CN110560685A - Metal 3D printing process parameter optimization method - Google Patents
Metal 3D printing process parameter optimization method Download PDFInfo
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- CN110560685A CN110560685A CN201810567448.XA CN201810567448A CN110560685A CN 110560685 A CN110560685 A CN 110560685A CN 201810567448 A CN201810567448 A CN 201810567448A CN 110560685 A CN110560685 A CN 110560685A
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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
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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
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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
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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
Abstract
The invention provides a method for optimizing metal 3D printing process parameters, which comprises the following steps: determining a quality characteristic Y value of the experiment; controllable factor selection and each factor level selection; designing an experiment orthogonal table and collecting experiment data based on a Taguchi method; analyzing experimental data; selecting and predicting the optimal conditions; confirming the reliability of the experiment at the present stage; the present phase experiment summary is performed and analyzed. The metal 3D printing process parameter optimization method is based on a Taguchi method, so that the experiment times are reduced, and the process parameter optimization cost is reduced; and disclosing the influence rule of controllable process parameters such as the powder spreading layer thickness, the laser power, the scanning speed, the scanning line spacing and the like on the specific density of the metal 3D printing part.
Description
Technical Field
The invention relates to the field of 3D printing, in particular to a method for optimizing metal 3D printing process parameters.
Background
The 3D printing technology, also called additive manufacturing technology, is a high and new manufacturing technology based on a material accumulation method. The method integrates mechanical engineering, CAD (computer aided design), reverse engineering, layered manufacturing technology, numerical control technology, material science and laser technology, and can automatically, directly, quickly and accurately convert the design idea into a prototype with certain functions or directly manufacture parts, thereby providing a high-efficiency and low-cost implementation means for aspects such as part prototype manufacture, new design idea verification and the like.
The basic principle of the 3D printing technique is layered manufacturing, layer-by-layer stacking: firstly, discretizing a CAD model of a part to be processed along a Z axis, namely slicing to obtain a plurality of 2D sections; and then printing layer by layer from bottom to top to finally obtain a complete part.
The influence factors of the forming effect of the existing metal 3D printing part mainly comprise specific density, precision and roughness. The specific density is the most important factor for the forming effect of the metal 3D printing part, and directly influences the printing quality of the metal 3D printing part. At present, the metal 3D printing process parameters mainly comprise four controllable process parameters of powder layer thickness, laser power, scanning speed and scanning line spacing, and a single parameter variable method is adopted in the process parameter optimization method. Generally, to obtain the optimal process parameter combination, 9 sets of data are required for each process parameter, and 6561 (9) is required for optimizing the process parameters by using a single parameter variable method4) And (5) carrying out secondary experiments. Therefore, the experiment frequency is high, the experiment period is long, the experiment efficiency is low, and more manpower and material resources are consumed.
Disclosure of Invention
In view of the above, it is necessary to provide a method for optimizing parameters of a metal 3D printing process to solve the deficiencies in the prior art.
the invention provides a method for optimizing metal 3D printing process parameters, which comprises the following steps:
s1, determining the quality characteristic Y value of the experiment;
S2 controllable factor selection and each factor level selection;
S3, designing an experiment orthogonal table and collecting experiment data based on a Taguchi method;
Analyzing the experimental data of S4;
S5 selection and estimation of optimal conditions;
S6 confirming the reliability of the experiment at the present stage;
S7 is summarized and analyzed in the present stage experiment.
Preferably, in S1, the value of the mass characteristic Y in the present experiment is the specific part density.
The influence factors of the forming effect of the existing metal 3D printing part mainly comprise density, precision and roughness. The specific density is the most important factor of the forming effect of the metal 3D printing part, and directly influences the printing quality of the metal 3D printing part. Therefore, the density of the selected part is the quality characteristic Y value of the experiment.
for comparison, the density is converted to specific density, i.e.:
In the formula, YiRepresenting specific density, piRepresents the density, ρ, of stainless steel 316L under a metallic 3D printing process316Lwhich represents the density of stainless steel 316L in a conventional rolling process.
Preferably, in S2, the specific density of the metal 3D printed part is influenced by the system hardware configuration, software data error and mechanical precision, and depends on the process parameters such as laser power, scanning speed, scanning line spacing, powder layer thickness, etc. According to the earlier investigation and the verification of the actual experiment, the influence of the laser power, the scanning speed, the scanning line interval, the powder layer thickness and other process parameters on the density is obvious, and the process is controllable in the experimental process. Therefore, the laser power, the scanning speed, the scanning line spacing and the powder layer thickness are used as controllable factors of the experiment, and the laser power, the scanning speed, the scanning line spacing and the powder layer thickness of each controllable factor are numbered in sequence as follows: A. b, C and D. Generally, when an experiment is designed based on the Taguchi method, each controllable factor takes three levels, so that each controllable factor of the experiment is respectively set to three levels.
Preferably, in S3, the Taguchi-based design of experiments generally employs orthogonal table design experiments, which have a total of 4 factors, each at a level of 3, so L is selected9(34) And (4) a direct-cross table.
Preferably, in S4, the experimental data analysis is performed by the snr resp table and the metal 3D printed part specific density average resp table.
The Y (specific density of parts) in this experiment is the expected characteristic, and the signal-to-noise ratio SN (eta) expression is:
In the formula, SN (. eta.) represents a signal-to-noise ratio, yiAnd the specific density value of the metal 3D printing part obtained in n times of experiments is shown.
Average specific density of metal 3D printed parts:
In the formula (I), the compound is shown in the specification,Means mean specific density of the parts, Ynand the specific density value of the metal 3D printing part obtained in n times of experiments is shown.
preferably, in S5, after selecting the optimum combination of process parameters, the sum of SN values under the condition can be estimated according to the response tableThe values are used as comparison reference for the next verification experiment.
due to the error of the experimental result corresponding to each factor, the estimated value is usually higher than the actual value. Therefore, only the significant factor should be selected for estimation.
preferably, in S6, a confirmation set of experiments is performed according to the selected optimal process parameter combination to confirm the reliability of the experiments at the present stage. The experimental reliability judgment standard is as follows:confirm the group SN value andThe value is the maximum value in all test groups at the present stage; confirming that the SN value of the group is close to the estimated SN value, confirming the groupValue and estimateThe values are close; the reliability of the experiment can be confirmed when the above two conditions are satisfied.
Preferably, in S7, the rule of the influence of the laser power, the scanning line spacing, the powder layer thickness, and the scanning speed on the specific density of the metal 3D printed part may be obtained through experiments, and it is determined whether an optimal process parameter combination is obtained. If the optimal process parameter combination is obtained, the process parameter optimization is finished; if the space can be optimized, the next field experiment is designed and executed according to the experimental result of the previous stage and the values of the factors such as the thickness of the powder layer, the scanning speed, the distance of the scanning lines, the laser power and the like according to the variation trend. And (5) determining the optimal process parameter combination through repeated experiments, and finishing the process parameter optimization.
compared with the prior art, the metal 3D printing process parameter optimization method is based on a Taguchi method, so that the experiment times are reduced, and the process parameter optimization cost is reduced; and disclosing the influence rule of controllable process parameters such as the powder spreading layer thickness, the laser power, the scanning speed, the scanning line spacing and the like on the specific density of the metal 3D printing part.
Drawings
FIG. 1 is a flow chart of experimental design procedures based on Taguchi method.
Fig. 2 is a flowchart of steps of a method for optimizing parameters of a metal 3D printing process according to the present invention.
FIG. 3 is a first stage experimental SN (η) response graph in an embodiment of the present invention.
FIG. 4 is a first stage experiment in an embodiment of the present inventiona value response map.
FIG. 5 is a graph of a second stage experimental SN (η) response in an embodiment of the present invention.
FIG. 6 is a second stage experiment in an embodiment of the present inventionA value response map.
fig. 7 is a third-stage experimental response diagram in an embodiment of the present invention.
Detailed Description
The metal 3D printing process parameter optimization method provided by the invention is based on a Taguchi method, and the experimental design of the Taguchi method is shown in figure 1. The metal 3D printing process parameter optimization method comprises the following steps:
S1, determining the quality characteristic Y value of the experiment;
S2 controllable factor selection and each factor level selection;
S3, designing an experiment orthogonal table and collecting experiment data based on a Taguchi method;
Analyzing the experimental data of S4;
S5 selection and estimation of optimal conditions;
s6 confirming the reliability of the experiment at the present stage;
S7 is summarized and analyzed in the present stage experiment.
Preferably, in S1, the value of the mass characteristic Y in the present experiment is the specific part density.
The influence factors of the forming effect of the existing metal 3D printing part mainly comprise density, precision and roughness. The specific density is the most important factor of the forming effect of the metal 3D printing part, and directly influences the printing quality of the metal 3D printing part. Therefore, the density of the selected part is the quality characteristic Y value of the experiment.
for comparison, the density is converted to specific density, i.e.:
in the formula, YiRepresenting specific density, pirepresents the density, ρ, of stainless steel 316L under a metallic 3D printing process316Lwhich represents the density of stainless steel 316L in a conventional rolling process.
Preferably, in S2, the specific density of the metal 3D printed part is influenced by the system hardware configuration, software data error and mechanical precision, and depends on the process parameters such as laser power, scanning speed, scanning line spacing, powder layer thickness, etc. According to the earlier investigation and the verification of the actual experiment, the influence of the laser power, the scanning speed, the scanning line interval, the powder layer thickness and other process parameters on the density is obvious, and the process is controllable in the experimental process. Therefore, the laser power, the scanning speed, the scanning line spacing and the powder layer thickness are used as controllable factors of the experiment, and the laser power, the scanning speed, the scanning line spacing and the powder layer thickness of each controllable factor are numbered in sequence as follows: A. b, C and D. Generally, when an experiment is designed based on the Taguchi method, each controllable factor takes three levels, so that each controllable factor of the experiment is respectively set to three levels.
Preferably, in S3, the Taguchi-based design of experiments generally employs orthogonal table design experiments, which have a total of 4 factors, each at a level of 3, so L is selected9(34) And (4) a direct-cross table.
Preferably, in S4, the experimental data analysis is performed by the snr resp table and the metal 3D printed part specific density average resp table.
The Y (specific density of parts) in this experiment is the expected characteristic, and the signal-to-noise ratio SN (eta) expression is:
In the formula, SN (. eta.) represents a signal-to-noise ratio, yiand the specific density value of the metal 3D printing part obtained in n times of experiments is shown.
Average specific density of metal 3D printed parts:
in the formula (I), the compound is shown in the specification,And expressing the average value of the specific density of the part, and Yn expresses the specific density value of the metal 3D printing part obtained in n times of experiments.
Preferably, in S5, after selecting the optimum combination of process parameters, the sum of SN values under the condition can be estimated according to the response tablethe values are used as comparison reference for the next verification experiment.
Due to the error of the experimental result corresponding to each factor, the estimated value is usually higher than the actual value. Therefore, only the significant factor should be selected for estimation.
Preferably, in S6, a confirmation set of experiments is performed according to the selected optimal process parameter combination to confirm the reliability of the experiments at the present stage. The experimental reliability judgment standard is as follows: confirm the group SN value andThe value is the maximum value in all test groups at the present stage; confirming that the SN value of the group is close to the estimated SN value, confirming the groupValue and estimateThe values are close; the reliability of the experiment can be confirmed when the above two conditions are satisfied.
Preferably, in S7, the rule of the influence of the laser power, the scanning line spacing, the powder layer thickness, and the scanning speed on the specific density of the metal 3D printed part may be obtained through experiments, and it is determined whether an optimal process parameter combination is obtained. If the optimal process parameter combination is obtained, the process parameter optimization is finished; if the space can be optimized, the next field experiment is designed and executed according to the experimental result of the previous stage and the values of the factors such as the thickness of the powder layer, the scanning speed, the distance of the scanning lines, the laser power and the like according to the variation trend. And (5) determining the optimal process parameter combination through repeated experiments, and finishing the process parameter optimization.
Some embodiments of the invention are described in detail below with reference to the accompanying drawings. In the following embodiments, features of the embodiments may be combined with each other without conflict.
The method for optimizing the metal 3D printing process parameters in the embodiment specifically comprises the following steps:
In the first step (S1), the mass characteristic Y value of the present experiment is determined.
The Taguchi method can carry out systematic experiment planning, and points to the optimal trend with a few experiments. The invention adopts a Taguchi method to design an experiment and analyzes the relation between the specific density of a metal 3D printing part and the process parameters of the powder layer thickness, the laser power, the scanning speed and the scanning line spacing.
Optimization experiment design and analysis based on Taguchi method: the core of the Tankou method is orthogonal table design and signal-to-noise ratio analysis, the orthogonal table is adopted to carry out optimization design on metal 3D printing process parameters, and the influence rule of the process parameters on the specific density of the metal 3D printing part is obtained through the signal-to-noise ratio analysis.
Experimental configuration: and the quality characteristic Y value of the experiment is the specific density of the metal 3D printing part. The influence factors of the forming effect of the existing metal 3D printing part mainly comprise density, precision and roughness; the density is an important factor of the forming effect of the metal 3D printing part, and directly influences the printing quality of the metal 3D printing part.
And (4) combining the analysis, and selecting the part density as the quality characteristic Y value of the experiment.
For comparison, the density is converted to specific density, i.e.:
In the formula, YiRepresenting specific density, piRepresents the density, ρ, of stainless steel 316L under a metallic 3D printing process316LWhich represents the density of stainless steel 316L in a conventional rolling process.
And step two (S2), controllable factor selection and each factor level selection.
The specific density of the metal 3D printing part is influenced by system hardware configuration, software data error and mechanical precision, and also depends on process parameters such as laser power, scanning speed, scanning line spacing, powder layer spreading thickness and the like. According to the earlier investigation and knowledge and the verification of the actual experiment, the influence of the laser power, the scanning speed, the scanning line spacing and the powder layer thickness process parameters on the density is obvious, and the laser power, the scanning speed, the scanning line spacing and the powder layer thickness process parameters are controllable in the experimental process. Therefore, the laser power, the scanning speed, the scanning line spacing and the powder layer thickness are used as controllable factors of the experiment, and the controllable factors of the laser power, the scanning speed, the scanning line spacing and the powder layer thickness are numbered in sequence as follows: A. b, C and D. Each controllable factor was set to three levels, as shown in table 1 below.
TABLE 1 controllable factors and levels
| A(W) | B(mm/s) | C(mm) | D(mm) | |
| 1 | 166 | 900 | 0.03 | 0.10 |
| 2 | 172 | 950 | 0.04 | 0.11 |
| 3 | 180 | 1000 | 0.05 | 0.12 |
And step three (S3), designing an experiment orthogonal table and collecting experiment data based on the Taguchi method.
The experimental design based on the Taguchi method generally adopts an orthogonal table design experiment, the orthogonal experiment has 4 factors, each factor is 3 levels, so that L is selected9(34) Orthogonal table, as shown in table 2 below.
TABLE 2L9(34) Orthogonal meter
According to L9(34) Orthogonal table configuration each set of experimental parameters is shown in table 3 below.
Table 3 table for experimental parameter configuration of each group
| Number/factor | A (Power) | B (speed) | C (line spacing) | D (layer thickness) |
| 1 | 166 | 900 | 0.03 | 0.10 |
| 2 | 166 | 950 | 0.04 | 0.11 |
| 3 | 166 | 1000 | 0.05 | 0.12 |
| 4 | 172 | 900 | 0.04 | 0.12 |
| 5 | 172 | 950 | 0.05 | 0.10 |
| 6 | 172 | 1000 | 0.03 | 0.11 |
| 7 | 180 | 900 | 0.05 | 0.11 |
| 8 | 180 | 950 | 0.03 | 0.12 |
| 9 | 180 | 1000 | 0.04 | 0.10 |
the experimental data collected according to the above experimental parameters are shown in table 4 below.
TABLE 4 statistical table of experimental data
And step four (S4), analyzing the experimental data.
The experimental data analysis specifically comprises signal-to-noise ratio analysis and metal 3D printing part specific density average value analysis.
in the Tankou method, the quality characteristic of a target is evaluated by using a signal-to-noise ratio SN (eta), and in the experiment, the influence rule of process parameters such as laser power, scanning speed, scanning line spacing, powder layer spreading thickness and the like on the specific density of the metal 3D printing part is analyzed by using the signal-to-noise ratio.
The specific density signal-to-noise ratio of the metal 3D printed part is defined as:
In the formula, SN (. eta.) represents a signal-to-noise ratio, yiAnd the specific density value of the metal 3D printing part obtained in n times of experiments is shown.
The average value of the specific density of the metal 3D printing part is as follows:
In the formula (I), the compound is shown in the specification,Means mean specific density of the parts, Ynand the specific density value of the metal 3D printing part obtained in n times of experiments is shown.
In order to reduce the influence caused by experimental measurement errors, the method carries out experimental analysis by using the average value of the specific density of the metal 3D printing part obtained by experiments during data processing.
SN (. eta.) response tables (Table 5 below) and response graphs (FIG. 3) were prepared.
making ofa value response table (table 6 below) and a response graph (fig. 4).
TABLE 5 SNR SN (η) response Table
| A (Power) | B (speed) | c (line spacing) | D (layer thickness) | |
| Level 1 | 37.77 | 37.96 | 38.14 | 38.03 |
| Level 2 | 37.94 | 37.97 | 37.92 | 37.94 |
| Level 3 | 38.1 | 37.88 | 37.74 | 37.84 |
| Difference in | 0.33 | 0.09 | 0.40 | 0.19 |
| sorting | 2 | 4 | 1 | 3 |
TABLE 6 specific DensityValue response table
| A (Power) | B (speed) | C (line spacing) | D (layer thickness) | |
| Level 1 | 77.43 | 79.07 | 80.76 | 79.73 |
| Level 2 | 78.84 | 79.14 | 78.71 | 78.88 |
| Level 3 | 80.3 | 78.37 | 77.11 | 77.96 |
| Difference in | 2.87 | 0.77 | 3.65 | 1.77 |
| Sorting | 2 | 4 | 1 | 3 |
And observing the influence of each factor on the Y value, and finding that the influence of each factor on the sintering effect is C > A > D > B.
and step five (S5), selecting and estimating the optimal condition.
Since the experiment is expected to have great characteristics, SN andthe values all tend to be great and excellent.
According to the SN response table (Table 5 above) and the response map (FIG. 3), the optimum level of the factors A, B, C and D is A3、B2、C1And D1。
in accordance withthe optimal level of each factor A in the value response table (Table 6 below) and response plots (FIG. 4), A, B, C and D is3、B2、C1And D1。
In summary, the optimal levels are selected as: a. the3、B2、C1and D1。
After the optimum process parameter combination is selected, the SN value sum under the condition can be estimated according to the response tableThe values are used as comparison reference for the next verification experiment.
Due to the error of the experimental result corresponding to each factor, the estimated value is usually higher than the actual value. Therefore, only the significant factor should be selected for estimation.
Specifically, the method comprises the following steps:
And step six (S6) for confirming the reliability of the current stage experiment.
To confirm the reliability of the experiment at the present stage, first, according to A3、B2、C1And D1The process parameter combinations were tested, and SN values were calculated from the experimental data (see table 7 below) and compared to the estimated SN values.
TABLE 7 confirmation of Experimental data
after calculating the SN value, the judgment standard of the success of the experiment is as follows: acknowledgement group SN andMaximum in all test groups; acknowledgement group SN andClose to the estimated value.
in the above, the SN value is almost the same as the predicted value and is the maximum value among all test groups;The values were close to the predicted values and were the maximum values in all experimental groups. Therefore, the reliability of the experiment can be confirmed.
Step seven (S7), the experiment at the present stage is summarized and analyzed.
From the experimental results of this stage (first stage), the experiments can be summarized as follows:
The metal 3D printing part data under the same group of parameters is stable, the difference is small, the repeatability is ok, and the errors of the manufacturing and measuring system are controllable;
Confirming the influence of each factor in the current factor level range and the influence trend of each factor level value on the result (see SN andResponding to the figures, fig. 3 and fig. 4): the A (power) is increased, and the specific density of parts is improved; c (line spacing) is reduced, and the specific density of parts is improved; d (layer thickness) is reduced, and the specific density of the part is improved; b (speed) has no obvious trend (probably because the horizontal value range is too small, the verification is needed at the next stage);
The overall design of the experiment and the data calculation are reliable (SN andThe estimated value is consistent with the actual value).
Due to the above-described problems, the next stage experiment (second stage experiment) was performed.
According to the experimental result and analysis of the first stage, the experimental plan of the second stage is as follows: A. and C and D factors are valued according to the change trend, B factor expands the value range, and a second-stage field experiment is carried out.
And (4) designing a second stage experiment, including factor level selection and orthogonal table selection.
according to the experimental results of the first stage: the A (power) is increased, and the specific density of parts is improved; c (line spacing) is reduced, and the specific density of parts is improved; d (layer thickness) is reduced, and the specific density of the part is improved; b (speed) has minimal effect on the results and no clear trend in the response plot (highest specific density at 950 mm/s) is seen. Therefore, the selection range needs to be expanded to confirm whether the horizontal selection range is too small.
Based on the above analysis, 3 levels of each factor were selected, as shown in table 8 below.
TABLE 8 second stage controllable factors and levels
| A(W) | B(mm/s) | C(mm) | D(mm) | |
| 1 | 184 | 550 | 0.02 | 0.07 |
| 2 | 190 | 750 | 0.025 | 0.08 |
| 3 | 196 | 950 | 0.03 | 0.09 |
the experiment has 4 factors in total, each factorSub 3 level, still select L9(34) The cross table is shown in table 9 below.
TABLE 9 second stage experiment orthogonal table
According to L9(34) Orthogonal table configuration each set of experimental parameters is shown in table 10 below.
TABLE 10 second stage experimental parameter configuration table
| Number/factor | A (Power) | B (speed) | C (line spacing) | D (layer thickness) |
| 1 | 184 | 550 | 0.02 | 0.07 |
| 2 | 184 | 750 | 0.025 | 0.08 |
| 3 | 184 | 950 | 0.03 | 0.09 |
| 4 | 190 | 550 | 0.025 | 0.09 |
| 5 | 190 | 750 | 0.03 | 0.07 |
| 6 | 190 | 950 | 0.02 | 0.08 |
| 7 | 196 | 550 | 0.03 | 0.08 |
| 8 | 196 | 750 | 0.02 | 0.09 |
| 9 | 196 | 950 | 0.025 | 0.07 |
The experimental data collected and performed the experiments according to the experimental parameters described above are shown in table 11 below.
TABLE 11 statistics of second stage experimental data
Analyzing the experimental data, and making an SN (. eta.) response table (Table 12 below) and a response chart (FIG. 3); making ofA value response table (table 13 below) and a response graph (fig. 4).
TABLE 12 second stage experiment SNR SN (η) response Table
| A (Power) | B (speed) | C (line spacing) | D (layer thickness) | |
| Level 1 | 38.89 | 39.19 | 38.90 | 39.06 |
| level 2 | 38.95 | 38.89 | 38.96 | 38.93 |
| Level 3 | 38.95 | 38.71 | 38.93 | 38.80 |
| Difference in | 0.06 | 0.48 | 0.06 | 0.26 |
| Sorting | 3 | 1 | 3 | 2 |
TABLE 13 second stage Experimental specific DensityValue response table
| A (Power) | b (speed) | C (line spacing) | D (layer thickness) | |
| Level 1 | 88.02 | 91.08 | 88.16 | 89.80 |
| Level 2 | 88.66 | 87.98 | 88.74 | 88.44 |
| Level 3 | 88.64 | 86.27 | 88.43 | 87.09 |
| Difference in | 0.64 | 4.81 | 0.58 | 2.71 |
| Sorting | 3 | 1 | 4 | 2 |
and observing the influence of each factor on the Y value, and finding that the influence of each factor on the sintering effect is B > D > A > C.
Selection of optimal conditions: since the experiment is expected to have great characteristics, SN andThe values all tend to be great and excellent.
According to the SN response table (Table 12 above) and the response map (FIG. 3), the optimum level of the factors A, B, C and D is A2/A3、B1、C2And D1；
in accordance withThe optimal level for each factor of the value response table (Table 13 above) and response map (FIG. 4), A, B, C and D is A2、B1、C2And D1；
In summary, the optimum level is selected as A2、B1、C2And D1。
Evaluation of the best results: after the optimum process parameter combination is selected, the SN value sum under the condition can be estimated according to the response tableThe values are used as comparison reference for the next verification experiment.
due to the error of the experimental result corresponding to each factor, the estimated value is usually higher than the actual value. Therefore, only significant factors are selected for prediction calculation during prediction, and B and D factors are selected in the experiment.
Specifically, the method comprises the following steps:
confirmation experiment: experiments were performed according to the combination of process parameters a2, B1, C2 and D1, and SN ratios were calculated from experimental data, see table 14 below, and compared to the estimated SN ratios.
TABLE 14 second stage experimental data validation
Determination criteria for success of the experiment: acknowledgement group SN andMaximum in all test groups; acknowledgement group SN andClose to the estimated value.
In the above, the SN value is almost the same as the predicted value and is the maximum value among all test groups;the values are very close to the predicted values and are the maximum values in all experimental groups. Therefore, the reliability of the experiment can be confirmed.
In summary, the experiments can be summarized as follows:
Optimum levels of lock laser power and scan line spacing: 190, 0.025 mm;
The scanning speed and the powder layer spreading thickness continuously show a trend of diminishing on the basis of the current stage;
The specific density is improved from 82.85% in the first stage to 92.40%.
the experimental plan of the next stage is: and locking the optimal level of power and line spacing, and continuously taking values of speed and layer thickness in a small direction for verification.
As described above, in this embodiment, step eight is added to perform the third stage experiment and summarized.
and (3) designing a third stage experiment, including selection of each factor level and parameter configuration.
According to the experimental results of the second stage: the A (power) is 190, and the C (line spacing) is 0.025mm and remains unchanged; the values of B (speed) and D (layer thickness) were taken in the decreasing direction, and 2 levels were selected, respectively, as shown in Table 15 below.
TABLE 15 third stage controllable factors and levels
| B(mm/s) | D(mm) | |
| 1 | 350 | 0.06 |
| 2 | 450 | 0.05 |
The experiment has a total factor 2 level, so the full factor experiment method is selected to generate 4 groups of experiments in total, as shown in the following table 16.
TABLE 16 table of orthogonal form of third stage experiment and parameter configuration
| Number/factor | Factor 1 | Factor 2 | b (speed) | D (layer thickness) |
| 1 | 1 | 1 | 350 | 0.06 |
| 2 | 2 | 1 | 450 | 0.06 |
| 3 | 1 | 2 | 350 | 0.05 |
| 4 | 2 | 2 | 450 | 0.05 |
The experimental data collected and experiments performed according to the experimental parameters described above are shown in table 17 below.
TABLE 17 statistics of experimental data of the third stage
Analyzing the above experimental data, makingA value response table (table 18 below) and a response graph (fig. 5).
TABLE 18 third stage Experimental specific DensityValue response table
| B (speed) | D (layer thickness) | |
| Level 1 | 97.23 | 96.23 |
| Level 2 | 96.21 | 97.21 |
| Difference in | 1.02 | 0.98 |
| Sorting | 1 | 2 |
From the above analysis, the best results are evaluated as: the optimal parameters at present are B1(350mm/s) and D2(0.05mm), and the specific density of the sample is 97.49%.
According to the analysis result, the parameters still have an optimization space, so that an experiment is added to achieve the aim that the specific density is more than or equal to 98%. And (4) continuously taking values of the speed and the layer thickness in a small direction by adding experimental parameters of the experiment. In the present embodiment, the experimental parameters of the additional experiment are shown in table 19 below.
Table 19 additional experimental parameter configuration table
In the present embodiment, the experimental data collected in the additional experiment are shown in table 20 below.
table 20 additional experimental data statistics table
All the above experimental data were analyzed, and the experimental summary of the present embodiment is shown in table 22 below.
TABLE 22 summary of the experiments
The method comprises the steps of firstly selecting the specific density of a metal 3D printing part as a research object, then designing an optimized experiment based on a Taguchi method, wherein the experiment frequency is 6561 (9)4) The times are reduced to 25, the process parameter optimization period is shortened, the process parameter optimization cost is saved, and the process parameter optimization efficiency is improved; an optimization model taking the process parameters of the powder layer thickness, the laser power, the scanning speed and the scanning line spacing as variables and the specific density of the metal 3D printing part as an optimization target is constructed; and analyzing the incidence relation between the specific density of the 3D printing part and the technological parameters of the powder laying layer thickness, the laser power, the scanning speed and the scanning line spacing through experimental data and an algorithm optimization result. Therefore, the influence rule of process parameters of the powder layer thickness, the laser power, the scanning speed and the scanning line spacing and the specific density of the metal 3D printing part in the metal 3D printing process is disclosed. Through three stagesThe optimal process parameters are obtained by the Taguchi experiment. When the laser power is 190W, the scanning speed is 250mm/s, the powder spreading layer thickness is 0.04mm, and the scanning line spacing is 0.025mm, the specific density of the metal 3D printing part can reach 98.69%.
Compared with the prior art, the metal 3D printing process parameter optimization method is based on a Taguchi method, so that the experiment times are reduced, and the process parameter optimization cost is reduced; and disclosing the influence rule of controllable process parameters such as the powder spreading layer thickness, the laser power, the scanning speed, the scanning line spacing and the like on the specific density of the metal 3D printing part.
it is understood that various other changes and modifications may be made by those skilled in the art based on the technical idea of the present invention, and all such changes and modifications should fall within the protective scope of the claims of the present invention.
Claims (7)
1. A metal 3D printing process parameter optimization method comprises the following steps:
Determining a quality characteristic Y value of the experiment;
Controllable factor selection and each factor level selection;
Designing an experiment orthogonal table and collecting experiment data based on a Taguchi method;
analyzing experimental data;
Selecting and predicting the optimal conditions;
confirming the reliability of the experiment at the present stage;
The present phase experiment summary is performed and analyzed.
2. The metal 3D printing process parameter optimization method of claim 1, wherein: and the quality characteristic Y value of the experiment is the specific density of the part.
3. The metal 3D printing process parameter optimization method of claim 2, wherein: the controllable factors comprise laser power, scanning speed, scanning line spacing and powder layer spreading thickness, each controllable factor is respectively set to three levels, and orthogonal table design is performed on experiments based on a Taguchi methodconsidering in the step of collecting experimental data, L is selected9(34) Orthogonal table design experiment.
4. the metal 3D printing process parameter optimization method of claim 3, wherein: in the step of experimental data analysis, the experimental data analysis comprises a signal-to-noise ratio (SN) value and a specific density mean value, and the calculation formula of the SN value is as follows:
Wherein SN (η) represents said signal-to-noise ratio value, yiThe specific density value of the metal 3D printing part obtained in n experiments is represented;
the calculation formula of the specific density mean value is as follows:
In the formula (I), the compound is shown in the specification,Representing the mean value of the specific density, Y, of the partnAnd the specific density value of the metal 3D printing part obtained in n times of experiments is shown.
5. the metal 3D printing process parameter optimization method of claim 4, wherein: in the step of selecting and predicting the optimal condition, after the optimal process parameter combination is selected according to the experimental data analysis, the SN value of the signal-to-noise ratio and the average value of the specific density under the condition are predicted according to the optimal process parameter combination.
6. The metal 3D printing process parameter optimization method of claim 5, wherein: in the step of confirming the experimental reliability at the present stage, the experimental reliability judgment standard includes two conditions: confirming that the SN value and the average value of the specific density of the group are maximum values in all test groups at the present stage; and confirming that the signal-to-noise ratio (SN) value of the group is consistent with the estimated SN value, and confirming that the specific density mean value of the group is consistent with the estimated specific density mean value.
7. The metal 3D printing process parameter optimization method of claim 6, wherein: in the step of summarizing and analyzing the experiment at the present stage, whether an optimal process parameter combination is obtained or not is determined according to the influence rule of the laser power, the scanning line interval, the powder spreading layer thickness and the scanning speed on the specific density of the metal 3D printing part, which is obtained through the experiment; if the optimal process parameter combination is obtained, the process parameter optimization is finished; if the space can be optimized, designing and executing a next-stage field method experiment according to the experimental result of the previous stage and the values of the powder laying layer thickness, the scanning speed, the scanning line spacing and the laser power according to the variation trend; and (5) determining the optimal process parameter combination through repeated experiments, and finishing the process parameter optimization.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111922480A (en) * | 2020-07-24 | 2020-11-13 | 中国航空工业集团公司沈阳飞机设计研究所 | Method for connecting components |
| CN113118458A (en) * | 2021-04-20 | 2021-07-16 | 江西省科学院应用物理研究所 | Prediction method for tensile property of metal component formed by selective laser melting |
| CN113320168A (en) * | 2021-06-16 | 2021-08-31 | 厦门天宇丰荣科技有限公司 | Technological parameter optimization method for 3D printing of planar solid graph by using biological ink |
| CN113427766A (en) * | 2021-06-16 | 2021-09-24 | 厦门天宇丰荣科技有限公司 | Technological parameter optimization method for 3D printing of straight line elements by using biological ink |
| CN113946903A (en) * | 2021-08-24 | 2022-01-18 | 北京航空航天大学 | Optimization test design method for preparation process of heat insulation layer of solid rocket engine |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103258082A (en) * | 2013-04-17 | 2013-08-21 | 西安工程大学 | Optimization method of production parameters for solid ground curing rapid prototyping system |
| CN104615077A (en) * | 2015-01-07 | 2015-05-13 | 重庆大学 | Efficient energy-saving optimizing method for numerical control milling processing process parameters based on Taguchi method |
| US20150363512A1 (en) * | 2014-06-11 | 2015-12-17 | Cheng Uei Precision Industry Co., Ltd. | System and method for optimizing connector design |
-
2018
- 2018-06-05 CN CN201810567448.XA patent/CN110560685A/en not_active Withdrawn
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103258082A (en) * | 2013-04-17 | 2013-08-21 | 西安工程大学 | Optimization method of production parameters for solid ground curing rapid prototyping system |
| US20150363512A1 (en) * | 2014-06-11 | 2015-12-17 | Cheng Uei Precision Industry Co., Ltd. | System and method for optimizing connector design |
| CN104615077A (en) * | 2015-01-07 | 2015-05-13 | 重庆大学 | Efficient energy-saving optimizing method for numerical control milling processing process parameters based on Taguchi method |
Non-Patent Citations (4)
| Title |
|---|
| N. RAGHUNATH: "Improving accuracy through shrinkage modelling by using Taguchi method in selective laser sintering", 《MACHINE TOOLS & MANUFACTURE》 * |
| 王毓芳，肖诗唐: "《质量改进的策划与实施》", 31 October 2005, 中国经济出版社 * |
| 王细洋: "《现代制造技术》", 30 June 2017, 国防工业出版社 * |
| 邹亚桐等: "AlSi10Mg激光选区熔化成形工艺参数对致密度的影响与优化", 《应用激光》 * |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111922480A (en) * | 2020-07-24 | 2020-11-13 | 中国航空工业集团公司沈阳飞机设计研究所 | Method for connecting components |
| CN113118458A (en) * | 2021-04-20 | 2021-07-16 | 江西省科学院应用物理研究所 | Prediction method for tensile property of metal component formed by selective laser melting |
| CN113118458B (en) * | 2021-04-20 | 2023-04-07 | 江西省科学院应用物理研究所 | Prediction method for tensile property of metal component formed by selective laser melting |
| CN113320168A (en) * | 2021-06-16 | 2021-08-31 | 厦门天宇丰荣科技有限公司 | Technological parameter optimization method for 3D printing of planar solid graph by using biological ink |
| CN113427766A (en) * | 2021-06-16 | 2021-09-24 | 厦门天宇丰荣科技有限公司 | Technological parameter optimization method for 3D printing of straight line elements by using biological ink |
| CN113320168B (en) * | 2021-06-16 | 2022-10-14 | 厦门天宇丰荣科技有限公司 | Technological parameter optimization method for 3D printing of planar solid graph by using biological ink |
| CN113946903A (en) * | 2021-08-24 | 2022-01-18 | 北京航空航天大学 | Optimization test design method for preparation process of heat insulation layer of solid rocket engine |
| CN113946903B (en) * | 2021-08-24 | 2023-04-18 | 北京航空航天大学 | Optimization test design method for preparation process of heat insulation layer of solid rocket engine |
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