A method for alloy composition optimization design for additive manufacturing

Abstract

The application discloses an alloy component optimization design method for additive manufacturing and belongs to the technical field of additive manufacturing. The alloy component optimization design method for additive manufacturing expands the content numerical range of the alloy component to include all existing alloy component contents on the basis of existing alloy components, combines thermodynamic calculation and high-throughput calculation, optimizes suitable alloy components according to a strain rate hot cracking criterion based on a hot cracking sensitivity index, prepares alloy powder for additive manufacturing according to the optimized alloy components, performs laser additive manufacturing, observes the microstructure of the sample after additive manufacturing and tests the performance of the sample, and selects the component optimization meeting the actual alloy performance. The application takes the alloy component optimization design as a main influencing factor, adopts thermodynamic software and computer language, optimizes the component through the hot cracking sensitivity index to reduce the hot cracking sensitivity of the additive manufacturing alloy, and is beneficial to industrial large-scale production and popularization and use.

Description

Technical Field

[0001] This invention belongs to the technical field of additive manufacturing and relates to a method for optimizing the design of alloy composition for additive manufacturing. Background Technology

[0002] Additive manufacturing technology, with its short manufacturing cycle, near-net-shape forming, excellent mechanical properties, strong structural adaptability, and high degree of design freedom, has obvious advantages over traditional processes in forming complex and precision structures and components made of difficult-to-machine materials. It can also process a variety of metals, such as aluminum alloys, titanium alloys, stainless steel, high-entropy alloys, and other alloys. Due to its good processing performance and high reliability, it is widely used.

[0003] However, during additive manufacturing, the quality of formed parts is often affected by solidification defects such as hot cracks. On the one hand, it is necessary to continuously improve the process conditions, and on the other hand, it is necessary to re-optimize the alloy composition of additive manufacturing to reduce the sensitivity to hot cracking.

[0004] Chinese patent CN112570732A discloses a method for reducing the hot cracking sensitivity of nickel-based superalloys manufactured by laser additive manufacturing. This method involves optimizing the process parameters of laser additive manufacturing. The composition optimization does not involve adjusting the content of existing elements, but rather adding 1.5% pure zirconium powder and 0.5% pure aluminum powder by mass. This method is only applicable to nickel-based superalloys and is not applicable to aluminum alloys or other alloys.

[0005] Chinese patent CN108994304A discloses a method for eliminating additive manufacturing cracks in metal materials and improving their mechanical properties. This method involves sequentially performing stress-relief annealing and spark plasma sintering on the additively manufactured parts. However, it does not consider adjusting the metal material composition to reduce additive manufacturing cracks. Instead, it provides heat treatment, which is obviously much more expensive than adjusting the metal material composition.

[0006] Chinese patent CN111235564A discloses a method for designing the composition of high-temperature alloys for additive manufacturing, which is also aimed at high-temperature alloys. Obviously, it is not applicable to aluminum alloys and other alloys. Moreover, the alloy composition selected by the orthogonal design method is limited by the experimental level. Since it does not consider the influence of hot cracking sensitivity index on composition selection, the subsequent optimization of several suitable composition selection bases using thermodynamic calculation software and electronic vacancy calculation methods does not take into account the aforementioned influence. The selected composition cannot reduce the hot cracking sensitivity of high-temperature alloys, let alone aluminum alloys and other alloys.

[0007] Chinese patent CN110010210A discloses a multi-component alloy composition design method based on machine learning and oriented towards performance requirements. Although it establishes a dataset based on historical data, builds and trains C2P and P2C models, the method of using the target performance as input data to P2C to obtain the initial design composition has certain empirical errors. The relationship between the hot cracking sensitivity index and the composition selection is extremely complex. Machine learning requires a lot of data, has a long learning time, and requires many accuracy verifications. It is inefficient and costly in selecting compositions to reduce the hot cracking sensitivity of high-temperature alloys, aluminum alloys and other alloys.

[0008] In summary, most existing technologies for reducing thermal cracking sensitivity involve optimizing the process parameters of laser additive manufacturing, particularly the laser process parameters and heat treatment. While composition selection is also considered as a way to reduce thermal cracking sensitivity, it is only used as a supplement to heat treatment rather than as the primary technical means. Furthermore, there are many methods for composition design, including orthogonal experimental design and machine learning, but they all have various drawbacks and are costly, which are not conducive to large-scale industrial production and widespread use. Summary of the Invention

[0009] The technical problem to be solved by this invention is that most of the existing technologies for reducing hot cracking sensitivity are achieved by adjusting the laser process parameters and heat treatment of laser additive manufacturing. Although there are individual methods such as laser process parameter control, heat treatment, and the addition of auxiliary components, the influence of composition optimization design on reducing hot cracking sensitivity has not been considered. Nor has it been considered to further use the combination of thermodynamic software Thermo-Calc and Python language to optimize the composition to reduce the hot cracking sensitivity of additive manufacturing alloys through hot cracking sensitivity index.

[0010] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0011] A method for optimizing the alloy composition for additive manufacturing is disclosed. This method expands the range of alloy composition values ​​to include all existing alloy compositions, combining thermodynamic and high-throughput calculations. Based on a hot cracking sensitivity index and a strain rate hot cracking criterion, a suitable alloy composition is optimized. Additive manufacturing alloy powder is prepared according to the optimized alloy composition, and laser additive manufacturing is performed. The microstructure of the additively manufactured sample is observed and its performance is tested. The optimized composition that best matches the actual alloy properties is then selected.

[0012] Preferably, the expansion of the alloy component content range to include all existing alloy component contents is achieved by expanding it according to the upper and lower limits of the alloy component content values ​​in national standards.

[0013] Preferably, the combination of thermodynamic calculation and high-throughput calculation is achieved by using high-throughput calculations performed by combining the thermodynamic software Thermo-Calc and the Python language to obtain the Scheil-Gulliver solidification curve between solid fraction and temperature, and outputting the data of the solid fraction fs = 0.9-0.99 interval on the curve.

[0014] Preferably, the combination of Thermo-Calc thermodynamic software and Python language is achieved by writing a program in Python to call Thermo-Calc software and calculate the combination of different element contents sequentially according to the step size.

[0015] Preferably, the hot cracking sensitivity index is obtained by batch processing the data output of the solid fraction fs = 0.9-0.99 interval on the curve, changing the vertical axis temperature to +273.15 to obtain the Kelvin temperature (K), and then taking the square root of the solid fraction on the horizontal axis; then taking the derivative of the square rooted solid fraction with respect to the Kelvin temperature, and taking the absolute value of the derivative value in turn to obtain the solidification hot cracking sensitivity index that meets the requirements; and taking the average value of the obtained solidification hot cracking sensitivity index in the solid fraction fs = 0.9-0.99 range.

[0016] Preferably, the strain rate hot cracking criterion is based on the interaction force between dendrite grain boundaries. When the solid fraction is close to 1, the tearing force on the liquid film is greater than the sum of the mutual growth between grains and the liquid filling, and cracks begin to initiate.

[0017]

[0018] (separation)(growth)(feeding)

[0019] Preferably, the alloy powder for additive manufacturing is an alloy powder prepared from the alloy ingot; or it is a powder made by mixing elemental metals or alloy powders according to a calculated content ratio.

[0020] Preferably, the amount of computational data for the high-throughput computing exceeds 3.0 × 10⁻⁶. 12 Group.

[0021] Preferably, the laser additive manufacturing process comprises: a laser power of 200-300W, a scanning speed of 800-1000mm / s, a scanning spacing of 100-120μm, a layer thickness of 20-40μm, a Z-scanning strategy, and an energy density of 60-120J / mm². 3 .

[0022] Preferably, the alloy properties corresponding to the optimized alloy composition in the additive manufacturing alloy composition optimization design method are as follows compared with the alloy properties before optimization: the yield strength is increased by at least 10%, the tensile strength is increased by at least 6.96%, and the elongation is increased by 14.5%.

[0023] Compared with the prior art, the present invention has the following advantages:

[0024] In the above scheme, the present invention combines Python language with the thermodynamic software Thermo-Calc, expands the element range and then screens alloy elements with a given step size, achieving a data volume of up to hundreds of millions of sets. The method is convenient and efficient, and can quickly screen out several alloy components with low hot cracking sensitivity index. Therefore, the method is relatively efficient, convenient, and low-cost, making it easy to promote and apply on a large scale in industry.

[0025] The optimized alloy composition alloy powder of the present invention is prepared by using alloy ingots with the same composition.

[0026] The optimized alloy composition obtained by the alloy composition optimization design method for additive manufacturing of the present invention is formed by conventional additive manufacturing process. During the additive manufacturing process, attention should be paid to oxygen content and protective atmosphere to prevent impurities such as O and N from entering the molten pool during printing. In addition, the laser beam energy density should meet the processing window of the composition to ensure that the formed part is free from defects such as large-sized pores, poor fusion, and spheroidization.

[0027] The alloys used in additive manufacturing according to this invention include various alloys such as aluminum alloys, titanium alloys, stainless steel, and high-entropy alloys.

[0028] Compared with the alloy properties before optimization, the alloy properties corresponding to the optimized alloy composition in the additive manufacturing alloy composition optimization design method of the present invention are: yield strength increased by at least 10%, tensile strength increased by at least 6.96%, and elongation increased by 14.5%.

[0029] In summary, this invention provides a method for optimizing the alloy composition for additive manufacturing. It considers the impact of alloy composition optimization on reducing hot cracking sensitivity and takes it as the main influencing factor. Furthermore, it uses a combination of thermodynamic software Thermo-Calc and Python language to select the optimal composition to reduce the hot cracking sensitivity of additive manufacturing alloys through hot cracking sensitivity index, which is beneficial for large-scale industrial production and widespread use. Attached Figure Description

[0030] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 This is a morphology diagram of martensitic stainless steel powder used in the additive manufacturing martensitic stainless steel composition optimization design method of Embodiment 1 of the present invention.

[0032] Figure 2 This is a flowchart of the high-throughput calculation process in the composition optimization design method for martensitic stainless steel for additive manufacturing in Embodiment 1 of the present invention.

[0033] Figure 3 This is a trend chart of the hot cracking sensitivity index of different elements in the composition optimization design method for martensitic stainless steel for additive manufacturing in Embodiment 1 of the present invention.

[0034] Figure 4 This is a cloud map of the hot cracking sensitivity index of composite element addition in the additive manufacturing martensitic stainless steel composition optimization design method of Embodiment 1 of the present invention;

[0035] Figure 5 This is a comparison diagram of the hot cracking of the new composition designed and optimized by the high-throughput method in the additive manufacturing martensitic stainless steel composition optimization design method of Embodiment 1 of the present invention and the original martensitic stainless steel printed part.

[0036] Figure 6 This is a comparison chart of the room temperature tensile properties of the new composition designed and optimized using the high-throughput method in the additive manufacturing martensitic stainless steel composition optimization design method of Embodiment 1 of the present invention with those of the original martensitic stainless steel. Detailed Implementation

[0037] The technical solutions and problems solved by the embodiments of the present invention will be described below with reference to the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them.

[0038] Example 1

[0039] Martensitic stainless steel is used for additive manufacturing.

[0040] The chemical composition of martensitic stainless steel is shown in Table 1:

[0041] Table 1: Chemical composition of martensitic stainless steel, in wt%.

[0042]

[0043] The specific steps of the alloy composition optimization design method for additive manufacturing are as follows:

[0044] S1, Extended compositional range of martensitic stainless steel

[0045] The element range has been expanded according to the national standard GB / T 20878-2007 to cover the upper and lower limits of the content of each element in martensitic stainless steel. The expanded element range and design step size are shown in Table 2.

[0046] Table 2: Extended elemental range for martensitic stainless steel, in wt%.

[0047]

[0048]

[0049] S2, Element combination for obtaining martensitic stainless steel composition

[0050] For the extended martensitic stainless steel composition in step S1, the combinations of different element contents were initially calculated sequentially according to the step sizes in Table 2. The possible element combinations exceeded 3.0 × 10⁻⁶. 12 Group;

[0051] S3. Obtain the Scheil-Gulliver solidification curve for martensitic stainless steel.

[0052] High-throughput calculations are performed using Python and the thermodynamic software Thermo-Calc, with the following workflow: Figure 2 As shown, the Scheil-Gulliver solidification curve between solid fraction fs and temperature (°C) is obtained based on the element combination in step S2, and the data of the solid fraction fs = 0.9-0.99 interval on the curve is output.

[0053] S4. Obtain the solidification hot cracking susceptibility index of martensitic stainless steel.

[0054] The Scheil-Gulliver data output in step S3 is batch-processed. The vertical axis temperature is changed to +273.15 to obtain the Kelvin temperature (K). Then, the solid fraction on the horizontal axis is square-rooted. The derivative of the square-rooted solid fraction with respect to the Kelvin temperature is then calculated, and the absolute values ​​of the derivatives are taken sequentially to obtain the solidification hot cracking sensitivity index that meets the requirements. The average value of the obtained solidification hot cracking sensitivity index in the range of solid fraction fs = 0.9-0.99 is taken. The trend of the hot cracking sensitivity index of different elements is as follows: Figure 3 As shown, the thermal cracking sensitivity index of composite elements is as follows: Figure 4 As shown;

[0055] S5. Optimize the suitable alloy composition of martensitic stainless steel.

[0056] The appropriate composition of martensitic stainless steel is optimized based on the strain rate hot cracking criterion and the average solidification hot cracking sensitivity index obtained in step S4.

[0057] Preparation of S6 martensitic stainless steel alloy powder

[0058] Based on the optimized alloy composition of martensitic stainless steel obtained in step S5, alloy powder can be prepared from the alloy ingot with this alloy composition for additive manufacturing; alternatively, alloy powder for additive manufacturing can be obtained by mixing several elemental metals or alloy powders with specific compositions according to the optimized alloy composition requirements of martensitic stainless steel obtained in step S5. The morphology of the alloy powder is as follows: Figure 1 As shown;

[0059] Additive manufacturing of S7 martensitic stainless steel

[0060] The alloy powder from step S6 is shaped using a conventional additive manufacturing process. The process parameters are: laser power of 200W, scanning speed of 1000mm / s, scanning spacing of 100μm, layer thickness of 30μm, scanning strategy of Z-scan, and energy density of 66.67J / mm². 3 ;

[0061] S8, Microstructural Observation and Performance Testing

[0062] The martensitic stainless steel prepared in step S7 was subjected to microstructure observation and performance testing. The microstructure was a typical state in which columnar and cellular crystals coexisted in the molten pool, and the molten pool was relatively regular. The hot cracking sensitivity was 1861, the density was 99.79%, the tensile strength was 945.8 MPa, the yield strength was 713.8 MPa, and the elongation was 13.9%.

[0063] Among them: the comparison between the new composition designed and optimized using the high-throughput method of this embodiment and the hot cracking of the original martensitic stainless steel printed parts, for example: Figure 5 As shown, the room temperature tensile properties of the new composition designed and optimized using the high-throughput method of this embodiment are compared with those of the original martensitic stainless steel. Figure 6 As shown.

[0064] As can be seen, the new composition designed and optimized by the high-throughput method in this embodiment has better tensile properties than the original martensitic stainless steel. The yield strength and tensile strength are both increased, with the yield strength increasing from 644.7 MPa to 713.8 MPa and the tensile strength increasing from 884.24 MPa to 945.8 MPa. Furthermore, the elongation increases from 12.1% to 13.9%.

[0065] Compared with the prior art, the present invention has the following advantages:

[0066] In the above scheme, the present invention combines Python language with the thermodynamic software Thermo-Calc, expands the element range and then screens alloy elements with a given step size, achieving a data volume of up to hundreds of millions of sets. The method is convenient and efficient, and can quickly screen out several alloy components with low hot cracking sensitivity index. Therefore, the method is relatively efficient, convenient, and low-cost, making it easy to promote and apply on a large scale in industry.

[0067] The optimized alloy composition alloy powder of the present invention is prepared by using alloy ingots with the same composition.

[0068] The optimized alloy composition obtained by the alloy composition optimization design method for additive manufacturing of the present invention is formed by conventional additive manufacturing process. During the additive manufacturing process, attention should be paid to oxygen content and protective atmosphere to prevent impurities such as O and N from entering the molten pool during printing. In addition, the laser beam energy density should meet the processing window of the composition to ensure that the formed part is free from defects such as large-sized pores, poor fusion, and spheroidization.

[0069] The alloys used in additive manufacturing according to this invention include various alloys such as aluminum alloys, titanium alloys, stainless steel, and high-entropy alloys.

[0070] Compared with the alloy properties before optimization, the alloy properties corresponding to the optimized alloy composition in the additive manufacturing alloy composition optimization design method of the present invention are: yield strength increased by at least 10%, tensile strength increased by at least 6.96%, and elongation increased by 14.5%.

[0071] In summary, this invention provides a method for optimizing the alloy composition for additive manufacturing. It considers the impact of alloy composition optimization on reducing hot cracking sensitivity and takes it as the main influencing factor. Furthermore, it uses a combination of thermodynamic software Thermo-Calc and Python language to select the optimal composition to reduce the hot cracking sensitivity of additive manufacturing alloys through hot cracking sensitivity index, which is beneficial for large-scale industrial production and widespread use.

[0072] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for alloy composition optimization design for additive manufacturing, characterized by, The alloy composition optimization design method for additive manufacturing expands the content range of alloy composition to include all existing alloy composition based on existing alloy composition. Combining thermodynamic calculations and high-throughput calculations, and based on the hot cracking sensitivity index, a suitable alloy composition is optimized according to the strain rate hot cracking criterion. Alloy powder for additive manufacturing is prepared according to the optimized alloy composition, and laser additive manufacturing is performed. The microstructure of the additively manufactured sample is observed and the performance is tested. The composition optimization that meets the actual alloy performance is selected from the results. The expansion of the range of alloy component content values ​​to include all existing alloy component contents is achieved by expanding the range based on the upper and lower limits of alloy component content values ​​in national standards. The combination of thermodynamic and high-throughput calculations involves using the thermodynamic software Thermo-Calc and the Python language to perform high-throughput calculations to obtain the Scheil-Gulliver solidification curve between solid fraction and temperature, and outputting the data for the solid fraction range fs = 0.9-0.99 on the curve.

2. The alloy composition optimization design method for additive manufacturing according to claim 1, characterized by, The method of combining the thermodynamic software Thermo-Calc with the Python language involves writing a program in Python to call the Thermo-calc software and calculate the combination of different element contents sequentially according to the step size.

3. The alloy composition optimization design method for additive manufacturing according to claim 1, characterized by, The aforementioned hot crack sensitivity index is obtained by batch processing the data output from the solid fraction range fs = 0.9-0.99 on the curve, changing the vertical axis temperature to +273.15 to obtain the Kelvin temperature (K), and then taking the square root of the solid fraction on the horizontal axis; then taking the derivative of the square root solid fraction with respect to the Kelvin temperature, and taking the absolute value of the derivative value in turn to obtain the solidification hot crack sensitivity index that meets the requirements; and then taking the average value of the obtained solidification hot crack sensitivity index in the solid fraction range fs = 0.9-0.

99.

4. The alloy composition optimization design method for additive manufacturing according to claim 3, characterized by, The strain rate hot cracking criterion considers the interaction force between dendrite grain boundaries. When the solid fraction is close to 1, the tearing force on the liquid film is greater than the sum of the mutual growth between grains and the liquid filling, and cracks begin to initiate.

5. The alloy composition optimization design method for additive manufacturing according to claim 1, characterized in that, The alloy powder used in additive manufacturing is an alloy powder prepared from an alloy ingot of the alloy composition; or it is a powder made by mixing elemental metals or alloy powders according to a calculated content ratio.

6. The alloy composition optimization design method for additive manufacturing according to claim 1, characterized in that, The high throughput computing computes a data volume exceeding 3.0 x 10 12 Group.

7. The alloy composition optimization design method for additive manufacturing according to claim 1, characterized by, The process of laser additive manufacturing is: laser power is 200-300 W, scanning speed is 800-1000 mm / s, scanning interval is 100-120 μm, layer thickness is 20-40 μm, scanning strategy is Z type scanning, and energy density is 60-120 J / mm 3 .

Images