Additive manufacturing method and system

Abstract

A method (100) of additively manufacturing a product comprises performing one or more additive manufacturing steps on a material to build the product having a target microstructure (110); obtaining atomic data relating to the location of atoms within the material during the additive manufacturing steps (130); performing an analysis of the material (140) and adjusting the additive manufacturing steps in response to a detected microstructure being substantially different to at least the target microstructure (150). The analysis (140) comprises performing a calculation on the atomic data to generate a plurality of mathematical functions relating to the spatial distribution of the atoms according to one or more predefined parameters (320); calculating a statistical distribution of the generated mathematical functions (330); determining the detected microstructure of the material based on the statistical distribution (340); and comparing the detected microstructure to the target microstructure (150). A system (600) is also provided.

Description

Additive Manufacturing Method and SystemCross-Reference to Priority Application

[0001] This application claims priority to Australian Provisional Patent Application No. 2024901771 filed on 12 June 2024, the contents of which are hereby incorporated by reference in their entirety.Field of the Disclosure

[0002] The disclosure relates to a method of additive manufacturing and an additive manufacturing system. The disclosure has been developed primarily for use in an additive manufacturing process and will be described hereinafter by reference to this application.Background of the Disclosure

[0003] The following discussion of the prior art is intended to present the disclosure in an appropriate technical context and allow its advantages to be properly appreciated. Unless clearly indicated to the contrary, however, reference to any prior art in this specification should not be construed as an express or implied admission that such art is widely known or forms part of common general knowledge in the field.

[0004] Additive manufacturing (AM) is a multi-stage, highly complex processing technology, where products are built layer upon layer by depositing a material according to a computer design file. Due to this process, AM is frequently used to manufacture products having a complex geometry or shape. Many products produced by AM need to be qualified or certified before they can be sold and used. Products may also need to comply with requirements of the product design. This involves testing the physical, mechanical, chemical or other properties of the product to ensure compliance. These properties of an AM product are a consequence of its microstructure, which in turn is consequence of the arrangement of atoms of the product / material. For example, mechanical properties like ductility and tensile strength, and chemical properties like corrosion resistance, are a consequence of the particular arrangements of the constituent atoms of the material. The critical parameters of these atomic arrangements are their spacings, symmetries and chemical distributions. These determine the local electronic density of states which govern the state of the material. This informationtogether with the grain size, phase, defect and textural distribution information are typically key variables that control the macro-behaviour of solid-state materials.

[0005] Hence, the properties of a product are governed by the microstructure of its material, which is a consequence of the manufacturing process used to produce the material, especially where the material is being manufactured into a particular part or component of a machine or other device.

[0006] It is common AM practice to use test coupons for validating the microstructure and properties of AM parts or components. These test coupons are manufactured adjacent or near to the “real” engineering parts as located on the build plate or in the build volume. However, these test coupons often vary from the “real” part to be manufactured due to different spatial-temporal distributions of the atoms in the test coupon and real part. As the spatial-temporal thermal distributions are different, then the microstructure and the resultant properties must also be different, resulting in deviation in the properties of the test coupon compared to the real part.

[0007] These differences arise from two primary issues. One issue is that some AM processes involve the heating of feedstock materials (such as powders or wire) in laser powder bed fusion (L-PBF) or electron-beam PBF (E-PBF) or directed energy deposition (DED) using arcs or lasers. Due to the fundamentals of heat transfer based on thermal conduction, convection, and radiation, the spatial- temporal thermal distribution of the test coupon will be different to that of the real part by virtue of their different proximation in the build volume. Another major issue is that test coupons usually have a different geometry to the real part. Test coupons are typically parallelepiped volumes from which tensile test specimens are extracted via subtractive machining. In contrast, the real parts to be built will usually always have some complexity in their geometry or shape, such as complex tapers, re-entrant angles, thin walls, multi-sized voids, and complex curvatures. This difference in the geometries of the test coupons and the real parts will impose different rates of heat transfer throughout the respective structures. This means that they will have different spatial-temporal thermal signatures, as well as mechanical signatures. This results in different microstructures and so different resultant properties by virtue of different geometry.

[0008] This results in the properties in the test coupons are not being representative of the properties of the real part being manufactured. Consequently, achievement ornon-achievement of the target properties in the test coupon does not necessarily mean that the various corresponding AM process variables are delivering the same microstructure and properties for the part that is to be certified or qualified. The limitations introduced by the reliance on test coupons include additional cost, time and risk because the validations may not be relevant to the real part, especially where the real part has to be qualified or certified as meeting an industry or regulatory standard, or meeting a contractual design requirement.

[0009] As a result, the qualification and certification process for products produced through AM processes can be prolonged since the test coupon may pass qualification / certification but the real part does not. This means that the test coupon or real part needs to be further modified to achieve compliance. Consequently, there is waste of resources, in terms of the material consumed, as well as time to obtaining qualification / certification of the product.

[0010] It is also difficult to ensure that manufactured materials (and the parts or components made of that material) have the desired properties initially designed for when commencing the AM process. That is, the initial design of a material or part to be manufactured may not exhibit the desired properties due to variances in the microstructure that form during the AM process and cannot be designed around. This results in significant wastage of resources and time due to the need to produce and test prototypes or samples to check that the desired properties are exhibited by the manufactured material or part based on the initial design requirements. This creates further problems when the manufactured material or part needs to be certified or qualified before it can be used, as discussed above. Often, a manufacturer will design a specific part have certain requirements as to its mechanical, chemical and / or physical properties, and invest capital expenditure into AM equipment, but find it difficult to manufacture the specific part with satisfactory reliability, such that it can pass end-user certification and qualification requirements.

[0011] It is an object of the present disclosure to overcome or substantially ameliorate one or more of the disadvantages of prior art, or at least to provide a useful alternative. It is an object of the disclosure in at least one embodiment to provide an improved additive manufacturing method and system that is time and cost efficient, and consistently reliable, in producing products of a specific geometry that meet end user requirements for industrial certification and / or qualification. Consequently, at least one embodiment ofthe disclosure avoids wastage of resources and minimises the number or need to use test coupons to obtain certification or qualification of the product.Summary of the Disclosure

[0012] A first aspect of the disclosure provides a method of additively manufacturing a product comprising at least a first material, comprising: performing one or more additive manufacturing steps on the first material to build the product having a target microstructure; obtaining atomic data relating to the location of atoms within the first material during the one or more additive manufacturing steps; performing an analysis of the first material, wherein the analysis comprises: performing a calculation on the atomic data to generate a plurality of mathematical functions relating to the spatial distribution of the atoms according to one or more predefined parameters; calculating a statistical distribution of the generated mathematical functions; determining a detected microstructure of the first material based on the statistical distribution; and comparing the detected microstructure to the target microstructure; and adjusting the one or more additive manufacturing steps in response to the detected microstructure being substantially different to at least the target microstructure.

[0013] In one or more embodiments, determining the detected microstructure comprises determining the concentration and / or arrangement of atoms according to the one or more predefined parameters from the statistical distribution. In one or more embodiments, the concentration of the atoms according to the one or more predefined parameters indicates a clustering effect of the atoms to exhibit the one or more properties of the material.

[0014] In one or more embodiments, the method comprises determining one or more properties of the material from the detected microstructure of the first material. In one or more embodiments, the one or more properties of the first material comprises one or more of tensile strength, shear strength, yield strength, ductility, malleability, combustibility, density, hardness, plasticity, elasticity, stiffness, fracture resistance, fatigue resistance, corrosion resistance, wear resistance, electrical conductivity, and both soft and hard magnetic properties.

[0015] In one or more embodiments, the plurality of mathematical functions comprises k-Nearest Neighbour (kNN) distributions. In one or more other embodiments, the plurality of mathematical functions comprises Short Range Order (SRO) parameters or values. In one or more further embodiments, the plurality of mathematical functions comprises probability functions representing local composition parameters. In one or more further embodiments, the plurality of mathematical functions comprises voxelisation of the spatial distribution of atoms.

[0016] In one or more embodiments, the method comprises assigning weights to one or more of the mathematical functions. In one or more embodiments, the method comprises calculating the statistical distribution based on the weighted mathematical functions.

[0017] In one or more embodiments, the method comprises selecting one or more of the plurality of mathematical functions as a representative mathematical function of a subset of mathematical functions. In one or more embodiments, the method comprises calculating the statistical distribution based on the representative mathematical functions.

[0018] In one or more embodiments, the method comprises obtaining the atomic data using a microscopy method. In one or more embodiments, the microscopy method comprises one or more of light-optical microscopy, scanning electron microscopy (SEM), transmission electron microscopy (TEM), atom probe microscopy (APM), X-ray scattering and neutron scattering. In one or more embodiments, the SEM method comprises at least one of backscattered electron imaging (BSI), secondary electron imaging (SEI), transmission Kikuchi diffraction (TKD), electron backscattered diffraction (EBSD) and energy dispersive X-ray spectroscopy (EDXS). In one or more embodiments, the TEM method comprises at least one of scanning TEM (STEM), electron energy loss spectrometry (EELS), EDXS and a TEM based diffraction technique. In one or more embodiments, the APM method comprises at least one of mass spectrometry, field ion microscopy (FIM), field evaporation microscopy (FEM) and atom probe tomography (APT).

[0019] In one or more embodiments, the method comprises retrieving a sample of the first material and the atomic data is obtained from the sample. In one or more other embodiments, the sample is taken during the one or more additive manufacturing steps. In one or more embodiments, the sample is retrieved using a focussed ion beam (FIB)specimen extraction method. In one or more embodiments, the sample is retrieved using precision mechanical sectioning, further cutting or electrolytic polishing.

[0020] In one or more embodiments, the atomic data is obtained from the sensor data.

[0021] In one or more embodiments, the atomic data comprises data relating to the state of the material at one or more predetermined times and / or locations. The state of the material may relate to a thermal state of the material. In one or more embodiments, the atomic data comprises thermal data relating to the temperature of the atoms within the material based on their location over a predetermined time period. In one or more embodiments, the atomic data comprises atomic-scale microstructural data.

[0022] In one or more embodiments, local measurements of properties may be made from the material at one or more locations adjacent or in proximity to where the sample was obtained. These local measurements may be local mechanical property measurements, such as hardness, but may also comprise local magnetic property measurements, or electrical property measurements.

[0023] In one or more embodiments, the one or more predefined parameters comprises atomic species. In one or more other embodiments, the one or more predefined parameters comprises distances between a designated atom from one or more other atoms.

[0024] In one or more embodiments, the method comprises obtaining sensor data relating to the first material during the one or more additive manufacturing steps. In one or more embodiments, the sensor data is obtained directly from the first material during the one or more additive manufacturing steps. In one or more embodiments, the sensor data comprises spatial-temporal thermal data relating to the first material.

[0025] In one or more embodiments, the analysis of the first material comprises performing a calculation on the sensor data to generate a predicted microstructure of the first material and the analysis of the first material comprises comparing the detected microstructure to the predicted microstructure. In one or or more embodiments, the analysis of the first material comprises comparing the detected microstructure to the predicted microstructure and / or the target microstructure. That is, the comparison is made between the detected microstructure and the predicted microstructure as analternative to or in addition to comparing the detected microstructure to the target microstructure. In one or more embodiments, the one or more additive manufacturing steps are adjusted in response to the detected microstructure being substantially different to the predicted microstructure and / or the target microstructure.

[0026] In one or more embodiments, the obtaining and analysis steps are repeated after the adjusting step. In one or more embodiments, the obtaining, analysis and adjusting steps are repeated until the detected microstructure is substantially the same as the target microstructure and / or the predicted microstructure.

[0027] In one or more embodiments, the target microstructure is generated from an initial set of data relating to the material. In one or more embodiments, the initial set of data is derived from theoretical values relating to the material or measurements taken prior to formation of the material.

[0028] In one or more embodiments, the adjusting step comprises changing one of the additive manufacturing steps to modify the detected microstructure of the first material to the target microstructure. In one or more embodiments the adjusting step comprises modifying a first additive manufacturing step to modify the detected microstructure.

[0029] In one or more embodiments, there is at least a first additive manufacturing step and a second additive manufacturing step, wherein the adjusting step comprises performing the second additive manufacturing step on the first material to modify the detected microstructure of the first material to the target microstructure. In one or more embodiments, the second additive manufacturing step comprises a post-processing step after stoppage or completion of the first additive manufacturing step. In one or more embodiments, the post-processing step comprises at least one of hot isostatic pressing (HIP), thermal annealing or aging of the first material.

[0030] In one or more embodiments, the adjusting step comprises adjusting one or more parameters of the one or more additive manufacturing steps. In one or more embodiments, the one or more parameters comprise at least one of an operational parameter of the one or more additive manufacturing steps, a build parameter of the product and a design parameter of the product.

[0031] In one or more embodiments, the adjusting step comprises pre-treating the first material prior to the one or more additive manufacturing steps.

[0032] In one or more embodiments, the adjusting step further comprises modifying one or more build or design parameters of the product prior to repeating the one or more additive manufacturing steps.

[0033] In one or more embodiments, the product comprises at least a second material, wherein the method is also performed in relation to the second material. That is, the one or more additive manufacturing steps are performed on the second material for producing a second target microstructure, the sensor data obtaining, atomic data obtaining and analysis steps are performed in respect of the second material and the one or more additive manufacturing steps are adjusted to to modify the detected microstructure of the second material to the second target microstructure. In one or more embodiments, the one or more additive manufacturing steps are performed simultaneously on the first and second materials. In one or more embodiments, the one or more additive manufacturing steps are performed sequentially on the first and second materials.

[0034] In one or more embodiments, the first material comprises a metal or metal alloy. In one or more embodiments, the metal alloy comprises a multicomponent alloy or a medium and high entropy alloy (M / HEA). In one or more embodiments, the first material comprises a ceramic material.

[0035] A second aspect of the disclosure provides a system for additively manufacturing a product comprising at least a first material, comprising: one or more additive manufacturing machines for performing one or more additive manufacturing steps on the first material to build the product having a target microstructure; a control unit configured to control the one or more additive manufacturing machines; a data retrieval unit for obtaining atomic data relating to the location of atoms within the first material; and a processor configured to perform an analysis method of the first material, wherein the analysis method comprises:performing a calculation on the atomic data to generate a plurality of mathematical functions relating to the spatial distribution of the atoms according to one or more predefined parameters; calculating a statistical distribution of the generated mathematical functions; determining a detected microstructure of the first material based on the statistical distribution; and comparing the detected microstructure to the target microstructure; wherein the control unit is configured to adjust operation of the one or more additive manufacturing machines in response to the detected microstructure being substantially different to at least the target microstructure.

[0036] In one or more embodiments, the system comprises one or more sensors for obtaining sensor data relating to the first material. In one or more embodiments, the processor is configured to: perform a calculation on the sensor data to generate a predicted microstructure of the first material; and compare the detected microstructure to the predicted microstructure. In one or more embodiments, the control unit is configured to adjust operation of the one or more additive manufacturing machines in response to the detected microstructure being substantially different to the target microstructure and / or predicted microstructure.

[0037] In one or more embodiments, the control unit adjusts operation of the one or more additive manufacturing machines such that the detected microstructure of the first material is modified to the target microstructure.

[0038] In one or more embodiments, the one or more additive manufacturing machines modifies a first additive manufacturing step to modify the detected microstructure. In one or more embodiments, there is at least a first additive manufacturing step, and the one or more additive manufacturing machines performs a second additive manufacturing step on the first material to modify the detected microstructure. In one or more embodiments, the one or more additive manufacturing machines perform the second additive manufacturing step after stoppage or completion of the first additive manufacturing step.

[0039] In one or more embodiments, the control unit adjusts one or more operational parameters of the one or more additive manufacturing machines. In one or moreembodiments, the control unit adjusts one or more build parameters of the product or one or more design parameters of the product.

[0040] In one or more embodiments, the control unit controls a pre-treatment unit to pre-treat the first material prior to the one or more additive manufacturing steps.

[0041] In one or more embodiments, the one or more sensors obtain the sensor data directly from the first material during the one or more additive manufacturing steps. In one or more other embodiments, the one or more sensors are thermal sensors.

[0042] In one or more embodiments, the data retrieval unit obtains the atomic data directly from the first material during operation of the one or more additive manufacturing machines. In one or more other embodiments, the data retrieval unit obtains the atomic data from a sample of the first material taken during operation of the one or more additive manufacturing machines.

[0043] In one or more embodiments, the data retrieval unit is in communication with the one or more sensors to obtain the atomic data from the sensor data.

[0044] In one or more embodiments, the data retrieval unit comprises a microscopy instrument. In one or more embodiments, the data retrieval unit comprises one or more of a light-optical microscope, an electron microscope, an atomic force microscope (AFM), a scanning tunnelling microscope, an atom probe microscope, and X-ray microscope.

[0045] In one or more embodiments, the processor is configured to generate the target microstructure from an initial set of data relating to the first material. The initial set of data may be derived from theoretical values or from data acquired from the first material prior to performing the one or more manufacturing steps.

[0046] In one or more embodiments, the data retrieval unit obtains an additional set of the atomic data and the processor performs the analysis method on the additional set of the atomic data after the control unit adjusts operation of the one or more additive manufacturing machines. In one or more embodiments, the system is configured so that the data retrieval unit obtains the atomic data, the processor performs the analysis method and the control unit adjusts operation of the one or more additive manufacturingmachines iteratively until the detected microstructure is substantially the same as the target microstructure and / or predicted microstructure.

[0047] In one or more embodiments, the product comprises at least a second material, wherein the system is configured to perform the analysis method in relation to the second material. That is, the one or more additive manufacturing machines performs the one or more additive manufacturing steps on the second material for producing a second target microstructure, the data retrieval unit obtains atomic data relating to the location of atoms in the second material, the processor performs the analysis method in relation to the second material and the control unit adjusts operation of the one or more additive manufacturing machines in response to the detected microstructure of the second material being substantially different to the second target microstructure.

[0048] One or more embodiments of the second aspect may have the features of one or more embodiments of the first aspect of the disclosure stated above, where applicable. In particular, the analysis step may be performed in accordance with one or more embodiments of the first aspect.

[0049] Unless the context clearly requires otherwise, throughout the description and the claims, the words “comprise”, “comprising”, and the like are to be construed in an inclusive sense as opposed to an exclusive or exhaustive sense; that is to say, in the sense of “including, but not limited to”.

[0050] Furthermore, as used herein and unless otherwise specified, the use of the ordinal adjectives "first", "second", "third", etc., to describe a common object, merely indicate that different instances of like objects are being referred to, and are not intended to imply that the objects so described must be in a given sequence, either temporally, spatially, in ranking, or in any other manner.Brief Description of the Drawings

[0051] Preferred embodiments of the disclosure will now be described, by way of example only, with reference to the accompanying drawings in which:

[0052] Figure 1 is a schematic drawing of the various scales by which a material can be viewed;

[0053] Figure 2 is a schematic drawing illustrating the connection between the atomic arrangement of a material and the material’s properties;

[0054] Figure 3 is a schematic drawing of a method of additively manufacturing a product according to an embodiment of the disclosure;

[0055] Figure 4 is a schematic drawing of an APT method to acquire atomic data;

[0056] Figure 5 is a schematic drawing of a tomogram or map showing atoms within an exemplary material obtained by using the APT method of Figure 4;

[0057] Figure 6 is a schematic drawing of the analysis method used in the method of Figure 3;

[0058] Figure 6 is a schematic drawing of a typical spatial distribution of the map;

[0059] Figure 7 is a schematic drawing of a distribution of the spatial distributions ofFigure 6;

[0060] Figure 8(a) is a schematic drawing illustrating atom maps of copper in a material, simulated and experimental;

[0061] Figure 8(b) is a schematic graph of SRO values for a copper-copper pair;

[0062] Figure 8(c) is a schematic graph of a distribution of the spatial distributions relating to Figures 8(a) and 8(b);

[0063] Figure 9 is a schematic drawing illustrating a nearest neighbour relationships between atomic species;

[0064] Figures 10(a) to 10(d) are schematic graphs comparing SRO values in two different samples of the same alloy following different heat treatment (or manufacturing) processes;

[0065] Figure 11 is a schematic drawing illustrating a system for manufacturing a material according to another embodiment of the disclosure; and

[0066] Figure 12 is a schematic drawing illustrating a system for manufacturing a material according to a further embodiment of the disclosure.Preferred Embodiments of the Disclosure

[0067] The present disclosure will now be described with reference to the following examples which should be considered in all respects as illustrative and non-restrictive. Although the disclosure has been described with reference to specific examples, it will be appreciated by those skilled in the art that the disclosure may be embodied in many other forms. In the Figures, corresponding features within the same embodiment or common to different embodiments have been given the same reference numerals.

[0068] Products are manufactured to have certain physical, mechanical chemical or other properties derived from the material from which they are made of. These properties are exhibited by the material due to its microstructure. The microstructure of a material, especially a crystalline material, such as a ceramic or a metal, may be conceived of as a multi-scale set of parameters that define the phenomenological attributes of, variously, the grain structure (including the size, shape and texture), the various defect structures (dislocations, twins, etc.), any precipitation that may occur, any segregation across relevant microstructural interfaces, and any clustering or anticlustering that may occur throughout the atoms within the crystal. Multiple matrix phases may exist adding further complexity. This description of microstructure of a material may be represented schematically in Figure 1 , which illustrates the multi-scale conception ranges from the atomic scale 10 at the left through to what could be tens of microns (or larger) scale 20 at the right.

[0069] This microstructure is in turn dependent on the arrangement of atoms within the crystalline material. Hence, the phenomenological attributes of the microstructure are the result of the particular arrangements of the constituent atoms within the material. The critical parameters of these atomic arrangements are their spacings, symmetries and chemical distributions. These determine the local electronic density of states which govern the state of the material, as illustrated in Figure 2. That is, the electronic structure 50 (i.e. the arrangement of atoms) dictates the structural, electronic, magnetic, optical and mechanical properties 60 of the material. These properties 60 in turn influence directly other related properties 70 of the material or combined with another property to influence additional properties 80 of the material. For example, the mechanical properties 60a of the material directly determines the stress-strainrelationship or the bulk / Young's / shear modulus 70a and in combination with the optical properties 60b determines the optomechanical 80b of the material.

[0070] Combined with the grain size, phase, defect information and textural distribution information, the above set of data represents the key variables that control the macro-behaviour of materials, especially solid-state materials. This macro-behaviour results in a particular material exhibiting its properties, such as tensile strength, shear strength, yield strength, ductility, malleability, combustibility, density, hardness, plasticity, conductivity, elasticity, stiffness, fracture resistance, fatigue resistance, corrosion resistance and wear resistance. This list is not exhaustive, and other mechanical, physical or other material properties can be measured, such as nanoindentation to uniaxial tensile strength.

[0071] The details of the atomic arrangements (their spacings, symmetries and chemical distributions), grain size, phase, defect information and textural distribution information are effectively captured, quantitatively, in atomic nearest neighbour (NN) distributions. These distributions are frequency histograms of the first (and / or second, third, fourth, up to a kth) atomic nearest neighbours in a material. For example, the 1stto kthNN distributions of an intragranular (matrix) region of a grain in a crystalline material will, for example, reflect the tendency for clustering, segregation, precipitation, or other microstructural phenomena. Moreover, the 1stto kthNN distributions may change from region to region throughout the microstructure depending on the local thermodynamic and kinetic influences on the materials imposed by the manufacturing process. As another example, the 1stto kthNN distributions of a region of a grain boundary that contains segregation will be different from the 1stto kthNN distributions of an interior region of the grain, which will also be very different from regions containing crystal defects or second phase precipitates.

[0072] The present inventor has developed a novel methodology for performing an AM process, where the material of the product is analysed based on quantified atomic data as it is being formed. This enables a go / no-go thresholding of the parameter-set for a given AM build. Necessarily, the tailoring of the AM build involves multiple process steps related to the powder management, the AM build parameters and the postprocessing treatments. The embodiments of the present disclosure enable these various process steps to be modified arrive at a suite of final properties that meets any certification, qualification and / or contractual requirements. As such, with the disclosedembodiments, the AM process can be modified to change the properties of the end product as the product is being built; i.e. before completion of the AM process. In other words, the product can be modified mid-process since in the AM process, the product is gradually formed by depositing the material in layers.

[0073] Thus, in one embodiment, there is provided a method 100 of manufacturing a product comprising at least a first material (the product could be composed entirely of the first material or of several compositions of which one is the first material), as best shown in Figure 3. The method 100 comprises performing one or more additive manufacturing steps on the first material to build the product having a target microstructure at step 110. At step 130, atomic data relating to the location of atoms within the first material is obtained during the additive manufacturing step(s). At step 140, an analysis method 140 is then performed on the first material. The analysis method 140 is described in more detail below, but as a result of the analysis method, a target microstructure and a detected microstructure of the material are determined. In step 150, the additive manufacturing step(s) are adjusted in response to the detected microstructure being substantially different to the target microstructure.

[0074] The adjusting step 150 may involve one or more options, as indicated by boxes 160, 165, 167. In option 160, the adjusting step 150 involves one or more of the additive manufacturing steps being altered or changed to modify the detected microstructure of the first material to the target microstructure. This may involve modifying an operational parameter of the additive manufacturing step to modify the detected microstructure, such as how the additive manufacturing step is performed. Additionally or alternatively, one or more build parameters may be modified. For example, the build direction, layer thickness, scan speed, hatch distance, period of heat treatment or any combination of these parameters may be varied to modify the microstructure of the first material.

[0075] In option 165, the adjusting step 150 comprises performing a second additive manufacturing step on the first material to modify the detected microstructure of the first material to the target microstructure. The second additive manufacturing step may be performed after stoppage or completion of a first additive manufacturing step.

[0076] In option 167, the adjusting step 150 comprises adjusting one or more design parameters of the product prior to repeating the manufacturing steps. This could involve changing the geometry of the product (in terms of its three-dimensional shape), the sizeand / or dimensions of the product. In one or more embodiments, it may involve pretreating the first material prior to the one or more additive manufacturing steps.

[0077] Each of the options 160, 165, 167 may be selected alone or in any combination when performing the adjusting step 150. Where the adjusting step 150 involves changing the additive manufacturing step(s) or the design parameter(s) under options 160 and 167, respectively, the method 100 may further comprise repeating the now adjusted additive manufacturing step(s) at step 110, as indicated by arrow 170. The sensor data obtaining step 120, the atomic data obtaining step 130 and the analysis step 140 are performed again. Where the adjusting step 150 involves performing an additional manufacturing step under option 165, the method 100 may further comprise repeating the sensor data obtaining step 120, the atomic data obtaining step 130 and the analysis step 140, as indicated by the arrow 180. This repetition or iteration of these steps may continue until the detected microstructure is substantially the same as the target microstructure.

[0078] The target microstructure can be a set of predetermined parameters or characteristics that are intended for the material to exhibit a set of physical, mechanical, chemical or other properties. The target microstructure can be generated from an initial set of data relating to the material. The initial set of data may be derived from theoretical values based on the assumed composition of the material or derived from computational modelling of the material, or based on an initial set of a parameters (such as the atomic components of the material) prior to creation or formation of the material. The initial set of data may be obtained from measurements made during the creation or formation of the material.

[0079] Optionally, the method 100 may comprise at step 120 obtaining sensor data relating to the first material during the additive manufacturing step(s). The sensor data in step 120 may be obtained by one or more sensors for measuring data relating to the first material. This sensor data may comprise thermal data relating to the first material. That is, the thermal data comprises the temperature of the atoms in the first material based on their location over a predetermined time period. In practice, the sensor data generates an accurate spatial-temporal thermo-mechanico-chemico signature of the real part to be manufactured. This may be achieved by using in situ sensors that serve as an accurate time-temperature recorder during the build. This accuracy may be improved by also using computational models to fill in gaps in the sensor data. For example, thermalsignatures during the AM process can be integrated with any subsequent post-AM processing, such as hot isostatic pressing (HIP), thermal annealing or ageing to create a complete through-process spatial-temporal materials informatics for the whole real part. This through-process distribution serves as a generator function for driving a computational model of the part microstructure given the material composition and AM process parameters. Consequently, this results in a location-specific microstructural and / or physical feature (e.g. porosity) map across the part in a voxel resolution dictated by the end use. This 3D information, in turn, will be used to generate location-specific property maps throughout the part that can be used to assign boundaries for the expected performance of the AM parts using commercial finite element models.

[0080] The atomic data in step 130 may be obtained by either acquiring the data directly from the material prior to the manufacturing process or in situ during the manufacturing process. Alternatively, the atomic data is obtained by retrieving a sample of the material. The sample is retrieved preferably using a focussed ion beam (FIB) specimen extraction method. Other methods include the use of precision mechanical sectioning, further cutting, and electrolytic polishing may be used.

[0081] The atomic data in step 130 may be obtained from the sample through various methods, but preferably a microscopy method is used. The microscopy method may be light-optical microscopy, transmission electron microscopy (TEM), atom probe microscopy (APM), X-ray scattering or neutron scattering. The TEM method may comprise comprises at least one of scanning TEM (STEM), electron energy loss spectrometry (EELS), energy dispersive X-ray spectroscopy (EDXS) and a TEM based diffraction technique. The APM method may comprise mass spectrometry, field ion microscopy (FIM), field evaporation microscopy (FEM) and atom probe tomography (APT).

[0082] APT is preferred to be used to obtain the atomic data in step 130, as this method provides a unique range of information compared to other microscopy methods related to the local arrangement of the individual atomic species and there is a relative ease of computation. The extremely high chemical and spatial resolution analyses provided in APT enable the direct microscopic imaging of atomic arrangements including the nature of atomic clusters which can represent a subtle, but significant, local chemistry perturbation that may contain just a few atoms in a solid solution. This distinguishes APT from many other techniques because it provides direct three-dimensional chemicalinformation on this length scale, whereas other microscopies cannot achieve the combined levels of chemical and spatial resolution required for such measurements. Thus, APT eliminates the substantial effort of measuring the atomic data using diffuse scattering experiments. With APT, it is possible to reconstruct the position and chemical identity of tens-of millions of atoms from a material in 3D with a resolution <1 nm in the lateral direction, and < 0.1 nm in the depth direction. This allows nanoscale microstructural features to be imaged in great detail, and in some cases crystallographic information may be determined. Accurate APT data reconstructions effectively transform detector ion-hit positions into atomic spatial coordinates.

[0083] Figure 4 shows the process of obtaining the atomic data using an APT method. In APT, surface atoms from a needle-shaped specimen 200 under a positive electrical potential undergo ionisation due to stimulation from either a high voltage pulse or an ultra-short laser pulse. That is, the surface atoms are evaporated and the created ions 210 are accelerated by an electric field (generated by electrode 220) toward a position sensitive detector 230 that registers the impact position and the time taken for each ion 210 to travel from the specimen to the detector. This enables a time-of-flight mass spectrometry to identify the ionised atomic species. The impact position and order of the detection can be used to approximate the original location of each atomic species in the specimen 200. An exemplary tomogram illustrating the location of various atoms is shown in Figure 5, where the spatial locations of aluminium 260 (blue), 270 magnesium (green) and 280 copper (red) atoms in a metal alloy specimen are shown.

[0084] While the atomic data obtained through APT relates to the spatial location of the atoms, in other embodiments the atomic data may relate to the state of the material at one or more predetermined times and / or locations. The state of the material may relate to a thermal state of the material. For example, the atomic data may comprise thermal data relating to the temperature of the atoms within the material based on their location over a predetermined time period. In further embodiments, the atomic data comprises atomic-scale microstructural data.

[0085] Referring to Figure 6, the analysis method 140 is described in more detail. The analysis method 140 comprises performing a calculation on the atomic data to generate a plurality of mathematical functions relating to the spatial distribution of the atoms according to one or more predefined parameters at step 320. The method 140 then involves calculating a statistical distribution of the generated mathematical functions atstep 330 and determining the detected microstructure of the material based on the statistical distribution at step 340. At step 350, the detected microstructure is compared to the target microstructure. The result of this comparison is then used to determine whether to adjust at least one of the additive manufacturing steps, as discussed above. Optionally, the analysis method 140 comprises the step 360 of determining one or more properties of the material from the detected microstructure as described above.

[0086] Optionally, the method 140 may also comprise the step 310 of performing a calculation on the sensor data to generate a predicted microstructure of the first material. The predicted microstructure in step 310 can provide another point of comparison for the detected microstructure and / or the target microstructure. The predicted microstructure is generated by from computational models derived from the sensor data. Consequently, the predicted microstructure is based on the actual characteristics of the first material as the product is being built in the AM process.

[0087] In this embodiment, the plurality of mathematical functions comprises k- Nearest Neighbour (kNN) distributions, as they best quantify the above information that determines the macro-behaviour of a material and thus the physical and mechanical properties that are exhibited by that material, such as conductivity, tensile strength, ductility, and so on. However, in other embodiments, the plurality of mathematical functions may comprise Short Range Order (SRO) values, probability functions representing local composition parameters or voxelisation of the spatial distribution of atoms. Each of these alternative mathematical functions also describe or indicate the macro-behaviour resulting in the above physical and mechanical properties.

[0088] By using a distribution of generated mathematical functions, the overall microstructure of the material can be viewed holistically. For example, using a distribution of kNN distributions (which may also be called a mosaic of kNN distributions) allows full microstructural picture of the material to be constructed for analysis. This means that patterns of particular concentrations of atoms can be recognised, which indicate that the material is likely or will have a particular microstructure at those locations, and thus exhibit a particular physical or mechanical property. For example, the addition of copper atoms at certain locations within a metal alloy will control the strength and conductivity at those locations in the metal alloy. In other words, one can determine the microstructure by determining the local arrangement of atoms according to the one or more predefined parameters from the statistical distribution. The concentration of atomsmay indicate a clustering or anti-clustering effect that results in the material exhibiting (where there is a clustering effect) or not exhibiting (where there is an anti-clustering effect) a particular property or set of properties. Other possibilities include segregation effects as in segregation to a microstructural interface such as a grain boundary or a defect, or alternatively, a near random solute distribution. Thus, this method 100 can validate the location-specific microstructure across a real world part composed of the material at multiple length-scales (nm to mm)

[0089] The predefined parameters may comprise atomic species, groups or pairings of different atomic species and / or distances between a designated atom from one or more other atoms. For example, in an aluminium alloy containing magnesium and silicon, the mathematical functions are generated according to the spatial distribution of magnesium atoms and / or silicon atoms. That is, there can be a set of mathematical functions for the spatial distribution of magnesium atoms and / or a set of mathematical functions for the spatial distribution of silicon atoms. As another example, the mathematical functions are generated according to the distances between the magnesium atoms and / or silicon atoms. Again, there can be a set of mathematical functions for the distances between magnesium atoms only, a set of mathematical functions for the distances between silicon atoms only and / or a set of mathematical functions for the distances between magnesium atoms and silicon atoms. As a further example, in an aluminium-copper-magnesium alloy, there can be a set of mathematical functions for the spatial distribution of aluminium atoms only, a set of mathematical functions for the spatial distribution of copper atoms only, a set of mathematical functions for the spatial distribution of magnesium atoms only, a set of mathematical functions for the spatial distribution of aluminium to copper atoms, a set of mathematical functions for the spatial distribution of aluminium to magnesium atoms, a set of mathematical functions for the spatial distribution of copper to magnesium atoms, a set of mathematical functions for the spatial distribution of aluminium to magnesium or copper atoms, etc.

[0090] In addition, depending on the predefined parameters selected, it is also possible to assign weights to one or more particular mathematical functions when calculating the distribution of mathematical functions. For example, it may be desirable for the material to exhibit a particular property (like conductivity or ductility) at a particular location of the material. Consequently, the mathematical functions, like kNN distributions, can be assigned weighting to place emphasis when calculating thedistribution of kNN distributions or when examining the distribution of kNN distributions to determine if the material will exhibit the desired property at the desired location of the material.

[0091] A mathematical function in a subset of mathematical functions may also be taken or selected as a representative of the subset; this so-called representative mathematical function can then be used as a substitute or proxy for the subset when calculating the distribution of mathematical functions. The representative mathematical function is generally selected from a wide sample of mathematical functions in the subset, and is usually indicative of or has significance in the material exhibiting a particular microstructure and hence property. Consequently, the use of a representative mathematical function may reduce the need for calculating each mathematical function in the subset. For example, instead of calculating the NN distribution from the 1stNN distribution up to the 100thNN distribution, where 1 < k < -250, a NN distribution, like the 34thNN distribution, may be selected as the representative kNN distribution and is generated for that location and / or atom type (atomic element). When the distribution of kNN distributions is calculated, the 34thNN distribution is used for the subset. This may be repeated for each location and / or atomic element. Accordingly, the distribution of kNN distributions can be calculated more quickly while still retaining its usefulness as an indicator of the microstructure (and hence properties) of the material.

[0092] In further embodiments, the representative mathematical function may be used as an effective proxy for the entire microstructure, and thus all or most of the microstructural attributes shown in Figure 1. This involves giving the representative mathematical functions more weight in assessing the distribution of mathematical functions to determine the microstructure of the material. For example, the 34thNN distribution may be shown to be particularly sensitive or significant in indicating whether a particular microstructure is present in the material and thus a particular property will be exhibited. Thus, the 34thNN distribution is given more weight in the distribution of kNN distributions when the microstructure is determined in step 140.

[0093] The distribution of the spatial locations of the atoms can be described by a mathematical function. In one embodiment, the kNN distribution of the atoms may be chosen. That is, the distances from a designated atom to its 1stnearest neighbour (NN) atom, its 2ndNN atom, its 3rdNN atom, up to the kth NN atom. This may be repeated for each atom. In the case of the exemplary tomogram of Figure 5, the kNN distributionsmay comprise the kNN distribution of a designated aluminium atom to other aluminium atoms (the distances from the designated aluminium atom to its 1stNN aluminium atom, its 2ndNN aluminium atom, its 3rdNN aluminium atom, up to the kth NN aluminium atom); the kNN distribution of a designated aluminium atom to other atoms of any type; the kNN distributions of a designated aluminium atom to other atoms of one type (magnesium or copper); and the kNN distribution of a designated aluminium atom to magnesium or copper atoms. The kNN distributions may be repeated for each aluminium atom, as well as repeated for each atom type; i.e. the kNN distributions of a designated magnesium or copper atom to other magnesium atoms, aluminium atoms, copper atoms, atoms of any type; atoms of one type or atoms of two types (aluminium and copper for the designated magnesium atom and aluminium and magnesium for the designated copper atom).

[0094] Figures 7(a) and 7(b) illustrate frequency histograms illustrating an exemplary set of kNN distributions 370, 375, 377 for an aluminium-copper-magnesium (Al-Cu-Mg) alloy, where the alloy is an AI-1.1Cu-1 ,7Mg (at. %) alloy aged for 60 seconds at 150°C, and k = 5 (i.e. 5thnearest neighbour or 5NN distributions). The frequency histogram in Figure 7(a) shows the 5NN distribution 370 for copper (i.e. a designated copper atom to other copper atoms), the 5NN distribution 375 for magnesium (i.e. a designated magnesium atom to other magnesium atoms) and the 5NN distribution 377 for both atoms (i.e. a designated atom, which can be magnesium or copper, to the other atoms). Each of the 5NN distribution curves are compared to random distributions of their respective atom-pairs (distribution 380 for copper-copper, distribution 385 for magnesium-magnesium and distribution 387 for atom-atom), showing the clustering of copper and magnesium atoms in these distributions are the result of a particular arrangement and are not simply randomly distributed. These NN distributions may be repeated for each NN for k = 1 up to a selected kthvalue. Figure 7(b) shows the cumulative frequency histogram for the 5NN distributions 370, 375, 377 showing the total number of atoms in each distribution fulfilling the threshold distance along the x-axis.

[0095] Referring to Figure 8, an exemplary distribution of kNN distributions is shown where a holistic view or overview of the material can be observed from the distribution. Figure 8(a) shows two atom maps for copper for two sets of data, one based on a computer simulated model and the other based on experimental data. The atom maps show the distribution of copper atoms in the material. Figure 8(b) is a graph showing the SRO values for copper-copper pairs for the simulated model. The SRO values assist in filtering out Figure 8(c) the distributions of kNN distributions for copper-copper pairs, withthe distribution 390 of kNN distributions for the simulated model are in red, while the distribution 400 of the kNN distributions for the experimental data are in black. Both distributions use k values of 1 < k < 100. As shown in this Figure, the distribution 400 for the experimental data indicates that there is in fact a lower frequency of kNN distributions compared to the distribution 390 for the simulated model. The distribution 400 can be expanded to other atomic species or combinations of atomic species in the alloy. The distribution(s) 400 can thus create a three-dimensional picture or map of the kNN distributions for the material. From this distribution, concentrations or clustering of selected atomic species can be identified in various regions of the material. The distribution 400 is an example in which there can be an assessment of whether there is any concentration of atoms or grouping of arrangement of atoms (including atoms of more than one type) to indicate that a particular microstructure is present, and thus the exhibition of a particular property associated with that microstructure. For example, it is known that the clustering of copper (Cu) and magnesium (Mg) atoms leads to the strengthening of Al-Cu-Mg 2xxx series alloys. The state of clustering is directly linked to the kNN distributions. Hence, by calculating the distribution of the kNN distributions of the atomic species, Cu and Mg, and to combinations of these atomic species, the clustering effect can be easily and quickly determined for the entire alloy. Consequently, the strength of the Al-Cu-Mg alloy may be easily assessed.

[0096] It has been found that producing multiple kNN distributions from 1 < k < -250 is sufficient to provide a distribution of kNN distributions to determine the microstructure of the material. However, it is possible to use a k value less than 100. It is preferred to use using all -250 of these kNN distributions as they provide a rich assessment of the solute (atom) distribution in the material. Taking kNN distributions from 1 < k < -250 at different locations in the microstructure builds an atomistic NN mosaic distribution or function that can be weighted. For example, if a microstructure is thought to comprise of grains having the following microstructural constituents: (i) matrix or intragranular regions, (ii) grain boundaries, (iii) precipitates, (iv) defect structures of some type, and (v) other microstructural constituents, a weighted function of the, in this example, five separate kNN distributions from 1 < k < -250 (actually five hundred distributions) can be used to describe the microstructural state. There would be various ways to weight the five distributions, such as volume fraction of the various microstructural constituents, solute fraction at these microstructural constituents, etc.

[0097] A similar process may be used where a different mathematical function is chosen, such as SRO values or local composition probabilities. Short-range order (SRO) is used in crystallography as a quantitative measure of the relative tendency for the constituent elements in a material to deviate from a random distribution. Specifically, it is a measure of the tendency for certain atomic species to exhibit particular short-range NN relationships. These relationships may be either random 500, preferred ('clustering' 510), or non-preferred ('anti-clustering' 520), as shown in Figure 9. The SRO value alpha (a) indicates how the atoms of a particular element (also called solutes) are distributed within a material. Generally, where a = 0, the solutes are distributed randomly. Where a > 0 the solutes have preferred interaction or “clustering” (tendency towards being grouped together) and when a < 0, the solutes tend not to interact or have an anticlustering effect. The SRO value a thus indicates how close or far apart the solutes are within a material, and enables a probability to be assigned to spatial relationship at a certain location in the material. The SRO values can be determined for different shells of the crystal.

[0098] An example of a distribution of SRO values is shown in Figures 10(a) and 10(b), where the distribution of SRO values is for various atomic species pairs (CoCo, CoCr, CoNi, etc.) in a CoCrNi M / HEA alloy that was arc melted using equal molar fractions of high purity Co, Cr and Ni in an argon atmosphere. The as-cast alloy was then homogenised at 1200°C for 24 hours followed by water quenching, and this sample was designated as the AH sample. Half of the AH sample was cut and placed in a salt bath at 500°C annealing temperature for 500 hours followed by a water quench (the sample at this condition is named the AN500 sample).

[0099] Figures 10(a) and 10(b) present the results of calculating the cumulative SRO parameter from the first nearest neighbour (1 NN) to the 200th nearest neighbour (200NN) of the CoCrNi MEAs in the AH and AN500 conditions by using the entire dataset with at least 2 million atoms. That is, the SRO values have been taken over multiple kNN distributions, where k values of 1 < k < 200 were used. Each SRO calculation considers all the atoms in a sphere out to the respective kNN value. After ~ 50NN, all pairwise SRO values in both conditions converged to a steady state, and the value at 200NN was used as the experimental SRO values between the different pairs. This result indicates that annealing has driven a clear departure from the random distribution apparent in the AH condition, with significant changes particularly for the Cr- Cr and Cr-Ni / Ni-Cr pairs.

[0100] SRO values are not the same as kNN distributions as they are a probability of solutes being near or far from each other, whereas kNN distributions involving the counting of atoms. Generally, kNN distributions are data rich but can be difficult to use due to the amount of data required to be processed. SRO values are simpler to process, being a single number, such as 0.00423, but are less data rich (and thus may be less accurate than kNN distributions).

[0101] The various kNN distributions (1 < k < -250) can be determined for individual atomic species in a multicomponent material (e.g. a five-component alloy comprising elements A-B-C-D-E) and then, as mentioned above, weights applied either to the distributions arising from the distribution from an individual k value, or ranges of k values for mathematical functions chosen that integrate the data. Other weights can be introduced to the kNN distributions from individual atomic species, or combinations of atomic species. For example, the kNN distributions from species ‘B’ in the above example might be given a different weighting from the kNN distributions of the species ‘C’, ‘D’ and ‘E’ or combinations thereof.

[0102] A similar process may be used where a different mathematical function is chosen to include SRO values from different atomic species with different weights assigned to each species. In the example above, the weights assigned to the SRO values can be tuned in a wide-ranging way. The SRO values of species ‘B’, can be assigned different weights to those from species ‘O’, ‘D’ and ‘E’. Similarly, the SRO values for species ‘B-C’, or ‘B-C-D’ or the SRO value for the species group ‘B-C-D-E’ could be assigned different weights from the SRO values calculated from other combinations of elements.

[0103] Table 1 below summarises the range of possible binary SRO values in the example of a five-component alloy comprising elements A-B-C-D-E. Table 1 represents only the binary SRO values. In fact, ternary, quaternary and quinary SRO values are also possible to calculate in a quinary alloy using combinatorial mathematics.Table 1 : Binary SRO values for a five-component alloy comprising elements A-B-C-D-E

[0104] For the avoidance of doubt, it is emphasised that the above discussion relates to the different weights that can be applied to the various kNN distributions or SRO values, respectively, from individual or combinatorial permutations of the species in the multicomponent system.

[0105] Local composition probabilities are based on local composition theory, where a mixture (or material) is assumed to have local regions have a specific composition of constituent atoms, so the properties of the mixture or material are determined by the local compositions. Local composition is expressed as a probability of a constituent atom being present at a local region of the material. For example, using the above example of an aluminium-magnesium-copper alloy, a particular area or region may have 40% aluminium, 30% magnesium and 30% copper. Based on these probabilities, the microstructure, and hence properties, of the material may be inferred.

[0106] Both of these alternative mathematical functions may be used for each localised region or area of the material to generate a set of mathematical functions for the entire material, from which a distribution of those mathematical functions are obtained.

[0107] As a result of performing the additive manufacturing method 100, the physical, mechanical and chemical properties of an AM product, as determined by quantifying its microstructure with the detected microstructure, can be compared with a target microstructure and / or predicted microstructure of the material. This enables control of the microstructure, and thus the properties of a materials properties, through the AM manufacturing process. This control may be enhanced by coupling the distribution of mathematical functions (especially, kNN distributions) with the grain structure of the material. Thus, these microstructural indicators can be directly determined and validated, serving to anchor the coupling between the additive manufacturing process and properties of a material. As such, the capacity to quantify the microstructure provides critical capacity to calibrate and validate computationally generated microstructural information. Consequently, this allows the material to be altered or modified in some way so that its microstructure will substantially match the target microstructure during the AM process, resulting in the end product exhibiting the designed properties, including properties required to obtain certification or qualification. This advantageously avoids or minimises the need to produce test coupons to achieve certification or qualification, increasing the efficiency of the AM process in both consumption of materials, time and labour involved with test coupon preparation and testing.

[0108] In addition, the additive manufacturing method 100 can be used to detect defects in the material, since deviations in the detected microstructure to the target microstructure will indicate some fault in the material. For example, such a deviation from the target microstructure in a particular location may indicate a defect or crack in the material, requiring remediation, reprocessing or discarding of the material.

[0109] Where the product comprises at least a second material in addition to the first material, the method 100 may also be performed in relation to the second material. That is, the additive manufacturing step(s) are performed on the second material for producing a second target microstructure, the analysis method 140 is performed in respect of the second material and the additive manufacturing step(s) are adjusted to to modify the detected microstructure of the second material to the second target microstructure. In this case, the additive manufacturing step(s) are performed simultaneously on the first and second materials. Alternatively, the additive manufacturing step(s) are performed sequentially on the first and second materials.

[0110] Referring to Figure 11, a system 600 for additively manufacturing a product comprising at least a first material is shown, comprising a plurality of additive manufacturing machines 610 for performing one or more additive manufacturing steps on the first material to build the product having a target microstructure. The first material is fed from an input 613 into the additive manufacturing machine(s) 610 and the product leaves as an output 617. A control unit 620 is configured to control the additive manufacturing machines 610. One or more sensors 625 are optionally provided for obtaining sensor data relating to the first material. A data retrieval unit 630 is provided for obtaining atomic data relating to the location of atoms within the first material. A processor 640 is configured to perform the analysis method 100 in respect of the first material, and compare the detected microstructure to at least the target microstructure. The processor 640 may also compare the detected microstructure to the predicted microstructure derived or calculated from the sensor data, either additionally or alternatively to the target microstructure.

[0111] The control unit 620 is further configured to adjust operation of the additive manufacturing machine(s) 610 in response to the detected microstructure being substantially different to the target microstructure and / or predicted microstructure. The control unit 620 may be in communication with each of the additive manufacturing machine(s) 610, wirelessly or through wired connections, to control the additive manufacturing machine(s). Thus, the control unit 620 is configured to adjust operation of the one or more additive manufacturing machines such that the detected microstructure of the first material is modified to the target microstructure and / or predicted microstructure. In some embodiments, there may be multiple control units 620, where a single control unit 620 controls a single additive manufacturing machine 610. In this case, there may be a master control unit (not shown) that controls each of the control units 620.

[0112] In this embodiment, the sensors 625 are thermal sensors for acquiring thermal data relating to the temperature of the first material. The thermal data comprises spatial and / or time data of the atoms within the first material based on their temperature. In this embodiment, there is a thermal sensor 625 is associated or connected to each additive manufacturing machine 610 to obtain the thermal data. However, in other embodiments, there may be a single sensor that is moved amongst the additive manufacturing machines 610 to obtain the sensor data from each additive manufacturing machine 610. The sensors 625 may be in wireless or wired communication with either the data retrievalunit 630 or the processor 640 to transfer the sensor data to the processor. For example, as shown in Figure 11, two sensors 625 are wiredly connected to the data retrieval unit while the third sensor 625 is in wireless communication with the processor 640.

[0113] The data retrieval unit 630 is connected to the additive manufacturing machine(s) 610 to acquire the atomic data from the material. It will be appreciated that there may be multiple data retrieval units 630, one for each additive manufacturing machine 610. Alternatively, the data retrieval unit 630 is removably connected to each additive manufacturing machine 610 to obtain the atomic data from a selected manufacturing machine 610.

[0114] The data retrieval unit 630 may obtain the atomic data directly from the first material during operation of the additive manufacturing machine(s) 610. In other embodiments, as shown in Figure 11, the data retrieval unit 630 obtains the atomic data from a sample of the first material taken during operation of the additive manufacturing machine(s) 610. In this case, the data retrieval unit may comprise a sampling unit 650 and a data processing unit 660. The sampling unit 650 may be a FIB instrument. The sample is then sent to the data processing unit 660, which may be a microscopy instrument, such as a light-optical microscope, electron microscope, atomic force microscope (AFM), scanning tunnelling microscope, a photonic force microscope, recurrence tracking microscope, atom probe and X-ray microscope. The sampling unit 650 and the data processing unit 660 may be integrated into one unit. For example, the data retrieval unit 630 may comprise an instrument that selectively uses ion beams or electrons, enabling it to operate as a FIB instrument or as a SEM instrument.

[0115] The control unit 420, data retrieval unit 630 and processor 640 operate independently of each other in performing the method 100. That is, data retrieval unit 630 operates independently to obtain the atomic data. This atomic data is then sent to the processor 640 to perform the method 100. The result of the method 100 from the processor 640 is then used to operate the control unit 620 to modify operation of the additive manufacturing machines 610. This process reflects the fact that it may take some time for the processor 640 to perform the method 100 to determine the determined microstructure and make the comparison against the target microstructure and / or predicted microstructure.

[0116] However, it is contemplated that in other embodiments, the processor 640 may be placed in communication with the control unit 620 and the data retrieval unit 630, either wirelessly or through wired connections. In this way, the processor 640 is able to perform the analysis method 100 on the first material upon receipt of the atomic data from the data retrieval unit 620. It also enables the processor 640 to communicate with the control unit 620 to instruct the control unit 620 to modify operation of the manufacturing machine(s) 610 in response to the comparison of the detected microstructure to the target microstructure and / or predicted microstructure. That is, the control unit 620 may control the manufacturing machine(s) 610 to modifying its operation to modify a first manufacturing step and so modify the microstructure of the first material. This may involve the manufacturing machine(s) 610 performing a second or additional manufacturing step on the first material to modify the microstructure of the first material. The manufacturing machine(s) 610 may perform the second manufacturing step after stoppage or completion of the first manufacturing step.

[0117] The processor 640 may also be configured to generate the target microstructure from an initial set of data relating to the first material, as described above in relation to the method 100. It will also be appreciated that the processor 640 may comprise a computer, computer processing unit (CPU) or similar computational device.

[0118] Hence, the system 600 is able to perform the manufacturing method 100 of Figure 3. This also includes the iteration of the analysis step 140. That is, the data retrieval unit 630 obtains an additional set of the atomic data and the processor 640 performs the analysis method 140 on the additional set of the atomic data after the control unit 620 adjusts operation of the additive manufacturing machine(s) 610. The system 600 is configured so that the data retrieval unit 630 obtains the atomic data, the processor 640 performs the analysis method and the control unit 620 adjusts operation of the additive manufacturing machine(s) 610 iteratively until the detected microstructure is substantially the same as the target microstructure and / or predicted microstructure.

[0119] Where the product comprises at least a second material, wherein the system 600 is configured to perform the method 100 in relation to the second material. That is, the additive manufacturing machine(s) 610 perform the additive manufacturing step(s) on the second material for producing a second predicted microstructure, the data retrieval unit 630 obtains atomic data relating to the location of atoms in the second material and the sensors 625 acquire sensor data relating to the second material to generate asecond predicted microstructure. The processor 640 performs the analysis method 100 in relation to the second material and the control unit 620 adjusts operation of the additive manufacturing machine(s) in response to the detected microstructure of the second material being substantially different to the target microstructure and / or second predicted microstructure.

[0120] Referring to Figure 12, another embodiment of an additive manufacturing method 700 is shown, where the AM product or part is designed with a particular geometry at step 710 and build parameters are determined at step 720, to have a target microstructure. A trial AM part is then produced with the additive manufacturing process 730 (comprising one or more additive manufacturing step(s)) based on the part geometry and build parameters from steps 710 and 720, respectively. The trial AM part then proceeds to a post-processing thermal treatment 740. During the additive manufacturing steps 730, in situ thermal measurements are made of the material at step 750. This thermal data is then used at step 760 to create a spatial-thermal model of the AM part. At step 770, the thermal model is then used to create a model of the microstructure of the AM part, enabling determination of a predicted microstructure for the AM part. At step 800, experimental data is taken of the AM part during the post-processing thermal treatment step. This experimental data corresponds to the atomic data relating to the material of the AM part, and thus step 800 mirrors step 130 of method 100. At step 810, experimental location-specific property information is then measured and at step 820 a through-process location-specific property model is generated. Steps 770, 810 and 820 are performed as part of the analysis method 140, which in this embodiment generates a plurality of kNN distributions 850.

[0121] A determination is made at step 860 as to whether the properties of the AM part (as derived from the detected microstructure) meet the design requirements (as derived from the target microstructure). Optionally, the predicted microstructure is used as another point of comparison. The design requirements are those properties of the AM part that are necessary to be qualified or certified for use, or to comply with specifications of the AM part or contractual requirements. If the determination is that the properties of the AM part do meet the design requirements at step 870, then the AM part has been qualified / certified as meeting the design requirements. Accordingly, the AM part geometry, AM build parameters and post-processing treatment are ’’locked” or fixed for future production of the AM part, as indicated by step 880. Further runs of the additivemanufacturing steps may be made to confirm that the AM part can repeatedly be manufactured with the same quality.

[0122] If the determination is that the properties of the AM part do not meet the design requirements at step 890, then the method 700 enters into a feedback loop 900 corresponding the adjusting step 150 of the method 100. In the feedback loop 900 comprises the step 910 of assessing the model properties against the experimental properties, as well as assessing the microstructure model and experimentally derived microstructure. At step 920 of the feedback loop 900, optionally there is an analysis of the trial build(s) of the AM part to tailor it to the thermal model developed at steps 760 and 770. At step 930, the AM build parameters, post-processing treatment, party geometry or any combination thereof are revised or adjusted so that the experimental model will match the thermal model. After passing through the feedback loop 900, another trial build of the AM part is then made by the AM process 730, as described above. The feedback loop 900 will be repeated until the properties of the AM part (as derived from the detected microstructure) meet the design requirements (as derived from the target microstructure).

[0123] The method 700 thus enables quantification of site-specific multi-scale microstructural information to derive information that supports the feedback loop 900 for rapid, iterative process design. As mentioned above, the properties of a material are the consequence of its microstructure.

[0124] This embodiment generates experimental atomic kNN distributions that can be used to rapidly calibrate the thermal distribution to the target mechanical properties. The distribution or mosaic of kNN distributions from region to region throughout the microstructure depends on the local thermodynamic and kinetic influences on the materials imposed by the additive manufacturing process, and so serves as an effective proxy for all or most of the microstructural attributes. As shown in Figure 12, the method 700 involves (static and dynamic) materials testing to determine location-specific materials property information. That is, the location-specific experimental information on the materials microstructure and properties from steps 800, 810 and 820 will be compared to the target microstructure and property information generated from the models based on the true thermal signatures from the sensors and thermal models at steps 760 and 770. This enables the feedback loop 900 for iteration of the thermal distribution via AM machine parameters, scan strategies, post-processing parametersand, if need be, part geometry, as indicated by steps 910, 920 and 930. Similarly, SRO values or mosaics thereof may be used instead of the kNN distributions.

[0125] From the viewpoint of the final materials properties (e.g. the mechanical properties) exhibited by a material against a particular end-use application, the additive manufacturing methods 100, 700 and system 600 may be used to provide a threshold requirement to determine whether a manufacturer should proceed with additively manufacturing the product using the material based on the current additive manufacturing steps and / or design of the product. For example, where the mathematical functions are kNN distributions, there will be kNN distributions that can be classified as nominal, marginal and unsatisfactory. Here, a “nominal” kNN distribution corresponds to good or satisfactory properties of the material, “marginal” corresponds to just meeting minimum property requirements of the material and thus the product, and “unsatisfactory” will not meet the minimum requirements. The notion of nominal, marginal and unsatisfactory may be considered as providing a tool for risk analysis in the additive manufacturing process according to the methods 100, 700 and system 600. For example, the parts of an engineering component or machine produced by an additive manufacturing process may need to be compliant with engineering performance standards that permit formal certification and qualification of that part. If the distribution of kNN distributions has most or all of its kNN distributions classified as nominal or marginal, then this would indicate that these requirements would be met by the additive manufacturing process. However, if the distribution of kNN distributions has most or all of its kNN distributions classified as unsatisfactory, then this would indicate that the requirements would not be met and thus modification of the additive manufacturing process is required. As noted above, in other embodiments, one or more representative kNN distributions may be selected for assessment as being nominal, marginal or unsatisfactory for this purpose, instead of viewing the overall distribution of kNN distributions. Similarly, nominal, marginal and unsatisfactory ranges of SRO values may be used.

[0126] Thus, the embodiments provide the ability to accelerate the certification and qualification of AM parts via control of the process-property relationship via microstructure. The capacity to generate location specific ANN data and ANN mosaics will unlock the process-property relationships. An even more specific application in AM is the case where in situ information (such as infra-red signal) during the AM build process can be coupled with post-processing treatments to generate a spatio-temporal thermaldistribution across the part or component. This spatio-temporal thermal distribution can serve as an input to a model to generate microstructure from which the materials properties can be calculated. The invention as described in the above embodiments relates to the multi-scale microstructural characterisation (including, explicitly, atomic- scale information about the atomistic arrangements as formulated through mosaics of kNN distributions or SRO values) coupled with local property measurements that can be used to calibrate and register these models.

[0127] Consequently, the method 700 may be used to enable the acceleration of the certification and qualification process for parts and components made by additive manufacturing. In particular, the method 700 may reduce or eliminate the use of test coupons for validating the microstructure and properties of AM parts, and focussing instead on the location-specific microstructure and properties of the real 3D part in voxel resolutions dictated by the end use case. By focussing the validation effort on the real part, the iteration of the AM process variables will converge on the true microstructural and property targets required for certification. This leads to a cost-efficient, time-efficient, high-fidelity workflow to establish AM process parameters that deliver parts of a specific geometry that meet the end-user requirements for industrial certification and qualification.

[0128] Overall, the additive manufacturing methods 100, 700 and system 600 enables measuring and correlating real-world assumption-free ground-truth data on the critical attributes of both the AM process and the part to be qualified / certified. These critical attributes are generally the thermal signature of the through-process AM-build, the atomic-scale microstructure, and the actual properties of the real part. The advantage of the described embodiments is that there is no opacity around proxies or model assumptions, etc. The embodiments are tether any predictive models to the real-world data that is acquired. Thus, the embodiments provide, a short-cut to iterate the AM process towards achieving the desired properties in the final AM part, since the data is based on the real AM part and not a test coupon, which may have different

[0129] It will further be appreciated that any of the features in the preferred embodiments of the disclosure can be combined together and are not necessarily applied in isolation from each other. For example, the system 600 may implement the method 700 instead or in addition to the method 100, or a combination of kNN distributions, SRO values and / or local composition parameters are used in the analysismethod 140. Similar combinations of two or more features from the above described embodiments or preferred forms of the disclosure can be readily made by one skilled in the art.

[0130] The above described embodiments provides methods of additive manufacturing, and an additive manufacturing system, where determining a distribution of a plurality of mathematical functions relating to the spatial distribution of atoms in the material, like kNN distributions, enables determining of the microstructure of a material. Consequently, this detected microstructure can be linked to the process parameters used in the additive manufacture of a material, creating a feedback loop for rapid, iterative process design that achieves a targeted suite of materials properties. The above described embodiments may also be used generally in the assessment of the microstructure of materials, including the presence of any faults or defects, as well as enabling a go / no-go threshold to be established in the additive manufacturing process. As a consequence, these embodiments result in an improvement to the additive manufacturing process, reducing the consumption of material resources and time to manufacture a material or product exhibiting the correct or desired physical, mechanical and chemical properties. That is, as the material can be tested for their properties (via the detected microstructure) during the additive manufacturing process, this avoids the need to produce test coupons that are usually required and that may be inaccurate compared to the real AM part. This in turn reduces the consumption of material and time in producing these test coupons, or at least reduce the number of test coupons required. Also, by being able to determine the properties of the material and final product during additive manufacture, the embodiments permit a quicker qualification or certification process that confirms whether an additively manufactured product will comply with any regulatory requirements, industry standards or contractual design requirements. Moreover, the embodiments enable an early assessment whether a designed AM product additively manufactured from the material is practically viable; that is, they can provide a threshold test to ensure whether the AM product exhibiting the required properties can be additively manufactured reliably. In all these respects, these disclosed embodiments represent a practical and commercially significant improvement over the prior art.

Claims

Claims1. A method of additively manufacturing a product comprising at least a first material, comprising: performing one or more additive manufacturing steps on the first material to build the product having a target microstructure; obtaining atomic data relating to the location of atoms within the first material during the one or more additive manufacturing steps; performing an analysis of the first material, wherein the analysis comprises: performing a calculation on the atomic data to generate a plurality of mathematical functions relating to the spatial distribution of the atoms according to one or more predefined parameters; calculating a statistical distribution of the generated mathematical functions; determining a detected microstructure of the first material based on the statistical distribution; and comparing the detected microstructure to the target microstructure; and adjusting the one or more additive manufacturing steps in response to the detected microstructure being substantially different to at least the target microstructure.

2. The method of claim 1, wherein determining the detected microstructure comprises determining the arrangement and / or concentration of atoms according to the one or more predefined parameters from the statistical distribution.

3. The method of claim 2, wherein the arrangement and concentration of the atoms according to the one or more predefined parameters indicates a localised arrangement of atoms that exhibits one or more target properties of the first material.

4. The method of any one of the preceding claims, wherein the plurality of mathematical functions comprises k-Nearest Neighbour (kNN) distributions.

5. The method of any one of claims 1 to 3, wherein the plurality of mathematical functions comprises Short Range Order (SRO) parameters or values.

6. The method of any one of claims 1 to 3, wherein the plurality of mathematical functions comprises probability functions representing local composition parameters.

7. The method of any one of the preceding claims, comprising assigning weights to one or more of the mathematical functions and calculating the statistical distribution based on the weighted mathematical functions.

8. The method of any one of the preceding claims, further comprising selecting one or more of the plurality of mathematical functions as a representative mathematical function of a subset of mathematical functions, and calculating the statistical distribution based on the representative mathematical functions.

9. The method of any one of the preceding claims, comprising obtaining the atomic data using a microscopy method, wherein the microscopy method comprises one or more of transmission electron microscopy (TEM), atom probe microscopy (APM), X- ray scattering, and neutron scattering.

10. The method of any one of the preceding claims, comprising obtaining sensor data relating to the first material during the one or more additive manufacturing steps, wherein: the analysis of the first material comprises: performing a calculation on the sensor data to generate a predicted microstructure of the first material; and comparing the detected microstructure to the target microstructure and / or the predicted microstructure; and the adjusting step comprises adjusting the one or more additive manufacturing steps in response to the detected microstructure being substantially different to the predicted microstructure and / or the target microstructure.

11. The method of any one of the preceding claims, wherein the obtaining, analysis and adjusting steps are repeated until the detected microstructure is substantially the same as the target microstructure and / or predicted microstructure.

12. The method of any one of the preceding claims, wherein the adjusting step comprises at least one of: changing one of the additive manufacturing steps to modify the detected microstructure of the first material to the target microstructure and / or predicted microstructure;performing a second additive manufacturing step, separate to a first additive manufacturing step, on the first material to modify the detected microstructure of the first material to the target microstructure and / or predicted microstructure; and adjusting at least one of an operational parameter of the one or more additive manufacturing steps, a build parameter of the product and a design parameter of the product.

13. The method of any one of the preceding claims, wherein the one or more predefined parameters comprises atomic species and / or distances between a designated atom from one or more other atoms.

14. A system for additively manufacturing a product comprising at least a first material, comprising: one or more additive manufacturing machines for performing one or more additive manufacturing steps on the first material to build the product having a target microstructure; a control unit configured to control the one or more additive manufacturing machines; a data retrieval unit for obtaining atomic data relating to the location of atoms within the first material; and a processor configured to perform an analysis method of the first material, wherein the analysis method comprises: performing a calculation on the atomic data to generate a plurality of mathematical functions relating to the spatial distribution of the atoms according to one or more predefined parameters; calculating a statistical distribution of the generated mathematical functions; determining a detected microstructure of the first material based on the statistical distribution; and comparing the detected microstructure to at least the target microstructure; wherein the control unit is configured to adjust operation of the one or more additive manufacturing machines in response to the detected microstructure being substantially different to at least the target microstructure.

15. The system of claim 14, comprising one or more sensors for obtaining sensor data relating to the first material, wherein:the processor is configured to: perform a calculation on the sensor data to generate a predicted microstructure of the first material; and compare the detected microstructure to the predicted microstructure; and the control unit is configured to adjust operation of the one or more additive manufacturing machines in response to the detected microstructure being substantially different to the target microstructure and / or predicted microstructure.

16. The system of claim 14 or 15, wherein the control unit adjusts operation of the one or more additive manufacturing machines such that the detected microstructure of the first material is modified to the target microstructure and / or predicted microstructure.

17. The system of any one of claims 14 to 16, wherein the control unit adjusts at least one of an operational parameter of the one or more additive manufacturing machines, a build parameter of the product and a design parameter of the product.

18. The system of any one of claims 14 to 18, wherein the data retrieval unit obtains an additional set of the atomic data and the processor performs the analysis method on the additional set of the atomic data after the control unit adjusts operation of the one or more additive manufacturing machines.

19. The system of any one of claims 14 to 18, wherein the system is configured so that the data retrieval unit obtains the atomic data, the processor performs the analysis method and the control unit adjusts operation of the one or more additive manufacturing machines iteratively until the detected microstructure is substantially the same as the target microstructure and / or predicted microstructure.

20. The system of any one of claims 14 to 29, wherein: the data retrieval unit obtains the atomic data directly from the first material during operation of the one or more manufacturing machines or obtains the atomic data from a sample of the first material taken during operation of the one or more manufacturing machines steps; and / or wherein the data retrieval unit comprises one or more of a light-optical microscope, an electron microscope, an atomic force microscope (AFM), scanningtunnelling microscope, the photonic force microscope, recurrence tracking microscope, atom probe, X-ray microscope or other microscopy instrument.

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