CN115146512A - Material multi-iteration hybrid design method driven by service performance of additive manufacturing component - Google Patents
Material multi-iteration hybrid design method driven by service performance of additive manufacturing component Download PDFInfo
- Publication number
- CN115146512A CN115146512A CN202210873856.4A CN202210873856A CN115146512A CN 115146512 A CN115146512 A CN 115146512A CN 202210873856 A CN202210873856 A CN 202210873856A CN 115146512 A CN115146512 A CN 115146512A
- Authority
- CN
- China
- Prior art keywords
- component
- service performance
- additive manufacturing
- performance
- strengthening
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/23—Design optimisation, verification or simulation using finite element methods [FEM] or finite difference methods [FDM]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2113/00—Details relating to the application field
- G06F2113/10—Additive manufacturing, e.g. 3D printing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2113/00—Details relating to the application field
- G06F2113/26—Composites
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/02—Reliability analysis or reliability optimisation; Failure analysis, e.g. worst case scenario performance, failure mode and effects analysis [FMEA]
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Powder Metallurgy (AREA)
Abstract
The invention discloses a material multi-iteration hybrid design method taking service performance of an additive manufacturing component as a drive, which comprises the following steps: classifying the service performance of the component and matching with a base material; determining a strengthening scale and a strengthening mechanism, screening strengthening materials, designing a single-addition molar ratio of the strengthening materials, and calculating entropy values of mixed materials; calculating thermophysical parameters of the mixed material, performing temperature field, stress field and deformation calculation, gradually judging optimal process parameters required by optimizing the corresponding material proportion based on a simulation result, performing tissue simulation, and judging to output a better material proportion; and performing sample preparation and performance test based on the parameters and the material components, and iteratively and preferably selecting the optimal material components by taking the test data falling into the service performance index range as a judgment basis. By using the method provided by the invention, the optimal design of the mixed material components driven by taking the service performance of the component as a core can be realized, and the aim of benchmarking and strengthening the service performance of the additive component at the initial port of material design is fulfilled.
Description
Technical Field
The invention relates to the technical field of 3D printing, in particular to a material multi-iteration hybrid design method driven by the service performance of an additive manufacturing component.
Background
3D printing is commonly used to refer to "additive manufacturing" techniques, and in a broad sense, materials (including powders, blocks, lines, or liquids) are automatically accumulated in 3D printing based on design data, which can be considered additive manufacturing techniques. The material is the basis of the development of industrial technology, and how the adopted material is produced and finished can reflect the productivity level of the current society. Materials are the basis and key of additive manufacturing, and applied materials in different technologies are different, and although there are thousands of materials in real life, additive manufacturing requires specific requirements and properties.
Pure metals are rarely used in any application because their properties are not suitable or appropriate for the specific requirements of the product. However, the addition of small amounts of the second or third element to the pure metal can result in significant changes in the properties of the alloy. Because of the extensive experimental capabilities required to design new alloys based on application needs, efforts have been made for decades to modify the surface of alloys rather than redesigning new alloy chemistries because the surface modification is easily done without affecting the overall performance.
In general, the need for new alloys can be foreseen from a specific and/or a series of applications that will require better performance than the material systems currently used, compared to the material systems present in the market or in a given supply chain. In many cases, the cost of manufacturing the product is also an important factor behind the design of the alloy, so new alloys can perform the same task using low cost and off-the-shelf starting materials. New alloys can be designed by computational methods, a large amount of experimental data, or first principles. An important factor in alloy development is how much research personnel need to perform internal alloy design and characterization. New products require small batch processing which can be an obstacle in the production line of existing products. It is also noted that this alloy design method requires many starting materials to produce the ingot. In contrast, using powder-based additive methods relies on obtaining raw elemental powders and using additive manufacturing equipment to manufacture end-use parts having the same or different chemical compositions. Since additive manufacturing techniques use conventional alloys without any compositional changes, new alloys designed by additive manufacturing techniques can also be used for conventional manufacturing with minimal changes to their chemical composition. Finally, innovations in alloy chemistry are expected to address long-term challenges in various applications.
Aiming at alloy powder required in the additive manufacturing process, the service performance of a final component is taken as a target index, a mixed material system design taking metal or alloy material as a base material and taking characteristic materials such as rare earth material, ceramic material, simple substance element material and the like as additional materials is developed, the combined regulation and control of the structure performance of an interface bonding area and a deposition layer area in the additive manufacturing process of the mixed material is taken as a core base point, and the laser additive repair strengthening material integrating the base alloy and the additional material on the surface of the composite light alloy is systematically designed, so that the aims of shortening the development period of the additive manufacturing material, improving the material development efficiency and directionally and quantitatively designing the material proportion are fulfilled.
Disclosure of Invention
The invention aims to design a material multi-iteration hybrid design method taking the service performance of an additive manufacturing component as a drive.
The invention mainly solves the problems that: the traditional experimental method is designed for additive manufacturing materials, and has high cost, long research and development period and difficult guarantee of performance on the standard effect and the strengthening effect.
The invention provides a material multi-iteration hybrid design method driven by the service performance of an additive manufacturing component, which specifically comprises the following steps:
(6) Selecting a component base material and determining a strengthening scale and a strengthening mechanism corresponding to a target strengthening material;
(7) Calculating entropy values of a high-throughput calculation screening reinforced material and mixed material system;
(8) Performing macroscopic temperature field, stress field and deformation calculation in the additive process of the mixed material, and preferably selecting optimal process parameters for additive manufacturing of the mixed material;
(9) Simulating and analyzing the microstructure distribution uniformity, element diffusivity and grain size of the interface bonding area and the deposition layer area of the mixed material additive manufacturing component to dynamically judge and identify whether the components of the mixed material meet the requirements and output the proportional components of the corresponding material;
(10) And preparing a sample piece based on the output material proportion components for performance test, and judging the matching degree of the sample piece performance and the service performance so as to finally obtain the material component proportion and the additive process parameters corresponding to the optimal service performance.
Preferably, the service environment analysis of the component is combined in the step (1) to determine the service performance of the component, the service performance of the component is disassembled in a one-to-one classification manner from two aspects of thermal performance and mechanical performance, a base material matched with multiple comprehensive service performances is searched and selected from a base material library comprising pure metals, alloys and the like, and the multiple service performances of the component are taken as targets to determine the reinforcement scale and the reinforcement mechanism corresponding to the requirements.
Preferably, in the step (2), based on the determined target reinforcement scale and reinforcement mechanism, a high-throughput computing technology is adopted to screen out a target performance reinforcement material from a reinforcement material library comprising rare earth materials, ceramic materials, simple substance elements and the like, the service performance of the component is taken as a target, the reinforcement material is set to be added in batches for multiple times, the single mixing molar ratio of the reinforcement material is set, the entropy value of the medium-entropy alloy is taken as a lower limit, the entropy value of the high-entropy alloy is taken as an upper limit to determine the entropy range of the mixture material, the mixture entropy value is computed for a mixture material system, and the entropy value is determined whether to be in a reasonable range, and the material parameter computation is carried out after the requirements are met, wherein the material parameter computation comprises thermodynamic parameters and kinetic parameters;
preferably, the step (3) is to establish a finite element model for the additive manufacturing of the sample based on the calculated material thermophysical parameters, initially input the process parameters of the basic component in the additive manufacturing process to perform macroscopic physical field simulation calculation including the temperature field, the stress field and the deformation, output the corresponding material additive process parameters after identifying and judging that the temperature field distribution, the residual stress distribution, the peak stress index, the deformation distribution and the deformation index all meet the criterion through step-by-step iteration, and return to reset the process parameters when the single factor does not meet the criterion.
Preferably, in the step (4), the evolution calculation of the microstructure and the solute field in the hybrid material additive manufacturing process is performed based on the optimal process parameters obtained by the macroscopic physical field simulation, the element diffusion degree and the microstructure evolution stability are iteratively determined step by step aiming at the interface bonding area, the material addition proportion is planned again after the multiple of the material addition proportion is increased when the single factor does not meet the criterion, the microstructure distribution uniformity and the grain size are iteratively determined step by step aiming at the deposition layer area, and the material proportion modification operation is performed again when the single factor does not meet the criterion.
The invention has the beneficial effects that:
the high-flux design of the mixed material is carried out by taking the service performance of the component as a target, and the systematic design of the mixed material integrating the basic material and the reinforced material in the additive manufacturing process can be realized by combining macro-micro simulation with performance evaluation. The method can well select the basic material and the reinforced material by taking the service performance of the material as a target, can consider the interaction of single or multiple service performances to correspond to the optimal selection direction in a material library, and determines the proportion of the mixed material by taking the entropy value of the mixed material as a basis and taking the distribution condition of the microstructure and the performance detection data as a core judgment basis. The mixed powder with optimized material proportion can meet the service performance requirement of the component to the maximum extent.
Drawings
FIG. 1 is an implementation flow diagram of a material multiple iteration hybrid design method for driving in additive manufacturing component service performance.
Detailed Description
In order to facilitate understanding of those skilled in the art, the present invention will be further described with reference to the following examples and drawings, which are not intended to limit the present invention.
The working flow of the method of the invention is shown in figure 1.
FIG. 1 is an implementation flow diagram of a material multiple iteration hybrid design method for driving in additive manufacturing component service performance.
Step 1, analyzing the service environment of the component, and determining the corresponding service performance of the component based on the service environment.
And 2, disassembling the service performance of the member from two aspects of thermal performance and mechanical performance according to the environment in which the member is positioned, wherein the service performance comprises friction resistance, ablation resistance, tensile performance, fatigue performance, creep resistance, corrosion resistance and the like.
And 3, seeking and selecting a base material matched with the multi-comprehensive service performance from a base material library comprising pure metals, alloys and the like.
And 4-5, determining a strengthening scale and a strengthening mechanism corresponding to the requirement by taking the multi-service performance of the component as a target, wherein the strengthening scale and the strengthening mechanism comprise the following steps:
(1) Determining the strengthening scale of the target material, wherein the strengthening scale is the size of microscopic grain boundary or phase boundary or nucleation scale;
(2) And determining the strengthening mechanism required by the added material, including grain boundary strengthening, fine grain strengthening and the like.
And 6, screening the target performance reinforced material from the reinforced material library comprising the rare earth material, the ceramic material, the simple substance element and the like by adopting a high-throughput computing technology based on the determined target reinforced scale and reinforced mechanism.
And 7, setting the reinforcing material to be added for multiple times in batches by taking the service performance of the component as a target, and setting the single mixing molar ratio of the reinforcing material.
And 8, determining the entropy range of the mixed material by taking the entropy of the medium-entropy alloy as a lower limit and the entropy of the high-entropy alloy as an upper limit.
And 9, calculating the mixing entropy value of the mixed material.
And step 10, judging whether the thermophysical property parameter calculation of the material is continued or not by judging whether the mixed entropy value of the mixed material is in the range, if not, entering step 7 to adjust the single addition molar ratio of the reinforced material, and if not, entering step 11.
Step 12 initially inputs process parameters of an empirically based base component during an additive manufacturing process.
And step 13, establishing a finite element model of the sample additive manufacturing based on the calculated material thermophysical parameters, and carrying out macroscopic physical field simulation calculation including a temperature field, a stress field and deformation based on the process parameters of the initial input basic component in the additive manufacturing process.
And step 15, judging whether to continue calculating by judging the uniform degree of the distribution of the temperature field, if so, entering step 12 to adjust the processing technological parameters, and if not, continuing calculating and entering step 16.
And step 17, judging whether to continue calculating by judging the distribution uniformity of the residual stress, if so, entering step 12 to adjust the processing technological parameters, and if not, continuing calculating and entering step 18.
And step 18, judging whether to continue calculating by judging whether the peak stress exceeds the index, if so, entering step 12 to adjust the processing technological parameters, and if not, continuing calculating and entering step 19.
And step 20, judging whether to continue calculating by judging the uniform degree of deformation distribution, if so, entering step 12 to adjust the processing technological parameters, and if not, continuing calculating and entering step 21.
And step 21, judging whether to continue calculating by judging whether the deformation exceeds the index, if so, entering step 12 to adjust the processing technological parameters, and if not, continuing calculating and entering step 22.
And step 23, performing microstructure and solute field evolution calculation in the additive manufacturing process of the mixed material based on the optimal process parameters obtained by macroscopic physical field simulation.
And step 25, judging whether the calculation is continued by judging whether the diffusion of the elements in the bonding area is uniform, if the elements are not uniformly distributed, performing multiple increase on the adding proportion of the added elements in step 26, and if not, continuing the calculation and performing step 27.
And step 26, performing multiple increase on the addition proportion of the additional elements, and returning to the secondary determination of the single-time mixing molar proportion of the reinforced material.
And step 27, judging whether to continue calculating by judging whether the combination area tissue evolution transition is stable, if the combination area tissue evolution transition is not stable, entering step 26 to multiply and increase the adding proportion of the additional elements, and if not, continuing calculating and entering step 28.
And 29, judging whether to continue calculating by judging whether the microstructure distribution of the deposition layer area is uniform, if so, performing multiple increase on the adding proportion of the additional elements in step 26, and otherwise, continuing calculating and performing step 30.
And step 30, judging whether to continue calculating by judging whether the grain size of the deposition layer area exceeds the index, if so, performing multiple increase on the adding proportion of the additional elements in step 26, and if not, continuing calculating and performing step 31.
Step 31 outputs the corresponding material composition ratio.
And 32, performing additive manufacturing preparation on an actual component by using the output process parameters and the mixed material of the components corresponding to the material proportion, wherein the shape of the component is the test shape of the basic part.
And step 33, performing performance detection on the prepared basic component, wherein the performance detection takes the service performance of the component as a guide, and the thermal performance and the mechanical performance are respectively tested to obtain corresponding test data and perform recording and sorting.
The embodiments of the present invention have been described in detail, but the description is only for the preferred embodiments of the present invention and should not be construed as limiting the scope of the present invention. All equivalent changes and modifications made within the scope of the present invention should be covered by this patent.
Claims (5)
1. A material multi-iteration hybrid design method taking service performance of an additive manufacturing component as a drive is characterized by comprising the following steps:
(1) Selecting a component base material and determining a strengthening scale and a strengthening mechanism corresponding to a target strengthening material;
(2) Entropy calculation of a high-throughput calculation screening reinforced material and mixed material system;
(3) Carrying out macroscopic temperature field, stress field and deformation calculation in the additive process of the mixed material, and preferably selecting optimal process parameters for additive manufacturing of the mixed material;
(4) Simulating and analyzing the microstructure distribution uniformity, element diffusivity and grain size of the interface bonding area and the deposition layer area of the mixed material additive manufacturing component to dynamically judge and identify whether the components of the mixed material meet the requirements and output the proportional components of the corresponding material;
(5) And preparing a sample piece based on the output material proportion components for performance test, and judging the matching degree of the sample piece performance and the service performance so as to finally obtain the material component proportion and the additive process parameters corresponding to the optimal service performance.
2. The method according to claim 1, wherein the step (1) is performed in combination with the analysis of the service environment of the component to determine the service performance of the component, the service performance of the component is disassembled in a one-to-one classification from the two aspects of thermal performance and mechanical performance, a base material matching with multiple comprehensive service performances is searched and selected from a base material library comprising pure metals, alloys and the like, and the multiple service performances of the component are used as targets to determine the reinforcement scale and the reinforcement mechanism corresponding to the requirements.
3. The method according to claim 1, wherein in the step (2), based on the determined target reinforcement scale and reinforcement mechanism, a high-throughput computing technique is used to screen out a target performance reinforcement material from a reinforcement material library including rare earth materials, ceramic materials, elemental elements, and the like, the member service performance is used as a target to set the reinforcement material to be added in batches for multiple times, the reinforcement material single-time mixing molar ratio is set, the entropy value of the medium-entropy alloy is used as a lower limit, the entropy value of the high-entropy alloy is used as an upper limit to determine a mixture entropy range, the mixture entropy calculation is performed on a mixture system, whether the mixture entropy value is within a reasonable range is determined, and the material parameter calculation including thermodynamic parameters and kinetic parameters is performed after requirements are met.
4. The material multi-iteration hybrid design method driven by the service performance of the additive manufacturing component according to claim 1, wherein in the step (3), a finite element model for additive manufacturing of the sample piece is established based on the calculated material thermophysical parameters, process parameters of the basic component in the additive manufacturing process are initially input to perform macroscopic physical field simulation calculation including a temperature field, a stress field and deformation, the temperature field distribution, the residual stress distribution, the peak stress index, the deformation distribution and the deformation index are identified and judged to be in accordance with the criterion through step-by-step iteration, then the corresponding material additive process parameters are output, and when the single factor does not meet the criterion, the process parameters are returned to be reset.
5. The material multi-iteration hybrid design method driven by the service performance of the additive manufacturing component according to claim 1, wherein in the step (4), the evolution calculation of the microstructure and solute field in the additive manufacturing process of the hybrid material is performed based on the optimal process parameters obtained by the macroscopic physical field simulation, the gradual iteration determination of the element diffusion degree and the microstructure evolution stability is performed on the interface bonding area, the material addition proportion planning is performed again after the multiple increase of the material addition proportion is performed when the single factor does not meet the criterion, the gradual iteration determination of the microstructure distribution uniformity and the grain size is performed on the deposition layer area, and the material proportion modification operation is performed again when the single factor does not meet the criterion.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210873856.4A CN115146512A (en) | 2022-07-22 | 2022-07-22 | Material multi-iteration hybrid design method driven by service performance of additive manufacturing component |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210873856.4A CN115146512A (en) | 2022-07-22 | 2022-07-22 | Material multi-iteration hybrid design method driven by service performance of additive manufacturing component |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115146512A true CN115146512A (en) | 2022-10-04 |
Family
ID=83415061
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210873856.4A Pending CN115146512A (en) | 2022-07-22 | 2022-07-22 | Material multi-iteration hybrid design method driven by service performance of additive manufacturing component |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115146512A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115740498A (en) * | 2022-11-23 | 2023-03-07 | 河北科技大学 | Ternary coupling regulation and control method for optimizing molding quality by selective laser melting |
-
2022
- 2022-07-22 CN CN202210873856.4A patent/CN115146512A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115740498A (en) * | 2022-11-23 | 2023-03-07 | 河北科技大学 | Ternary coupling regulation and control method for optimizing molding quality by selective laser melting |
| CN115740498B (en) * | 2022-11-23 | 2023-08-04 | 河北科技大学 | Ternary coupling regulation and control method for optimizing forming quality of selective laser melting |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11511491B2 (en) | Machine learning assisted development in additive manufacturing | |
| CN111627503B (en) | Prediction method of stress field in laser cladding manufacturing process of alumina ceramic matrix composite coating | |
| JP2005002472A (en) | Rapid prototyping machine, selective laser sintering machine, and method for improving production part from automated production process for just in time inventory system | |
| MX2012001976A (en) | Material property distribution determination for fatigue life calculation using dendrite arm spacing and porosity-based models. | |
| CN115146512A (en) | Material multi-iteration hybrid design method driven by service performance of additive manufacturing component | |
| Rajeswari et al. | Investigation on mechanical properties of aluminium 7075-silicon carbide-alumina hybrid composite using Taguchi method | |
| RaviKumar et al. | Mechanical and wear behavior of Al7075/Al2O3/SiC hybrid composite | |
| Zhang et al. | Effects of interfacial debonding on the rate-dependent response of metal matrix composites | |
| CN115440326A (en) | High-strength high-plasticity Zr-based amorphous alloy and machine learning-based method thereof | |
| Chate et al. | Application of statistical modelling and evolutionary optimization tools in resin-bonded molding sand system | |
| Raza et al. | Modeling of the mechanical properties of directionally solidified Al-4.3% Cu alloy using response surface methodology | |
| CN113868912A (en) | Parameter identification and correction method for J-C constitutive model of titanium alloy | |
| Tyagunov et al. | Application of artificial neural networks for prediction of nickel-based superalloys service properties based on the chemical composition | |
| Wen et al. | High-throughput exploration of the composition-dependent elasto-plastic properties in Co–Ni–W system | |
| US20230367929A1 (en) | Optimisation fo alloy properties | |
| US20230166332A1 (en) | Optimising process parameters in additive manufacturing | |
| JPH06264137A (en) | Method for determining conditions for controlling material structure of polycrystalline material | |
| Ghafari et al. | NEW ROBUST DESIGN APPROACH FOR OPTIMIZED SUSTAIBALE UHPC | |
| Pai et al. | Application of response surface methodology and enhanced non-dominated sorting genetic algorithm for optimisation of grinding process | |
| EP4109456A1 (en) | Methodology for developing sustainable aluminium products | |
| Sokhal et al. | A Review on Aluminum Metal Matrix Composites: A Multidimensional Attribute Approach | |
| CN114252471B (en) | Rapid prediction method for high-temperature fatigue performance of nickel-based superalloy small-angle grain boundary | |
| Mallian | Multi-optimization of empirical models for the material extrusion process | |
| Özel et al. | Physics-Based Microstructure Modeling for Grain Tailoring and Refinement in Wire Arc Additively Manufactured Ti-6Al-4V Alloy | |
| CN113183452B (en) | Multi-material complex structure 4D printing method with variable mechanical properties and product |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |