system receives the defect information and maps the defect information into the additive part model to predict the internal defects of the overall additive part, and the service life of the additive part in the service environment is simulated and analyzed by changing the initial conditions and the boundary conditions;
(4) The real-time adjusting system obtains information transmitted by the service life predicting system and adjusts the current process parameters in the laser additive manufacturing process so as to achieve the appropriate process parameters to adjust heat input, reduce defects of air holes, unmelted materials and cracks and prolong the service life of the additive parts.
2. The laser additive manufacturing life prediction method based on coaxial damage detection according to claim 1, wherein the additive part model is a geometric model constructed in modeling software according to an actual structural size of the additive part, and the additive area, the heat affected area and the substrate area in the additive part geometric model are gridded in gridding software in a sparse-dense transition gridding manner, so as to ensure the efficiency and accuracy of calculation.
3. The laser additive manufacturing life prediction method based on coaxial damage detection as claimed in claim 1, wherein the material database is obtained by experimental measurement, literature collection and data input, the database comprises density, young modulus, thermal expansion coefficient, poisson's ratio, thermal conductivity, specific heat capacity and yield strength material parameters, the types of the material parameters comprise metal materials, ceramic materials, composite materials and gradient materials, and corresponding material physical property parameters are searched from the database according to the input material types and transmitted to the life prediction system.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116277952A (en) * | 2023-04-07 | 2023-06-23 | 苏州壹哲智能科技有限公司 | 3D printing equipment, method, device and medium |
| CN116275114A (en) * | 2023-05-12 | 2023-06-23 | 山东海纳智能装备科技股份有限公司 | Process monitoring control system based on laser additive manufacturing |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116277952A (en) * | 2023-04-07 | 2023-06-23 | 苏州壹哲智能科技有限公司 | 3D printing equipment, method, device and medium |
| CN116277952B (en) * | 2023-04-07 | 2023-11-17 | 苏州壹哲智能科技有限公司 | 3D printing equipment, method, device and medium |
| CN116275114A (en) * | 2023-05-12 | 2023-06-23 | 山东海纳智能装备科技股份有限公司 | Process monitoring control system based on laser additive manufacturing |
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