Correction of the development strategy of parts manufactured by additive manufacturing by the study of tool trajectories

Abstract

Method for configuring the manufacturing of a part using a machine for additive manufacturing, by means of a production strategy indicating at least one manufacturing trajectory followed by the machine, comprising steps of: - determining (310) a thermal map for at least one layer of the part, associating a local temperature rise with the points of the trajectory; - determining (314) critical thermal thresholds as a function of a correlation between the local temperature rise and a measurement relating to a defect representative of the quality of the part; - adapting (316) the production strategy as a function of the thermal thresholds and the thermal map. Figure for the abstract: Fig. 3

Description

Title of the invention: Correction of the development strategy of parts manufactured by additive manufacturing by the study of tool trajectories FIELD OF THE INVENTION

[0001] The present invention relates to additive manufacturing (or printing) and in particular to the configuration of the manufacturing of a part by means of a machine for additive manufacturing, or "3D printer", by means of a development (or lasing) strategy.

[0002] Additive manufacturing (AM), or 3D printing, is a family of processes in which parts are produced by successively adding layers of materials.

[0003] The parts produced can be of different natures, different dimensions and varied complexity. Some parts must also meet strict specifications and may be subject to certifications. For example, in the aeronautical field, additive manufacturing can concern high or low pressure distributors for turbomachines, turbomachine rectifier vanes, injectors, casings, heat exchangers, hydraulic blocks, etc.

[0004] Generally speaking, we can consider any additive manufacturing process by melting or consolidating material (metallic, polymer, ceramic, etc.) by successive or simultaneous applications of one or more heat sources provided by tools following a given trajectory. These tools can in particular be lasers.

[0005] Among the methods currently available, we can notably cite - powder bed laser sintering processes, such as selective laser sintering (SLS), - laser powder bed fusion processes, such as selective laser melting (SLM), laser beam melting (LBM), laser powder bed fusion (LPBF), direct metal laser sintering (DMLS), etc. - electron beam powder bed fusion processes, such as electron beam melting (EBM), electron powder bed fusion (EPBF), etc.

[0006] In particular, in powder bed laser fusion processes (SLM, LBM, LPBF, DMLS, etc.), the parts are manufactured by laser fusion and solidification of a succession of powder layers.

[0007] For each layer, a scraper spreads a bed of metal powder of fixed thickness. Then a laser whose movement is controlled melts only the surface corresponding to the cut of the part to be built, by scanning this surface according to a development strategy (including a trajectory) fixed by the operator stored in a computer file. This part of the operation is often called "lasing". The molten powder resolidifies after the laser passes and the solid surface is thus created. The plate is then lowered by the thickness of the new layer of powder which will be spread, and the process is repeated until the part is completely produced. The power, speed and focal plane of the laser are controlled throughout the process. These parameters determine the width and depth of the melted zone when the laser passes (called "lasing bead").

[0008] The laser trajectory is also set by the operator. This laser trajectory is also called a laser pattern, or path, a scanning pattern, or a vectorization strategy. This trajectory is predefined during the manufacturing preparation phase (CAM for "computer-aided manufacturing") before being supplied to the additive manufacturing machine.

[0009] The state of the material is thus strongly impacted by the thermal cycles imposed by the passage of the laser beam, and the successive coolings. We generally seek to eliminate or, at least, to limit these "hot spots", because they are the source of numerous defects and non-conformities.

[0010] Proposals have been made to determine the thermal behavior of a part during manufacture, particularly upstream of its manufacture.

[0011] However, tools for a priori evaluation of the thermal behavior of a part encounter limitations linked to the scale effects associated with additive manufacturing processes, and in particular with laser powder bed fusion processes such as the LPBF process.

[0012] Thus, there are digital tools capable of finely simulating the thermal history of the process at the scale of a few beads, or even at the scale of a layer, with relatively high associated calculation times (from a few hours to a few tens of hours per layer and per part). There are also tools that make it possible to estimate in a few hours the macroscopic temperature fields during manufacturing, but with simplifying assumptions (for example, a homogeneous heat source at the scale of a layer) which do not take into account the local effects that one wishes to analyze and therefore do not make it possible to anticipate anomalies in the product during manufacturing with sufficient precision and finesse.

[0013] This has the particular disadvantage of being based on the geometric description of the part to be manufactured. When the part is large and complex, the descriptive data can become very cumbersome to manage and generate processing times. calculation incompatible with industrial constraints. In addition, basing oneself on the geometric description of a part necessarily implies that the simulation is only valid for this part and therefore cannot correctly take into account the laser paths when several parts are manufactured simultaneously.

[0014] Furthermore, the energy supply strategy is not considered (lasing path, possible presence of several lasers, etc.). Thermal modeling is therefore very macroscopic and does not allow precise and efficient prediction of anomalies linked to the thermal behavior of the part, in a limited modeling time allowing a reduction in modeling and manufacturing cycle times.

[0015] Furthermore, generally speaking, the elaboration (or lasing) strategies which specify the lasing trajectory and the associated parameters (laser power, etc.) are generated by tools provided by the manufacturer of the additive manufacturing machine.

[0016] The user of the machine therefore does not have full control and mastery of these generations, and in particular of the criteria used. As a result, when defects appear, the user can hardly establish reliable and repeatable corrective measures.

[0017] Certainly, some manufacturers provide the user with various settings that can theoretically allow the lasing strategy to be adapted. However, these settings are often inappropriate. For example, it may be planned to identify areas likely to overheat or "hot spots", or more generally to identify risk areas identified by strong thermal heterogeneities, in order to apply suitable lasing parameters. However, the discrimination of these areas generally relies on purely geometric parameters, which are insufficient since they do not take into account the thermal nature of the overheating phenomenon. Furthermore, no rule is provided for adjusting these parameters rationally: the user must proceed by trial and error, which represents a significant cost for a development phase.

[0018] There is therefore a need to improve the current proposals of the state of the art. In particular, the state of the art does not propose a method, and a tool implementing this method, for configuring the manufacturing of a part by means of an additive manufacturing machine by means of a development or lasing strategy, responding to the problems raised above.

[0019] In particular, a method is sought indicating at least one manufacturing trajectory followed by said machine so as to avoid overheating areas of the part, with, in addition, processing times compatible with industrial constraints (a few seconds per layer). Summary of the invention

[0020] For these purposes, according to a first aspect, the present invention can be implemented by a method of configuring the manufacturing of a part by means of a machine for additive manufacturing, by means of a development strategy indicating at least one manufacturing trajectory followed by said machine, said method comprising steps of: - determination of a thermal map for at least one layer of said part, said thermal map associating a local temperature rise with the points of said trajectory; - determination of critical thermal thresholds based on a correlation between said local temperature rise and a measurement relating to a defect representative of the quality of said part; - adaptation of said development strategy based on said thermal thresholds and said thermal map.

[0021] Thus the method makes it possible to generate a specific file, allowing the manufacturing of a part by an additive manufacturing machine (or 3D printer).

[0022] According to preferred embodiments, the invention comprises one or more of the following features which can be used separately or in partial combination with each other or in total combination with each other: - the method further comprises a prior step of determining basic metric values, relating to said manufacturing, as a function of said trajectory; - the method further comprises a step of determining a set of indicators as a function of said basic metric values and a correlation function linking said indicators, the heat map being determined as a function of said indicators; - said set of indicators is calculated by first determining a set of aggregated values of metrics on elementary volumes, then determining said indicators for said elementary volumes; - said aggregated metric values are determined according to a first discretization scale and then transformed into a digital image for each basic metric, in order to apply an image processing operation to determine said set of aggregated values; - said determination of indicators comprises the determination of an indicator dependent on a temperature rise and said correlation function, said correlation function being estimated by previously varying values for basic metrics and by simulating an associated temperature rise so as to constitute an abacus; - said adaptation step includes the determination of a scenario comprising the grouping of vectors of said development strategy and assignment of a value of at least one parameter of said machine to each group; - said adaptation step comprises the determination of a scenario comprising the allocation of a progression of at least one parameter of said machine to at least one part of a vector of said development strategy; - said adaptation step comprises the determination of a scenario comprising the segmentation of at least one vector into a set of vectors, each associated with a different value of at least one parameter of said machine; - the adaptation of said development strategy comprises a modification of at least one parameter among a laser power, a lasing speed, a laser beam diameter, a pause time, an intervector jump time and a gap between vectors.

[0023] According to another aspect, the invention relates to a method of manufacturing a part by additive manufacturing using a machine for additive manufacturing, comprising a configuration method as previously described, and in which said manufacturing is carried out according to said development strategy.

[0024] According to another aspect, the invention relates to a computer program comprising instructions for implementing a method as previously described when executed on a computer.

[0025] According to another aspect, the invention relates to an optimization device comprising a processor and associated circuits, adapted to implement a configuration method as previously described for configuring a machine for the manufacture of a part.

[0026] According to one embodiment, said machine is suitable for producing a part from among a high or low pressure distributor for a turbomachine, a turbomachine rectifier vane, an injector, a casing, a heat exchanger, a hydraulic block, etc.

[0027] Other characteristics and advantages of the invention will appear on reading the following description of a preferred embodiment of the invention, given by way of example and with reference to the appended drawings. BRIEF DESCRIPTION OF THE FIGURES

[0028] The attached drawings illustrate the invention: [Fig.l] schematically illustrates a system according to one embodiment of the invention, comprising a machine for production by additive manufacturing.

[0029] [Fig.2] schematically represents a flowchart of the steps of a method according to one embodiment of the invention.

[0030] [Fig.3] schematically illustrates a possible embodiment for a step correction of the process illustrated in [Fig.2], according to one embodiment.

[0031] FIGS. 4A to [Fig.4C] schematically illustrate a possible embodiment for a step of determining aggregated values of basic metrics for an elementary volume.

[0032] [Fig.5A] and [Fig.5B] illustrate examples of elementary volumes, according to embodiments of the invention.

[0033] [Fig.6] illustrates the result of a set of simulations for a step of estimating a correlation function linking indicators, according to an implementation of the invention.

[0034] [Fig.7] illustrates an example of a heat map obtained according to implementations of the invention.

[0035] [Fig.8] illustrates a method for determining thermal thresholds, according to one embodiment of the invention.

[0036] [Fig.9A] and [Fig.9B] illustrate a first scenario for adapting the lasing strategy, according to an embodiment of the invention.

[0037] [Fig.10A] and [Fig.10B] illustrate a second scenario for adapting the lasing strategy, according to an embodiment of the invention.

[0038] [Fig. 11 A] to [Fig. 11F] illustrate a third scenario for adapting the lasing strategy, according to an embodiment of the invention.

[0039] DETAILED DESCRIPTION OF EMBODIMENTS OF THE INVENTION

[0040] The proposed method applies to various printing or manufacturing techniques. additive manufacturing (or 3D printing), but more particularly to techniques generating risks of local overheating, such as LPBF (Laser Powder Bed Fusion) type techniques. Generally speaking, additive manufacturing involves the movement of one or more tools along a predefined path (which may be called a "tool path") in order to provide a point source of heat on a material.

[0041] The additive manufacturing process by laser fusion of powder beds makes it possible to manufacture parts from the selective fusion of successively stacked powder bed layers. It allows the manufacture of lighter complex parts such as fine structures (lattice type), and is therefore applicable to the field of aeronautics.

[0042] It is found that the metallurgical and mechanical state (phases present, residual stresses, etc.), the health of the material (porosity rate, cracking, etc.), the state of the surface, and therefore, the thermomechanical properties of the resulting parts are determined to the first order by the thermal cycles imposed by the additive manufacturing process.

[0043] It is therefore essential to understand the influence of heating / cooling cycles development on the thermal observed in the parts in order to be able, if necessary, to optimize the manufacturing parameters to eliminate, or reduce, the harmful effects.

[0044] In the particular case of an LPBF process, the thermal cycles are controlled by the vectorization (or lasing) paths imposed on each layer. Depending on these development strategies and the local geometric configuration of the part, hot spots may develop, linked to an energy input that is too high compared to the part's capacity to evacuate heat locally. This is for example the case of areas with small sections where the laser makes numerous back-and-forth movements in a relatively short period of time and / or undercut areas overhanging powder masses which have a relatively low thermal diffusivity compared to that of the dense material.

[0045] These “hot spots” should be avoided because: - Heat accumulation in a localized area can cause coalescence of the molten pools, which form a protrusion during solidification. When this local excess thickness exceeds the thickness of the powder layer, the scraping system can collide with the part being manufactured, which can cause degradation of the manufactured geometry or even a manufacturing stoppage. As a result, the fused material is lost and manufacturing times are extended. - Hot spots can promote cracking phenomena (solidification cracking or macroscopic cracking) - Overheating areas can promote the formation of "keyholes" or steam capillaries: these are microcavities that form under the surface and are often unstable and, when they collapse and solidify, form porosities in the part, the cross-section of which can take on the appearance of keyholes.

[0046] In order to anticipate the appearance of these hot spots, it is proposed to carry out digital modeling and simulations upstream of the actual manufacturing.

[0047] Indeed, by simulating the heat, or energy, input into a part produced by additive manufacturing, and by taking into account the vectorization (or lasing) schemes, we can evaluate a priori the evolution of the thermal field during manufacturing. We can thus anticipate the appearance of overheating zones, and possibly trigger corrective actions.

[0048] The proposed method thus aims to identify risk zones, which could possibly become “hot spots” and induced by the exact lasing strategy on the parts to be produced, and this with a calculation time compatible with industrial constraints (i.e. a few hours at most per production platform).

[0049] The proposed method is based on the strategy, or scheme, of development, or lasing and not, like certain proposals of the state of the art, on the geometry of the parts to be produced. This makes it possible on the one hand to drastically reduce the calculation times required while maintaining a high precision in the estimation of the risk zones, and also to take into account the entire production tray, which may include several parts whereas previous proposals opt for a part-by-part approach. It is important to note that since the lasing scheme is common for the entire production tray, a product-by-product approach necessarily introduces a bias into the estimation.

[0050] [Fig.l] schematically illustrates a context of use of a proposed method of configuring the manufacturing of a part by additive manufacturing.

[0051] A machine, or printer, 200 for additive manufacturing consumes materials 210 in order to produce one, or more, parts 220. This machine is configured by a set of parameters which specify its operation and which can be modified during manufacturing: laser movement speed, laser power, etc.

[0052] In the field of aeronautics, the machine 200 can be adapted to produce parts as varied as, for example, a high or low pressure distributor for turbomachines, a turbomachine rectifier vane, an injector, a casing, a heat exchanger, a hydraulic block, etc.

[0053] An optimization device 230 is also provided and adapted for simulating the thermal behavior of a part 220 with a view to its production by additive manufacturing using the machine 200. This simulation is implemented upstream of production by the machine for additive manufacturing and aims to determine risk zones which are in particular likely to give rise to hot spots, or, more generally, risk zones characterized by strong thermal heterogeneities. The optimization device 230 comprises, for example, a processing unit comprising calculation means (such as one or more processors) on which instructions are loaded to implement a method for configuring the manufacturing of a part by additive manufacturing.

[0054] These risk areas can be indicated to the user. For example, a heat map can be displayed on a screen, possibly highlighting the risk areas.

[0055] Depending on the risk zones determined in the thermal behavior of the part 220 to be produced, a user can intervene on the parameters of the machine in order to avoid, or reduce, the appearance of these risk zones, or else so that these risk zones actually become hot spots.

[0056] The optimization device 230 can be adapted to directly determine adaptations in the machine parameter set in order to optimize the additive manufacturing process in order to avoid or minimize the occurrence of hot spots. In particular, heating and cooling cycles during additive manufacturing can be optimized using the results of the simulation phase.

[0057] The optimization device 230 can therefore provide the machine 200 with an optimized production strategy 240. This optimization strategy specifies a laser trajectory and associated parameters (laser power per vector, etc.). The additive manufacturing machine uses this production strategy for its control and thus to produce the requested part 220.

[0058] [Fig.2] schematically illustrates a flowchart of an overall process for developing and manufacturing a part, according to one embodiment.

[0059] An ideal geometric model is first designed in step 100 with Computer Aided Design (CAD) software, based on specifications. The geometric model 100 is generally designed with a surface representation formalism (or B-Rep, for “Boundary Representation”). The STEP file format is an example of a format associated with the B-Rep representation.

[0060] Then, the model resulting from step 100 is prepared in step 102, via Computer Aided Manufacturing (CAM) software. This software may be identical to that used in step 100, but it may also be software dedicated to CAM. The preparation step 102 consists, mainly, in designing the geometric model of the manufacturing blank, which takes up the ideal CAD model resulting from step 100, by increasing it, for example, with manufacturing overthicknesses (intended to be eliminated during finishing operations) or manufacturing supports. At the end of this step, the geometric model is generally saved in STL format, which gives a meshed (“triangularized”) representation of the exterior surfaces of the model.

[0061] Then, the model from step 102 is virtually “sliced” in step 104. There are as many digital slices as there are manufacturing layers. Each slice is then represented by its contour(s), which are stored, for example, in a file in CLI format. The manufacturing layers are generally of constant thickness, but it is possible to vary this thickness according to geometric criteria (for example, the local curvature of the part profile), so as to attenuate the “staircase effect” specific to AM processes.

[0062] Then, the set trajectory of the manufacturing tool(s) is defined in step 106. A part of the trajectories delimits the outline of the slices, while another part defines a filling pattern. The trajectories of the tool(s) are generally discretized by a set of vectors (which is why we also speak of the “vectorization” step), defined by a starting point and an arrival point.

[0063] All the coordinates of the vectors are stored in a file 108 which is presented, for example, in tabular form, where each row includes the coordinates of the start and end points of the vectors. Other data structures are possible for the 108 file, for example, in the form of lists. Examples of vectorization file formats are TXT, CLI, SLI, OVF or HDF5.

[0064] The manufacturing layers can, for example, be segmented by strips which are traversed one after the other, by parallel vectors (separated by a distance called "vector gap", corresponding to a microscopic scale) which are oriented perpendicular to the main direction of the strip. This strategy is known to homogenize the energy input at the scale of each strip.

[0065] Another popular strategy is to segment the build layers into a checkerboard pattern, composed of square cells. Each square of the checkerboard is lasered by parallel vectors, and the order of lasing the cells as well as the orientation of the vectors inside (two possible directions inside a square) are adjusted so as to limit the thermomechanical constraints at the part scale. There is, moreover, a variant of this strategy, where the square cells are replaced by hexagons, which offer an additional degree of freedom (three possible directions inside a hexagon) in reducing overheating phenomena and residual stresses, and to homogenize the heat input at the layer scale. Other more complex vectorization strategies are also found, such as fractal patterns. Finally, the build layers can be lasered without any particular pattern.In this case, the vectors cross the layers of each part from one side to the other.

[0066] Steps 104 and 106 are for example executed by the proprietary software suite of the machine manufacturer (also called “Build Processor”), but they can also be managed by external software, in particular with the same software as that used in step 102.

[0067] Then, in step 110, the setpoint manufacturing parameters are assigned to the setpoint trajectory of the tool(s). In the case of LPBF, these are, for example, the laser power, the scan speed, the vector deviation, the laser spot diameter, the inter-vector jump or pause times, etc. Depending on the strategy adopted and the pattern chosen, the manufacturing parameters can be assigned to the entire layer, or to subunits of the layers (for example, a set of parameters per strip, per checkerboard square, or even per vector).

[0068] As a result, the parameters of the instruction manufacturing tool can be stored in the vectorization file 108. For example, in the case of a tabular structure of the file 108, following the coordinates of the starting point and the arrival point of the vector, may include the laser power, the scan speed, the spot diameter, the nature of the vector (e.g., fill or contour vector), etc. Similarly, if the file 108 is in the form of lists, where each list contains, for example, the coordinates of all the vectors included in a unit of the lasing pattern (e.g., one list per band, per checkerboard cell, per vector, etc.), then separate manufacturing parameters may be associated with each of the lists (in this case, the vector(s) included in the same list are lased with the same parameters).

[0069] More abstractly, we can consider that the vectorization file 108 is composed of objects (sets or subsets of vectors described by their coordinates) and properties (process parameters), defining the way of lasering these objects (power, speed, spot diameter, etc.)

[0070] Finally, the obtained file 108 is converted into a machine command, and the part is manufactured by the additive manufacturing machine in a step 112.

[0071] According to a particular implementation, an additional step 103 of mechanical or thermomechanical simulation of the parts can be implemented. This calculation step 103 makes it possible to anticipate the rate of deformation of the parts resulting from the manufacturing process, in order, if necessary, to correct it by applying a pre-deformation adapted to the CAD model (correction represented in [Fig.2] by the feedback loop going from step 103 to the CAD file).

[0072] A step 111 for correcting, or adapting, the production strategy may also be provided. This correction 111 occurs at the end of the cycle and may, if necessary, be made to the file 108 representing the production strategy in order to adapt it, before its transmission to the machine for manufacturing 112.

[0073] [Fig.3] schematically illustrates a possible embodiment for this correction step 111.

[0074] According to this embodiment, a first step 302 comprises determining values of a set of basic metrics according to a first discretization scale.

[0075] This first discretization scale can correspond to a vector deviation, which can be defined according to the additive manufacturing technology (for example between 50 qm and 200 qm, for example of the order of 100 qm for LPBF technology).

[0076] According to one embodiment, in a step 304, aggregated values of this same set of basic metrics will be calculated according to a second, more macroscopic discretization scale, corresponding to elementary volumes.

[0077] According to one embodiment, to summarize, these aggregated metric values are determined according to a first discretization scale then transformed into a digital image for each basic metric, in order to apply an operation of image processing to determine said set of aggregated values

[0078] More precisely, according to one embodiment, in order to be able to easily move from the first to the second scale (elementary volumes), a digital image is constructed from the values corresponding to the first scale on which an image processing operation (convolution) can be applied in order to obtain the values corresponding to the second scale.

[0079] Thus, for each basic metric, the values are transformed into a digital image in which the pixels correspond to the first discretization scale.

[0080] Such a process is schematized in FIG. 4A, where the vectorization scheme 400 is intersected with a grid of cells (or pixels) 402, within which basic metrics m; are calculated, thereby generating an image 404 (in which the gray level is representative of the value of the metric for the given pixel).

[0081] The basic metrics locally characterize the vectorization scheme.

[0082] Since it is from these that a thermal map is subsequently calculated, these basic metrics must best capture all the characteristics of the lasing strategy applied to the material. A set of basic metrics must therefore be considered, each providing a set of values that can be transformed into a digital image. Consequently, there are as many images generated in step 302 as there are basic metrics.

[0083] A first basic metric can be the total length of vector fragments, contained in each pixel (denoted in FIG. 4B). This total length can be calculated as the sum of the lengths of segments of the laser path passing through the pixel. As indicated in the example referenced 407, the path passes through the pixel twice. The lengths of each segment, [2 are summed to give the total length: =

[0084] A second basic metric can be the smallest input time of the lasing vectors in each pixel (denoted in [Fig.4C]). This time can be easily deduced from the vector lengths contained in the file 108 specifying the elaboration strategy, i.e. the lasing trajectory decomposed into vectors and the manufacturing parameters, in particular the scan speed and the inter-vector jump or pause times.

[0085] A third basic metric can be the largest output time of the vectors of each pixel (denoted in [Fig.4C]). As before, this quantity is deduced from the vector lengths and the process parameters, in particular the scan speed and the inter-vector jump or pause times.

[0086] For these three metrics, we therefore seek to exclude the intermediate times t and tn which correspond to the exit time of a first (chronologically) vector crossing the pixel and, respectively, the re-entry time of a second vector crossing this same pixel, to keep only the extrema, that is to say the times of first entry and last exit of a given pixel.

[0087] A fourth basic metric may be the distance to the edges of each point located in the manufacturing layer. This quantity may be calculated from the coordinates of the vectors contained in the laser strategy file 108 resulting from the vectorization step 106.

[0088] The four quantities mentioned above are the main basic metrics, but others can be considered to the extent that they can participate in the calculation of the heat map.

[0089] Generally speaking, these basic metrics are selected because they are identified as having a first-order influence on thermal behavior at the local scale. In other words, the set of basic metrics can be chosen so as not to exclude any important factor likely to impact the thermal behavior of the material and the part to be produced.

[0090] Generally speaking, these basic metrics relating to additive manufacturing production concern the intended tool path for this production, the material and the tool(s) to be used as a point source of heat.

[0091] Thus, in the case of a laser, this set of basic metrics includes relative parameters: - to the laser diagram planned for the production of one (or more) parts, - to the material used for this production and - to the laser(s) (for example, power, speed, spot diameter, etc.)

[0092] It should be noted that the basic metrics do not relate to the geometry of the part to be produced. It is indeed noted that the geometry of the part to be produced can be extracted from the lasing diagram (or more generally from the trajectory of the tools used). Indeed, the analysis of the segments on a neighborhood allows to have sufficient knowledge of the geometry of the part on this neighborhood, since the segments are only present on parts of the part. The absence of a segment on an area indicates, implicitly, that the area does not belong to the part to be produced.

[0093] This has the advantage of not having to take into account the files describing the geometry of the parts to be produced which are traditionally quite heavy, but only the file 108 describing the lasing. The latter is substantially smaller in size and easier to manage in structure, which can allow a significant gain in performance.

[0094] In a step 304, the basic metrics are aggregated within an elementary volume, or representative elementary volume (REV), which corresponds, as previously stated, to a second larger scale (mesoscopic scale) than the first scale corresponding to pixels and vector deviations (microscopic scale).

[0095] The elementary volume is a volume of any shape (cubic, hemispherical, etc.), whose upper surface coincides with the surface of the part being manufactured, and whose underlying volume contains the quantity of material of the part already densified. This elementary volume can be associated with a point considered.

[0096] [Fig.5A] and [Fig.5B] illustrate examples of elementary volumes ELM for a point M located on the laser diagram.

[0097] This volume is characterized by a surface centered on this point M considered, by a dimension of this surface (diameter in the example of [Fig.5A], side a in the example of [Fig.5B].

[0098] The characteristic dimension (diameter, side, etc.) has a magnitude which is equivalent to a few vector deviations (i.e. distance between two consecutive vectors), for example between 5 and 10 vector deviations, and contains portions of the vectors which have a thermal influence on point M. This characteristic dimension can therefore depend on different parameters relating to the laser (power, etc.) and the material (thermal conductivity, etc.).

[0099] [Fig.5A] illustrates all or part of a part 220 to be produced. The upper surface of this part is hatched, these hatchings schematically represent the lasing diagram.

[0100] This laser pattern can be seen as a set of lines corresponding to the path of the laser beam (or other heat source) on the surface of the layer of the part being manufactured.

[0101] This lasing scheme, or strategy, mainly specifies the trajectory of the laser path and the inter-cord distance, i.e. the distance between two neighboring (and generally parallel) lines of the lasing scheme. In the figure, the lines are parallel and the inter-cord distance corresponds to the distance between two lines of the hash.

[0102] [Fig.5A] represents an elementary volume ELM of hemispherical shape, corresponding to a zone 221 of the part 220, and centered on a point M belonging to the laser diagram.

[0103] This elementary volume ELM contains several vectors of the lasing pattern. Here, we call "vector" any rectilinear section that discretizes the path of the laser on the surface. The lasing pattern is thus also commonly called "vectorization pattern". It is possible during the design to define curved lines for lasing, but these are generally discretized into a set of straight line segments, or vectors.

[0104] The intersection of the laser pattern with the upper surface of the elementary volume representative forms a set of segments between an entry point and an exit point (the lasing pattern being oriented in the direction of the laser path).

[0105] Thus, in [Fig.5A], we observe the oriented segments [1; 2], [3; 4], [5; 6], [7; 8], [9, 10]. Depending on the definition of an elementary volume, the passage of the laser over these different segments can influence the thermal behavior of the material at point M (located in the middle of the segment [5; 6]).

[0106] The elementary volume ELM can have different shapes: cube, hemisphere, hemi-ellipsoid, etc., depending on its relevance to the thermal problem that one wishes to resolve, and the practical constraints that this choice imposes on the implementation of the proposed method.

[0107] [Fig.5B] shows another form of elementary volume ELM. Its cubic form can be characterized by a characteristic side a.

[0108] The method aims to determine data representative of the thermal behavior of the part to be produced. This representative data can for example be a thermal map for each layer of the part to be produced. This thermal map can associate a value of a thermal quantity (or temperature) with each point M at which this quantity is to be evaluated. It is assumed, according to the proposed modeling, that this quantity depends on valid primary parameters (and assumed to be constant) on the representative elementary volume centered on the point M considered.

[0109] The elementary volume ELM can correspond to a mesoscopic scale. This mesoscopic scale is intermediate between the macroscopic and microscopic scales; it is sufficiently large to include a large number of particles (such that their statistical properties do not fluctuate significantly), while remaining sufficiently fine so that the thermodynamic quantities (pressure, temperature, etc.) remain local (point-like at the macroscopic scale).

[0110] In particular, the mesoscopic scale can be defined by the influence that the laser has on a point of the lasing pattern but also on the points close to this point, on this lasing pattern, upstream or downstream.

[0111] In this step 304, in order to carry out the aggregation of the values for the pixels into values for elementary volumes ELM, an image processing operation can be applied. For example, a digital mask corresponding to the second discretization scale can be applied to the digital images as illustrated in FIG. 4A.

[0112] In this example, the digital mask is illustrated by the square 406 which is applied to the digital image 404. This mask slides over the digital images with a minimum step equal to the resolution of the pixels (first discretization scale). At each step, mathematical operations make it possible to obtain an aggregated value for the elementary volume ELM corresponding to the position of the mask.

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[0124] Computing the basic metrics in two steps (first at the pixel scale, then at the elementary volume scale) speeds up the execution time of the calculation. Indeed, without the first preprocessing step, the calculation of the basic metrics would have to be looped for each position of the ELM elementary volumes. By going through a first "grid" of pixels, we reduce the calculation at the elementary volume scale to simple convolution operations (where the convolution kernel is the shape of the elementary volume), which are faster to execute than looping operations. For example, an aggregate value for the basic metric corresponding to the total length of the vector segments included in an elementary volume, denoted [ lJot 1 is obtained by the sum: LVJ ELM [l“1 L ELM ^ELM Similarly, the smallest entry time of the vectors to the surface of an elementary volume ELM, noted [ fpm ] is easily calculated using: ' 1 m * ELM 6 =min(C w ) L m *ELM ELM m Also, the largest exit time of the vectors on the surface of an elementary volume ELM, noted [ Æ3] is calculated via: They have 1ELM r ^max ] — mov / fliax \ iELM~^âW^ According to one embodiment, the method then comprises a step 306 of determining a set of indicators based on the values of the basic metrics. In particular, the indicators for each elementary volume ELM can be determined based on the values aggregated thereon. According to one embodiment, it is also desired that the number of indicators be minimal. Indeed, the fewer parameters there are, the simpler the problem is, and the simpler it is to construct an abacus as will be seen below. As a result, it is necessary to reduce the set of parameters from which they can be calculated. Since these parameters must also capture as much information as possible about the additive manufacturing process, this set must be judiciously chosen. These parameters preferably include parameters relating to the laser pattern (Car» b, b') which can be directly determined from the values basic metrics, for a given elementary volume, material parameters (thermal conductivity 2, thermal diffusivity a) and laser parameters (irradiance Io). According to a particular embodiment, 6 parameters are used:

[0125]

[0126]

[0127]

[0128]

[0129]

[0130]

[0131]

[0132] - the irradiance of the laser, I0 (or the absorbed irradiance Iabs=I0.A with A the absorbance of the material); - the cumulative exposure time t$xpo - the duration of the tscan age; - the depth of dense matter b, or the equivalent depth b'; - thermal conductivity 2; - thermal diffusivity a; A seventh parameter of the expressions of the indicators is formed by a deviation from the reference temperature, or temperature rise, A T^- H this is the parameter that we seek to estimate, in order to obtain a thermal map. A laser of incident irradiance Io travels across the upper surface of a representative elementary volume at a speed VL. We can write: [Math.3] T _ P with P the laser power, RL the radius at 1 / e2 of the laser beam (Gaussian or single-mode). The constant "e" here represents e=exp(l). In this embodiment, the laser is considered to be Gaussian, i.e. the spatial distribution of irradiance follows a two-dimensional Gaussian curve centered on the point of impact. For a Gaussian laser, the radius of the focal spot is arbitrarily defined as the radial distance from the optical axis such that the incident irradiance falls to 13.5% of the maximum irradiance, measured at the center of the spot (1 / e2 = 0.135). Obviously, it is possible to consider other types of spatial distribution of the irradiance of the laser, or more generally of the heat source, around an impact point, and to characterize this spatial distribution by a characteristic radius of the focal spot, or any other quantity or set of quantities. The cumulative laser exposure time [ tS^111]^ can be defined as the sum of the lengths of the vector segments contained in the representative elementary volume, divided by this laser speed. It can be expressed by the ratio between the total length [ pot ] of the vector segments included in an ele- LV .ELM and the tool displacement speed VL (or laser speed in LPBF), that is to say: cumulative 'expo [READ] _ THE ELM ELM ~ vl Similarly, the total scanning, or lasing, time on the surface of an elementary volume, noted [ , is calculated by the difference between the greatest exit time of the vectors on the surface of this elementary volume ELM and the smallest entry time of the

[0133]

[0134]

[0135]

[0136]

[0137]

[0138]

[0139]

[0140]

[0141]

[0142]

[0143]

[0144] vectors on its surface, that is to say: rf 1 _ r jnax 1 _ [ tmin 1 L scan\ELM L ont l^y L in 1ELM Each individual laser pass is actually separated from the others by a lasing time outside the representative elementary volume considered plus a hopping time between two vectors (i.e. the time taken for the laser to travel from the end of one vector to the beginning of the next vector). Thus[ and [ ^^m]ELM are constrained by the following inequality: ELM Furthermore, the elementary volume ELM includes a certain quantity of dense matter 223 which diffuses heat. Potentially, it also contains a complementary quantity of powder 222 (not exposed to the laser) which acts as a thermal insulator. Simplifying the problem further, considering a unidirectional elementary volume, we denote by b the depth of dense matter 223 located under the lasered surface. We can write: b < a In a real part, however, the dense material portions in the elementary volume are generally not unidirectional, especially in the draft and undercut areas. Instead, we can consider a thickness b' equivalent to the smallest distance in 3D, between the point considered on the lasered surface and the edge of the part. Furthermore, the dense matter has a thermal conductivity 2 and a thermal diffusivity a assumed to be constant, while the powder 222 is assumed to be a perfect insulator (adiabatic conditions). Finally, we can evaluate the temperature rise at point M corresponding to the elementary volume ELM considered by the variable A: ATm = Tm-T0 Tm is a characteristic temperature at point M, resulting from the vectorization sequence. It can be the maximum temperature or the average temperature seen during the exposure sequence or any other homogeneous parameter at a temperature, which can characterize local heating of the part. To is the reference temperature of the elementary volume, ELM, or of the layer. This quantity A determined for all the points M makes it possible to determine a thermal map of the part(s) to be produced. The thermal map can therefore associate with each point M of the trajectory of the tool (here the laser) a value representative of a temperature, that is to say a rise in temperature (compared to a reference temperature).

[0145] From these parameters, indicators can be determined. These indicators can be any.

[0146] According to one embodiment, these indicators Ib I2, .. .In, can be universal dimensionless numbers, which are linked together by a mathematical function noted f().

[0147] Thus, the parameters depend on 4 fundamental units (mass, length, time and temperature).

[0148] According to the Vaschy-Buckingham theorem (or theorem ji), if a physical equation involves p physical variables, these depending on u fundamental units, then there exists an equivalent equation involving pu dimensionless variables constructed from the original variables.

[0149] In the embodiment mentioned based on 7 parameters, there is therefore a mathematical function f(), involving pu=3 independent dimensionless numbers, which can be noted jti, ir2, ir3. We can write:

[0150] 7t] = f (tt2, tt3).

[0151] Subsequently, we will call this function the correlation function, since it allows to correlate the variations of the dimensionless parameters jti, ir2, ir3.

[0152] In a classic way, by exploiting the properties of these 3 numbers, we can define:

[0154] We can define ô as the characteristic diffusion length, such that: 101551 6 = Z^a.[t ra „]

[0156]

[0157] The definition of dimensionless numbers can then be written: 7r'i~Tï î

[0158] We note that ^ depends on the temperature TiD calculated at the surface of a semi-infinite medium absorbing the intensity Iabs with a one-dimensional heat flow. This temperature is notably described in Carslaw HJ, Jaeger, JC, “Conduction of heat in solids”, Oxford University Press, 1959. It can be defined by the expression:

[0159] rp _ Jaby^ 1 1D — TT?

[0160] The definition of the dimensionless parameters can then be rewritten:

[0161] ^scan !ELM

[0162] Thus, the 3 dimensionless numbers, or indicators, obtained after dimensional analysis can be described as follows:

[0163] is proportional to a normalized temperature rise. This quantity is unknown a priori: it is the one that we wish to calculate along the laser diagram in order to obtain the thermal map.

[0164] ^2 is proportional to the normalized diffusion length. This number characterizes the ability of the part to dissipate heat locally. The smaller the depth of dense material b, or the equivalent depth b', compared to the characteristic diffusion length ô, the less the part is able to dissipate heat locally (all other things being equal). As described previously, this value can be calculated only from the data structure describing the laser scheme (for example in file 108), considering the fact that only dense areas are crossed by vectors.

[0165] ^3 is proportional to the normalized exposure time. The higher the cumulative duration laser exposure [ tèxpo1 ]ELM tends towards the lasing duration [tscan]^ M, and the faster the heat buildup locally (all other things being equal). Just like K2, this number can be calculated only from the data structure describing the lasing pattern.

[0166] In an illustrative embodiment in which a dimensionless number L can be expressed as a function exclusively of a dimensionless number I3, one can then write:

[0167] I1 = f(l3)

[0168] Either:

[0169] Lbs[&]ELM \ /

[0170] d is the thermal conductivity of the material, is the absorbed laser irradiance and [ fi] = laï t 1 is the thermal diffusion length (with a the diffusivity 1 *elm \ 1 thermal performance of the material).

[0171] In a step 310, a thermal map can be determined for the considered layer of the part (corresponding to the elementary volumes previously determined) as a function of these indicators. This thermal map is a data structure which associates a temperature rise value FA Tl with the points (or LJ ELM elementary volumes) of the layer considered.

[0172] According to one embodiment, this step 310 of determining a heat map may be based on the results of step 306 of determining a set of indicators. Other embodiments are however conceivable for determining a heat map for at least one layer of the part to be manufactured.

[0173] The temperature rise value for a given elementary volume ELM results from the expression:

[0174] ~ fl \ L 1 hLM~

[0175] This expression clearly shows that the temperature rise is only deduced from the process parameters (here lobs), the thermophysical properties of the manufacturing material (here / . and a) and the basic metrics (here, [ ] ELM and [ tscan ] EL^'

[0176] This case, given for illustrative purposes, is a simplified example intended to improve understanding of the method. In more complex cases, other relationships are likely to be obtained, depending on the indicators chosen. On the other hand, whatever the complexity of the case studied, the temperature rise will always, ultimately, be deduced (1) from the parameters of the manufacturing process, (2) from the thermophysical properties and (3) from the basic metrics.

[0177] Thus, in another case where we have three indicators linked by the equation jti=f(jt 2 ; Jt3) stated previously, we can express iri as a function of ir2 and ir3, so as to calculate the characteristic temperature rise [ A T | along the diagram of lasage:

[0178]

[0179] This expression shows that the temperature rise [AT]ELM is propor tional at temperature TiD, weighted by a function of the normalized diffusion length § / b and the normalized exposure time [ texpiul ]ELM / [ tscan] ELM-

[0180] This embodiment is based on 3 dimensionless numbers. However, other embodiments are entirely possible, and in particular other embodiments based on dimensionless numbers. These can, among other things, be chosen according to the basic metrics that are chosen to model the thermal behavior of the part to be produced. Indeed, if the basic metrics are changed,

[0181]

[0182]

[0183]

[0184]

[0185] the dimensional analysis described above will provide a different set of dimensionless numbers. Thus, in the embodiment described above, the absorption process of the incident energy provided by the laser was considered to be a surface phenomenon. The absorbed irradiance Iabs was therefore considered as the primary parameter. This hypothesis works particularly well for metal alloys irradiated by YAG lasers, in "conduction" mode. Alternatively, the irradiated material can be considered to absorb the incident radiation in volume (this is for example the case of ceramics irradiated by YAG lasers or, as a first approximation, that of metals in "keyhole" mode). In this case, the absorbed irradiance Iabs (expressed in W / m2) can be replaced by a volumetric heat source Qabs (expressed in W / m3). After dimensional analysis, three dimensionless parameters ji'b ji'2, jt'3 can be calculated: 4HAT] ' 1 e„A ^2“ A 2 2» [ tc wnu l 1 Æ3~ H ] We can consider that Qabs is expressed as the product of the absorbed irradiance Iabs and a volume absorption coefficient [3 such that 1 / P is homogeneous at an absorption thickness. We can then write: ' 2k.AT h 2 712 ~ 77^ —Tï^ Or again:

[0186]

[0187]

[0188] ^3 = ^3 According to one embodiment, the volume absorption coefficient [3 can be considered as a basic metric. Therefore, the dimensional analysis can provide 4 dimensionless numbers, jt”b ir”2, jt”3, ir”4. These can be expressed as :

[0190] Furthermore, in the embodiments previously described, the heat source (laser) is modeled by an irradiance alone (Iabs) or a heat source alone (Qabs)•

[0191] However, depending on the application, it may be desirable to take into account the effect of the lasing speed VL and that of the characteristic radius RL of the energy source: in fact, a laser scan on a given surface is not strictly equivalent to an equivalent irradiance imposed statically on this same surface.

[0192] We can then determine five dimensionless numbers jtb ir2, ir3, ir4, ir5:

[0193] tK~iJn]ELM-VL

[0194] Thus, many mechanisms can be developed to determine a heat map, and the configuration method described does not depend on a particular mechanism but can instead be based on any heat map, regardless of its method of determination (and regardless of the modeling of the manufacturing parameters).

[0195] In a more general case, at least one indicator is provided to be expressed as a function of the temperature rise [ AT], and of the correlation function g(). So we have:

[0197] The other indicators I2,I3... In relate to the causes of overheating, such as undercuts, or short inter-vector transients. Thus, h and f are linked via the function f:

[0198] / ( — with i = 2, 3 ...n

[0199] n being the number of indicators.

[0200] Consequently, to generate the heat map from the indicator map, it is sufficient to apply, in step 310, the following operation: [02011

[0202] In all cases, the determination of the heat map depends on a correlation function linking the indicators. In particular, it depends on the correlation function making it possible to link an indicator dependent on the temperature rise (the desired quantity) to indicators not dependent on the temperature rise (and therefore calculable).

[0203] This correlation function can be determined in a step 308. This step consists of generating a law ) or a tabulated response surface, which makes it possible to mathematically link a temperature rise [ A 7' ]^ ^ to the different identified indicators. The law f ( ) or the tabulated response surface are generated using numerical simulations of the vectorization at the scale of an elementary volume for different manufacturing conditions (different process parameters, different materials, different geometries), so as to form an abacus.

[0204] These simulations can be carried out beforehand (via different numerical methods, such as finite elements, finite volumes or the discontinuous Galerkin method), or by thermal measurements carried out during the manufacture of previous parts.

[0205] Step 308 can therefore be carried out prior to triggering the machine configuration process for a given production. In particular, the result of this step (an abacus) can be shared for a plurality of different configurations (different products, etc.)

[0206] This step can be called the calibration step, or calibration, since it involves relating the known values of a subset of indicators (for example of dimensionless numbers) to a correlation function to obtain the value of the unknown indicator, i.e. the one which depends on the temperature rise (the correspondence between the temperature rise and this last indicator being immediate, via the equations given above).

[0207] Several methods can be used to obtain an estimate of this function.

[0208] For example, this estimation can be carried out by simulating the vectorization and the associated thermal behavior at the elementary volume scale, ELM, by varying the different primary parameters of the additive manufacturing process in a range of values relevant to the capabilities of the machines or the window of the additive manufacturing process implemented. We can thus describe a space of relevant values and, for each n-tuples of the parameter space, we can obtain by calculation or simulation the indicators { / ■] 9 (or for example the parameters dimensionless jti, ir2, Jt3.)

[0209] We can therefore consider that it is a question of carrying out a plan of numerical experiments with the objective of constructing a response surface making it possible to correlate the indicators [ L} . When a function f( ) is difficult to determine, it is 1 b J 7=1, 2, 3.. .n possible to construct a database in which the indicators [Zj . are tabulated.

[0210] In other words, from these n-tuples of indicator values, we can determine a general law (by linear approximation or other, for example), or a table, whose knowledge of n-1 entries {Zj allows us to determine the last value of the p-tuple, F.

[0211] This simulation can be carried out by different methods of the state of the art such as for example the finite difference method, the finite element method, the finite volume method, the discontinuous Galerkin method (GD), etc.

[0212] Simulating the vectorization at the elementary volume scale, ELM, to correlate the indicators with each other, instead of doing it at the scale of an entire layer, makes it possible to considerably reduce the calculation times: thus, the simulation can take a few minutes for an elementary volume against a few hours, or even a few tens of hours, for an entire layer.

[0213] FIG. 6 illustrates the result of such a set of simulations. Each point represents a simulation, and has as its abscissa the value of the correlation function applied to the indicators not involving the temperature rise {A} 3 ' and as its ordinate the value of the dimensionless quantity h or more generally of the indicator as a function of the temperature rise.

[0214] We note that the points are approximately aligned for all parametric configurations, and in particular for different materials (since, during the simulations, the types of materials and therefore the parameters X and a were varied). This therefore validates the universal nature of the scaling law thus obtained. Therefore, if the experimental plan is well designed, this simulation can be carried out only once and reused for different simulations of the thermal behavior of a part for its production by additive manufacturing.

[0215] Thus, the computational aspects of the simulation are transferred to the construction of this abacus (in the form of a general law (function), tabular, etc.). They can be carried out upstream of its use and be shared for several uses. These calculations therefore no longer penalize the processing of the determination of the thermal behavior of a part to be produced.

[0216] This abacus can therefore be created upstream and reused for a set of simulations of the thermal behavior of a part to be produced due to the universal nature of its values.

[0217] Following the determination of the values of the basic metrics, in step 302, it is possible, in step 310, to calculate the values of the indicators which do not depend on the temperature rise [AT].

[0218] This calculation can be carried out for all or part of the layers of the part to be produced. Each layer considered is subdivided into elementary volumes, ELM. The characteristic dimension of these and the distance between two elementary volumes depend on the spatial and temporal resolution desired for the analysis.

[0219] Each ELM elementary volume contains portions of the laser diagram, forming segments between an entry point and an exit point in the elementary volume (Thus, as a reminder, in [Fig.5A], we observe the segments oriented [1; 2], [3; 4], [5; 6], [7; 8], [9, 10]). We can calculate the indicators not depending on the temperature rise for each ELM elementary volume from these segments and without resorting to the geometric model.

[0220] The calculation of certain indicators can be done directly for a layer, that is to say that their values do not depend on the other layers. The value of other indicators can depend on the other layers, in particular the elementary volumes located on the same vertical as the current one.

[0221] In the example given above based on dimensionless numbers, the calculation of the dimensionless number ir3 is direct: it depends on the values of the durations [ and [The value of these durations depends only on the layer considered.

[0222] On the contrary, the calculation of the dimensionless number ir2 depends on a set of layers: it is necessary to aggregate the information contained in the previous layers in the depth a of the elementary volume, in particular, to sum all the lengths of portions of vectors allowing the calculation of the volume of dense matter.

[0223] As described previously, using this correlation function, which can be precalculated in the form of an abacus, the temperature rise can be determined in each elementary volume ELM. All of these values define the thermal map

[0224] [Fig.7] illustrates such a heat map very schematically.

[0225] This figure represents a fictitious part to be produced 220. It is assumed that the lasing is carried out horizontally. The different filling patterns of the zones 225, 226, 227 correspond to distinct values of the temperature rise [AT]. Only 3 values are represented for the clarity of the figure, but in practice, the map can present a continuity of values (each corresponding to a gray level, in a graphical representation) or to a larger number of discrete values.

[0226] In zone 225 (shown in dotted lines), heating is minimal because the laser travels a long distance, within the part (or parts, in the general case) before returning to the vicinity of the same point. The heat therefore has time to dissipate substantially between two passes of the laser in the same vicinity (according to the predefined lasing pattern).

[0227] In zone 227 (represented by bold hatching), heating is strong because the laser makes very short back and forth movements in the part which, in this zone, has a very small dimension in the direction of the lasing. Between two passes of the laser, the heat does not have enough time to be evacuated, and the temperature rises.

[0228] Zone 226 (represented by finer hatching) corresponds to a situation intermediate to those of zones 225 and 227, in which the zone is oriented at 45° relative to the lasing pattern so that the travel time of the laser within this zone is longer than in zone 227, but not sufficiently to allow sufficient heat evacuation.

[0229] Thus, this thermal map shows the different areas of the manufacturing layer which are likely to overheat, due to the lasing strategy which may be locally unsuitable.

[0230] According to particular embodiments, post-processing 311, 313 can be implemented to improve the heat map generated in step 310 and allow better adaptation to step 316.

[0231] These post-processing operations may consist of digital image processing operations, insofar as the thermal map can be considered as a digital image (the value of each pixel corresponding to a rise in temperature.)

[0232] In particular, thresholding steps 311 and segmentation 313 of the heat map can be implemented.

[0233] This thermal map can thus make it possible to determine risk areas, i.e. areas likely to give rise to hot spots which could lead to surface protrusions, cracks, cavities (“keyholes”), or other anomalies on the part to be produced.

[0234] The determination of these risk areas can be carried out in different ways and follows directly from the determination of the heat map.

[0235] For example, a threshold can be established beyond which a temperature difference is indicative of a risk zone.

[0236] The detection of one or more risk zones can trigger several actions on the part of the optimization device 230 aimed at adapting the configuration of the machine 200 by means of the laser strategy 240. In particular, from the heating law determined in step 308, it is possible to deduce rules of cor actions to mitigate or even eliminate anticipated hot spots.

[0237] The method may therefore include a correction step 316. This step may consist of calculating the condition(s) for which, at any point in the lasered layer, the temperature rise remains below a critical threshold, noted ATcrit (critical temperature constraint), established upstream in a step 314.

[0238] This step 314 of determining critical thermal thresholds is based on a correlation between the local temperature rise and a measurement relating to a defect representative of the quality of the part 220, but this correlation can be established more generally between temperature rise and defect, independently of a specific part, in particular using a test piece. This makes it possible to establish the correlation upstream, once and for all, without needing to recalculate the correlation for each part.

[0239] One way of establishing this (or these) critical threshold consists of training a regression model h( ) between thermal elevation values [AT calculated as indicated previously and an experimental measurement of a material defect measured in different positions of a representative test piece.

[0240] This measurement relates to a representative defect, i.e. one on which one can base oneself to establish the conformity of the part produced to a specification. It is therefore the objective to be achieved as quality of the part produced. A relevant experimental measurement of a representative defect can be, for example, the porosity rate measured at the element scale, noted [n%]

[0241] [Fig.8] illustrates this step 314 of determining thermal thresholds.

[0242] A measurement of a defect is carried out, for example by local tomography, on a layer 801 of a test piece 800. A data structure 804 is thus obtained providing a measurement of a representative defect for each point (or element) of the layer 801.

[0243] Furthermore, the temperature rise [AT] can be simulated using the lasing strategy 108 and the indicators in order to obtain a thermal map 803.

[0244] It is then easy to match the thermal map of the test piece 800 with the defect map 804, and thus establish the correlation 805 between representative defects 804 and temperature increases 803.

[0245] This can be done layer by layer.

[0246] Generally speaking, at any point of the representative test piece, we therefore calculate:

[0247] rp%i = h(f AT] )

[0248] In other words, we establish, upstream, a correspondence between, at the input, a temperature rise, and, at the output, a representative fault. We can then establish a critical temperature threshold ATcrit based on a desired threshold on the representative fault.

[0249] Thus, depending on the quality criterion required on a part, the critical threshold ATcrit is positioned so that the porosity rate in the part resulting from the lasing strategy remains lower than the desired quality criterion. This reasoning can be applied to other types of measurements on representative test pieces.

[0250] Of course, other optimization objectives can be considered in the adaptation / correction step 316.

[0251] For example, we can mention an objective of minimizing the deviations of [ A T ] calculated at the layer scale (homogenization constraint). This different objective can influence the adaptation strategies implemented in step 316 of adaptation of the lasing strategy.

[0252] In practice, detecting an overheating problem from the thermal map can allow the triggering of different actions.

[0253] For example, these actions may include one or more of: - Alerting a user by highlighting the risk area(s) on a graphical representation of the heat map; - Alert the user so as to prevent the triggering of additive manufacturing as is; - Trigger an optimization of the configuration parameters of the additive manufacturing machine 200, in order to avoid, or reduce, the appearance of these risk zones appearing on the heat map.

[0254] In particular, the method may therefore comprise a step 316 of adapting the production strategy as a function of the thermal thresholds and the thermal map. The thermal thresholds form an adaptation objective making it possible to search for the manufacturing parameters making it possible to generate a thermal map conforming to the objectives (thermal thresholds). It is thus proposed to base the adaptation of the production strategy on thermal thresholds, themselves based on a measurement of a criterion (or defect) representative of the final quality of the produced part 220.

[0255] The parameters that can be adjusted for this optimization include: - The power of the laser. The laser pattern can be kept but the power can be reduced on one or more segments of the laser pattern that correspond to risk areas; - The laser speed. The laser pattern can be maintained, but the laser speed can be increased on one or more segments that correspond to risk areas. - Pause times between two segments. For example, you can keep the laser pattern, but add a pause time between two successive segments in or around a risk area to allow the area to cool. - The order of the segments. You can keep the laser pattern, but skip one or more segments in a risk area and then return to them later, in order to let the area cool down in the meantime. This option penalizes manufacturing time less than introducing pause times. - Segment length. In the case of a strip laser strategy, the width of the strips can be increased to increase the time between two laser passes in the same area. Two adjacent segments can also be joined together if one of them is too short. - The laser diagram, in its entirety. - The geometry of the part to be produced.

[0256] This adaptation of parameters may for example comprise the modification of the data structure expressing the lasing scheme. The lasing parameters (laser power, laser speed, laser beam diameter, irradiance profile, etc.) may be directly modified segment by segment in this data structure, the order of the segments may be modified, new lines may be added to insert a pause between two segments, etc.

[0257] We therefore see that a large number of correction combinations are possible. Indeed, each parameter of the process considered in the modeling (laser power, scan speed, laser spot diameter, pause time or inter-vector jump, etc.) can be adjusted individually. Similarly, several parameters can be adjusted jointly for this purpose.

[0258] However, in practice, there are constraints linked to the manufacturing process (e.g.: melting threshold power, maximum laser movement speed, response dynamics of the opto-mechanical chain of the manufacturing machine, productivity, etc.), known to those skilled in the art which, once taken into account, limit the number of possible combinations.

[0259] According to one embodiment, the adaptation step 316 is provided to generate a set of adaptation scenarios. Each scenario may have advantages and disadvantages.

[0260] The choice between the different scenarios can be made in different ways: manually by a human operator, or automatically according to a selection criterion which may depend on other constraints present in a specification (cost, etc.)

[0261] A first possible scenario consists of carrying out a grouping of vectors of the development strategy 308 and to assign a value of at least one parameter of the machine 200 to each group (for example the power of the laser).

[0262] The grouping, or splitting, can relate to sets of vectors having the same parameterization (e.g.: a band) into several sets of vectors having different parameters (e.g.: sub-bands), without changing the laser order between the vectors.

[0263] This strategy is shown diagrammatically in [Fig.9A] and [Fig.9B].

[0264] Element 900 of [Fig.9A] represents a trapezoidal vectorization diagram, which generates an overheating zone in the converging zone of the tool path (i.e. at the top of the part, in the figure).

[0265] This overheating zone is represented by the schematic heat map 902, which has a color gradient going towards a darker gray in the upper (convergent) zone of the parallelepiped shape.

[0266] Thanks to the adaptation step 316, a new lasing strategy 904 is calculated, where new process parameters (laser power) are assigned to subgroups of vectors. This splitting with preservation of the lasing order, here consisting of six groups of three vectors, is schematized by the use of different dotted lines.

[0267] Then, a new thermal map 906 can be produced from the new lasing strategy, so as to verify that the overheating zone has indeed been attenuated, or even eliminated. The figure shows a uniform gray level across the entire part, illustrating a homogeneous thermal map with no areas exceeding an established critical threshold.

[0268] [Fig.9B] illustrates what the laser power setpoint would give schematically, if the correction of the lasing strategy concerned this parameter. We see that the laser power is modulated by group of three successive vectors, as suggested in the diagram of [Fig.9A].

[0269] In the illustrative example, the vector groups contain equal numbers of vectors. However, the number of vectors contained in each group can be calculated in a self-consistent manner by the correction method. A limiting case of this adaptation method would consist of forming groups of a single vector: a value of the parameter (laser power or other) is then assigned to each vector independently.

[0270] In addition to laser power, machine parameters that can be optimized in this way may include lasing speed, intervector jump or pause times, etc.,

[0271] The parameters can also be modulated jointly. For example, it is possible to act first on the laser power, up to a lower power threshold, then compensate for the residual overheating by adding pause time. inter-vectors.

[0272] Another possible scenario includes assigning a progression of at least one parameter of the machine 200 to at least a portion of a vector of the elaboration strategy.

[0273] This possibility therefore allows a gradual adaptation to a detected thermal problem, by increasing or decreasing a parameter along a vector in order to optimize the thermal response of the material. This growth can be continuous or in stages.

[0274] Like the previous strategy, this method allows the initial laser pattern and order to be preserved; and the correction acts only on the process parameters.

[0275] This scenario makes it possible to respond to both an object of not exceeding a critical threshold of temperature rise, but also of homogeneity of the temperature rise on the surface of the lasered material. However, it assumes that certain parameters of the machine can be modulated (continuously) along a given vector, which is not currently possible on all the machines available on the market.

[0276] This second scenario, called continuous adaptation of the process parameters, is illustrated in [Fig.10A] and [Fig.10B].

[0277] [Fig.10A] schematizes a vectorization diagram 1000. From this vectorization diagram and the process parameters, a thermal map 1002 is calculated. On this map, a gradient of gray levels is observed, starting from the darkest along the left lateral edge of the laser pattern, schematically indicating an overheating zone, towards a lighter zone along the right lateral edge.

[0278] On the basis of the thermal thresholds calculated in step 314, a new production strategy is calculated, schematized by the laser pattern 1004. It is assumed that this new production strategy makes it possible to satisfy, at any point of the fabricated layer, the equality [ A T]EL^ — ATref (where AT ref < ATcn-r is a reference temperature rise, deemed to give satisfactory material health, therefore lower than the critical threshold temperature).

[0279] One or more parameters of the process are therefore continuously adjusted along the vectors, using equation (1) given above and linking temperature rise and indicators, so as to achieve the set homogenization objective. This adaptation is shown diagrammatically on the vectors 1004 by the use of a dotted line. This new development strategy is saved, a new thermal map 1006 is generated so as to verify that the new calculated development strategy allows the thermal map to be homogenized, below the critical threshold ATcrih as wish.

[0280] If the parameter (or combination of parameters) chosen for the correction does not modify the temporality of the tool path (e.g.: modulation of the power), then the correction step is direct, i.e. it is carried out in a single calculation step. On the other hand, if the corrective measure changes the temporality of the tool path (e.g.: modulation of the speed or addition of pause time), then the corrected strategy is obtained after an iterative process, because by changing the temporality of the lasing strategy, the basic metrics must be entirely updated.

[0281] Let us assume in this first example that only the laser power has been modified. This strategy is illustrated in [Fig. 10B], which shows a vector representative of all the vectors included in the corrected vectorization diagram 1004. In the area where the thermal map is homogeneous and shows no signs of overheating, the laser power is kept constant, as shown in graph 1011. This absence of power modulation is shown diagrammatically on the vector by the use of the solid line 607 and by a plateau on the power curve 1011 between points Mi and M2.

[0282] On the other hand, in the area where signs of overheating were initially observed, the laser power is reduced along the vector between points M2 and Mn. This adaptation is shown diagrammatically on the vector by the use of a dotted line 1009 (portions 1007 and 1009 are part of the same vector). Thus, on graph 1011, the laser power is continuously reduced along the vector.

[0283] In order to carry out this operation, each vector can be discretized into a variable number of control points Mi (distinct from the geometric starting and ending points of the vectors), to which the power values calculated in step 314 are assigned.

[0284] The coordinates of the control points and the associated powers must appear in the vectorization file (or laser strategy) 106 as properties.

[0285] Thus, in FIG. 6B, the portion 1007 of the vector which has not been modified, is only discretized by two control points M1 and M2, delimiting the start and the end of the zone in which the initial laser power is preserved. On the other hand, in the portion 1009, the vector is discretized by a greater number of control points, making it possible to restore the power correction setpoint with sufficient resolution. Finally, between two control points, the interpolation depends on the mechanical or opto-mechanical chain (in the case of the LPBF process) used in the manufacturing machine.

[0286] Therefore, the number of control points and their positions must be chosen in accordance with the response dynamics of the opto-mechanical chain, so that the correction instruction can be achieved in practice.

[0287] Concretely, if two control points are located in a lasing time interval lower than the characteristic response times of the opto-mechanical chain, then the correction calculated in step 316 cannot be adequately taken into account by the manufacturing machine.

[0288] In fact, the lasing time interval between two control points tCTRL and the characteristic response time(s) of the opto-mechanical chain tdyn are constrained by the following inequality: CTRL ^-tdyn

[0289] where k is a proportionality factor. The characteristic time tdyn can represent the time for the laser to ramp up to the set power, the characteristic time for the tool (galvanometers) to ramp up to the set speed, etc.

[0290] The opto-mechanical characteristics specific to each machine thus act as constraints which are taken into account in correction step 316.

[0291] Obviously, similar correction strategies can be considered for the other parameters of the LPBF process, such as the scanning speed (lasing), the laser beam diameter, the inter-vector pause or jump times, the vector deviation, etc.

[0292] A third possible scenario, called “segmentation”, is illustrated by [Fig. 11 A] to [Fig. 11F],

[0293] This scenario differs from the previous ones in that it leads to "cutting" the vectors, that is to say to creating new vectors from the old ones.

[0294] From the lasing strategy 1100, a thermal map 1102 is generated which, as previously, shows a gradient of the temperature rise illustrated by a gray level gradient, ranging from a dark gray along the left lateral edge to a light gray along the right lateral edge. This thermal map therefore reveals a thermal problem on the leftmost part of the part.

[0295] According to this embodiment, at least one threshold is established within the thermal map. To do this, an additional thresholding step can be implemented in order to obtain a thresholded thermal map 603 on which (a single threshold being used) two zones appear. In the diagram, one threshold is represented, but of course, more thresholds can be added, so as to create as many laser zones as necessary.

[0296] From this thresholded heat map, a new segmented vectorization scheme 1105 can be generated.

[0297] Then, we can identify two variants of this method of correcting the development strategy, depending on whether the order of lasering the vectors is preserved or not.

[0298] A first embodiment where the lasing order is preserved is illustrated in [Fig. 11B] and [Fig. 11C],

[0299] In this case, the vectors resulting from the segmentation operation are lasered one after the other in the same order as in the initial lasering strategy, as illustrated in element 1113 of [Fig. 1 IB].

[0300] In the illustrated example, each vector is segmented into two vectors. Thus a first vector is segmented into two vectors [1 - 2] and [3 - 4], the next vector is segmented into two vectors [5 - 6] and [7-8], and so on until the last vector, segmented into two vectors [21 - 22] and [23 - 24].

[0301] Therefore, if we take the previous example of the reduction of the laser power, then we obtain the graph 1119 of [Fig. 1 IC], where the set laser power is modulated discontinuously, between a first level (at high power) on a first vector 1115 and a second level (of lower power) on a second vector 1117. These first and second vectors correspond to the vectors in solid lines, respectively in dotted lines, on [Fig. 11B].

[0302] Compared to the previous implementation, the vectors are segmented into two (or more) distinct vectors, whereas previously, the elements 1007 and 1009 were portions of the same vector.

[0303] We then understand that the greater the number of thresholds, the closer this new method (discontinuous method) is to the first (continuous method).

[0304] The discontinuous method can help to overcome the difficulties encountered by the continuous method, linked to possible incompatibilities between the opto-mechanical dynamics of the manufacturing machine and the time intervals of the correction instructions for the process parameters.

[0305] The discontinuous method, however, has the disadvantage of imposing stopping points between two new vectors (increasing the manufacturing time and the probability of generating defects when stopping and restarting the vectors), where there were none with the continuous method. In addition, with the discontinuous method, the correction instruction obtained is not necessarily the optimal solution, since it amounts to calculating a corrected parameter for an entire vector, where in the continuous method, there was one corrected parameter per control point (therefore with a finer spatial resolution).

[0306] Finally, continuous and discontinuous methods can be cleverly combined to benefit from the advantages of each. They can also be combined with the fractionation method.

[0307] Obviously, similar correction strategies can be considered for the other parameters of the LPBF process, such as the scan speed, the laser beam diameter, the inter-vector pause or jump times, the vector deviation, etc.

[0308] According to an alternative embodiment, the lasing order is modified. This case is illustrated in [Fig. 11D] to [Fig. 11F]. In this example, it can be seen on element 1121 of [Fig. 11D] that the right band resulting from the segmentation operation is lased first (from point 1 to point 12), and that the remaining left band is lased second (from point 13 to point 24). In this configuration, different correction instructions can be envisaged. Two are illustrated in [Fig. 11E] and [Fig. 11F].

[0309] As in the two previous strategies, it is possible to reduce the laser power in the left band, so as to reduce overheating. This is illustrated in graph 1123 of [Fig.l 1E].

[0310] It is also possible to maintain the laser power (for example, if it is impossible to reduce it for material health reasons), but to add pause times between two successive vectors, so as to allow the material to cool between two passes. This is what is illustrated in graph 1125 of [Fig.l 1F].

[0311] Also, it is possible to combine the two approaches. For example, reduce the power to a minimum threshold (guaranteeing material health), and supplement with an inter-vector delay when necessary.

[0312] . Other parameters can also be modulated, such as scan speed, laser beam diameter, vector spacing, lasing order of vectors within each zone, etc. Also, this segmentation strategy without preservation of the lasing order can be combined with the previous ones, which would therefore be applied within each new lasing zone created.

[0313] We can also note the difference between the segmentation strategy of vectorization without preservation of the lasing order, and the global reordering strategies, presented in the literature. As a reminder, in the latter, the vectors which constitute the lasing pattern are numbered, and a correction algorithm changes the global lasing order of the vectors (or lasing patterns) so as to limit the hot spots. However, the global lasing order can be constrained in industrial parts, for example to obtain specific micro structures.

[0314] In the proposed method of segmentation of the vectorization without preservation of the lasing order, the order of the vectors is altered only at the margin. In particular, in the example proposed in [Fig. 11D], the lasing order inside the pattern 1105 is globally preserved with respect to the initial strategy 1100 (from bottom to top, from right to left), and adapted only locally so as to correct the overheating zone. This method is therefore more advantageous than those presented in the literature, because it is more flexible.

[0315] Of course, the present invention is not limited to the examples and the embodiment described and shown. It is in particular susceptible to numerous variants accessible to those skilled in the art.

[0316] Based on the lasing strategy adapted by step 316 of the method described, regardless of the embodiment, the machine 200 can undertake the manufacture of the part 220. The adaptation carried out makes it possible to optimize the thermal response of the part and, consequently, to minimize the problems resulting from local overheating and / or heterogeneities in the temperature rise. The defects in the material are thus minimized, which makes it possible to achieve, on the one hand, better qualities of the parts produced and also a more robust industrial process (with fewer scraper / material collisions, in particular, causing less downtime, loss of material, etc.).

Claims

Claims

1. Method for configuring the manufacturing of a part (220) by means of a machine (200) for additive manufacturing, by means of a production strategy (240) indicating at least one manufacturing trajectory followed by said machine, said method comprising steps of: - determining (310) a thermal map for at least one layer of said part, said thermal map associating a local temperature rise with the points of said trajectory; - determining (314) critical thermal thresholds as a function of a correlation between said local temperature rise and a measurement relating to a defect representative of the quality of said part (220); - adapting (316) said production strategy as a function of said thermal thresholds and said thermal map.

2. Method according to the preceding claim, comprising a prior step of determining (302, 304) basic metric values, relating to said manufacturing, as a function of said trajectory.

3. Method according to the preceding claim, comprising a step of determining (306) a set of indicators as a function of said basic metric values and a correlation function linking said indicators, the heat map being determined (310) as a function of said indicators, said set of indicators preferably being calculated by first determining a set of aggregated metric values on elementary volumes (ELM) and then determining said indicators for said elementary volumes.

4. The method of claim 3, wherein said determining indicators comprises determining an indicator dependent on a temperature rise and said correlation function, said correlation function being estimated (308) by previously varying values for basic metrics and simulating an associated temperature rise so as to constitute an abacus.

5. Method according to one of the preceding claims, in which said adaptation step (316) comprises the determination of a scenario comprising the grouping of vectors of said development strategy and assignment of a value of at least one parameter of said machine (200) to each group.

6. Method according to one of the preceding claims, in which said adaptation step (316) comprises the determination of a scenario comprising the assignment of a progression of at least one parameter of said machine (200) to at least a part of a vector of said development strategy.

7. Method according to one of the preceding claims, in which said adaptation step (316) comprises the determination of a scenario comprising the segmentation of at least one vector into a set of vectors, each associated with a different value of at least one parameter of said machine (200).

8. A method of manufacturing a part by additive manufacturing using a machine (200) for additive manufacturing, comprising a configuration method according to any one of the preceding claims, and in which said manufacturing is carried out according to said development strategy.

9. Computer program comprising instructions for implementing a method according to one of claims 1 to 7 when executed on a computer.

10. Optimization device (230) comprising a processor and associated circuits, adapted to implement a method according to one of claims 1 to 7 for configuring a machine (200) for the manufacture of a part (220).

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