Correcting a strategy for producing parts manufactured by additive manufacturing, by studying toolpaths

Abstract

The invention relates to a method for configuring the manufacture of a part using an additive manufacturing machine, by means of a production strategy indicating at least one manufacturing path 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 points on the path; determining (314) critical thermal thresholds as a function of a correlation between the local temperature rise and a measurement relating to a fault representative of the quality of the part; adapting (316) the development strategy as a function of the thermal thresholds and of the thermal map.

Description

DESCRIPTION TITLE: Correction of the development strategy of parts manufactured by additive manufacturing by the study of tool trajectories TECHNICAL FIELD

[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 types, different dimensions, and varying in complexity. Some parts must also meet strict specifications and may be subject to certification. For example, in the aeronautics sector, additive manufacturing can involve high or low pressure distributors for turbomachines, turbomachine guide 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 processes currently available, we can notably cite laser powder bed sintering processes, such as selective laser sintering (SLS), laser powder bed fusion processes, such as for example selective laser melting (SLM), laser beam melting (LBM), laser powder bed fusion (LPBF), direct metal laser sintering (DMLS), etc.

[0006] electron beam powder bed fusion processes, such as electron beam melting (EBM), electron powder bed fusion (EPBF), etc.

[0007] In particular, in laser powder bed fusion processes (SLM, LBM, LPBF, DMLS, etc.), parts are manufactured by laser fusion and solidification of a succession of powder layers.

[0008] For each layer, a scraper spreads a bed of metal powder of a fixed thickness. Then a laser whose movement is controlled melts only the surface corresponding to the cross-section of the part to be built, scanning this surface according to a development strategy (including a trajectory) set by the operator and stored in a computer file. This part of the operation is often called "lasing". The molten powder resolidifies after the laser passes over it and the solid surface is thus created. The plate is then lowered by the thickness of the new layer of powder that 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 as the laser passes (called the "lasing bead").

[0009] The laser path is also set by the operator. This laser path is also called a laser pattern, or path, scan pattern, or vectorization strategy. This path is predefined during the manufacturing preparation phase (CAM for "computer-aided manufacturing") before being supplied to the additive manufacturing machine.

[0010] 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, limit these "hot spots", because they are the source of many defects and non-conformities.

[0011] Proposals have been made to determine the thermal behavior of a part during manufacture, particularly upstream of its manufacture.

[0012] However, tools for a priori evaluation of the thermal behavior of a part encounter limitations linked to the scale effects associated with manufacturing processes. additive, and in particular to laser powder bed fusion processes such as the LPBF process.

[0013] Thus, there are numerical 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 dozen hours per layer and per part). There are also tools that allow the macroscopic temperature fields to be estimated in a few hours 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 we wish to analyze and therefore do not allow us to anticipate anomalies in the product during manufacturing with sufficient precision and finesse.

[0014] 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 calculation times incompatible with industrial constraints. In addition, the fact of basing itself 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.

[0015] 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.

[0016] Furthermore, generally speaking, the elaboration (or lasing) strategies that specify the lasing trajectory and associated parameters (laser power, etc.) are generated by tools provided by the manufacturer of the additive manufacturing machine.

[0017] The machine user therefore does not have full control and mastery over these generations, and in particular the criteria used. As a result, when defects appear, the user can hardly establish reliable and repeatable corrective measures.

[0018] Certainly, some manufacturers offer the user various settings that can theoretically allow the lasing strategy to be adapted. However, these settings are often inappropriate. For example, it may be intended 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 appropriate 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 rules are provided for adjusting these parameters rationally: the user must proceed by trial and error, which represents a significant cost for a development phase.

[0019] 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.

[0020] In particular, we are looking for a process 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). STATEMENT OF THE INVENTION

[0021] 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: determining basic metric values, relating to said manufacturing, as a function of said trajectory, determining a heat map for at least one layer of said part from said basic metric values, said heat map associating a local temperature rise with the points of said trajectory, determining 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, adapting said development strategy as a function of said thermal thresholds and said thermal map, comprising determining a scenario comprising: grouping vectors of said development strategy and assigning a value of at least one parameter of said machine to each group, and / or assigning a progression of at least one parameter of said machine to at least one part of a vector of said development strategy, and / or segmenting at least one vector into a set of vectors, each associated with a different value of at least one parameter of said machine.

[0022] The process thus makes it possible to generate a specific file, allowing the manufacturing of a part by an additive manufacturing machine (or 3D printer).

[0023] 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 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 metric values ​​on elementary volumes, then determining said indicators for said elementary volumes; said aggregated values ​​of metrics 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; 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 inter-vector jump time and a gap between vectors.

[0024] According to another aspect, the invention relates to a method of manufacturing a part by additive manufacturing using an additive manufacturing machine, comprising a configuration method as previously described, and in which said manufacturing is carried out according to said development strategy.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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 DRAWINGS

[0029] The accompanying drawings illustrate the invention: FIG. 1 schematically illustrates a system according to an embodiment of the invention, comprising a machine for production by additive manufacturing. FIG. 2 schematically represents a flowchart of the steps of a method according to an embodiment of the invention. FIG. 3 schematically illustrates a possible embodiment for a correction step of the method illustrated in FIG. 2, according to one embodiment. FIGS. 4A to FIG. 4C schematically illustrate one possible embodiment for a step of determining aggregated values ​​of basic metrics for an elementary volume. FIG. 5A and FIG. 5B illustrate examples of elementary volumes, according to embodiments of the invention. 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. FIG. 7 illustrates an exemplary heat map obtained according to implementations of the invention. FIG. 8 illustrates a method for determining thermal thresholds, according to one embodiment of the invention. FIG. 9A and FIG. 9B illustrate a first scenario for adapting the lasing strategy, according to one embodiment of the invention. FIG. 10A and FIG. 10B illustrate a second scenario for adapting the lasing strategy, according to one embodiment of the invention. FIGS. 11A to FIG. 11F illustrate a third scenario for adapting the lasing strategy, according to one embodiment of the invention. DETAILED DESCRIPTION OF SPECIFIC EMBODIMENTS

[0030] The proposed method applies to various printing or additive manufacturing (or 3D printing) techniques, 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.

[0031] The laser powder bed fusion additive manufacturing process 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 aeronautics sector.

[0032] 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.

[0033] It is therefore essential to understand the influence of heating / cooling cycles 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.

[0034] 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 can develop, linked to an excessive energy input 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 many round trips in a relatively short period of time and / or undercut areas overlooking powder masses which have relatively low thermal diffusivity compared to that of dense matter.

[0035] 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 scraper system can collide with the part being manufactured, which can cause degradation of the manufactured geometry or even stop production. As a result, 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.

[0036] In order to anticipate the appearance of these hot spots, it is proposed to carry out digital modeling and simulations before the actual manufacturing.

[0037] Indeed, by simulating the heat, or energy, input into a part produced by additive manufacturing, and by taking into account the vectorization (or lasing) diagrams, 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.

[0038] The proposed process thus aims to identify risk areas, 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).

[0039] The proposed method is based on the strategy, or scheme, of development, or lasing and not, like some 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 necessary calculation times while maintaining a high precision in the estimation of risk areas, and also to take into account the entire production tray, which may include several parts while previous proposals opt for a part-by-part approach. It is important to note that the lasing scheme being common for the entire production tray, a product-by-product approach necessarily introduces a bias in the estimation.

[0040] FIG. 1 schematically illustrates a context of use of a proposed method of configuring the manufacturing of a part by additive manufacturing.

[0041] 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.

[0042] In the aeronautics field, the 200 machine 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.

[0043] 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 additive manufacturing machine 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.

[0044] 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.

[0045] Depending on the risk areas determined in the thermal behavior of the part 220 to be produced, a user can intervene on the machine parameters in order to avoid, or reduce, the appearance of these risk areas, or else these risk areas actually become hot spots.

[0046] 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, the heating and cooling cycles during additive manufacturing can thus be optimized using the results of the simulation phase.

[0047] 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.

[0048] FIG. 2 schematically illustrates a flowchart of an overall process for developing and manufacturing a part, according to one embodiment.

[0049] An ideal geometric model is first designed in step 100 using Computer Aided Design (CAD) software, based on a specification. The geometric model 100 is generally designed using a surface representation (or B-Rep, for "Boundary Representation") formalism. The STEP file format is an example of a format associated with B-Rep representation.

[0050] Then, the model from step 100 is prepared in step 102, using 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, of designing the geometric model of the manufacturing blank, which takes up the ideal CAD model 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 provides a meshed (“triangularized”) representation of the exterior surfaces of the model.

[0051] 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 CLI format file. 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 mitigate the “staircase effect” specific to AM processes.

[0052] Then, the target trajectory of the manufacturing tool(s) is defined in step 106. 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 end point.

[0053] All vector coordinates are stored in a 108 file, which is presented, for example, in tabular form, where each line contains the coordinates of the vectors' starting and ending points. 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.

[0054] The manufacturing layers can, for example, be segmented by strips that are traversed one after the other, by parallel vectors (separated by a distance called "vector gap", corresponding to a microscopic scale) that are oriented perpendicular to the main direction of the strip. This strategy is said to homogenize the energy input at the scale of each strip.

[0055] 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 cell lasering order and the orientation of the vectors within them (two possible directions within a square) are adjusted to limit thermomechanical stresses at the part scale. There is, moreover, a variant of this strategy, where square cells are replaced by hexagons, which offer an additional degree of freedom (three possible directions within a hexagon) in reducing overheating phenomena and residual stresses, and to homogenize the heat input at the layer scale. There are also other more complex vectorization strategies, such as fractal patterns. Finally, the manufacturing 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.

[0056] Steps 104 and 106 are for example executed by the machine manufacturer's proprietary software suite (also called "Build Processor" in English), but they can also be managed by external software, in particular with the same software as that used in step 102.

[0057] 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).

[0058] Therefore, the parameters of the setpoint 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 end point of the vector, the laser power, the scan speed, the spot diameter, the nature of the vector (e.g.: filling or contour vector), etc. can appear. 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.: a list per band, per checkerboard cell, per vector, etc.), then separate manufacturing parameters can be associated with each of the lists (in this case, the vector(s) included in the same list are lased with the same parameters).

[0059] 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 how to laser these objects (power, speed, spot diameter, etc.)

[0060] 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.

[0061] 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).

[0062] 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.

[0063] FIG. 3 schematically illustrates a possible embodiment for this correction step 111.

[0064] According to this embodiment, a first step 302 comprises determining values ​​of a set of basic metrics according to a first discretization scale.

[0065] This first discretization scale can correspond to a vector deviation, which can be defined according to the additive manufacturing technology (for example between 50 .m and 200 pm, for example of the order of 100 .m for LPBF technology).

[0066] 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.

[0067] According to one embodiment, to summarize, these 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.

[0068] 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.

[0069] Thus, for each basic metric, we transform the values ​​into a digital image in which the pixels correspond to the first discretization scale.

[0070] 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 mi are calculated, thereby generating an image 404 (in which the gray level is representative of the value of the metric for the given pixel).

[0071] The basic metrics locally characterize the vectorization scheme.

[0072] Since it is from these that a heat 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 at step 302 as there are basic metrics.

[0073] A first basic metric can be the total length of vector fragments, contained in each pixel (denoted L t v ot 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 shown in the example referenced 407, the path passes through the pixel twice. The lengths of each segment, L^, L^ are summed to give the total length

[0074] A second basic metric can be the smallest input time of the laser vectors into each pixel (denoted t™ n in FIG. 4C). This time can be easily deduced from the vector lengths contained in the file 108 specifying the production strategy, i.e. the laser trajectory decomposed into vectors and the manufacturing parameters, in particular the scan speed and the inter-vector jump or pause times.

[0075] 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.

[0076] For these three metrics, we therefore seek to exclude the intermediate times t o ' ut and t- nwhich 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.

[0077] 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.

[0078] 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.

[0079] Generally speaking, these basic metrics are selected because they are identified as having a primary 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.

[0080] Generally speaking, these basic metrics for additive manufacturing production relate to the intended tool path for that production, the material, and the tool(s) to be used as a point heat source.

[0081] So, in the case of a laser, this basic set of metrics includes parameters relating to: the lasing pattern planned for the production of one (or more) part, the material used for this production and the laser(s) (e.g., power, speed, spot diameter, etc.)

[0082] 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 laser 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.

[0083] 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 108 file describing the lasing. The latter is substantially smaller in size and easier to manage in structure, which can allow a significant gain in performance.

[0084] 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 the pixels and the vector deviations (microscopic scale).

[0085] 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.

[0086] FIG. 5A and FIG. 5B illustrate examples of ELM elementary volumes for a point M located on the laser diagram.

[0087] 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.

[0088] The characteristic dimension (diameter, side, etc.) has a magnitude 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 that 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.).

[0089] 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.

[0090] 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.

[0091] This lasing scheme, or strategy, mainly specifies the trajectory of the laser path and the inter-bead distance, that is, the distance between two neighboring (and usually parallel) lines of the lasing pattern. In the figure, the lines are parallel and the inter-bead distance corresponds to the distance between two lines of the hash.

[0092] 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.

[0093] This elementary volume ELM contains several vectors of the laser pattern. Here, a "vector" is any rectilinear section that discretizes the laser path on the surface. The laser pattern is therefore also commonly called a "vectorization pattern". It is possible during design to define curved lines for lasering, but these are generally discretized into a set of straight line segments, or vectors.

[0094] The intersection of the laser pattern with the upper surface of the representative elementary volume forms a set of segments between an entry point and an exit point (the laser pattern being oriented in the direction of the laser path).

[0095] 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]).

[0096] The elementary volume ELM can have different shapes: cube, hemisphere, hemi-ellipsoid, etc., depending on its relevance to the thermal problem that we wish to solve, and the practical constraints that this choice imposes on the implementation of the proposed process.

[0097] FIG. 5B shows another form of elementary volume ELM. Its cubic shape can be characterized by a characteristic side a.

[0098] 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.

[0099] The elementary volume ELM can correspond to a mesoscopic scale. This mesoscopic scale is intermediate between the macroscopic and microscopic scales; it is large enough to include a large number of particles (such that their statistical properties do not fluctuate significantly), while remaining fine enough so that the thermodynamic quantities (pressure, temperature, etc.) remain local (point-like at the macroscopic scale).

[0100] 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 near this point, on this lasing pattern, upstream or downstream.

[0101] 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.

[0102] 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 pixel resolution (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.

[0103] Calculating the basic metrics in two steps (first at the pixel scale, then at the elementary volume scale) allows for faster calculation execution time. Indeed, without the first preprocessing step, it would be necessary to loop the calculation of the basic metrics for each position of the ELM elementary volumes. By going through a first "grid" of pixels, we reduce the calculation at the scale of the elementary volumes to simple convolution operations (where the convolution kernel is the shape of the elementary volume), which are faster to execute than looping operations.

[0104] For example, an aggregate value for the basic metric corresponding to the total length of the vector segments included in an elementary volume, denoted [L^V ^ELM is obtained by the sum:

[0105] Similarly, the smallest entry time of the vectors to the surface of an elementary volume ELM, noted [ti in ] ELM is easily calculated using:

[0106] Also, the largest output time of vectors on the surface of an elementary volume ELM, noted is calculated via:

[0107] 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.

[0108] 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.

[0109] 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.

[0110] These parameters preferably include parameters relating to the lasing pattern t SC an, b, b') which can be directly determined from the values ​​of the basic metrics, for a given elementary volume, of the material parameters (thermal conductivity X, thermal diffusivity a) and laser parameters (irradiance 10).

[0111] According to a particular embodiment, 6 parameters are used: the irradiance of the laser, lo (or the absorbed irradiance l a bs=lo. A with A the absorbance of the material); the cumulative exposure time the laser duration tscan / the dense matter depth b, or the equivalent depth b'; the thermal conductivity X; the thermal diffusivity a;

[0112] A seventh parameter of the indicator expressions is formed by a deviation from the reference temperature, or temperature rise, AT^. This is the parameter that we seek to estimate, in order to obtain a heat map.

[0113] A laser of incident irradiance lo travels along the upper surface of a representative elementary volume at a speed VL. We can write: PZ° RL 2

[0114] with P the laser power, RL the radius at 1 / e 2of 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 / e 2 = 0.135).

[0115] 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.

[0116] The cumulative duration of laser exposure can be defined [tlxpo 11]^^ as the sum of the lengths of the portions of vectors contained in the representative elementary volume, divided by this laser speed. It can be expressed by the ratio between the total length [L^V ^ELM of the vector segments included in an elementary volume and the displacement speed VL of the tool (or laser speed in LPBF), that is to say:

[0117] Similarly, the total scanning, or laser, time on the surface of an elementary volume, noted [t scan ] ELM , is calculated by the difference between the largest exit time of the vectors on the surface of this elementary volume ELM and the smallest entry time of the vectors on its surface, that is to say:

[0118] Each individual laser pass is in fact separated from the others by a lasing time outside the representative elementary volume considered plus a jump time between two vectors (i.e. the time taken for the laser to move from the end of one vector to the beginning of the next vector). Thus [texpo Ul ] ELM et [tscanlELM are constrained by the following inequality:

[0119] 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

[0120] 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.

[0121] Furthermore, the dense matter has a thermal conductivity X and a thermal diffusivity a assumed to be constant, while the powder 222 is assumed to be a perfect insulator (adiabatic conditions).

[0122] Finally, we can evaluate the temperature rise at point M corresponding to the elementary volume ELM considered by the variable AT^: A r * _ r * rp

[0123] 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.

[0124] This quantity AT^ 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).

[0125] From these parameters, indicators can be determined. These indicators can be any.

[0126] According to one embodiment, these indicators li, I2, ..., I n , can be universal dimensionless numbers, which are linked together by a mathematical function denoted f().

[0127] Thus, the parameters depend on 4 fundamental units (mass, length, time and temperature).

[0128] According to the Vaschy-Buckingham theorem (or theorem n), 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.

[0129] 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 ni, Ti2, ns. We can write: TTi = f(TT2, TT3)

[0130] Subsequently, we will call this function the correlation function, since it allows us to correlate the variations of the dimensionless parameters ni, n2, ns.

[0131] In a classic way, by exploiting the properties of these 3 numbers, we can define:

[0132] We can define 5 the characteristic diffusion length, such that:

[0133] The definition of dimensionless numbers can then be written:

[0134] We note that ni depends on the temperature T1D calculated at the surface of a semi-infinite medium absorbing the intensity l a bs 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:

[0135] The definition of the dimensionless parameters can then be rewritten:

[0136] Thus, the 3 dimensionless numbers, or indicators, obtained after dimensional analysis can be described as follows:

[0137] TCi is proportional to a standardized temperature rise. This quantity is unknown a priori: it is the one that we want to calculate along the laser diagram in order to obtain the thermal map.

[0138] TT2 is proportional to the normalized diffusion length. This number characterizes the part's ability to dissipate heat locally. The smaller the depth of dense material b, or the equivalent depth b', compared to the characteristic diffusion length 8, 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 lasing scheme (for example in file 108), considering the fact that only dense areas are crossed by vectors.

[0139] ns is proportional to the standardized exposure time. The longer the cumulative laser exposure time [texpo Ul ] ELM tends towards the duration of lasing [t scan ] ELM, and the faster the heat builds up locally (all other things being equal). Just like TT2, this number can be calculated only from the data structure describing the lasing pattern.

[0140] In an illustrative embodiment in which a dimensionless number li can be expressed as a function exclusively of a dimensionless number I3, one can then write: = fCi3)

[0141] Either :

[0142] X is the thermal conductivity of the material, labs is the absorbed laser irradiance and [< ]ELM = / a Ascani ELM is the a thermal diffusion length (with a the thermal diffusivity of the material).

[0143] In a step 310, a thermal map can be determined for the considered layer of the part (corresponding to the elementary volumes previously determined) based on these indicators. This thermal map is a structure of data that associates a temperature rise value [AT] FLM at the points (or elementary volumes) of the layer considered.

[0144] 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.

[0145] The temperature rise value for a given elementary volume ELM is derived from the expression:

[0146] This expression clearly shows that the temperature rise is only deduced from the process parameters (here labs), the thermophysical properties of the manufacturing material (here and a) and the basic metrics (here,

[0147] 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.

[0148] Thus, in another case where we have three indicators linked by the equation ni=f(n2; 1x3) stated previously, we can express ni as a function of 1x2 and 1x3, so as to calculate the characteristic temperature rise [AT] FLM along the laser diagram:

[0149] This expression shows that the temperature rise [AT] FLM is proportional to the temperature T1D, weighted by a function of the normalized diffusion length 5 / b A' and the standardized exposure time [tex^, ul ] ELM / [t scan ] ELM .

[0150] This example implementation is based on 3 dimensionless numbers. However, other implementations are entirely possible, including other implementations based on dimensionless numbers. These can be chosen, among others: depending on the basic metrics that we choose to model the thermal behavior of the part to be produced. Indeed, if we change the basic metrics, the dimensional analysis described above will provide a different set of dimensionless numbers.

[0151] 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 labs 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).

[0152] In this case we can replace the absorbed irradiance labs (expressed in W / m 2 ) by a volumetric heat source Qabs (expressed in W / m3). After dimensional analysis, three dimensionless parameters n'i, n'2, n'3 can be calculated:

[0153] We can consider that Qabs is expressed as the product of the absorbed irradiance labs and a volume absorption coefficient P such that 1 / p is homogeneous at an absorption thickness. We can then write:

[0154] Or again:

[0155] According to one embodiment, the volume absorption coefficient P can be considered as a basic metric. Therefore, the dimensional analysis can provide 4 dimensionless numbers, n"i, n"2, n' , n"4. These can be expressed as:

[0156] Furthermore, in the previously described embodiments, the heat source (laser) is modeled by an irradiance alone (l a bs) or a heat source alone (Qabs).

[0157] 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.

[0158] We can then determine five dimensionless numbers ni, n2, ns, n4, n5:

[0159] 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).

[0160] In a more general case, at least one indicator is provided to be expressed as a function of the temperature rise [AT] ELMand the correlation function g(). We therefore have:

[0161] The other indicators h, I3... In relate to the causes of overheating, such as undercuts, or short inter-vector transients. Thus, li and h are linked via the function f: I r = / i)), with i = 2, 3 ...nn being the number of indicators.

[0162] Consequently, to generate the heat map from the indicator map, it is sufficient to apply, in step 310, the following operation:

[0163] 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 allowing to link an indicator dependent on the temperature rise (the desired quantity) to indicators not dependent on the temperature rise (and therefore calculable).

[0164] 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 [AT] ELM to the different identified indicators. The law ( ) 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.

[0165] 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.

[0166] 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.)

[0167] This step can be called the calibration step, or calibration, since it involves relating the known values ​​of a subset of indicators (for example, dimensionless numbers) to a correlation function to obtain the value of the unknown indicator, i.e. the one that depends on the temperature rise (the correspondence between the temperature rise and this last indicator being immediate, via the equations given above).

[0168] Several methods can be used to obtain an estimate of this function.

[0169] For example, this estimation can be done 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-tuple of the parameter space, we can obtain by calculation or simulation the indicators { / i=i,2,3...n (or for example the dimensionless parameters ni, 2, ns)

[0170] 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 allowing the indicators to be correlated When a function f( ) is difficult to determine, it is possible to construct a database in which the indicators { / i =1,2,3...n are tabulated.

[0171] 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 { / i=i,2,3...n allows us to determine the last value of the p-tuple, li.

[0172] This simulation can be carried out by different state-of-the-art methods such as for example the finite difference method, the finite element method, the finite volume method, the discontinuous Galerkin (GD) method, etc.

[0173] Simulating vectorization at the elementary volume scale, ELM, to correlate indicators with each other, instead of doing it at the scale of an entire layer, allows for a considerable reduction in computation times: thus, the simulation can take a few minutes for an elementary volume compared to a few hours, or even a few dozen hours, for an entire layer.

[0174] 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 Ui}i=i,2,3...n> and as its ordinate the value of the dimensionless quantity li, or more generally of the indicator as a function of the temperature rise.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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] ELM .

[0179] 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 dimension characteristic of these and the distance between two elementary volumes depend on the spatial and temporal resolution desired for the analysis.

[0180] 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.

[0181] The calculation of some 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 on elementary volumes located on the same vertical as the current one.

[0182] In the example given above based on dimensionless numbers, the calculation of the dimensionless number n3 is direct: it depends on the values ​​of the durations [texpo Ul ] ELMet ^scaniELM - The value of these durations depends only on the layer considered.

[0183] On the contrary, the calculation of the dimensionless number 1x2 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 to calculate the volume of dense matter.

[0184] As described previously, using this correlation function, which can be precalculated in the form of an abacus, we can determine the temperature rise in each elementary volume ELM. All of these values ​​define the thermal map

[0185] FIG. 7 illustrates such a heat map very schematically.

[0186] 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, 1 1 correspond to distinct values ​​of the temperature rise [AT] ELM Only 3 values ​​are shown for clarity of the figure, but in practice the map may show a continuity of values ​​(each corresponding to a gray level, in a graphical representation) or a larger number of discrete values.

[0187] 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).

[0188] In zone 1 1 (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 laser. Between two passes of the laser, the heat does not have enough time to be evacuated, and the temperature rises.

[0189] Zone 226 (represented by finer hatching) corresponds to an intermediate situation to those of zones 225 and 227, in which the zone is oriented at 45° relative to the lasing pattern so that the laser travel time within this zone is longer than in zone 227, but not long enough to allow sufficient heat evacuation.

[0190] Thus, this heat map shows the different areas of the manufacturing layer which are likely to overheat, due to the lasing strategy which may be locally unsuitable.

[0191] 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.

[0192] These post-processing operations can consist of digital image processing operations, since the thermal map can be considered as a digital image (the value of each pixel corresponding to a temperature rise.)

[0193] In particular, thresholding steps 311 and segmentation 313 of the heat map can be implemented.

[0194] This thermal map can thus be used to determine risk areas, i.e. areas likely to give rise to hot spots that could lead to surface protrusions, cracks, cavities ("keyholes"), or other anomalies on the part to be produced.

[0195] The determination of these risk areas can be carried out in different ways and follows directly from the determination of the heat map.

[0196] For example, a threshold can be established beyond which a temperature difference indicates a risk area.

[0197] 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 correction rules making it possible to attenuate, or even eliminate, the hot spots which have been anticipated.

[0198] 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 AT cr it (critical temperature constraint), established upstream in a step 314.

[0199] 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.

[0200] One way to establish this (or these) critical threshold is to train a regression model h ) between thermal elevation values ​​[AT] FLM calculated as indicated previously and an experimental measurement of a material defect measured in different positions of a representative test piece.

[0201] 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 [p%] EEM -

[0202] FIG. 8 illustrates this step 314 of determining thermal thresholds.

[0203] 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.

[0204] Furthermore, the temperature rise [AT] can be simulated ELM using the 108 laser strategy and indicators to obtain an 803 heat map.

[0205] 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.

[0206] This can be done layer by layer.

[0207] Generally speaking, at any point on the representative specimen, we therefore calculate:

[0208] In other words, we establish, upstream, a correspondence between, at the input, a rise in temperature, and, at the output, a representative defect. We can then establish a critical temperature threshold AT cr it according to a desired threshold on the representative defect.

[0209] Thus, depending on the quality criterion required for 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 specimens.

[0210] Of course, other optimization objectives can be considered in the adaptation / correction step 316.

[0211] For example, we can mention an objective of minimizing the deviations of [AT] ELMcalculated at the layer scale (homogenization constraint). This different objective can influence the adaptation strategies implemented in step 316 of adaptation of the laser strategy.

[0212] In practice, detecting an overheating problem from the heat map can trigger various actions.

[0213] For example, these actions may include one or more of: Alert a user by highlighting the risk area(s) on a graphical representation of the heat map; Alert the user to prevent additive manufacturing from being triggered 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 areas appearing on the heat map.

[0214] 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 in accordance with 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.

[0215] The parameters that can be adjusted for this optimization include: The laser power. The laser pattern can be maintained but the power can be reduced on one or more segments of the laser pattern that correspond to risk areas; Lasing speed. The lasing 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. Segment order. You can maintain the laser pattern, but skip one or more segments in a high-risk area and then return to them later to allow the area to cool in the meantime. This option penalizes manufacturing time less than introducing pause times. Segment length. In the case of a strip laser strategy, the strip width 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.

[0216] This parameter adaptation can, for example, include modifying the data structure expressing the lasing scheme. The lasing parameters (laser power, laser speed, laser beam diameter, irradiance profile, etc.) can be directly modified segment by segment in this data structure, the order of the segments can be modified, new lines can be added to insert a pause between two segments, etc.

[0217] We can 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.

[0218] 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.

[0219] According to one embodiment, the adaptation step 316 is provided to generate a set of adaptation scenarios. Each scenario may have advantages and disadvantages.

[0220] 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.)

[0221] A first possible scenario consists of carrying out a grouping of vectors of the development strategy 308 and assigning a value of at least one parameter of the machine 200 to each group (for example the power of the laser).

[0222] Grouping, or splitting, can involve sets of vectors having the same parameter (e.g., a band) into several sets of vectors having different parameter (e.g., sub-bands), without changing the laser order between the vectors.

[0223] This strategy is shown schematically in FIG. 9A and FIG. 9B.

[0224] Element 900 of FIG. 9A represents a trapezoidal-shaped vectoring scheme, which generates an overheating zone in the converging zone of the tool path (i.e., at the top of the part, in the figure).

[0225] This overheating area is represented by schematic heat map 902, which shows a color gradient toward a darker gray in the upper (converging) area of ​​the parallelepiped shape.

[0226] 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, consisting here of six groups of three vectors, is schematized by the use of different dotted lines.

[0227] Then, a new 906 heat map can be produced from the new laser strategy, so as to verify that the overheating area has been mitigated or even eliminated. The figure shows a uniform gray level across the entire part, illustrating a homogeneous heat map with no areas exceeding an established critical threshold.

[0228] FIG. 9B illustrates schematically what the laser power setpoint would give, if the correction of the laser 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.

[0229] 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: we then assign a value of the parameter (laser power or other) to each vector independently.

[0230] In addition to laser power, machine parameters that can be optimized include laser speed, intervector jump or pause times, etc.

[0231] The parameters can also be modulated jointly. For example, it is possible to first act on the laser power, down to a lower power threshold, and then compensate for the residual overheating by adding inter-vector pause times.

[0232] 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.

[0233] 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.

[0234] Like the previous strategy, this method allows the initial laser pattern and order to be preserved; and the correction only acts on the process parameters.

[0235] This scenario makes it possible to respond to both the requirement of not exceeding a critical temperature rise threshold, but also of homogeneity of the temperature rise on the surface of the lasered material. However, it assumes that certain machine parameters can be modulated (continuously) along a given vector, which is not currently possible on all machines available on the market.

[0236] This second scenario, called continuous adaptation of process parameters, is illustrated in FIG. 10A and FIG. 10B.

[0237] FIG. 10A schematically depicts a vectorization scheme 1000. From this vectorization scheme and the process parameters, a thermal map 1002 is calculated. In 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.

[0238] Based on the thermal thresholds calculated in step 314, a new development strategy is calculated, schematized by the lasing pattern 1004. It is assumed that this new development strategy makes it possible to satisfy, at any point of the manufactured layer, the equality [T] ELM = AT re f (where AT re f < AT critis a reference temperature rise, deemed to give satisfactory material health, therefore lower than the critical threshold temperature).

[0239] One or more process parameters 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 using a dotted line. This new development strategy is saved, a new heat map 1006 is generated so as to verify that the new calculated development strategy allows the heat map to be homogenized, below the critical threshold ATcrit, as desired.

[0240] If the parameter (or combination of parameters) chosen for the correction does not modify the temporality of the tool path (e.g., power modulation), 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., speed modulation or adding 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 fully updated.

[0241] Let us assume in this first example that only the laser power has been modified. This strategy is illustrated in FIG. 10B, which presents 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 diagrammed 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.

[0242] In contrast, in the area where signs of overheating were initially observed, the laser power is decreased along the vector between points M2 and M nThis adaptation is shown diagrammatically on the vector by using 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.

[0243] 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.

[0244] The coordinates of the control points and the associated powers must be included in the vectorization file (or laser strategy) 106 as properties.

[0245] Thus, in FIG. 6B, the portion 1007 of the vector which has not been modified, is only discretized by two control points Mi and M2, delimiting the beginning 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.

[0246] 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.

[0247] Concretely, if two control points are located in a laser 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.

[0248] In fact, the lasing time interval between two control points tcTRL and the characteristic response time(s) of the opto-mechanical chain td yn are constrained by the following inequality:

[0249] where k is a proportionality factor. The characteristic time td yn can represent the laser power ramp-up time to the set power, the characteristic tool speed ramp-up time (galvanometers) to the set speed, etc.

[0250] The opto-mechanical characteristics specific to each machine thus act as constraints which are taken into account in correction step 316.

[0251] Obviously, similar correction strategies can be considered for other LPBF process parameters, such as scanning (lasing) speed, laser beam diameter, inter-vector pause or jump times, vector deviation, etc.

[0252] A third possible scenario, called “segmentation,” is illustrated by FIG. 11A to FIG. 11F.

[0253] This scenario differs from the previous ones in that it leads to "cutting" the vectors, that is, to creating new vectors from the old ones.

[0254] From the laser strategy 1100, a thermal map 1102 is generated which, as before, shows a gradient of the temperature rise illustrated by a gray level gradient, ranging from a dark gray along the left side edge to a light gray along the right side edge. This thermal map therefore reveals a thermal problem on the leftmost part of the part.

[0255] According to this embodiment, at least one threshold is established within the heat map. To do this, an additional thresholding step can be implemented in order to obtain a thresholded heat 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.

[0256] From this thresholded heat map, a new segmented vectorization scheme 1105 can be generated.

[0257] 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.

[0258] A first embodiment where the lasing order is preserved is illustrated in FIG. 1B and FIG. 11C.

[0259] 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. 11B.

[0260] 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].

[0261] Therefore, if we take the previous example of the reduction of the laser power, then we obtain the graph 1119 of FIG. 11C, 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, in FIG. 11B.

[0262] Compared to the previous implementation, the vectors are segmented into two (or more) separate vectors, whereas previously, elements 1007 and 1009 were portions of the same vector.

[0263] We then understand that the greater the number of thresholds, the closer this new method (discontinuous method) is to the first (continuous method).

[0264] 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.

[0265] The discontinuous method, however, has the disadvantage of imposing stopping points between two new vectors (increasing manufacturing time and the probability of generating defects when stopping and restarting the vectors), where there were none with the continuous method. Moreover, 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).

[0266] 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.

[0267] Obviously, similar correction strategies can be considered for other LPBF process parameters, such as scan speed, laser beam diameter, inter-vector pause or jump times, vector gap, etc.

[0268] According to an alternative embodiment, the lasing order is modified. This case is illustrated in FIGS. 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 FIGS. 11E and FIG. 11F.

[0269] As in the two previous strategies, it is possible to reduce the laser power in the left band, so as to mitigate overheating. This is illustrated in graph 1123 of FIG. 11E.

[0270] 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. 11F.

[0271] It is also 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.

[0272] . Other parameters can also be modulated, such as scan speed, laser beam diameter, vector deviation, vector lasing order 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.

[0273] 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 that constitute the lasing pattern are numbered, and a correction algorithm changes the global lasing order of the vectors (or lasing patterns) in order to limit the hot spots. However, the global lasing order can be constrained in industrial parts, for example to obtain specific microstructures.

[0274] 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 compared 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.

[0275] 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.

[0276] 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 development strategy (240) indicating at least one manufacturing trajectory followed by said machine, said method comprising steps of: determining (302, 304) basic metric values, relating to said manufacturing, as a function of said trajectory, determining (310) a thermal map for at least one layer of said part from said basic metric values, 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 development strategy as a function of said thermal thresholds and said thermal map,comprising determining a scenario comprising grouping vectors of said development strategy and assigning a value of at least one parameter of said machine (200) to each group, and / or assigning a progression of at least one parameter of said machine (200) to at least part of a vector of said development strategy, and / or segmenting at least one vector into a set of vectors, each associated with a different value of at least one parameter of said machine (200)., 2. 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) then by determining said indicators for said elementary volumes.

3. The method of claim 2, 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.

4. Method for 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.

5. Computer program comprising instructions for implementing a method according to one of claims 1 to 4 when executed on a computer.

6. Optimization device (230) comprising a processor and associated circuits, adapted to implement a method according to one of claims 1 to 4 for configuring a machine (200) for the manufacture of a part (220)

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