Method for rapid strength evaluation of additively manufactured titanium using resistivity

Abstract

The title of the invention is a method for rapid additive manufacturing with resistivity for titanium strength evaluation. A method for evaluating the strength of an additively manufactured material is provided. The method includes calibrating a baseline resistivity of a multiphase additive material for a set dislocation density as a function of phase fraction and phase composition, where individual phases of the material have different resistivity values. During additive manufacturing, the phase fraction, phase composition, and resistivity of the additive material are characterized after the additive material undergoes a plurality of heating and cooling cycles. The dislocation density of the additive material is then determined from the resistivity after additive manufacturing, taking into account the influence of the phase fraction and phase composition determined from the characterization.

Description

Technical Field

[0001] The present disclosure relates generally to additive manufacturing, and more particularly to using microstructural characterization and resistivity to determine dislocation density in additively manufactured metals. Background Art

[0002] A dislocation is a defect or irregularity, such as the termination of an atomic plane within a crystal structure. This defect causes the surrounding plane of atoms to bend around the edge of the termination plane. Dislocations affect various properties of a material, including strength. Dislocation density is the number of dislocations per unit volume of a crystalline material.

[0003] Increasing the dislocation density in a material increases its yield strength. Dislocation density is imparted to forged materials through mechanical cycles, such as rolling or forging. In such applications, dislocation density can be determined by tracking the amount of mechanical deformation performed. However, in the case of additive manufacturing, the material in question undergoes thermal cycling rather than mechanical cycling. Summary of the Invention

[0004] The illustrative embodiment provides a method for evaluating the strength of additively manufactured materials. The method includes calibrating the baseline resistivity of a multiphase additive material as a function of phase fraction and phase composition for a set dislocation density, wherein each phase of the material has a different resistivity value. During the additive manufacturing process, the additive material undergoes multiple heating and cooling cycles, and the phase fraction, phase composition, and resistivity of the additive material are characterized. The dislocation density of the additive material is then determined based on the resistivity after additive manufacturing, taking into account the effects of the phase fraction and phase composition determined by the characterization.

[0005] Another illustrative embodiment provides a system for evaluating the strength of additively manufactured materials. The system includes a bus system; a memory device connected to the bus system, wherein the memory device stores program instructions; and a plurality of processors connected to the bus system, wherein the plurality of processors execute the program instructions to: calibrate a baseline resistivity of a multiphase additive material as a function of phase fraction and phase composition for a set dislocation density, wherein each phase of the material has a different resistivity value; characterize the phase fraction, phase composition, and resistivity of the additive material after the additive material undergoes multiple heating and cooling cycles during additive manufacturing; and determine the dislocation density of the additive material based on the resistivity after additive manufacturing, taking into account the effects of the phase fraction and phase composition determined by the characterization.

[0006] Another illustrative embodiment provides a computer program product for evaluating the strength of additively manufactured materials. The computer program product includes a non-volatile computer-readable storage medium having program instructions embodied therein, the program instructions being executable by a plurality of processors to cause a computer to perform the following steps: calibrating a baseline resistivity of a multiphase additive material as a function of phase fraction and phase composition for a set dislocation density, wherein each phase of the material has a different resistivity value; characterizing the phase fraction, phase composition, and resistivity of the additive material after the additive material undergoes multiple heating and cooling cycles during additive manufacturing; and determining the dislocation density of the additive material based on the resistivity after additive manufacturing, taking into account the effects of the phase fraction and phase composition determined by the characterization.

[0007] The features and functions can be implemented independently in various examples of the present disclosure or may be combined in yet other examples in which further details can be seen with reference to the following description and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] The novel features which are believed to be characteristic of the illustrative examples are set forth in the appended claims. However, the illustrative examples, together with the preferred modes of use, further objects and features thereof, will be best understood by reference to the following detailed description of illustrative examples of the disclosure when read in conjunction with the accompanying drawings, in which:

[0009] Figure 1 is a block diagram illustrating a material strength assessment system for additive materials according to an illustrative embodiment.

[0010] Figure 2 is a flow chart illustrating a method flow for evaluating dislocation density of an additive material according to an illustrative embodiment.

[0011] Figure 3 is a flow chart illustrating a method flow for calibrating the baseline resistivity of an additive material in accordance with an illustrative embodiment.

[0012] Figure 4 A method of calculating material strength of a titanium alloy using dislocation density is illustrated in accordance with an illustrative embodiment.

[0013] Figure 5 is an illustration of a block diagram of a data processing system in accordance with an illustrative embodiment.

[0014] Figure 6 illustrates a dislocation density graph of a titanium alloy after additive manufacturing according to an illustrative embodiment; and

[0015] Figure 7 Illustrated is a photomicrograph of a titanium alloy microstructure after additive manufacturing according to an illustrative embodiment. DETAILED DESCRIPTION

[0016] The illustrative examples recognize and take into account different considerations. For example, the illustrative examples recognize and take into account that a significant contribution to the yield strength of additively manufactured metals (e.g., titanium) comes from the dislocation density introduced into the material by the thermal cycling inherent in the rapid heating and cooling cycles of additive manufacturing (AM) processes. However, the dislocation density imparted by thermal cycling in AM is not as easily determined as the mechanical deformation of wrought materials.

[0017] The illustrative embodiments also recognize and take into account that rapid quantitative microstructure-based methodologies can use microstructure measurements as a way to reduce mechanical testing. To be useful, microstructure measurements must have a certain fidelity, timeliness, and cost-effectiveness. For example, transmission electron microscopy (TEM) methods are well suited for quantitative evaluation of titanium alloys, but can only quantitatively evaluate dislocation density within an order of magnitude above a certain threshold. In addition, TEM methods are expensive, time-consuming, and destructive measurements. Similarly, X-ray diffraction is another useful method, but it also has accuracy and resolution limitations and requires flat samples.

[0018] The illustrative embodiments also recognize and take into account that resistivity measurements provide a relatively quick and inexpensive way to non-destructively determine the microstructural input of titanium alloys for AM production.

[0019] Thus, the illustrative embodiments provide methods for characterizing microstructures and using electrical resistivity as an evaluation technique for determining dislocation density in metals used for additive manufacturing (e.g., titanium) as part of a combined approach to assessing yield strength. For a multiphase additive material with a defined dislocation density, the electrical resistivity is calibrated as a function of phase fraction and phase composition. After undergoing multiple AM ​​heating and cooling cycles, the phase fraction, phase composition, and resistivity of the material are characterized. Dislocation density is determined from the resistivity after AM, taking into account the effects of phase fraction and phase composition.

[0020] Figure 1 1 is a block diagram illustrating a material strength assessment system for additive materials according to an illustrative embodiment. System 100 includes an additive manufacturing (AM) system 102 that performs an AM process on additive material 120. AM refers to the process of joining materials to create an object from three-dimensional model data. This is typically, but not necessarily, performed layer by layer. Some examples of AM include three-dimensional (3-D) printing, rapid prototyping, and direct digital manufacturing (DDM).

[0021] The controller 104 can prepare digital data representing a 3-D object for printing and control the operation of the AM system 102. In this example, the controller 104 is located within the AM system 102, but can also be external to the AM system 102 and communicate via wired and / or wireless communications. Control functions can also be distributed among units, and not all control functions can be within the AM system 102. For example, a stand-alone unit (such as a personal computer or workstation) or a processing unit within a supply source (such as a cartridge) can provide some control or data storage capabilities.

[0022] The heating element 106 provides heat for the thermal cycle of the AM process. The heat provided by the heating element 106 is sufficient to put the additive material 120 into a flowable state for use in the AM process.

[0023] The positioner 108 or other suitable movement device controls the movement and position of the dispenser head 110 during the extrusion of the additive material 120 during the AM process.

[0024] Additive material 120 has properties that can change during repeated, rapid heating and cooling thermal cycles during the AM process. These properties include dislocation density 122, resistivity 124, phase fraction 126, and phase composition 128. These properties and their response to thermal cycling will depend on the material in question. In one embodiment, additive material 120 is titanium or a titanium alloy.

[0025] Phase fraction 126 describes the relative amounts of the different phases present in additive material 120. For example, the titanium alloy Ti-6Al-4V has two phases, α and β. Generally, alloys do not have a single melting point, but rather melt within a range of temperatures. The α phase is characterized as a solid solution, in which some atoms of a first element are dissolved in a second element. Conversely, the β phase is a solid solution, in which some atoms of a second element are dissolved in the first element.

[0026] Phase composition 128 describes the individual chemical compositions of each phase. The alpha phase has one chemical composition, while the beta phase has another. The overall average is the alloy's "chemical composition."

[0027] Different phases of the additive material 120 have different resistivity values, which are also chemically sensitive.

[0028] Resistivity 124 can be measured using an ohmmeter 130, which can be, for example, a four-point contact probe ohmmeter. Phase fraction can be determined using micrographs 140 (e.g., scanning electron micrographs 142, optical micrographs 144, or X-ray diffraction 146). Phase composition 128 can be determined using spectroscopy 150, which can be, for example, electron dispersion spectroscopy 152 or inductively coupled plasma spectroscopy 154.

[0029] The ohmmeter 130, micrograph 140, and spectrum 150 can be controlled and analyzed by a computer system 160. The computer system 160 includes a calibration algorithm 162, a characterization algorithm 164, and a dislocation density algorithm 166. The calibration algorithm 162 first generates a calibration curve for the additive material 120 to evaluate the phase fraction and chemical composition at a near-constant level of dislocation density. This can be achieved by heat treating the material to reduce the dislocation density to a specified minimum value, which contributes negligibly to the material's strength. Changes in the phase content (α and β) can be evaluated, as can changes in the individual chemical elements that have a minor effect on resistivity, independent of dislocation density. The baseline calibration can also create factors to evaluate vacancies and stacking faults, if their contribution is significant and exceeds a specified threshold.

[0030] To evaluate additive materials after they undergo AM thermal cycling, they are characterized using characterization algorithms 164 for phase fraction, chemical composition (both specific and bulk), and resistivity. This characterization can be performed on a per-sample basis or on a broader scale, where the boundaries or distribution of a range of materials are evaluated. Phase fraction can be determined using a variety of techniques, depending on the desired length scale and resolution, including but not limited to measurements from scanning electron micrographs 142, optical micrographs 144, and X-ray diffraction 146.

[0031] Phase composition may also be determined by a variety of techniques, including but not limited to electron dispersive spectroscopy (EDS) 152 and inductively coupled plasma spectroscopy (ICP) 154 , depending on the resolution desired.

[0032] Resistivity can be determined by, but is not limited to, using an ohmmeter (e.g., ohmmeter 130) having sufficient resolution to determine resistivity variations related to phase composition, chemistry, and dislocation density. In an embodiment, ohmmeter 130 includes a multi-point (e.g., 4-point) contact probe with which multiple measurements are taken to account for inherent probe variability.

[0033] Based on the characterization of the additive material 120 after AM cycling, the dislocation density algorithm 166 applies empirical relationships from the scientific literature to determine dislocation density. By comparing the baseline resistivity with the resistivity after AM thermal cycling, the contribution of dislocation density can be determined. Calibration and characterization of the additive material 120's microstructure isolate the effects of changes in phase fraction and chemical composition that result from variations in properties unique to additively manufactured materials.

[0034] The calibration algorithm 162, characterization algorithm 164, and dislocation density algorithm 166 enable the computer system 160 to transform the computer system into a specialized computer system compared to currently available general computer systems that do not have the means to perform dislocation density and strength evaluations.Figure 1 Computer system 160.

[0035] Figure 2 is a flow chart illustrating a method flow for evaluating the dislocation density of an additive material according to an illustrative embodiment. The method 200 begins by calibrating the baseline resistivity of the additive material (step 202). This step is performed using an additive material having a dislocation density at a near constant level. In an embodiment, the resistivity is calibrated for an additive material that has been heat treated to reduce the dislocation density to a specified minimum value (see below). Figure 3 ).

[0036] Once the baseline value has been calibrated, the additive material undergoes multiple heating and cooling cycles during AM (step 204). During the repeated, rapid heating and cooling thermal cycles, dislocations and dislocation density in the additive material increase, thereby increasing the strength of the material. Figure 6 Illustrated are dislocation density maps of titanium alloys based on micrographs of low dislocation density (graph 602) and high dislocation density (graph 604).

[0037] The increase in dislocation density in multiphase additive materials, such as titanium alloys, is similar to the increase in dislocations in wrought materials, but in this case the dislocations are created by thermal cycling rather than mechanical folding or rolling.

[0038] After the AM process is completed, the additive material is characterized for phase fraction, phase composition, and resistivity (step 206 ).

[0039] Then, after AM, the method 200 determines the dislocation density of the additive material based on the resistivity, taking into account the effects of the phase fraction and phase composition determined by the characterization (step 208). The calibration before AM and the characterization after AM allow the effects of phase content and chemical composition on the resistivity variation to be separated from the results.

[0040] Figure 3 is a flow chart illustrating a method flow for calibrating the baseline resistivity of an additive material according to an illustrative embodiment. Figure 2 The method of calibrating the baseline curve is described in detail in step 202 of FIG. Figure 3 The sequence of steps shown in , however, these steps can occur in a different order.

[0041] To minimize the effect of dislocation density on material strength, the additive material is heat treated to reduce the dislocation density to a specified minimum value (step 302 ).

[0042] In the case of an additive material having a dislocation density approaching a constant level, the method 300 determines the phase fraction and phase composition in the additive material (step 304). This step allows for establishing a resistivity baseline as a function of the phase fraction and phase composition.

[0043] The method 300 may also include determining the contribution of vacancies and stacking faults to the resistivity, if significant (step 306 ).

[0044] With the initial characterization in place, the resistivity of the additive material is measured to establish a baseline value (step 308 ).

[0045] Figure 4 A method for using dislocation density in calculating the material strength of titanium alloys according to an illustrative embodiment is illustrated. Material strength can be predicted based on several physical parameters. For AM-produced Ti-6Al-4V, the general equation for predicting strength is given in Equation 1 below.

[0046] Equation 1:

[0047]

[0048] Where σO is the intrinsic strength of the material, σ ss is the intrinsic strength of the solid solution, F V represents the volume fraction of a specified phase / microstructure, C i represents the prefactor of some term, t 特征 is the thickness of specific features within the microstructure (i.e., α-lamellae, β-ribs, and colony scale factors) (see Figure 7 ), α is the prefactor term, M is the Taylor factor, G is the shear modulus, and ρ is the dislocation density.

[0049] With the exception of the dislocation density, ρ, the parameters in Equation 1 are generally well known in the art and have been empirically characterized. As discussed above, current methods for determining dislocation density in additive materials are expensive, destructive, time-consuming, and unsuitable for rapid quantification in a manufacturing environment. The illustrative embodiments provide a cost-effective method that uses the resistivity of the additive material to determine this value and its reinforcement contribution as part of the evaluation represented by Equation 1.

[0050] like Figure 4 As shown, the resistivity is a function of the dislocation density p, the α:β volume fraction, and the percentage of elements in the alloy. Graph 402 illustrates the resistivity as a function of the percentage of elements (e.g., Al, V, Fe) determined from, for example, EDS / ICP. Graph 404 illustrates the resistivity as a function of the volume fraction of phases determined from, for example, scanning electron microscopy (SEM) or X-ray powder diffraction (XRD). By establishing these functions, it is possible to Figure 2The value of dislocation density is back-calculated from the resistivity during the calibration and characterization of steps 202 and 206, as shown in graph 406. The missing value of p can then be provided for a general evaluation of the Taylor hardening portion of Equation 1.

[0051] Now go to Figure 5 , which depicts an illustration of a block diagram of a data processing system according to an illustrative embodiment. Data processing system 500 may be used to implement one or more computers, such as Figure 1 The computer system 160 is configured to execute Figures 2-4 In this illustrative example, data processing system 500 includes communications framework 502, which provides communications between processor unit 504, memory 506, persistent storage 508, communications unit 510, input / output unit 512, and display 514. In this example, communications framework 502 may take the form of a bus system.

[0052] Processor unit 504 is used to execute instructions of software that may be loaded into memory 506. Processor unit 504 may be multiple processors, multiple processor cores, or some other type of processor, depending on the specific implementation. In one embodiment, processor unit 504 includes one or more conventional general-purpose central processing units (CPUs). In an alternative embodiment, processor unit 504 includes multiple graphics processing units (GPUs).

[0053] Memory 506 and permanent storage 508 are examples of storage devices 516. A storage device is any hardware capable of storing information, such as, but not limited to, at least one of data, program code in functional form, or other suitable information, either temporarily, permanently, or both temporarily and permanently. In these illustrative examples, storage devices 516 may also be referred to as computer-readable storage devices. In these examples, memory 516 may be, for example, random access memory or any other suitable volatile or non-volatile storage device. Persistent storage 508 may take a variety of forms, depending on the specific implementation.

[0054] For example, permanent storage 508 may include one or more components or devices. For example, permanent storage 508 may be a hard drive, flash memory, a rewritable optical disk, a rewritable magnetic tape, or some combination thereof. The media used by permanent storage 508 may also be removable. For example, a removable hard drive may be used for permanent storage 508. In these illustrative examples, communication unit 510 provides for communication with other data processing systems or devices. In these illustrative examples, communication unit 510 is a network interface card.

[0055] Input / output unit 512 allows for input and output of data to and from other devices that may be connected to data processing system 500. For example, input / output unit 512 may provide a connection for user input via at least one of a keyboard, a mouse, or other suitable input devices. Furthermore, input / output unit 512 may send output to a printer. Display 514 provides a mechanism for displaying information to a user.

[0056] Instructions for at least one of an operating system, applications, or programs may be located in storage devices 516, which are in communication with processor unit 504 via communications framework 502. The methods of the different embodiments may be performed by processor unit 504 using computer-implemented instructions, which may be located in a memory, such as memory 506.

[0057] These instructions are referred to as program code, computer usable program code, or computer readable program code that can be read and executed by a processor in processor unit 504. Program code in different embodiments may be embodied on different physical or computer-readable storage media, such as memory 506 or persistent storage 508.

[0058] Program code 518 is located in functional form on a selectively removable computer-readable medium 520 and can be loaded or transferred to data processing system 500 for execution by processor unit 504. Program code 518 and computer-readable medium 520 form computer program product 522 in these illustrative examples. In one example, computer-readable medium 520 may be computer-readable storage medium 524 or computer-readable signal medium 526.

[0059] In these illustrative examples, computer readable storage media 524 is a physical or tangible storage device used to store program code 518 rather than a medium that propagates or transmits program code 518. Alternatively, program code 518 may be transferred to data processing system 500 using computer readable signal media 526.

[0060] Computer readable signal media 526 may be, for example, a propagated data signal containing program code 518. For example, computer readable signal media 526 may be at least one of an electromagnetic signal, an optical signal, or any other suitable type of signal. These signals may be transmitted via at least one of a communication link, such as a wireless communication link, a fiber optic cable, a coaxial cable, an electrical wire, or any other suitable type of communication link.

[0061] The different components illustrated for data processing system 500 are not meant to provide architectural limitations to the manner in which different embodiments may be implemented. The different illustrative embodiments may be implemented in a data processing system including components in addition to or in place of those illustrated for data processing system 500. Figure 5 Other components shown can be varied from the illustrative examples shown. The various embodiments can be implemented using any hardware device or system capable of running program code 518.

[0062] Figure 6 Illustrated is a micrograph of a titanium alloy after additive manufacturing, according to an illustrative embodiment.

[0063] Figure 7 An illustration of a dislocation density plot of a titanium alloy after additive manufacturing is shown in accordance with an illustrative embodiment.

[0064] As used herein, the phrase "number" means one or more. When used with a list of items, the phrase "at least one" means that one or more different combinations of the listed items can be used, and only one of each item in the list may be required. In other words, "at least one" means that any combination of items and number of items from the list can be used, but not all items in the list are required. The item can be a specific object, thing, or category. As used herein, when used with respect to measurement, the term "substantially" or "approximately" is determined by ordinary skill in the art and is within acceptable engineering tolerances within the regulatory scheme of a given authority, such as, but not limited to, the Federal Aviation Administration Federal Aviation Regulations.

[0065] The flowcharts and block diagrams in the various described embodiments illustrate the architecture, functionality, and operations of some possible implementations of the apparatus and methods in the illustrative embodiments. In this regard, each block in a flowchart or block diagram may represent at least one of a module, segment, function, operation, or portion of a step. The steps shown in the flowcharts may occur in an order different from the specific order of the blocks shown.

[0066] Furthermore, the present disclosure includes implementations according to the following clauses:

[0067] Clause 1. A computer-implemented method (200) for evaluating the strength of a material for additive manufacturing, the method comprising:

[0068] calibrating (202) a baseline resistivity of a multiphase additive material as a function of phase fraction and phase composition for a set dislocation density, wherein each phase of the material has a different resistivity value, via a plurality of processors;

[0069] characterizing the phase fraction, phase composition, and resistivity of the additive material through a plurality of processors after the additive material undergoes multiple heating and cooling cycles during additive manufacturing; and

[0070] Based on the resistivity after additive manufacturing, a dislocation density of the additive material is determined (208) by a plurality of processors taking into account the effects of the phase fraction, phase composition, and resistivity determined by the characterization.

[0071] Clause 2. The method of clause 1, wherein the baseline resistivity is calibrated for an additive material that has been heat treated (302) to reduce the dislocation density to a specified minimum value.

[0072] Clause 3. The method of any of the preceding clauses, wherein calibrating the baseline resistivity further comprises determining (304) a phase fraction and a phase composition in the additive material.

[0073] Clause 4. The method of any of the preceding clauses, wherein calibrating the baseline resistivity further comprises determining (306) contributions of vacancies and stacking faults to the resistivity.

[0074] Clause 5. The method of any preceding clause, wherein the phase fraction of the additive material is determined using at least one of:

[0075] Scanning electron micrographs (142);

[0076] Light micrograph (144); or

[0077] X-ray diffraction (146).

[0078] Clause 6. The method of any preceding clause, wherein the phase composition of the additive material is determined using at least one of:

[0079] Electron dispersion spectroscopy (152); or

[0080] Inductively coupled plasma spectroscopy (154).

[0081] Clause 7. The method of any preceding clause, wherein the resistivity is determined using a multi-point contact probe ohmmeter (130).

[0082] Clause 8. The method of any preceding clause, wherein the additive material comprises a titanium alloy.

[0083] Clause 9. A system (160) for evaluating the strength of an additively manufactured material, the system comprising:

[0084] bus system (502);

[0085] a storage device (508) coupled to the bus system, wherein the storage device stores program instructions (518); and

[0086] A plurality of processors (504) coupled to the bus system, wherein the plurality of processors execute program instructions to:

[0087] calibrating (202) a baseline resistivity of a multiphase additive material for a set dislocation density as a function of phase fraction and phase composition, wherein each phase of the material has a different resistivity value;

[0088] characterizing (206) the phase fraction, phase composition, and resistivity of the additive material after the additive material has been subjected to multiple heating and cooling cycles during additive manufacturing; and

[0089] Based on the resistivity after additive manufacturing, the dislocation density of the additively manufactured material is determined (208) taking into account the effects of the phase fraction and phase composition determined by characterization.

[0090] Clause 10. The system of clause 9, wherein the baseline resistivity is calibrated for an additive material that has been heat treated (302) to reduce dislocation density to a specified minimum value.

[0091] Clause 11. The system of any of clauses 9-10, wherein the instructions executed by the processor to calibrate the baseline resistivity further comprise instructions to determine (304) a phase fraction and a phase composition in the additive material.

[0092] Clause 12. The system of any of clauses 9-11, wherein the instructions executed by the processor to calibrate the baseline resistivity further include instructions to determine (306) contributions of vacancies and stacking faults to the resistivity.

[0093] Clause 13. The system of any of clauses 9-12, wherein the phase fraction of the additive material is determined using at least one of:

[0094] Scanning electron micrographs (142);

[0095] Light micrograph (144); or

[0096] X-ray diffraction (146).

[0097] Clause 14. The system of any of clauses 9-13, wherein the phase composition of the additive material is determined using at least one of:

[0098] Electron dispersion spectroscopy (152); or

[0099] Inductively coupled plasma spectroscopy (154).

[0100] Clause 15. The system of any of clauses 9-14, further comprising a multi-point contact probe ohmmeter (130) configured to determine resistivity.

[0101] Clause 16. The system of any of clauses 9-15, wherein the additive material comprises a titanium alloy.

[0102] Clause 17. A computer program product (522) for evaluating the strength of an additively manufactured material, the computer program product comprising:

[0103] A non-transitory computer-readable storage medium (508) having program instructions (518) embodied therein, the program instructions being executable by the plurality of processors (504) to cause the computer to perform the following steps:

[0104] calibrating (202) a baseline resistivity of a multiphase additive material for a set dislocation density as a function of phase fraction and phase composition, wherein each phase of the material has a different resistivity value;

[0105] characterizing (206) the phase fraction, phase composition, and resistivity of the additive material after the additive material has undergone a number of heating and cooling cycles during additive manufacturing; and

[0106] Based on the resistivity after additive manufacturing, the dislocation density of the additive material is determined (208) taking into account the effects of the phase characteristics and phase composition determined by the characterization.

[0107] Clause 18. The computer program product of clause 17, wherein the baseline resistivity is calibrated for an additive material that has been heat treated (302) to reduce dislocation density to a specified minimum value.

[0108] Clause 19. The computer program product of any of clauses 16-17, wherein the instructions for calibrating the baseline resistivity further comprise instructions for determining (304) a phase fraction and a phase composition in the additive material.

[0109] Clause 20. The computer program product of any of clauses 16-18, wherein the instructions for calibrating the baseline resistivity further comprise instructions for determining (306) contributions of vacancies and stacking faults to the resistivity.

[0110] The descriptions of the various illustrative examples have been presented for purposes of illustration and description and are not intended to be exhaustive or to limit the examples to the disclosed forms. Many modifications and variations will be apparent to those skilled in the art. Further, the various illustrative examples may provide different features compared to other contemplated examples. The selected one or more examples are selected and described in order to best explain the principles and practical applications of the examples and to enable others skilled in the art to understand that the disclosure of the various examples may be adapted to the specific intended use.

Claims

1. A computer-implemented method for evaluating the strength of a material for additive manufacturing, the method comprising: calibrating, by a plurality of processors, a baseline resistivity of a multiphase additive material as a function of phase fraction and phase composition for a set dislocation density, wherein each phase of the material has a different resistivity value; characterizing, via a plurality of processors, the phase fraction, phase composition, and resistivity of the additive material after the additive material undergoes a plurality of heating and cooling cycles during the additive manufacturing process; and Based on a comparison of the baseline resistivity and the resistivity after additive manufacturing thermal cycling, a dislocation density of the additive material is determined by a plurality of processors taking into account the effects of phase fraction and phase composition determined by characterization.

2. The method of claim 1 , wherein the baseline resistivity is calibrated for an additive material that has been heat treated to reduce dislocation density to a specified minimum value.

3. The method of any one of the preceding claims, wherein calibrating the baseline resistivity further comprises determining a phase fraction and a phase composition in the additive material.

4. The method of any one of claims 1-2, wherein calibrating the baseline resistivity further comprises determining contributions of vacancies and stacking faults to the resistivity.

5. The method according to any one of claims 1-2, wherein the phase fraction of the additive material is determined using at least one of: Scanning electron micrographs; Light micrograph; or X-ray diffraction.

6. The method according to any one of claims 1-2, wherein the phase composition of the additive material is determined using at least one of the following: Electron dispersion spectroscopy; or Inductively coupled plasma spectroscopy.

7. The method of any one of claims 1-2, wherein the resistivity is determined using a multi-point contact probe ohmmeter.

8. The method of any one of claims 1-2, wherein the additive material comprises a titanium alloy.

9. A system for evaluating the strength of an additively manufactured material, the system comprising: bus system; a storage device connected to the bus system, wherein the storage device stores program instructions; and a plurality of processors connected to the bus system, wherein the plurality of processors execute the program instructions to: calibrating a baseline resistivity of a multiphase additive material as a function of phase fraction and phase composition for a set dislocation density, wherein each phase of the material has a different resistivity value; characterizing the phase fraction, phase composition, and resistivity of the additive material after the additive material has undergone multiple heating and cooling cycles during additive manufacturing; and Based on a comparison of the baseline resistivity and the resistivity after additive manufacturing thermal cycling, the dislocation density of the additive material is determined, taking into account the effects of phase fraction and phase composition determined by characterization.

10. The system of claim 9, wherein the baseline resistivity is calibrated for an additive material that has been heat treated to reduce dislocation density to a specified minimum value.

11. The system of any one of claims 9-10, wherein the instructions executed by the processor to calibrate the baseline resistivity further include instructions to determine a phase fraction and a phase composition in the additive material.

12. The system of any one of claims 9-10, wherein the instructions executed by the processor to calibrate the baseline resistivity further include instructions to determine contributions of vacancies and stacking faults to resistivity.

13. The system of any one of claims 9-10, wherein the phase fraction of the additive material is determined using at least one of: Scanning electron micrographs; Light micrograph; or X-ray diffraction.

14. The system of any one of claims 9-10, wherein the phase composition of the additive material is determined using at least one of: Electron dispersion spectroscopy; or Inductively coupled plasma spectroscopy.

15. The system of any one of claims 9-10, further comprising a multi-point contact probe ohmmeter configured to determine resistivity.

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