Alloy analysis and development methods

Abstract

The present invention relates to a porous metal plasticity method for analyzing and strengthening alloys. The method involves the use of computed tomography scanning to model the material structure and determine the location and density of porosity region by region. The porosity regions are then processed by using a voxel mesh method with tessellation after pre-characterizing the alloy by dividing it into "n" different virtual regions to create a substructure. The main objective is to improve the strength and development of alloys by employing at least one computer method for analyzing and modeling porosity, especially in deformation regions.

Description

[Technical Field]

[0001] The present invention relates to a method aimed at increasing the strength of alloys by analyzing and addressing the porosity inherent in the alloy. Specifically, the method focuses on improving alloys through porosity analysis to increase strength relative to deformation zones. [Background technology]

[0002] The design phase and component design can be facilitated based on various analysis programs performed. For example, the forces acting on the component and elongation scenarios can be analyzed in a computer environment, allowing for the determination of stress distribution within the component. However, these studies are not sufficient for the development of new alloys.

[0003] Therefore, the obtained samples are scanned using computed tomography (CT) scanning (CT) technology to examine their structural characteristics. However, the evaluation of these structural characteristics can vary due to numerous factors, from the alloy casting process to the processing stage. For example, the structural characteristics of samples taken from the same alloy mixture may differ between a part obtained from casting into a sand mold and a part obtained from casting into a steel mold, even if they are taken from the same ladle.

[0004] Porosity / defects may exist in the structure of alloy components, and these defects can directly affect the service life of the part. In practical applications, the detection of such defects and the improvement of the alloy structure have become significant technical challenges.

[0005] Methods used in the prior art cannot accurately determine the porosity density of a scanned part. For example, when examining a given porosity of a part with loosely distributed pores throughout the part and comparing it to the porosity of a part that exhibits porosity density only over specific regions, the overall porosity may appear similar. However, the quality of these two parts is quite different. Therefore, innovations are needed to address technical challenges such as this example scenario.

[0006] During the conditioning stage of alloys, especially aluminum alloys (e.g., aluminum-titanium-magnesium), metal oxides tend to form. These oxides are hydrodynamically oriented during flow and can exhibit different opening / shape characteristics, causing irregularities in the part being cast. At this point, these metal oxides tend to act like pores on the part. Parameters such as turbulence and cooling rate during casting directly affect the formation of metal oxides. Therefore, it is very important to analyze alloy samples to determine the casting parameters.

[0007] As a result, all of the above-mentioned problems have necessitated the need for innovation in the relevant fields. Object of the invention

[0008] The present invention is proposed with the aim of eliminating the aforementioned problems and therefore bringing about a technological breakthrough in the relevant field.

[0009] The main objective of the present invention is to establish a method that allows for the analysis of porosity distribution after computer scanning of a part, where the results of the computer scanning are partitioned into a mesh / grid structure based on the area porosity distribution of the part.

[0010] Another object of the present invention is to prevent weakening of component strength in aluminum alloys due to the formation of metal oxides.

[0011] Another object of the present invention relates to establishing quality indices and minimum quality levels that provide consistent results in all situations during the determination of the quality of liquid metals and during the determination of the quality of parts with complex shapes.

[0012] Another object of the present invention is to prevent the weakening of parts due to the formation of metal oxides in the structure, especially in aluminum-titanium-magnesium alloys. Another object of the present invention is to develop alloy properties that behave within the desired properties, as well as to determine parameters such as casting angle, casting temperature, and cooling temperature for the developed alloy.

[0013] Another object of the present invention is to enable a method for the prediction of material / alloy strength.

[0014] Another object of the present invention is to partition the entire sub-region based on the local defect density of the sub-region during defect analysis of the sub-region, rather than considering the overall defect density of the sub-region.

[0015] The present invention also enhances the method for identifying the cause of failure by analyzing alloy samples based on computed tomography scan results.

[0016] Another object of the present invention is to allow for better analysis of parts with localized / regional porosity distributions when compared to parts with non-localized / global porosity distributions across the entire part area.

[0017] Yet another object of the present invention is to develop alloys with improved properties by using scanning parameters during the development stage of the alloy. Summary of the Invention

[0018] The present invention, as described above and in detail below, is an alloy analysis and development method designed to achieve all of the above objectives.

[0019] The present invention relates to a method for scanning and modeling an alloy using at least one computed tomography scanning method that allows for the analysis of inherent porosity, in particular metal oxide fraction in deformation zones, in order to increase and facilitate the improvement of the alloy's strength, characterized by obtaining a sample during at least one casting process, performing a computer scan to determine the location and density of the porosity zones of the material based on the scan results, dividing the porous portion into "n" different virtual zones to establish a substructure, processing the identified porosity using mesh methods after tessellation, and then analyzing the determined porosity of the metal using plasticity methods. 1. A method comprising scanning and modeling an alloy using at least one computed tomography scanning method that allows for the analysis of inherent porosity, in particular metal oxide fraction in deformation zones, in order to increase and facilitate the improvement of the alloy's strength, characterized by: obtaining a sample during at least one casting process; performing computer scanning to determine the location and density of the porosity zones of the material based on the scanning results; dividing the porous portion into "n" different virtual zones to establish a substructure; processing the identified porosity using mesh methods after tessellation; and then analyzing the determined porosity of the metal using plasticity methods.

[0020] In another preferred embodiment of the present invention, increasing the strength of the alloy and facilitating its development by enabling analysis of the porosity, in particular the metal oxide ratio, within the deformation zone of the part, is carried out by the following steps: a) preparing the alloy for production and carrying out mechanical testing; i) Determination of effective global / local display volume dimensions through computed tomography of the alloy; ii) storing surface area and volume data of defects detected in a computed tomography scan of the alloy; iii) Construction of test-tomography correlation matrix / card; b) input of alloy and part specific parameters; c) scanning of the part by CT; i) Determination of effective global / regional display volume through computed tomography (CT); ii) storing data on the surface area and volume of defects detected in CT scans of the alloy; iii) performing CT analysis to determine porosity and percent metal oxide by using correlation data including heat treatment and cooling rate (solidification) data; d) Determining the local density difference of the virtual area and dividing the virtual data of the alloy / part into a mesh grid; e) dividing the shape data and / or grid into "n" different regions based on the determined regions having varying densities; f) Construction of substructures from "n" regions; g) Separation of the regions into a mesh structure using a tessellation algorithm by determining the porosity / metal oxide fraction of each region; h) correlating data obtained from the testing process with data obtained from casting and CT scanning; ii) retrieving the necessary data from the embedded data matrix from the testing and CT scanning stages; j) Determination of alloy quality index and its projection onto the shape; k) indication of the location of the alloy quality index of the region for each subregion and the corresponding identification of weak points in the casting; l) creating a porous metal plasticity model, defining "n" different materials for "n" different regions, writing parameters from a data matrix and running a simulation (e.g., finite element); m) Projecting the obtained results onto a part and identifying vulnerabilities in that part.

[0021] In another preferred embodiment of the present invention, the alloy quality index referred to in step j is determined by assigning volumetric defect amount and surface area defect amount from the CT scan to each unit volume. The alloy quality index is calculated using the following formula:

[0022] Alloy quality index = (yield strength + tensile strength + elongation) - A *(Defect volume × defect surface area)b

[0023] In another preferred embodiment of the invention, the calculation is performed for each volume previously determined by subdivision. In the same preferred embodiment, a tessellation algorithm is used to perform the meshing process referred to in step d.

[0024] In another preferred embodiment of the present invention, before step i, the heat treatment results and area porosity are checked together with the data from the mechanical tensile test to construct a data matrix.

[0025] In another preferred embodiment, strength results are developed through correlation of volume defects, surface defects, and fv (initial porosity coefficient) recovered from test data.

[0026] In another preferred embodiment of the present invention, an AI sparse learning algorithm is used to perform modeling via damage initiation plasticity, life calculations, and strength calculations.

[0027] In the same preferred embodiment, the alloy quality index is determined according to the following formula:

[0028] Alloy quality index = (yield strength + tensile strength + elongation) - A * (Defect volume)bA * (Defect volume × defect surface area)b

[0029] In another preferred embodiment, the Gurson algorithm is used for the porous metal plasticity model.

[0030] In the same preferred embodiment, the Gurson-Tvergaard-Needleman algorithm is used for the porous metal plasticity model.

[0031] In another preferred embodiment, the Rousselier algorithm is used for the porous metal plasticity model.

[0032] In the same preferred embodiment, the Gologanu algorithm is used for the porous metal plasticity model.

[0033] In another preferred embodiment of the present invention, the part or parts scanned via CT are segmented into regions and mesh grids based on porosity obtained from the same CT scan results.

[0034] In another preferred embodiment of the present invention, following tomographic scanning, the constructed tessellation grid is further divided into sections to create a substructure based on the inherent porosity density and location of said porosity, which is then meshed by a voxel mesh method to perform the analysis through porous metal plasticity.

[0035] In another preferred embodiment of the present invention, the method includes a database matrix created with heat treatment results and area porosity data for use with porous metal plasticity algorithms.

[0036] In another preferred embodiment of the present invention, while performing porous metal plasticity analysis by defining "n" different material parameters into "n" subregions, the parameters are assigned to the subregions.

[0037] In another preferred embodiment of the present invention, global and local porosity analysis of the part is performed to determine different porosity densities in different regions of the part.

[0038] In another preferred embodiment of the present invention, after creating a substructure from "n" subregions at the associated pore level, stress intensity factors are assigned to the regions by defining interface / boundary elements.

[0039] In another preferred embodiment of the present invention, the stress intensity factor is calculated by correlating it with the sphericity / compactness value.

[0040] In another preferred embodiment of the invention, following step F, the stress concentrations are calculated with J2 plasticity.

[0041] In another preferred embodiment of the present invention, the calculation of the aforementioned coefficients is performed by J2 plasticity and assigned as coefficients of local stress concentration according to the local stress concentration.

[0042] In another preferred embodiment of the present invention, damage sensitivity is determined by considering the projection of the local or global dimensions of the defect along the x, y, z axes or xy, xz, yz planes, which represent the random orientation of the defect, pinpointing the location of the crack initiation point, and revealing the crack size.

[0043] In another preferred embodiment of the present invention, a voxel mesh method is used.

[0044] It is important to note that the scope of the present invention, as defined in the claims, is not limited to the specific details described in this summary and detailed description. It is obvious that a person skilled in the art can provide similar embodiments in light of the above description without departing from the main theme of the present invention. [Brief explanation of the drawings]

[0045] [Figure 1] A flowchart of the algorithm of the present invention is provided illustrating the operational steps. [Figure 2] A flow chart illustrating the method used in a preferred embodiment of the present invention is presented. [Figure 3] A flow chart illustrating the method used in a preferred embodiment of the present invention is presented. [Figure 4] An exemplary CT scan image is provided showing the results of the CT scan. [Figure 5] 1 shows an image of the sample portion that will become the mesh / section after CT scanning. [Figure 6A] A diagram of the distribution of defects identified in the analysis is presented. [Figure 6B]A diagram of the distribution of defects identified in the analysis is presented. [Figure 6C] A diagram of the distribution of defects identified in the analysis is presented. [Figure 7] Another algorithm is provided that illustrates the operation of the present invention.

[0046] The drawings are not intended to limit the scope defined in the claims, and should not be referenced independently without consideration of the technical description in the present disclosure to interpret the scope defined in those claims. These drawings are intended to contribute to the clarity of the description of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0047] In this detailed description, the subject of the present invention, "Method for Alloy Analysis and Development," is illustrated by way of example, without imposing any limiting effect, but simply for the purpose of better understanding.

[0048] The present invention relates to a method for increasing the strength of an alloy by analyzing the inherent pores and modifying the alloy to increase the strength of its deformation zones, in particular a method comprising sectioning the zones in the structure of the alloy / part according to their defect content level and individually localizing them by computer tomography scanning of the structure.

[0049] The present invention is a method for enhancing the strength and development of alloys by enabling analysis of porosity, and in particular, metal oxide fraction within deformed (deformed) regions of the material. The method involves scanning and modeling the alloy / part using at least one computed tomography scanning method. Unique features of the method include obtaining samples through at least one casting process and using the computer scan results to analyze the plasticity of the porous metal to determine the location and density of the porosity regions of the material. Additionally, the method includes characterizing the porous part by dividing it into "n" distinct virtual regions to create a framework before being processed using a mesh method after tessellation.

[0050] The present invention is characterized by the following steps. a) preparing the alloy for production and carrying out mechanical testing; i) Determination of effective global / local display volume dimensions through computed tomography of the alloy; ii) storing data on the surface area and volume of defects detected in a computed tomography scan of the alloy; iii) Construction of test-tomography correlation matrix / card; b) input of alloy and part specific parameters; c) scanning of the part by CT; i) Determination of effective global / regional display volume through computed tomography (CT); ii) storing the surface area and volume data of defects detected in CT scans of the alloy; iii) performing CT analysis to determine porosity and metal oxide fraction by using correlation data including heat treatment data and cooling rate data; d) Determining regional density differences and dividing the alloy / part data into grids according to said regions with varying densities; e) division of the shape data and / or grid into "n" different virtual regions based on said determined regions having varying densities; f) Construction of substructures from "n" regions; g) Separation of the regions into a mesh structure using a tessellation algorithm by determining the porosity / metal oxide fraction of each region; h) correlating data obtained from the testing process with data obtained from casting and CT scanning; ii) retrieving the necessary data from the embedded data matrix from the testing and CT scanning stages; j) Determination of alloy quality index and its projection onto the shape; k) indication of the location of the alloy quality index values ​​for each sub-zone and the corresponding identification of weak points in the casting; l) creating a porous metal plasticity model to define "n" different materials for "n" different virtual regions, writing parameters from a data matrix and running a simulation; m) Projecting the obtained results onto a part and identifying vulnerabilities in that part.

[0051] The present invention allows for the segmentation of a part based on the porosity density and spatial distribution of said porosity within the part as determined by computed tomography scan results, and further allows for the analysis of said segmentation using porous metal plasticity methods.

[0052] FIG. 2 is a flow diagram illustrating a preferred embodiment of the present invention.

[0053] Within the scope of the present invention, alloy parameters are first determined and the alloy is virtually scanned, said scanning being carried out by computed tomography. As a result of this scanning, the surface of the part is segmented using a tessellation method. The structure segmented by tessellation is divided into substructures based on the calculated porosity density distribution and location in the part. The number of these substructures can be "n". The above-mentioned "n" substructures are preferably meshed as a network through three-dimensional positioning of pixels using a voxel mesh method.

[0054] In a preferred embodiment of the present invention, global or local porosity analysis is performed based on the CT scan analysis results to determine the porosity of different area densities. In a preferred configuration, during scanning, global analysis can be performed by dividing into 1000 cubic millimeter cubes and performing global porosity analysis on 100 cubic millimeter cubes. The division parameters can be customized according to the shape of the part. The above-mentioned partitioning defines the unit volume and enables the determination of porosity through correlation. Then, based on the determined area ratio, a tessellation algorithm works to divide the shape / network into "n" regions and form substructures from these "n" regions.

[0055] The computed tomography results metrology produces measurement outputs including the porosity and defects of the part. Locations corresponding to these defects and pores, as well as volume and area outputs, are obtained. Exemplary tomography outputs are provided in Table 1. [Table 1]

[0056] In another preferred embodiment of the present invention, scanning of the alloy / part can be accomplished using X-ray techniques. In another preferred embodiment of the present invention, the scanning volume can vary depending on the equipment used. For example, a scan of a volume of one cubic meter can be performed. The purpose of the scan is to analyze how much sphericity and compactness occurs in different sections of the material by segmenting the shape.

[0057] As provided in Table 1, information specifically relating to defects / porosity (potential) obtained from computed tomography (CT scan) is used to reveal undesirable defects or porous structures within the alloy-part and used for analysis to provide the improvements necessary to eliminate defects in the alloy by the method of the present invention.

[0058] In another preferred embodiment of the present invention, a re-tessellation algorithm can be applied to a shape with "n" pre-tessellated regions.

[0059] The material is segmented based on the location of the defect rate detected in the CT scan results. This segmentation can be adjusted according to the level of defects in the material. This segmentation allows for a more thorough analysis and enhances the development of the material. When examining each of the aforementioned segmented regions, it is observed that the defect rate in each region varies (compared to analysis performed as a single piece), thus providing an improved resolution of the analysis process.

[0060] For example, when evaluating CT scans of two pieces, the calculated total defect density on both pieces may be the same. However, using the method described above, defects concentrated in specific areas can be identified. A method in which defects are assumed to be homogeneously distributed on a single piece is compared to another scenario in which defects are concentrated in specific areas on the other piece.

[0061] Figure 4 provides an image showing the results of a CT scan. For example, if substructure generation and segmentation are not performed based on defect distribution, the leftmost piece in the image may have the same total defect density as the adjacent piece. However, the distribution of defects is not homogenous across the piece, but is concentrated in a specific region, making it impossible to detect a significant defect in the second-to-the-left piece. If segmentation is performed based on defect density, the highest density defects are considered as a separate segment (or region), allowing for a more thorough analysis of the alloy, increasing the accuracy of the analysis.

[0062] The porosity of the substructure consisting of "n" regions is cross-checked with the heat treatment results and local porosity together with the correlation data obtained from the tensile test to generate a database matrix based on which "n" different material parameters are defined for the geometry, thereby completing the analysis.

[0063] Within the scope of the present invention, the analysis is carried out using the porous metal plasticity method, in which the analysis can be carried out using the Gurson method or the Rousselier method depending on the suitability of the analysis.

[0064] Porous metal plasticity, a type of continuum mechanical analysis, involves the stress tensor written in the following form, which allows obtaining deflection and volume partitioning: [σ 11 σ 12 σ 13 σ 21 σ 22 σ 23 σ 31 σ 32 σ 33 ]

[0065] Within the scope of the present invention, J2-type plasticity utilizes the deviatoric part. In porous metal plasticity, the volumetric part σ 11 , σ 22 , σ 33 The changes occurring in are translated into deformation behavior through porosity and other factors, thus simulating the stress distribution associated with porosity.

[0066] Porous metal plasticity is analyzed using local or global porosity. The analysis is calculated using direct or explicit / implicit methods (similar to the Newton-Raphson model). Starting from an initial structure with a "p" state (p=0, t=0) at time "t", it is calculated iteratively towards the (p+1)(t+1) state. In this iterative method, the successive number sequence of (p+1)(t+1) is monitored to reach a value in the strength test of the material. In the final stage, the analysis is done explicitly / implicitly by ensuring correlation.

[0067] In a preferred embodiment of the present invention, parameters are assigned for the "n" regions based on the analytical results, which allows for an improved porosity distribution.

[0068] FIG. 5 provides an exemplary image of "n" regions (selected as five). The aforementioned segmentation allows for the determination of defect locations, which correlate with the defect rate. In another preferred embodiment of the present invention, after creating a substructure from the "n" regions, a stress concentration factor is assigned to the determined porous regions by defining interface elements. The stress concentration factor is the ratio of the highest stress to the nominal stress, which causes irregularities that result in discontinuities in the material or geometry. This stress concentration factor is defined separately for each created region, allowing for local analysis.

[0069] In another preferred embodiment of the present invention, the stress concentration factor can be calculated by correlating the sphericity / compactness value for the factor.

[0070] In another preferred embodiment of the present invention, after creating the substructure of "n" regions, the stress concentration is calculated using J2 plasticity. The J2 plasticity and stress concentration values ​​are then converted into coefficients of local stress concentration.

[0071] The Alloy Quality Index (AQI) is determined using a data map containing heat treatment, tensile strength, and elongation data obtained from experimental results. Correlation maps (tensile-CT volume and surface area fractions) are constructed using mechanical properties and tomography results on the sample. The tomography data of the part is divided into random sections / regions, and AQI calculations are performed. The calculated AQI data is then reflected in the part design data / scan data, thereby allowing for observation of porosity / metal oxide density. [Table 2]

[0072] The alloy quality index (AQI) is obtained from the tomographic data by assigning defect volume and surface area values ​​per unit volume according to the following formula: (Yield strength + Tensile strength + Elongation) - A * (Defect volume (DVof) - defect surface area (Dsurf)b

[0073] In this formula, A and b are alloy material coefficients. The alloy quality index is calculated for each divided volume. Thus, the alloy quality index is calculated for the smallest point of the structure.

[0074] In another preferred embodiment of the present invention, the damage initiation plasticity model, life calculation, and strength calculation are performed using an AI sparse learning algorithm.

[0075] In another preferred embodiment of the present invention, various algorithms are employed, such as Gerson-type damage initiation plasticity in a continuous procedure where localization specifications are incorporated into the model formulation, or / and empirical Coffin-Manson, Paris-Erdogan, and / or energy methods based on the definition of a minimum flaw size as the initial damage state.

[0076] In a preferred embodiment of the invention, experimental design data management is ensured. In this context, mechanical test results and casting simulation results are considered. The obtained results allow the creation of a database matrix.

[0077] In another preferred embodiment of the present invention, the aforementioned "n" regions are meshed by a tessellation algorithm, and then a sparse learning algorithm is used to determine the properties of the "n" regions. The determined / calculated properties are then projected onto the shape of the part, and a quality index is calculated over the shape. This provides quality control for the part using the quality index. Table 3 gives the results for two samples. [Table 3]

[0078] In the example given in Table 3, sample 5 has larger volumetric defects with higher relative compactness, resulting in larger defect openings. Thus, the projected defect length along the yz axis is longer. The algorithm of the present invention can represent defects by incorporating these individual values ​​into a model according to severity.

Claims

1. 1. A method for improving the strength of an alloy, comprising scanning and modeling said alloy using at least one computed tomography scanning method that allows for the analysis of inherent porosity, in particular metal oxide fraction in deformation zones, in order to facilitate the improvement, characterized in that: a sample is obtained during at least one casting process; computer scanning is performed to determine the location and density of porosity zones in the material based on the scanning results; dividing the porous portion into "n" different virtual zones to establish a substructure; processing the identified porosity using mesh methods after tessellation; and then analyzing the determined porosity of the metal using plasticity methods.

2. In order to increase the strength of the alloy and to facilitate the development of the alloy, a method is provided according to claim 1 that allows the analysis of porosity, metal oxide content, and in particular porosity in the elongation / deformation zone, comprising the following steps: a) preparing the alloy for production and performing mechanical testing; i) determining the effective global / local display volume dimensions through computed tomography of said alloy; ii) storing surface area and volume data of defects detected in a computed tomography scan of said alloy; iii) Construction of test-tomography correlation matrix / card; b) input of alloy and part specific parameters; c) scanning said part by CT; i) Determination of effective global / regional display volumes through computed tomography (CT); ii) storing the surface area and volume data of the defects detected in the CT scan of the alloy; iii) performing CT analysis to determine porosity and metal oxide fraction by using said correlation data including heat treatment data and cooling rate data; d) Determining local density differences in the virtual region and dividing the virtual data of said alloy / said part into a mesh grid; e) dividing the shape data and / or the mesh grid into "n" different specific regions based on the determined regions having varying densities; f) constructing a substructure from said "n" virtual regions; g) partitioning the regions into a mesh structure using a tessellation algorithm by determining the porosity / metal oxide fraction of each region; h) correlating data obtained from the testing process with data obtained from the casting and the CT scan; ii) retrieving the necessary data from the embedded data matrix from the examination and CT scanning steps; j) Determination of alloy quality index and projection onto the shape; k) indication of the location of the alloy quality index value for each sub-zone and corresponding identification of weak spots in the casting; l) creating a porous metal plasticity model to define "n" different materials for said "n" different regions, retrieving parameters from said data matrix and running a simulation; m) projecting the obtained results onto said part and identifying vulnerabilities of said part.

3. The following formula: Alloy quality index = (yield strength + tensile strength + elongation) - A ＊ (Defect volume ＊ 3. The method of claim 2, wherein the calculation of the alloy quality index defined in process step j using the surface area of ​​a defect)b, wherein the volume and surface area of ​​the defect are assigned for each unit volume obtained from a tomographic scan.

4. 4. The method for analyzing alloys according to claim 3, wherein the calculation is performed for each volume determined by division.

5. 3. The method of claim 2, characterized in that it includes the process of partitioning into a mesh grid using the tessellation algorithm referred to in process step "d".

6. 3. The method of claim 2, wherein prior to said processing step "i", said data matrix is ​​constructed using heat treatment results and area porosity values, including tensile test results.

7. 10. The method of claim 1, characterized in that the strength results are retrieved through correlation of test data with defect volume, defect surface area, and fv (initial porosity coefficient).

8. 8. The method of claim 7, wherein an AI sparse learning algorithm is utilized to perform damage initiation plasticity modeling for life calculations and strength analysis.

9. AQI = (yield strength + tensile strength + elongation) - A ＊ (defect volume) b-A ＊ 2. The method of claim 1, wherein the alloy quality index is determined as (volume of defect x surface area of ​​defect)b.

10. The method of claim 1 , wherein the Gurson algorithm is used for the porous metal plasticity model.

11. The method of claim 1 , wherein the Rousselier algorithm is used for the porous metal plasticity model.

12. The method of claim 1, wherein the Gurson-Tvergaard-Needleman algorithm is used for the porous metal plasticity model.

13. The method of claim 1 , wherein the Gologanu algorithm is used for the porous metal plasticity model.

14. 2. The method of claim 1, wherein the data of the part or parts is divided into sections and a grid / mesh is constructed based on the porosity density obtained by tomographic scanning.

15. 15. The method of claim 14, characterized in that based on the location and density of the porosity acquired in the tomographic scan, a tessellated substructure of the part is created, the part is divided into sections, meshed with a voxel mesh method, and analyzed with a porous metal plasticity method.

16. 2. The method of claim 1, characterized in that damage sensitivity is determined by defining crack size considering the projection of local or global dimensions of the defect along the x, y, z axes or along the xy, xz, yz plane, which exhibits random orientation of the defect, which precisely locates the crack initiation point.

17. 10. The method of claim 1, further comprising creating a data matrix containing heat treatment results and regional / local porosity metrics to implement a porous metal plasticity algorithm.

18. 2. The method of claim 1, characterized in that "n" different material parameters are defined for a substructure having "n" different regions, and the parameter assignment is performed after a porous metal plasticity analysis of the "n" different material substructures.

19. 10. The method of claim 1, further comprising performing a global and local porosity analysis to determine different porosity densities between different regions of the part.

20. 3. The method of claim 2, wherein after the substructure is constructed from "n" virtual regions, stress intensity factor assignment is performed by defining interface / boundary elements for the regions where porosity is determined.

21. 11. The method according to claim 10, characterized in that the calculation of the stress intensity factor is carried out by correlating it with the sphericity / compactness value.

22. 3. The method according to claim 2, characterized in that step f is followed by the calculation of a stress intensity factor according to J2 plasticity.

23. 10. A method according to claim 1 or claim 9, characterized in that local / regional stress intensity factors are calculated and transferred by J2 plasticity and by the stress concentrations that occur.

24. The method of claim 1, characterized by the use of a voxel mesh method.

Images