CN115415542B - Prediction method for performance of duplex stainless steel 3D printing piece based on response surface method - Google Patents
Prediction method for performance of duplex stainless steel 3D printing piece based on response surface method Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 67
- 229910001039 duplex stainless steel Inorganic materials 0.000 title claims abstract description 37
- 238000010146 3D printing Methods 0.000 title claims abstract description 16
- 230000004044 response Effects 0.000 title claims abstract description 16
- 230000008569 process Effects 0.000 claims abstract description 26
- 239000000463 material Substances 0.000 claims description 13
- 238000013507 mapping Methods 0.000 claims description 6
- 239000002245 particle Substances 0.000 claims description 6
- 239000000843 powder Substances 0.000 claims description 6
- 238000009864 tensile test Methods 0.000 claims description 3
- WYTGDNHDOZPMIW-RCBQFDQVSA-N alstonine Natural products C1=CC2=C3C=CC=CC3=NC2=C2N1C[C@H]1[C@H](C)OC=C(C(=O)OC)[C@H]1C2 WYTGDNHDOZPMIW-RCBQFDQVSA-N 0.000 claims description 2
- 239000011229 interlayer Substances 0.000 claims description 2
- 238000000465 moulding Methods 0.000 claims description 2
- 238000002844 melting Methods 0.000 abstract description 8
- 230000008018 melting Effects 0.000 abstract description 8
- 230000001808 coupling effect Effects 0.000 abstract description 5
- 238000005516 engineering process Methods 0.000 abstract description 5
- 238000010309 melting process Methods 0.000 abstract description 2
- 238000007639 printing Methods 0.000 abstract description 2
- 238000012360 testing method Methods 0.000 description 5
- 238000012986 modification Methods 0.000 description 4
- 230000004048 modification Effects 0.000 description 4
- 238000012545 processing Methods 0.000 description 4
- 230000007547 defect Effects 0.000 description 3
- 239000010410 layer Substances 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 238000000691 measurement method Methods 0.000 description 2
- 239000002184 metal Substances 0.000 description 2
- 238000002360 preparation method Methods 0.000 description 2
- 238000004904 shortening Methods 0.000 description 2
- 229910001220 stainless steel Inorganic materials 0.000 description 2
- 239000010935 stainless steel Substances 0.000 description 2
- TVZRAEYQIKYCPH-UHFFFAOYSA-N 3-(trimethylsilyl)propane-1-sulfonic acid Chemical compound C[Si](C)(C)CCCS(O)(=O)=O TVZRAEYQIKYCPH-UHFFFAOYSA-N 0.000 description 1
- 230000006978 adaptation Effects 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 229910001566 austenite Inorganic materials 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000007797 corrosion Effects 0.000 description 1
- 238000005260 corrosion Methods 0.000 description 1
- 238000011056 performance test Methods 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 238000005482 strain hardening Methods 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 229910000859 α-Fe Inorganic materials 0.000 description 1
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B22—CASTING; POWDER METALLURGY
- B22F—WORKING METALLIC POWDER; MANUFACTURE OF ARTICLES FROM METALLIC POWDER; MAKING METALLIC POWDER; APPARATUS OR DEVICES SPECIALLY ADAPTED FOR METALLIC POWDER
- B22F10/00—Additive manufacturing of workpieces or articles from metallic powder
- B22F10/20—Direct sintering or melting
- B22F10/28—Powder bed fusion, e.g. selective laser melting [SLM] or electron beam melting [EBM]
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B22—CASTING; POWDER METALLURGY
- B22F—WORKING METALLIC POWDER; MANUFACTURE OF ARTICLES FROM METALLIC POWDER; MAKING METALLIC POWDER; APPARATUS OR DEVICES SPECIALLY ADAPTED FOR METALLIC POWDER
- B22F10/00—Additive manufacturing of workpieces or articles from metallic powder
- B22F10/30—Process control
- B22F10/36—Process control of energy beam parameters
- B22F10/366—Scanning parameters, e.g. hatch distance or scanning strategy
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B22—CASTING; POWDER METALLURGY
- B22F—WORKING METALLIC POWDER; MANUFACTURE OF ARTICLES FROM METALLIC POWDER; MAKING METALLIC POWDER; APPARATUS OR DEVICES SPECIALLY ADAPTED FOR METALLIC POWDER
- B22F10/00—Additive manufacturing of workpieces or articles from metallic powder
- B22F10/80—Data acquisition or data processing
- B22F10/85—Data acquisition or data processing for controlling or regulating additive manufacturing processes
-
- 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
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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
- 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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- 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
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- Engineering & Computer Science (AREA)
- Chemical & Material Sciences (AREA)
- Materials Engineering (AREA)
- Manufacturing & Machinery (AREA)
- Physics & Mathematics (AREA)
- Plasma & Fusion (AREA)
- Automation & Control Theory (AREA)
- Laser Beam Processing (AREA)
- Investigating Strength Of Materials By Application Of Mechanical Stress (AREA)
Abstract
The invention provides a prediction method of the performance of a duplex stainless steel 3D printing piece based on a response surface method, which comprises the steps of firstly adopting selected area laser melting process parameters of different levels to prepare a duplex stainless steel sample; further measuring the relevant performance parameters of the duplex stainless steel sample; and finally, establishing a selected area laser melting technology to prepare the duplex stainless steel performance prediction model according to the technological parameters and the performance parameters. According to the method provided by the invention, the coupling effect among different process parameters is comprehensively considered, the performance prediction model is reasonably constructed, the compactness and mechanical properties of the sample can be effectively predicted through the process parameters, the reasonable process parameter range is further determined, the exploration flow of the SLM printing process parameters can be effectively simplified, and theoretical reference is provided for preparing the high-performance duplex stainless steel.
Description
Technical Field
The invention belongs to the technical field of metal 3D printing, and particularly relates to a prediction method of the performance of a duplex stainless steel 3D printing piece based on a response surface method.
Background
Duplex stainless steel (Duplex STAINLESS STEEL, DSS) has the advantages of ferrite stainless steel and austenite stainless steel, has good comprehensive mechanical property and excellent corrosion resistance, and is widely applied to industries such as petrochemical industry, equipment manufacturing industry, aerospace, ocean engineering, automobile industry and the like. However, due to different strain hardening behaviors of two phases in the duplex stainless steel, the deformation of the two phases is uneven and the coordination is poor, so that the heat processing difficulty of the duplex stainless steel is high, the defects of edge and surface cracks and the like are extremely easy to occur during processing, and the expansion of the duplex stainless steel in the aspect of preparing complex parts is severely restricted. The selective laser melting (SELECTIVE LASER MELTING, SLM) technology is an important branch in the 3D printing technology, can well solve the forming problem of complex parts, builds a three-dimensional model based on a computer system, adopts laser melting metal powder, and accumulates and forms solid parts layer by layer from bottom to top. The SLM technology has the advantages of direct forming, no need of a die, capability of manufacturing high-precision parts and the like, endows materials with excellent mechanical properties, and is widely applied to aerospace, biomedical, power energy and related fields.
SLM-former performance is greatly affected by process parameters such as laser power, scan speed and scan pitch. The process parameters are unreasonably selected, the defects of insufficient powder melting, discontinuous melt channel, splashing of melt and the like can occur in the material, and the mechanical properties of the material are deteriorated while the density of the sample is reduced. Therefore, reasonable technological parameters are selected, so that the compactness and mechanical properties of the sample can be effectively improved, and the high-quality duplex stainless steel is prepared. However, no theory or method has been found to accurately predict the density and mechanical properties of the test sample by the process parameters.
Therefore, a method capable of effectively predicting the compactness and mechanical properties of the sample through the process parameters is needed, so that the compactness and mechanical properties of the sample under different process parameters are predicted, a reasonable process parameter range is determined, and theoretical reference is provided for preparing the high-performance duplex stainless steel.
Disclosure of Invention
Aiming at the blank and the defect existing in the prior art, the invention aims to provide a prediction method for the performance of a duplex stainless steel 3D printing piece based on a response surface method. The compactness and mechanical properties of the duplex stainless steel sample prepared by 3D printing are predicted through the process parameters, so that a reasonable process parameter range is determined, and the selection of the process parameters of the parts is guided.
In order to achieve the aim, the invention provides a high-efficiency and accurate prediction method for the density and the performance of the duplex stainless steel prepared by 3D printing by adopting a statistical method, and a nonlinear mapping relation between technological parameters (laser power, scanning speed and scanning interval) and material performance (density and tensile strength) is established by adopting a response surface method, so that the prediction of the performance of the duplex stainless steel prepared by 3D printing is realized.
Firstly, preparing a duplex stainless steel sample by adopting different levels of selective laser melting process parameters; further measuring the relevant performance parameters of the duplex stainless steel sample; and finally, establishing a selected area laser melting technology to prepare the duplex stainless steel performance prediction model according to the technological parameters and the performance parameters. According to the method provided by the invention, the coupling effect among different process parameters is comprehensively considered, the performance prediction model is reasonably constructed, the compactness and mechanical properties of the sample can be effectively predicted through the process parameters, the reasonable process parameter range is further determined, the exploration flow of the SLM printing process parameters can be effectively simplified, and theoretical reference is provided for preparing the high-performance duplex stainless steel.
The invention adopts the following technical scheme:
the prediction method of the performance of the duplex stainless steel 3D printing piece based on the response surface method is characterized by comprising the following steps of:
Step 1: selecting different laser powers, scanning speeds and scanning intervals, and preparing a duplex stainless steel sample by the SLM;
step 2: measuring the compactness and tensile strength of the sample;
Step 3: and a response surface method is adopted to establish a nonlinear mapping relation among laser power, scanning speed and scanning interval, material density and tensile strength, and a performance prediction model is established to realize the prediction of technological parameters on the material density and the tensile strength.
Further, in step 1, the particle sample and dumbbell sample are SLM formed with duplex stainless steel as a material by varying process parameters including laser power, scan speed and scan pitch.
Further, in step 2, the archimedes drainage method is adopted to measure the density of the sample, the density is calculated, the unidirectional tensile test is adopted to measure the tensile strength of the sample, and the sample of the sample required by the response surface method is obtained.
Further, in step 3, a nonlinear mapping relation among laser power, scanning speed and scanning interval, material density and tensile strength is established by adopting a response surface method, so that the prediction of the performance of the duplex stainless steel prepared by 3D printing is realized.
Further, the prediction model established in step 3 is as follows:
Density prediction model ：Z=19.57544+0.314458×P+0.004998×V+1094.8642×S+0.000012×P×V-1.12346×P×S+0.130864×V×S-0.00047×P2-0.000015×V2-6375.85734×S2.
Tensile strength prediction model ：K=-2059.75579+14.04277×P-1.5585×V+49318.27628×S+0.004184×P×V-71.77963×P×S+15.84432×V×S-0.022328×P2-0.000617×V2-2.85E+05×S2.
Wherein Z is density, K tensile strength, P is laser power, V is scanning speed, S is scanning interval.
Compared with the prior art, the method and the device take the coupling effect among different process parameters into consideration, and can reliably and accurately predict the performance of the SLM for preparing the duplex stainless steel, thereby further evaluating the rationality of the process parameters of the selective laser melting preparation and effectively shortening the processing period of the product.
Drawings
The invention is described in further detail below with reference to the attached drawings and detailed description:
FIG. 1 is a flow chart of a method for predicting the performance of a duplex stainless steel 3D printing piece based on a response surface method according to an embodiment of the invention;
FIG. 2 is a graph of predicted versus actual values for a density model in accordance with an embodiment of the present invention;
FIG. 3 is a graph of predicted versus actual values for a tensile strength model according to an embodiment of the present invention.
Detailed Description
In order to make the features and advantages of the present patent more comprehensible, embodiments accompanied with figures are described in detail below:
In order to further understand the method proposed by the present invention, the following description is made with reference to specific examples. The present invention provides preferred embodiments for further description of the invention, and should not be construed as limited to the embodiments set forth herein, nor should it be construed as limiting the scope of the invention, since numerous insubstantial modifications and adaptations of the invention will be apparent to those skilled in the art in light of the foregoing disclosure, and yet fall within the scope of the invention.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the application. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the present application. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.
As shown in fig. 1, an overall flowchart of a method for predicting the performance of the 3D printing duplex stainless steel provided in this embodiment includes the following steps:
Step 1: and selecting different laser powers, scanning speeds and scanning intervals, and preparing a duplex stainless steel sample by the SLM. The method comprises the following steps: the particle size of the molding powder is 10-53 mu m, and the components are C less than or equal to 0.02%, si less than or equal to 0.45%, mn less than or equal to 1.0%, S less than or equal to 0.02%, P less than or equal to 0.03%, ni: 4.5-6.5%, cr: 21-23%, mo: 2.5-3.5%, N: 0.1-0.3%, and the balance being Fe. The forming process adopts serpentine scanning, the interlayer rotation angle is 90 degrees, the laser power is 220-280W, the scanning speed is 500-800 mm/s, the scanning interval is 63-77 mu m, the powder laying thickness is fixed by 30 mu m, and the printed particle sample and dumbbell sample are used for measuring the density and the tensile strength.
Step 2: and measuring the compactness and mechanical properties of the sample. Specifically, the density and tensile strength of the formed sample in step 1 were measured and calculated. First, the density of the sample was measured by an Archimedes drainage method, and the density was calculated from the theoretical density of 7.8g/cm 3. And then, a tensile test is carried out by using a universal tester to obtain the tensile strength, and the loading speed is 2mm/min. It should be noted that the above measurement method is merely an example, and other measurement methods to obtain the above related parameters are equivalent to the equivalent techniques, and also belong to the protection scope of the present invention. The test results of the test pieces are shown in Table 1.
TABLE 1 test sample Performance test results
Step 3: and establishing a nonlinear mapping relation among laser power, scanning speed and scanning interval, material density and tensile strength by adopting a quadratic polynomial regression model in a response surface method. The density and tensile strength prediction model is established as follows:
Density prediction model ：Z=19.57544+0.314458×P+0.004998×V+1094.8642×S+0.000012×P×V-1.12346×P×S+0.130864×V×S-0.00047×P2-0.000015×V2-6375.85734×S2.
Tensile strength prediction model ：K=-2059.75579+14.04277×P-1.5585×V+49318.27628×S+0.004184×P×V-71.77963×P×S+15.84432×V×S-0.022328×P2-0.000617×V2-2.85E+05×S2.
Wherein Z is density, K tensile strength, P is laser power, V is scanning speed, S is scanning interval. The coupling effect among different technological parameters is comprehensively considered in the construction of the prediction model. The relationship between the predicted value and the actual value of the density and tensile strength prediction model is shown in fig. 2 and 3, respectively. The graph shows that the actual value is very close to the predicted value, and the established model has higher accuracy.
The prediction model established by the method is utilized to randomly select two groups of process parameter predictions and conduct actual tests, and the results are shown in Table 2. As can be seen from Table 2, the errors of all the indexes are within 5%, which shows that the prediction model established by the method of the embodiment can well predict the density and the tensile strength of the SLM formed duplex stainless steel under different process parameters.
TABLE 2 comparison of model predicted and actual values
The invention comprehensively considers the coupling effect among different process parameters, and can reliably and accurately predict the performance of the SLM for preparing the duplex stainless steel, thereby further evaluating the rationality of the process parameters of the selective laser melting preparation and effectively shortening the processing period of the product. The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the invention.
The above description of the embodiments is only for aiding in the understanding of the method of the present invention and its core ideas. Meanwhile, the above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention in any way, and any person skilled in the art may make modifications or alterations to the above disclosed technical content to equivalent embodiments. However, any simple modification, equivalent variation and variation of the above embodiments according to the technical substance of the present invention still fall within the protection scope of the technical solution of the present invention.
Claims (1)
1. The prediction method of the performance of the duplex stainless steel 3D printing piece based on the response surface method is characterized by comprising the following steps of:
Step 1: selecting different laser powers, scanning speeds and scanning intervals, and preparing a duplex stainless steel sample by the SLM; the method comprises the following steps: the particle size of the molding powder is 10-53 mu m, and the components are C less than or equal to 0.02%, si less than or equal to 0.45%, mn less than or equal to 1.0%, S less than or equal to 0.02%, P less than or equal to 0.03%, ni: 4.5-6.5%, cr: 21-23%, mo: 2.5-3.5%, N: 0.1-0.3%, and the balance being Fe; the forming process adopts serpentine scanning, the interlayer rotation angle is 90 degrees, the laser power is 220-280W, the scanning speed is 500-800 mm/s, the scanning interval is 63-77 mu m, the powder spreading thickness is fixed by 30 mu m, and a particle sample and a dumbbell sample are printed for measuring the density and the tensile strength;
step 2: measuring the compactness and tensile strength of the sample;
step 3: a response surface method is adopted to establish a nonlinear mapping relation among laser power, scanning speed and scanning interval, material density and tensile strength, a performance prediction model is established, and the prediction of technological parameters on the material density and the tensile strength is realized;
In the step 1, taking duplex stainless steel as a material, and forming a particle sample and a dumbbell sample by changing technological parameters including laser power, scanning speed and scanning interval through an SLM;
In the step 2, measuring the density of a sample by adopting an Archimedes drainage method, calculating the density, measuring the tensile strength of the sample by adopting a unidirectional tensile test, and obtaining a sample required by a response surface method;
In the step 3, a nonlinear mapping relation among laser power, scanning speed and scanning interval, material density and tensile strength is established by adopting a response surface method, so that the prediction of the performance of the duplex stainless steel prepared by 3D printing is realized;
The prediction model established in step 3 is as follows:
density prediction model ：Z=19.57544+0.314458×P+0.004998×V+1094.8642×S+0.000012×P×V-1.12346×P×S+0.130864×V×S-0.00047×P2-0.000015×V2-6375.85734×S2
Tensile strength prediction model ：K=-2059.75579+14.04277×P-1.5585×V+49318.27628×S+0.004184×P×V-71.77963×P×S+15.84432×V×S-0.022328×P2-0.000617×V2-2.85E+05×S2
Wherein Z is density, K tensile strength, P is laser power, V is scanning speed, S is scanning interval.
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