CN114254455A - Method for predicting fatigue life of 3D printing metal component - Google Patents
Method for predicting fatigue life of 3D printing metal component Download PDFInfo
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Abstract
The application discloses a method for predicting the fatigue life of a 3D printing metal component, which comprises the following steps: carrying out section pore distribution analysis on metal powder to determine the porosity of the metal powder; obtaining a 3D printing metal component from the metal powder through an additive manufacturing process; carrying out fatigue life test on the 3D printed metal component under the specific cycle characteristic, and carrying out S-N curve fitting on the obtained test data to obtain a fatigue life S-N curve of the 3D printed metal component; predicting the fatigue life of the 3D printed metal component based on the porosity of the metal powder and the fatigue life S-N curve of the 3D printed metal component. By the method, the fatigue life of the corresponding 3D printing metal component can be dynamically predicted based on the porosity of the metal powder, and technical support is provided for research and evaluation of the fatigue life of the 3D printing metal component.
Description
Technical Field
The application relates to a method for predicting fatigue life of a printing component, in particular to a method for predicting fatigue life of a 3D printing metal component.
Background
The selective laser melting technique is a typical metal additive manufacturing technique (3D printing technique) that produces a three-dimensional solid model from a computer-generated three-dimensional digital model using discrete materials in a layer-by-layer printed form. At present, the 3D printing technology is widely applied to the fields of aerospace, medical instruments, automobile parts and the like due to the characteristics of material saving, high speed, individuation and the like. However, 3D printed products are less dense than conventional castings, where the stacking effect roughens the surface and the internal voids cannot be completely eliminated, which can seriously affect their fatigue life.
Disclosure of Invention
The application mainly aims to provide a method for predicting the fatigue life of a 3D printed metal component, and aims to solve the technical problem that the fatigue life of the 3D printed metal component cannot be dynamically predicted in the prior art.
To achieve the above object, the present application proposes a method for predicting fatigue life of a 3D printed metal member, comprising the steps of:
carrying out section pore distribution analysis on metal powder to determine the porosity of the metal powder;
obtaining a 3D printing metal component from the metal powder through an additive manufacturing process;
carrying out fatigue life test on the 3D printed metal component under the specific cycle characteristic, and carrying out S-N curve fitting on the obtained test data to obtain a fatigue life S-N curve of the 3D printed metal component;
predicting the fatigue life of the 3D printed metal component based on the porosity of the metal powder and the fatigue life S-N curve of the 3D printed metal component.
Optionally, the expression of the fatigue life S-N curve of the 3D printed metal component is:
lgN＝A1+A2lgσmax
where N represents the number of cycles leading to failure, σ max represents the peak stress, A1And A2Is the undetermined coefficient.
Optionally, the step of analyzing the cross-sectional pore distribution of the metal powder to determine the porosity of the metal powder includes:
and measuring the occupied area of the pores in the metal powder section and the sectional area of the metal powder by image processing software, and obtaining the ratio of the occupied area to the sectional area of the metal powder to determine the porosity of the metal powder.
Optionally, the fatigue life test comprises an axial stress fatigue test.
Optionally, the additive manufacturing process comprises a selective laser melting forming process.
Optionally, before performing a cross-sectional pore distribution analysis on the metal powder to determine the porosity of the metal powder, the method further includes:
observing the surface morphology of metal powder for 3D printing fatigue samples to obtain the sphericity of the metal powder so as to judge whether the metal powder meets the morphology requirement of a laser additive manufacturing forming process;
and if the metal powder meets the morphology requirement of the laser additive manufacturing and forming process, performing section pore distribution analysis on the metal powder to determine the porosity of the metal powder.
Further, the step of observing the surface topography of the metal powder used for the 3D printing fatigue test sample to obtain the sphericity of the metal powder comprises:
and observing the surface appearance of the metal powder by using a scanning electron microscope, and obtaining the sphericity of the metal powder.
Optionally, before the step of analyzing the cross-sectional pore distribution of the metal powder and determining the porosity of the metal powder, the method further comprises:
performing powder particle size distribution analysis on metal powder for a 3D printing fatigue sample to obtain the average particle size of the metal powder so as to judge whether the metal powder meets the particle size requirement of a laser additive manufacturing and forming process;
and if the metal powder meets the particle size requirement of the laser additive manufacturing and forming process, performing section pore distribution analysis on the metal powder to determine the porosity of the metal powder.
Further, the step of analyzing the powder particle size distribution of the metal powder for the 3D printing fatigue sample to obtain the average particle size of the metal powder includes:
and analyzing the powder particle size distribution of the metal powder by using a laser particle sizer, and calculating to obtain the average particle size of the metal powder.
Optionally, before performing a fatigue life test on the 3D printed metal component under a specific cycle characteristic to obtain fatigue life test data, the method further includes:
performing profile pore distribution analysis on the 3D printed metal component, determining the porosity of the 3D printed metal component, and obtaining the density of the 3D printed metal component so as to judge whether the 3D printed metal component meets the density requirement of a laser additive manufacturing forming process;
if the 3D printing metal component meets the density requirement of the laser additive manufacturing forming process, carrying out a fatigue life test on the 3D printing metal component, and determining fatigue life test data of the 3D printing metal component.
The present study found that metal powder properties are critical to the forming quality of the printed metal component, especially the powder porosity, which can have a significant impact on the powder metallurgy process and pore formation, potentially reducing the fatigue life of the 3D printed metal component. Therefore, in order to ensure the reliability and repeatability of the fatigue life of the 3D printed metal component, the method for predicting and regulating the fatigue life of the 3D printed metal component based on the metal powder characteristic parameters is established.
The method comprises the following steps of testing the porosity of metal powder, and performing a fatigue test after the metal powder is subjected to additive manufacturing to obtain a 3D printed metal component; and performing mathematical fitting on the fatigue life of the 3D printed metal component corresponding to the metal powder with different porosities to obtain an S-N curve fitting equation.
Based on the S-N curve fitting equation, the damage rate and the service life of the 3D printing metal component can be calculated according to the stress, the cycle number and the S-N curve of the 3D printing metal component under the load spectrum condition, whether the working life of the 3D printing metal component meets the design requirement or not can be visually judged according to the stress, the cycle number and the position of the S-N curve of the 3D printing metal component under the load spectrum condition, whether the porosity of the selected original metal powder can meet the expected design requirement or not can be judged, and therefore the selected metal powder can be regulated and controlled.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the structures shown in the drawings without creative efforts.
FIG. 1 is a basic flowchart of a method for predicting fatigue life of a 3D printed metal component according to an embodiment of the present application;
FIG. 2 is a schematic flow chart illustrating a method for predicting fatigue life of a 3D printed metal component according to an embodiment of the present disclosure;
FIG. 3 is a graph of the morphology of a metal powder according to an embodiment of the present application;
FIG. 4 is a fatigue life S-N curve of a 3D printed metal component corresponding to a metal powder porosity of 0.1% according to an embodiment of the present disclosure;
FIG. 5 is a fatigue life S-N curve of a 3D printed metal component corresponding to a metal powder porosity of 0.5% according to an embodiment of the present disclosure;
fig. 6 is a fatigue life S-N curve of a 3D printed metal component when the porosity of the metal powder is 1% according to an embodiment of the present disclosure.
The implementation, functional features and advantages of the objectives of the present application will be further explained with reference to the accompanying drawings.
Detailed Description
The technical solutions in the embodiments of the present application will be described clearly and completely with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Referring to fig. 1, fig. 1 is a schematic flowchart of a method for predicting fatigue life of a 3D printed metal component according to an embodiment of the present application.
As shown in fig. 1, the method for predicting the fatigue life of a 3D printed metal component comprises the following steps:
carrying out section pore distribution analysis on metal powder to determine the porosity of the metal powder;
obtaining a 3D printing metal component from the metal powder through an additive manufacturing process;
carrying out fatigue life test on the 3D printed metal component under a specific cycle characteristic, and carrying out S-N curve fitting on the obtained test data to obtain a fatigue life S-N curve of the 3D printed metal component;
predicting the fatigue life of the 3D printed metal component based on the porosity of the metal powder and the fatigue life S-N curve of the 3D printed metal component.
In some embodiments of the present application, the fatigue life S-N curve of the 3D printed metal component is expressed as:
lgN＝A1+A2lgσmax
where N represents the number of cycles leading to failure, σ max represents the peak stress, A1And A2Is the undetermined coefficient.
According to the expression of the fatigue life S-N curve, the damage rate and the service life of the 3D printing metal component are calculated by combining the stress, the cycle number and the S-N curve of the 3D printing metal component under the load spectrum condition, whether the working life of the 3D printing metal component meets the design requirement or not can be visually judged according to the stress, the cycle number and the position of the S-N curve of the 3D printing metal component under the load spectrum condition, whether the porosity of the selected original metal powder can meet the expected design requirement or not can be judged, and therefore the selected metal powder can be regulated and controlled.
In some embodiments of the present application, the step of analyzing the cross-sectional pore distribution of the metal powder to determine the porosity of the metal powder comprises: and measuring the occupied area of the pores in the metal powder section and the sectional area of the metal powder by image processing software, and obtaining the ratio of the occupied area to the sectional area of the metal powder to determine the porosity of the metal powder. The measuring method adopts image processing software to carry out measurement, and the detection result is not influenced by artificial subjective impression and is objective and reliable. However, this does not mean that the method for measuring the porosity of the metal powder described in the present application is limited to this type, but the present application provides only one possibility for implementation, and other effective methods for measuring the porosity may be used instead.
To study the dynamic fatigue performance of the 3D printed metal components, in some embodiments of the present application, the fatigue life test comprises an axial stress fatigue test.
In some embodiments of the present application, the additive manufacturing process comprises a selective laser melt forming process; the selective laser melting forming process is a typical metal additive manufacturing technology, is a new direction for the development of the mechanical forming processing field, uses high-energy laser beams as energy sources, melts metal raw materials such as powder and the like, then superposes layer by layer, solidifies into compact entities, and provides a revolutionary integrated forming method for high-performance metal components.
In order to improve the accuracy of predicting the fatigue life of a 3D printing metal component, some pre-steps are added on the basis of the flow shown in FIG. 1, as shown in FIG. 2:
in some embodiments of the present application, before performing a cross-sectional pore distribution analysis on a metal powder to determine a porosity of the metal powder, the method further comprises: observing the surface morphology of metal powder for 3D printing fatigue samples to obtain the sphericity of the metal powder so as to judge whether the metal powder meets the morphology requirement of a laser additive manufacturing forming process; if the metal powder meets the morphology requirement of the laser additive manufacturing and forming process, performing section pore distribution analysis on the metal powder to determine the porosity of the metal powder;
in some embodiments of the present application, before the step of analyzing the cross-sectional pore distribution of the metal powder to determine the porosity of the metal powder, the method further comprises: performing powder particle size distribution analysis on metal powder for a 3D printing fatigue sample to obtain the average particle size of the metal powder so as to judge whether the metal powder meets the particle size requirement of a laser additive manufacturing and forming process; and if the metal powder meets the particle size requirement of the laser additive manufacturing and forming process, performing section pore distribution analysis on the metal powder to determine the porosity of the metal powder.
In order to obtain a metal powder morphology map with high definition and good contrast, as a preferable scheme of the above embodiment, the step of performing surface morphology observation on the metal powder for 3D printing fatigue samples to obtain the sphericity of the metal powder comprises: and observing the surface appearance of the metal powder by using a scanning electron microscope, and obtaining the sphericity of the metal powder.
In order to accurately and intuitively reflect the particle size and the particle size distribution of the metal powder, as a preferable aspect of the above embodiment, the step of performing powder particle size distribution analysis on the metal powder for the 3D printing fatigue sample to obtain the average particle size of the metal powder includes: and analyzing the powder particle size distribution of the metal powder by using a laser particle sizer, and calculating to obtain the average particle size of the metal powder. The laser particle analyzer can analyze the particle size and the particle size distribution of the powder through the diffraction of the powder or the spatial distribution (scattering spectrum) of scattered light.
In order to predict the fatigue performance of the 3D printed metal component and screen out some 3D printed metal components with extremely poor fatigue performance, in some embodiments of the present application, before performing a fatigue life test on the 3D printed metal component under a specific cycle characteristic and obtaining fatigue life test data, the method further includes: performing profile pore distribution analysis on the 3D printed metal component, determining the porosity of the 3D printed metal component, and obtaining the density of the 3D printed metal component so as to judge whether the 3D printed metal component meets the density requirement of a laser additive manufacturing forming process; if the 3D printing metal component meets the density requirement of the laser additive manufacturing forming process, carrying out a fatigue life test on the 3D printing metal component, and determining fatigue life test data of the 3D printing metal component.
It can be seen that the method for predicting the fatigue life of a 3D printed metal component described in the present application mainly comprises:
testing the porosity of the metal powder, and respectively performing fatigue testing after the metal powder with different porosities is subjected to additive manufacturing to obtain corresponding 3D printed metal components; and performing S-N curve fitting on the fatigue test data of the 3D printed metal component corresponding to the metal powder with different porosities to obtain an S-N curve fitting equation. Based on the S-N curve fitting equation, the damage rate and the service life of the 3D printing metal component are calculated according to the stress, the cycle times and the S-N curve of the 3D printing metal component under the load spectrum condition, whether the working life of the 3D printing metal component meets the design requirement or not can be visually judged according to the stress, the cycle times and the position of the S-N curve of the 3D printing metal component under the load spectrum condition, whether the porosity of the selected original metal powder can meet the expected design requirement or not can be judged, and therefore the selected metal powder can be regulated and controlled.
Example 1
(1) Observing the surface topography of TC4 metal powder A by using a scanning electron microscope to obtain the sphericity of the TC4 metal powder A, wherein the metal powder topography is shown in FIG. 3; it can be seen that the particles of TC4 metal powder a have smooth surfaces and high sphericity, meeting the requirements of the laser additive manufacturing forming process;
(2) powder particle size distribution analysis is carried out on the TC4 metal powder A by using a laser particle sizer, and the obtained TC4 metal powder A has the particle size D10-29.81 μm, D50-46.87 μm and D90-66.00 μm, is in normal distribution and meets the requirements of a laser additive manufacturing and forming process;
(3) performing a cross-sectional pore distribution analysis of the TC4 metal powder A by using an image method, and determining the porosity eta by measuring the ratio of the occupied area of pores in the cross section of the TC4 metal powder A to the cross-sectional area of the TC4 metal powder A, namely, the porosity of the TC4 metal powder A is mainly distributed in the range of 0.0-0.3%, and the average porosity is 0.1%;
(4) printing and forming the TC4 metal powder A by using a selective laser melting technology; the laser process parameters used were: the laser power is 300W, the scanning speed is 1000mm/s, the spot diameter is 70 μm, the scanning interval is 60 μm, the powder layer thickness is 30 μm, and the scanning strategy is subarea island scanning; subsequently, obtaining a 3D printing metal component for fatigue test through machining;
(5) performing microstructure characterization on the 3D printed metal member by using an optical microscope, and calculating the porosity of the 3D printed metal member by using an image method to obtain that the density of the 3D printed metal member is 99.37%, so that the 3D printed metal member is basically compact, and the fatigue life of the 3D printed metal member is predicted to be good;
(6) the 3D printed metal member was subjected to a high cycle fatigue test, and the 3D printed metal member was obtained in which R was 0.1 and the cycle base N was 107Lower fatigue strength sigma0.1An S-N curve of axial high cycle fatigue of the 3D printed metal component was fitted, 705MPa, as shown in fig. 4: horizontal sittingMarked Nf(predicted high Cycle fatigue life of the invention in cycles) with ordinate σmax(maximum stress in use, in MPa).
From the above, when the TC4 metal powder a porosity is 0.0% to 0.3%, the fitting equation of the S-N curve corresponding to the obtained 3D printed metal member is 61.2241 to 19.2195lg σ, which is lgN ═ 61.2241 to 19.2195lg σmaxCoefficient of regression r20.8981, the working range is 3.5X 104<N<107。
Based on the S-N curve fitting equation, the damage rate and the service life of the 3D printing metal component can be calculated according to the stress, the cycle number and the S-N curve of the 3D printing metal component under the load spectrum condition, whether the working life of the 3D printing metal component meets the design requirement or not can be visually judged according to the stress, the cycle number and the position of the S-N curve of the 3D printing metal component under the load spectrum condition, whether the porosity of the selected original metal powder can meet the expected design requirement or not can be judged, and therefore the selected metal powder can be regulated and controlled.
Example 2
(1) Observing the surface topography of TC4 metal powder B by using a scanning electron microscope to obtain the sphericity characteristic of the TC4 metal powder B, namely, the particles of the TC4 metal powder B have smooth surfaces and high sphericity and meet the requirements of a laser additive manufacturing and forming process;
(2) powder particle size distribution analysis is carried out on the TC4 metal powder B by using a laser particle sizer, the particle size D10-29.46 mu m, D50-44.89 mu m and D90-63.71 mu m of the TC4 metal powder B are obtained, and the particle size D is normally distributed and meets the requirements of a laser additive manufacturing and forming process;
(3) performing a cross-sectional pore distribution analysis on the TC4 metal powder B by using an image method, and determining the porosity eta by measuring the ratio of the occupied area of pores in the cross section of the TC4 metal powder B to the cross-sectional area of the TC4 metal powder B, namely, the porosity of the TC4 metal powder B is mainly distributed in the range of 0.3-0.8%, and the average porosity is 0.5%;
(4) printing and forming the TC4 metal powder B by using a selective laser melting technology, wherein the used laser process parameters are as follows: the method comprises the following steps of (1) obtaining a 3D printed metal component for a fatigue test by machining, wherein the laser power is 300W, the scanning speed is 1000mm/s, the spot diameter is 70 micrometers, the scanning interval is 60 micrometers, and the powder layer spreading thickness is 30 micrometers;
(5) performing microstructure characterization on the 3D printed metal component by using an optical microscope, and calculating the porosity of the 3D printed metal component by using an image method to obtain that the density of the 3D printed metal component is 98.78%, wherein small-size circular pores exist and can influence the fatigue life of the 3D printed metal component to a certain extent;
(6) the 3D printed metal member was subjected to a high cycle fatigue test, and the 3D printed metal member was obtained in which R was 0.1 and the cycle base N was 107Lower fatigue strength sigma0.1Fitting an S-N curve of axial high cycle fatigue of the 3D printed metal component, as shown in fig. 5, 669 MPa: the abscissa is Nf(predicted high Cycle fatigue life of the invention in cycles) with ordinate σmax(maximum stress in use, in MPa).
From the above, when the porosity of the TC4 metal powder B is 0.3% to 0.8%, the fitting equation of the S-N curve corresponding to the obtained 3D printed metal member is lgN ═ 61.3728-20.4102lg σ max, the regression determination coefficient r2 is 0.7699, and the working range is 6 × 104< N < 107.
Based on the S-N curve fitting equation, the damage rate and the service life of the 3D printing metal component can be calculated according to the stress, the cycle number and the S-N curve of the 3D printing metal component under the load spectrum condition, whether the working life of the 3D printing metal component meets the design requirement or not can be visually judged according to the stress, the cycle number and the position of the S-N curve of the 3D printing metal component under the load spectrum condition, whether the porosity of the selected original metal powder can meet the expected design requirement or not can be judged, and therefore the selected metal powder can be regulated and controlled.
Example 3
(1) Observing the surface topography of TC4 metal powder C by using a scanning electron microscope to obtain the sphericity characteristic of the TC4 metal powder C, namely, the particles of the TC4 metal powder C have smooth surfaces and high sphericity and meet the requirements of a laser additive manufacturing and forming process;
(2) powder particle size distribution analysis is carried out on the TC4 metal powder C by using a laser particle sizer, and the obtained TC4 metal powder C has the particle size D10-28.94 μm, D50-45.24 μm and D90-64.36 μm, is in normal distribution and meets the requirements of a laser additive manufacturing and forming process;
(3) performing a cross-sectional pore distribution analysis of the TC4 metal powder C by using an image method, and determining the porosity eta by measuring the ratio of the occupied area of pores in the cross section of the TC4 metal powder C to the cross-sectional area of the TC4 metal powder C, namely, the porosity of the TC4 metal powder C is mainly distributed in the range of 0.8-1.2%, and the average porosity is 1.0%;
(4) printing and forming the TC4 metal powder C by using a selective laser melting technology, wherein the used laser process parameters are as follows: the laser power is 300W, the scanning speed is 1000mm/s, the spot diameter is 70 μm, the scanning interval is 60 μm, the powder layer thickness is 30 μm, and the scanning strategy is subarea island scanning; subsequently, obtaining a 3D printing metal component for fatigue test through machining;
(5) performing microstructure characterization on the 3D printed metal member by using an optical microscope, and calculating the porosity of the 3D printed metal member by using an image method to obtain that the density of the 3D printed metal member is 98.12%, wherein large-size irregular pores exist and can significantly influence the fatigue life of the member;
(6) the 3D printed metal member was subjected to a high cycle fatigue test, and the 3D printed metal member was obtained in which R was 0.1 and the cycle base N was 107Lower fatigue strength sigma0.1Fitting an S-N curve of axial high cycle fatigue of the 3D printed metal component at 587.5MPa, as shown in fig. 6: the abscissa is Nf(predicted high Cycle fatigue life of the invention in cycles) with ordinate σmax(maximum stress in use, in MPa).
As can be seen from the above, when the porosity of the TC4 metal powder C is 0.8% to 1.2%, the fitting equation of the S-N curve corresponding to the obtained 3D printed metal member is lgN ═ 60.4342 to 19.5371lg σ max, the regression determination coefficient r2 is 0.5655, and the working range is 3.5 × 104< N < 107.
Based on the S-N curve fitting equation, the damage rate and the service life of the 3D printing metal component can be calculated according to the stress, the cycle number and the S-N curve of the 3D printing metal component under the load spectrum condition, whether the working life of the 3D printing metal component meets the design requirement or not can be visually judged according to the stress, the cycle number and the position of the S-N curve of the 3D printing metal component under the load spectrum condition, whether the porosity of the selected original metal powder can meet the expected design requirement or not can be judged, and therefore the selected metal powder can be regulated and controlled.
The above description is only an alternative embodiment of the present application, and not intended to limit the scope of the present application, and all modifications and equivalents of the technical solutions that can be directly or indirectly applied to other related fields without departing from the spirit of the present application are intended to be included in the scope of the present application.
Claims (10)
1. A method of predicting fatigue life of a 3D printed metal component, comprising the steps of:
carrying out section pore distribution analysis on metal powder to determine the porosity of the metal powder;
obtaining a 3D printing metal component from the metal powder through an additive manufacturing process;
carrying out fatigue life test on the 3D printed metal component under the specific cycle characteristic, and carrying out S-N curve fitting on the obtained test data to obtain a fatigue life S-N curve of the 3D printed metal component;
predicting the fatigue life of the 3D printed metal component based on the porosity of the metal powder and the fatigue life S-N curve of the 3D printed metal component.
2. The method for predicting the fatigue life of a 3D printed metal component according to claim 1, wherein the expression of the fatigue life S-N curve of the 3D printed metal component is as follows:
lgN＝A1+A2lgσmax
where N represents the number of cycles leading to failure, σ max represents the peak stress, A1And A2Is the undetermined coefficient.
3. The method of predicting the fatigue life of a 3D printed metal part as recited in claim 1, wherein said step of subjecting the metal powder to a cross-sectional pore distribution analysis to determine the porosity of the metal powder comprises:
and measuring the occupied area of the pores in the metal powder section and the sectional area of the metal powder by image processing software, and obtaining the ratio of the occupied area to the sectional area of the metal powder to determine the porosity of the metal powder.
4. The method of predicting fatigue life of a 3D printed metallic component of claim 1, wherein the fatigue life test comprises an axial stress fatigue test.
5. The method of predicting the fatigue life of a 3D printed metallic component of claim 1, wherein the additive manufacturing process comprises a selective laser melting forming process.
6. The method of predicting the fatigue life of a 3D printed metal part as recited in claim 1, further comprising, before subjecting the metal powder to a cross-sectional pore distribution analysis to determine the porosity of the metal powder:
observing the surface morphology of metal powder for 3D printing fatigue samples to obtain the sphericity of the metal powder so as to judge whether the metal powder meets the morphology requirement of a laser additive manufacturing forming process;
and if the metal powder meets the morphology requirement of the laser additive manufacturing and forming process, performing section pore distribution analysis on the metal powder to determine the porosity of the metal powder.
7. The method for predicting the fatigue life of the 3D printed metal component as claimed in claim 6, wherein the step of observing the surface topography of the metal powder used for the 3D printed fatigue sample to obtain the sphericity of the metal powder comprises the following steps:
and observing the surface appearance of the metal powder by using a scanning electron microscope, and obtaining the sphericity of the metal powder.
8. The method of predicting the fatigue life of a 3D printed metal part as recited in claim 1, further comprising, prior to the step of subjecting the metal powder to a cross-sectional pore distribution analysis to determine the porosity of the metal powder:
performing powder particle size distribution analysis on metal powder for a 3D printing fatigue sample to obtain the average particle size of the metal powder so as to judge whether the metal powder meets the particle size requirement of a laser additive manufacturing and forming process;
and if the metal powder meets the particle size requirement of the laser additive manufacturing and forming process, performing section pore distribution analysis on the metal powder to determine the porosity of the metal powder.
9. The method for predicting the fatigue life of the 3D printed metal component as claimed in claim 8, wherein the step of analyzing the powder particle size distribution of the metal powder used for the 3D printed fatigue sample to obtain the average particle size of the metal powder comprises the following steps:
and analyzing the powder particle size distribution of the metal powder by using a laser particle sizer, and calculating to obtain the average particle size of the metal powder.
10. The method of predicting fatigue life of a 3D printed metallic component of claim 1, further comprising, prior to subjecting the 3D printed metallic component to a fatigue life test at a specified cycle characteristic to obtain fatigue life test data:
performing profile pore distribution analysis on the 3D printed metal component, determining the porosity of the 3D printed metal component, and obtaining the density of the 3D printed metal component so as to judge whether the 3D printed metal component meets the density requirement of a laser additive manufacturing forming process;
if the 3D printing metal component meets the density requirement of the laser additive manufacturing forming process, carrying out a fatigue life test on the 3D printing metal component, and determining fatigue life test data of the 3D printing metal component.
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