METHOD FOR PRODUCING A 3D STRUCTURE IN A 3D PRINTING PROCESS

Description

[0001] The invention relates to a method for producing a 3D structure in a 3D printing process, in which, for a 3D structure to be produced from 3D printing data, which are data of a target geometry of the 3D structure, first layer data for the individual layers of the 3D structure to be produced are provided, with which the production of the 3D structure in a 3D printing process is controlled.

[0002] It is well known that 3D printing, or a 3D printing process, is used to manufacture individual or serial components, workpieces, or molds. In such printing processes, three-dimensional components or workpieces are built up layer by layer.

[0003] The process is computer-controlled and uses one or more liquid or solid materials to create components according to predefined dimensions and shapes. Specifications for the components or workpieces to be printed (3D structures) can be provided, for example, by computer-aided design (CAD) systems in the form of 3D printing data.

[0004] During the printing of 3D structures or components, physical or chemical hardening processes or a melting process take place within a particulate building material, also known as a molding material. Materials used for such 3D printing processes, for example in powder bed or layer-by-layer processes, include building materials or molding materials such as plastics, synthetic resins, ceramics, unconsolidated sediments like minerals or sands, and metals. Furthermore, extruding or contour-based processes using plastics or metals are also known.

[0005] Various manufacturing process sequences are known for the implementation of 3D printing processes.

[0006] However, several of these processes include the following exemplary process steps: Partial or full-surface application of particulate building material, also referred to as particle material or powdered build material, onto a so-called build area to form a layer of unsolidified particulate material, wherein the partial or full-surface application of particulate building material includes the discharge and smoothing of the particulate building material; selective solidification of the applied layer of unsolidified particulate building material in predetermined areas, for example by selective compaction, printing, or application of treatment agents, such as a binder, using a print head or a laser; repetition of the preceding process steps in a further layer layer to build up the component or workpiece layer by layer. For this purpose, it is intended that the component or workpiece, which is built up layer by layer on the build area, is transferred to a build platform.The process involves lowering the build area by one layer level or layer thickness, or raising the 3D printing device by one layer level or layer thickness relative to the build area before a new layer is applied partially or fully; subsequent removal of loose, unsolidified particulate build material surrounding the manufactured component or workpiece.

[0007] Particulate building materials are generally understood to be an accumulation of individual particles of a substance or mixture, each particle having a three-dimensional shape. Since these particles are predominantly round, oval, or elongated, it is possible to specify an average diameter for such a particle, which is usually in the range of 0.01 mm to 0.4 mm. Such particulate building materials can exhibit fluid properties.

[0008] Various methods for creating a 3D structure or for applying and discharging particulate building material onto a construction area to create a 3D structure are known from the prior art.

[0009] From DE 10 117 875 C1 a method and a device for applying fluids and their use are known.

[0010] The method for applying fluids according to DE 10 117 875 C1 relates in particular to particle material which is applied to an area to be coated, wherein, in front of a blade, viewed in the forward direction of movement of the blade, the fluid is applied to the area to be coated and then the blade is moved over the applied fluid.

[0011] The objective of DE 10 117 875 C1 is to provide a device, a method and a use of the device with which a distribution of fluid material that is as even as possible can be achieved on an area to be coated.

[0012] The solution described in DE 10 117 875 C1 is that the blade performs a vibration similar to a rotary motion. This oscillating rotary motion fluidizes the fluid applied to the area to be coated. This allows not only for the even and smooth application of particle material that is highly prone to agglomeration, but also makes it possible to influence the fluid's compaction through the vibration.

[0013] In a preferred embodiment of DE 10 117 875 C1, the fluid is applied to the area to be coated in excess. The constant oscillating movement of the blade, which rotates in a rotary motion, homogenizes the excess fluid in a roller formed by the fluid and / or particle material in the forward direction of the blade's movement. This allows any voids between individual particle agglomerations to be filled, and larger agglomerations of the particle material to be broken up by the roller movement.

[0014] In the field of manufacturing, the concept of dimensional accuracy is known, which means that the actual dimensions of a workpiece should lie within the agreed permissible deviation from the specified nominal dimension.

[0015] According to the state of the art, measures are taken during the production of 3D structures to achieve this dimensional accuracy or to reduce deviations between a 3D structure to be manufactured and a 3D structure produced in 3D printing.

[0016] DE 10 2012 022 435 A1 discloses a method for producing a three-dimensional object using an additive manufacturing process. The following process steps are disclosed: a) producing the object based on a binary data set specifying the object's spatial shape, hereinafter referred to as the target data set, using the additive manufacturing process; b) capturing the actual spatial shape of at least one sub-area of ​​the produced object and generating a binary data set describing the actual spatial shape of at least one sub-area of ​​the object, hereinafter referred to as the actual data set; c) determining geometric deviations between the spatial shape of at least one sub-area of ​​the object specified by the target data set and the spatial shape of at least one sub-area of ​​the object on which the actual data set is generated.d) Modifying the target data set based on at least one correction function, which is derived at least from the geometric deviations, to obtain a binary correction data set, and e) Generating the object based on the binary correction data set using the additive manufacturing process.

[0017] From DE 10 2017 108 031 A1, a method and a manufacturing device for the layer-by-layer construction of a shaped body defined by geometry description data are known, wherein the layer-by-layer construction is carried out by melting and depositing a preferably wire-shaped material. The material is deposited in individual material webs, whereby process parameters are used that are determined based on relationships stored in a database between process parameters, material properties, and the resulting geometry of a material web or its deviation from the target geometry of the shaped body. Each material web is measured after deposition using a measuring system, and its deviation from the target shape is entered into the database along with the process parameters used.

[0018] US Patent 2010 / 125356 A1 discloses a system and a method for producing a three-dimensional object. The method includes receiving a predefined object pattern representing part of a three-dimensional object, modifying the predefined object pattern to correct the geometric distortion of a pattern generator, and generating the modified pattern using the pattern generator. The generated pattern interacts with a reactive material to form the part of the three-dimensional object defined by the predefined object pattern.

[0019] From US patent 10 769 324 B2, a method is known for scanning a second part of a printed 3D structure and for modifying 3D printing data for a first part of a 3D structure, in which modified, pre-distorted 3D specifications for the first part are created based on the scan of the second part, taking into account the printing characteristics.

[0020] One disadvantage of the known state of the art is that there is usually insufficient quality control of the generated 3D structures for deviations from specified dimensions.

[0021] In the event that quality control of the generated 3D structures is carried out, for example by measuring the generated 3D structures, the detected deviations from specified dimensions of the 3D structures can be corrected, for example by mechanically readjusting components or assemblies of the 3D printer.

[0022] Such mechanical readjustments are usually complex, as they sometimes require disassembling the 3D printer to access the components or assemblies that need adjusting. Furthermore, these readjustments also necessitate a shutdown of the 3D printer, thus interrupting the creation of a 3D structure.

[0023] This is particularly disadvantageous in areas where very tight tolerances are specified for the production of 3D structures. Such tolerance ranges, for example, lie between +0.3 mm and -0.3 mm for specified maximum deviations. Consequently, a manufactured 3D structure may have an oversize or undersize of up to 0.3 mm in a first dimension, such as its length, in order to comply with the specified tight tolerance.

[0024] Somit besteht According to the current state of the art, there is only an insufficient and / or costly possibility of suitable quality control or quality assurance in the production of 3D structures.

[0025] Therefore, there is a need for an improvement of the known state of the art and thus for an improved method for producing a 3D structure using a 3D printing process.

[0026] The object of the invention is to provide a method for producing a 3D structure in a 3D printing process, whereby an automated correction of the 3D printing data used for the 3D printing process is carried out for the 3D structure to be produced when deviations occur during the production of the 3D structures.

[0027] In particular, this should reduce the deviations that occur during the production of the 3D structure in the 3D printing process.

[0028] This automated correction of the 3D printing data used for the 3D printing process is intended to be carried out particularly in areas on the surface of the 3D structure that are not accessible to a three-dimensional scan of the surface.

[0029] The problem is solved by a method for producing a 3D structure using a 3D printing process, having the features according to claim 1 of the independent patent claims. Further developments are specified in the dependent patent claims.

[0030] Such 3D printing processes include all methods in which the component geometry is created by applying a liquid or paste-like medium layer by layer onto a substrate and subsequent solidification. Examples, without claiming to be exhaustive, include stereolithography, laser and electron beam melting, binder and material jetting, cladding, digital light processing, and fused filament fabrication. The intention is to use a 3D printing process to create a 3D structure according to the known state of the art, for example, using a powder bed-based binder jetting process.

[0031] Although the present method for producing a 3D structure in a 3D printing process will only be described below using the example of the binder jetting process, this does not represent a restriction of the method to this 3D printing process.

[0032] It is known that in such a 3D printing process, 3D printing data of the target geometry of the 3D structure are converted into specific instructions for the creation of individual layers in the 3D printing process, which are also referred to as layer data and with which the creation of the 3D structure in a 3D printing process is controlled.

[0033] After the desired 3D structure is created on a build platform of the 3D printer in an additive manufacturing process such as the binder jetting process, it hardens, the unsolidified particulate building material is removed and the 3D structure is thus exposed.

[0034] Due to the 3D printing process itself, the subsequent curing, or necessary post-processing in which residual particles of the building material are mechanically and / or air-jet removed from the created 3D structure, it is possible that the desired target geometry of the 3D structure, as specified by the 3D printing data, will not match the actual geometry of the created 3D structure. Provided that such deviations of the actual geometry from the target geometry do not exceed previously defined tolerances, the 3D structure is dimensionally accurate; if the tolerances are exceeded, the 3D structure is not dimensionally accurate.

[0035] Such deviations are also referred to as warping or shrinkage. These warps can also be caused by handling or transporting the manufactured 3D structures.

[0036] Such distortions can be divided into two groups: deterministic and non-deterministic distortions. Deterministic distortions are characterized by the fact that they reappear when multiple 3D structures are produced using the same 3D printing data or printing parameters, and are therefore reproducible.

[0037] Examples of such deterministic delays: Material-related, due to deviations in the grain size and grain distribution of the particulate building material; system-related, due to deviations in manufacturing tolerances, machine vibrations, or mechanical distortions within the 3D printer; manufacturing process-related, due to deviations between target and actual speed values ​​of the 3D printer's working components such as a print head, an application element, or a smoothing agent, due to deviations in the timing of the exact nozzle activation of the print head, or due to deviations in the amount of binder dispensed from a print head nozzle, etc.; diffusion processes, for example, in a microwave oven or a sintering furnace during the curing of the 3D structure; cleaning steps, post-processing steps, and / or transport steps.

[0038] Non-deterministic delays: They do not occur in a stable, systematic way, but rather randomly and are not reproducible.

[0039] Due to such deterministic distortions, the process of generating 3D structures must be initiated for each new geometry or 3D structure. To avoid the need for complex mechanical adjustments to the 3D printer's components and assemblies, the 3D printing data can be manually adjusted to achieve the desired geometry.

[0040] In this context, the generated 3D structure must be captured at least once, for example by means of a 3D scan, in order to provide data on the geometry of the generated 3D structure. The use of other suitable methods for capturing the dimensions of the generated 3D structure is possible.

[0041] One problem with measuring a generated 3D structure, for example using a 3D scanning method, is that such a method may not be able to provide scan data, for example in the form of a so-called point cloud, for all areas of the generated 3D structure. Such a point cloud comprises the three-dimensional measurement points generated by the 3D scanning method, which describe the surface of the 3D structure.

[0042] The reason for missing scan data describing the surface of the 3D structure is that there are areas of the generated 3D structure that cannot be reached by the sensors of a 3D scanning device, as these areas lie behind the areas "visible" to the sensors. Such areas are also referred to as undercuts, dead zones, blind spots, or, subsequently, unscanned areas.

[0043] Areas where very few measurement points were generated during a 3D scan can also be considered unscanned. An insufficient density of measurement points allows for only limited or no conclusions to be drawn regarding potential deviations between the actual model of a 3D structure and the target model of that 3D structure.

[0044] The model of the surface of the generated 3D structure, created using a 3D scanning process, does not fully represent the generated 3D structure in the form of data of the scanned actual geometry and therefore has so-called unscanned areas.

[0045] Such unscanned areas can be closed, for example, by interpolation, which creates a surface over the open area. Due to the limited number of measurement points generated by the 3D scanning process in or near the unscanned area, which are usually located only at the edge of the unscanned area, an exact reproduction of the precise outer contours of the 3D structure's surface is not possible in these unscanned areas. If a few measurement points are generated in the unscanned area, the reproduction of the surface or contours of the 3D structure is sometimes stepped or stair-like and therefore does not correspond to the exact surface or outer contours of the 3D structure.

[0046] For less complex 3D structures, this process of area or surface closure represents a possible approximation of the generated actual geometry of the 3D structure's surface. However, in applications with small predefined tolerances between the target geometry of the 3D structure and the actual geometry, such area or surface closure methods are usually too inaccurate.

[0047] According to the present method for producing a 3D structure using a 3D printing process, it is intended that that for a 3D structure to be produced from 3D printing data, which are data of a target geometry of the 3D structure, initial layer data for the individual layers of the 3D structure to be produced are provided, with which the production of the 3D structure in a 3D printing process is controlled; that a produced 3D structure is measured three-dimensionally, whereby in practice incomplete three-dimensional data of a scanned actual geometry of the 3D structure, a surface of the produced 3D structure, are generated, which are represented in a model and have one or more unscanned areas; that in a projection step initial deviations between corresponding points P and P' in the scanned areas are determined, wherein point P is located on a surface of the target geometry of the 3D structure and the corresponding point P' is located on the model of the surface of the scanned actual geometry of the 3D structure.that these determined initial deviations are stored as deviation data for associated points P1, P2, P3, ..., Pn of the data of the target geometry of the 3D structure with their value, that in a completion step, the further deviations belonging to the unscanned areas of the surface of the generated 3D structure are completed using the data of the target geometry of the 3D structure as well as the already determined initial deviation data, and that the generated further deviation data for associated points P of the data of the target geometry of the 3D structure are stored with their value, and that such complete deviation data are generated that corrected 3D printing data are generated in a deformation step.At which point P of the data of the target geometry of the 3D structure, depending on the first or subsequent deviation determined and stored for the respective point P, are shifted in a direction opposite to the determined first or subsequent deviation by an amount of the stored value dependent on the first or subsequent deviation, so that the production of subsequent 3D structures in a 3D printing process is controlled by means of these corrected 3D printing data.

[0048] Data of a target geometry of the 3D structure are, as is known from the state of the art, converted into data for the individual layers of the 3D structure to be produced, and the production of a 3D structure in a 3D printer is controlled using this data.

[0049] The resulting 3D structure is then removed from the 3D printer's print bed, post-processed, and cleaned. This step can also include the curing process of the 3D structure.

[0050] The generated 3D structure is then measured three-dimensionally in a surveying step. This measurement process can be carried out, for example, using a suitable laser scanning device. The result of such a measurement is three-dimensional data of the actual geometries of the generated 3D structure, often incomplete in practice, representing a model of the actual geometry. This data is incomplete because it lacks information for unscanned areas that were not reached during the three-dimensional measurement.

[0051] Subsequently, initial deviations between corresponding points P' and P on a surface of the model of the actual geometry of the 3D structure and associated points on the surface of the target geometry of the 3D structure in the scanned areas are determined in a projection step.

[0052] The determination of these initial deviations and their magnitude can be performed for each point P' present in the 3D printing data on the surface of the model representing the actual geometry of the 3D structure and a corresponding point P on the surface representing the target geometry of the 3D structure. For example, if a point P' determined by a laser scan on the surface of the model representing the actual geometry of the generated 3D structure lies below the surface of the target geometry of the 3D structure to be generated, the deviation at this point P is undersized.

[0053] In the event that a point P' determined by means of a laser scan lies on the surface of the model of the actual geometry of the generated 3D structure above the surface of the target geometry of the 3D structure to be generated, the deviation at this point P is excessive.

[0054] In the event that a point P' determined by means of a laser scan lies on the surface of the model of the actual geometry of the generated 3D structure on the surface of the target geometry of the 3D structure to be generated, no deviation occurs at this point P.

[0055] When determining deviations, such as the first deviations, corresponding points P' and P on the model of the actual geometry of the generated 3D structure and the target geometry of the 3D structure to be generated are compared pairwise, which would lie on top of each other without any deviation occurring between the surfaces of the actual geometry and the target geometry of the 3D structure.

[0056] For each three-dimensional point P on the target geometry of the 3D structure, for which a corresponding point P' was determined in the model of the scanned actual geometry during the scanning process, an initial deviation between the actual geometry of the 3D structure and the target geometry of the 3D structure can be determined and stored in a projection step. Therefore, the initial deviations cannot be determined in unscanned areas, as no point P' could be provided in the model of the scanned actual geometry for these areas.

[0057] Alternatively, these initial deviations can be determined for predefined areas on the surface of the 3D structure. Such deviations can also represent an average value for a predefined area on the surface of the 3D structure.

[0058] The initial deviations determined in this way are stored as deviation data for corresponding points P of the data of the target geometry of the 3D structure with their value and can thus be used in the further process flow.

[0059] The initial deviations determined for the scanned areas are used in the process of determining further deviations or deviation data in the unscanned areas of the surface of the generated 3D structure model. If initial deviations are known at the edge of an unscanned area, it can be assumed that these initial deviations will also propagate into the unscanned area with a comparable magnitude or value.

[0060] Since the initial deviations identified at the edges of the unscanned areas are known, further deviations or deviation data in these areas can be determined by successively transferring this information from known points in the scanned area to points in the unscanned area, using small, assumed sub-areas. This allows for a step-by-step completion of the missing deviations for the unscanned areas. The points are located at the vertices of these small, assumed sub-areas, which are, for example, the triangular sub-areas used in a triangulation process of the surface of the target structure to be generated.

[0061] Such sub-areas filling the unscanned area can be n-gons or freeform surfaces with vertices, especially triangles. The method is explained below preferably using target geometries described by triangular sub-areas, which does not restrict the method to these sub-areas.

[0062] This process determines and stores both the initial deviation data already collected and the further deviation data to be determined for the unscanned areas at points P on the corners of the sub-areas. When using freeform surfaces, the deviation data is stored at corresponding, definable points.

[0063] The plan is to begin identifying further deviations in the unscanned areas at the edge of the unscanned area and to continue this process towards an assumed center of the unscanned area.

[0064] Furthermore, it is intended that the small sub-areas of the target geometry, with their respective vertices, which are used to determine further deviations in the unscanned areas, are, for example, triangular sub-areas. Using these small triangular sub-areas, further deviations are determined iteratively, starting at the edge of the unscanned area, until all further deviations for the unscanned area have been determined and stored at the respective points.

[0065] The process of determining further deviations or deviation data is explained in more detail below using an example: A prerequisite for determining further deviations is that the surface of the target component, i.e., the surface of the target geometry of the 3D structure, already exists in a triangulated form. Such triangulation is performed in a conversion step, whereby the surface of the target geometry of the 3D structure to be generated is completely covered or replicated, for example, by means of several triangular sub-surfaces, with points in the form P₁, P₂, P₃, ..., Pₙ being assigned to the vertices of each sub-surface. Thus, data on the proximity relationships of points P₁, P₂, P₃, ..., Pₙ at the vertices of the triangular sub-surfaces is available, which is then used.

[0066] Triangular sub-areas are defined on the surface of the target geometry. These sub-areas lie at the edge of the unscanned area and have already had initial deviation data determined at two corners in a projection step. In this example, this could be a triangular sub-area of ​​a triangle with vertices P1, P2, and P3. Initial deviations to vertices P1 and P2 are known for this sub-area because two initial deviations were determined between corresponding points P1 and P1', and P2 and P2'. However, determining deviations between corresponding points P and P' is not possible in the unscanned areas because data for points P' is missing in these areas.

[0067] When determining further deviations, it is assumed that the value or magnitude of the initial deviations determined at the edges of the unscanned area extends into the unscanned areas with comparable values ​​or magnitudes for the subsequent deviations to be determined. In the example, a further deviation for vertex P3 can be determined using a mathematical function, for instance, by calculating the arithmetic mean of the initial deviations known for points P1 and P2. The initial and subsequent deviations determined for points P1, P2, and P3 each have a value or magnitude for the determined deviation, with the directions of the deviations extending along the vertex normals of the triangular sub-areas corresponding to points P1, P2, or P3, respectively.

[0068] Determining a further deviation for corner point P3 using a mathematical function, such as arithmetic averaging the initial deviations known for points P1 and P2, is an example. In practice, other functions or calculation rules can be used besides averaging. These can take into account, for example, more complex sub-area configurations encompassing multiple points, priority weightings for significantly differing initial deviations, or the degree of uncertainty of a specific further deviation based on its distance from a determined initial deviation.

[0069] A function can also be used that involves area-weighted averaging, angle-weighted averaging, or distance-weighted averaging, so that not only the neighborhood relationships are included in the calculation, but also the existing geometric conditions, for example, in distance weighting, closer points have a greater influence than more distant ones.

[0070] After determining the further deviation for vertex P3, another triangular sub-area is defined on the surface of the target geometry. This sub-area lies at the edge of the unscanned area and has already had initial deviation data determined at two vertices during the projection step. In this example, it could be a triangle P2, P4, P5, for which initial deviations to vertices P2 and P4 are known, as the first deviations between corresponding points P2 and P2', as well as P4 and P4', could be determined. The further deviation for vertex P5 is again determined, for example, by calculating the arithmetic mean of the initial deviations known for points P2 and P4.

[0071] Such a determination of further deviations for the corresponding vertex of additional triangular sub-areas can, for example, be continued along the edge of the unscanned area until an initial series of triangular sub-areas has been used to determine the further deviations along the entire edge area. This process of determining further deviations based on known initial deviations for corresponding vertices or points P1, P2, P3, ..., Pn on the surface of the target geometry of the 3D structure to be generated is an iterative process in which the triangular sub-areas generated during the triangulation of the target geometry's surface are used, and not those generated during the determination of further deviations.

[0072] For example, if an initial series of further deviations has been identified near the edge of the unscanned area, the identification of further deviations can be continued in an area that is further away from the edge of the unscanned area, for example in a second series.

[0073] The determination of further deviations can be carried out using another triangular sub-area with the vertices P 3 , P 5 , P 6, to which the further deviations to the vertices P 3 and P 5 were determined as described above in the determination of the further deviations to the vertices P 3 and P 5.

[0074] .The further deviation to corner point P 6 can be determined in the manner described above, for example by means of an arithmetic mean calculation.

[0075] This continuation of the determination of further deviations in another series, whereby this further series is located further from the edge of the unscanned area than the first series, is exemplary.

[0076] The process of determining further deviations from points P 7 , P 8 , P 9 , ..., P n in the unscanned area is continued iteratively until further deviations have been generated, for example for the entire unscanned area.

[0077] The procedural determination of further deviations to points P 1 , P 2 , P 3 , ..., P n is carried out independently of the surface profile of the generated 3D structure, which represents a particular advantage of the method.

[0078] With this approach, steps can occur between adjacent points P, particularly in the area of ​​an imaginary center of the unscanned region, for example, due to different initial deviations at different edges of the unscanned region, during subsequent deformation. These steps are caused by excessively different values ​​or magnitudes of the determined further deviations, for example, between adjacent points P.

[0079] The plan is to smooth out any further deviations identified within the unscanned area.

[0080] Alternatively, it is planned that the first deviations and the subsequent deviations will be smoothed out.

[0081] In this smoothing process, steps that would otherwise occur in a subsequent deformation step are reduced by minimizing such abrupt deviations between adjacent deviation values. This is achieved by correcting the deviation values ​​determined for the corresponding points P1, P2, P3, ..., Pn using smoothing rules that take into account, for example, weighted averages or other values ​​such as distances, angles, and geometric configurations.

[0082] For this purpose, a weighted mean value for a point P1 is determined, for example, by processing the deviation value for point P1 itself and several deviation values ​​for the immediately surrounding, adjacent points P2, P3, ..., Pn that are also referred to as adjacent points. When using triangular sub-areas, at least the deviation values ​​of three adjacent points P2, P3, P4 and the deviation value of point P1 itself are used in the weighted mean calculation.

[0083] It is further planned that the surface normals belonging to the, for example, triangular sub-areas will be calculated for each triangle and converted into the corresponding corner normals of the vertices describing the triangle, which will be used in the further course of the procedure.

[0084] A surface normal is a normalized, outward-pointing vector perpendicular to the surface of the triangular sub-area.

[0085] For each point, the corner normal is calculated by angle- and area-weighted addition of the area normals belonging to the adjacent or neighboring triangles and subsequent normalization.

[0086] For each point P1, P2, P3, ..., Pn of the target model or the target geometry of the 3D structure, a first or further deviation value or magnitude and a corner normal are now determined according to the procedure. The vector resulting from the multiplication of the deviation value and the corner normal now corresponds in direction and magnitude to the displacement vector that moves each point P of the target geometry of the 3D structure to its actual position P' in the generated actual model, i.e., the actual geometry of the 3D structure.

[0087] If this displacement vector points outwards at a point P1, P2, P3, ..., Pn of the target geometry of the 3D structure, this means that the generated 3D structure has an excess at this point compared to the data of the specified target structure. If the displacement vector has a length of zero, this means that the generated 3D structure has no deviation at this point compared to the data of the specified target structure. If the displacement vector has a length within a tolerance, the deviation does not lead to a loss of dimensional accuracy. If the displacement vector points inwards, this means that the generated 3D structure has an undersize at this point compared to the data of the specified target structure.

[0088] The system also envisages the ability to determine the actual geometries of several generated 3D structures within the models, and to compare these geometries with the data of the desired target geometry of the 3D structure and the data of the number of actual geometries. This will generate a statistically determined average for the deterministic deviations, i.e., the deviations determined at different points.

[0089] For example, a different error procedure can be initiated for a single initial or subsequent deviation, or for a single excessively large deviation, than for systematic deviations. This also applies to multiple initial or subsequent deviations.

[0090] During the deformation process, point P can be shifted in the opposite direction to the determined deviation or displacement vector, and for example by the same amount, to generate the corrected 3D printing data. In one example, point P1, for which a deviation of 0.2 mm undersize has been determined, can be shifted outwards by 0.2 mm in the opposite direction to the determined deviation. In this way, deterministic distortions are corrected.

[0091] Conversely, for example, an excess of 0.1 mm at a specific point P2 on the surface of the generated 3D structure can be eliminated by specifying in the corrected 3D printing data for this point the generation of a subsequent 3D structure with an undersize of 0.1 mm. In this way, deterministic distortions are also corrected.

[0092] Alternatively, during deformation, a point P1, P2, P3, ..., Pn can be shifted in the opposite direction to the determined deviation by only a partial amount or an increased amount of the determined deviation. These partial amounts can be defined by the user according to specified areas of the 3D structure or based on the existing geometry. For example, a shift can be applied in steps of 50%, 75%, 90%, 110%, 125%, or 150% of the determined amount of a first or subsequent deviation.

[0093] It is further planned that, in the deformation step, the points P1, P2, P3, ..., Pn of the data of the target geometry of the 3D structure are shifted by a product that results from a value stored for a first or subsequent deviation as the first factor and a second factor that lies in the range between 0.3 and 1.7. In this way, the deformation can be continuously adjusted within the described range.

[0094] The plan is to also provide data for the individual layers of the 3D structure to be produced from the corrected 3D printing data, which will then be used to control the creation of the 3D structure in a 3D printing process. Such data is also referred to as layer data.

[0095] Alternatively, the identified initial and subsequent deviations can be used to correct the 3D printing data for the individual layers already available for the first print. This generates corrected 3D printing data for each layer of the 3D structure to be produced, which can then be used directly to create the subsequent 3D structure in the 3D printing process. The conversion of the corrected 3D printing data into data for the individual layers of the 3D structure to be produced is therefore unnecessary.

[0096] The subsequent generation of a further 3D structure is controlled using the corrected 3D printing data. This corrected 3D printing data thus takes into account the deformations or deviations of the actual geometry of the generated 3D structure compared to the specified target geometry of the 3D structure to be generated. For example, a detected initial or subsequent deviation, such as an undersize of 0.2 mm at a specific point on the surface of the generated 3D structure, can be eliminated by the corrected 3D printing data specifying the generation of a subsequent 3D structure with an oversize of 0.2 mm for that point.

[0097] In this example, the determined deviation would be stored relative to a point P on the specified target geometry of the 3D structure to be generated, with its direction according to the corner normal and its magnitude of 0.2 mm. In the case of an undersize, the direction of the determined deviation would be inward, towards the 3D structure to be generated, or towards the specified target geometry. In the case of an oversize, the direction of the determined deviation would be outward, away from the 3D structure to be generated, or away from the specified target geometry.

[0098] The procedure envisages that the 3D printing process will be controlled to compensate for identified deviations or deterministic distortions. This control is based on the determination of initial and subsequent deviation data between the actual geometry of the 3D structure to be produced and the target geometry of the 3D structure to be produced, across the entire surface or outer contour of the 3D structure. By determining deviations or deviation data for points in unscanned areas, deviations or deterministic distortions can also be determined for unscanned areas that could not otherwise be captured.

[0099] If the process of determining further deviation data in the unscanned areas yields complete deviation data, a so-called deformed 3D structure is generated, representing the corrected 3D printing data. It is assumed that the production of a 3D structure using this corrected 3D printing data compensates for the deterministic distortions caused by the material, process, and equipment, thus creating a 3D structure that corresponds as closely as possible to the data of the target geometry of the specified 3D structure, i.e., the 3D printing data obtained from the CAD system.

[0100] The generation of the deformed 3D structure, or rather the corrected 3D printing data, comprises the following steps: In the first step, the surface normals of the triangulated target component are calculated from the originally specified 3D printing data, which represents the target geometry of the 3D structure. Such surface normals are determined, for example, for small triangular sub-surfaces that replicate the surface of the 3D structure. Replicating the surface of the 3D structure can also be achieved using sub-surfaces that deviate from a triangle.

[0101] The corner normals are calculated from these surface normals.

[0102] For each triangular sub-area, the direction of translation is now defined by the corner normals, along which the specific deviations are assigned. Every point P₁, P₂, P₃, ..., Pₙ of a triangular sub-area can be translated along its corner normals.

[0103] The magnitude of the displacement along the corner normal is determined by the value of the respective deviation measured at a point P₁, P₂, P₃, ..., Pₙ through which the corner normal passes. The displacement can be understood as a vector originating at a point P₁, P₂, P₃, ..., Pₙ on the surface of the target geometry of the 3D structure to be generated, through which a corner normal passes. This vector is parallel to the corresponding corner normal and oriented in one of two possible directions. If the deviation measured for a point P₁, P₂, P₃, ..., Pₙ on the surface of the target geometry is greater than zero, the vector points away from the 3D structure. If the deviation measured for a point on the surface of the target geometry is less than zero, the vector points in the opposite direction, towards the 3D structure.

[0104] In the process of generating the deformed 3D structure or the corrected 3D printing data, referred to as deformation or deformation step, a point on the surface of the 3D structure to be created, which is contained in the data of the target geometry of the 3D structure, is moved along its corresponding corner normal. This point is moved by an amount dependent on the determined first or subsequent deviation at that point and in a direction opposite to the determined deviation.

[0105] This deformation of points P1, P2, P3, ..., Pn of the surface of the target geometry of the 3D structure is carried out for a specific number of points, for selected points or for all points, thus generating a deformed 3D structure or the corrected 3D printing data.

[0106] These corrected 3D printing data of the deformed 3D structure are sliced ​​into layer data as usual, thus generating the geometry data or layer data that control the 3D printing process.

[0107] In an alternative variant of the method for producing a 3D structure in a 3D printing process, instead of deforming the target component, it is intended to transform the points P 1 , P 2 , P 3 , ...,P n, which are provided with deviation data, into the coordinate system of the layer data already available from the first print, and to obtain thus corrected layer data with which the generation of a 3D structure in the 3D printer is controlled.

[0108] The system also allows for the creation of corrected 3D printing data, or corrected 3D printing data for individual layers of the 3D structure to be produced, regardless of the size of the deviations. This ensures that even the smallest detected deviations during deformation are processed and affect the resulting corrected 3D printing data.

[0109] A program implementing the present method for producing a 3D structure using a 3D printing process is executed, for example, in a control unit preparing a print job, in a CAD computer, or in a central control unit of the 3D printer. This central control unit also manages the creation of the 3D structure based on the 3D data of the dimensions of the structure to be produced, which is transferred to it. Such data can, for example, be generated by a computer-aided design system and transferred to the central control unit.

[0110] Thus, the central control unit provides or generates parameters for controlling the 3D printer, such as the nozzle activation time or the movement speed of the printer's tools above the build area. For example, the nozzle activation time can be influenced by the central control unit. This nozzle activation time can be shifted by the central control unit relative to its predefined value, so that the shifted activation time is either before or after the predefined value. The direction of this shift depends on the direction of the measured dimensional deviation.

[0111] While, according to the known state of the art, usually only a small number of, for example, 30 supposedly representative measuring points on a 3D structure are defined and used for quality assurance, the inventive method massively improves the quality control situation.

[0112] The method solves this problem by completing incomplete measurements, by allowing the merging of multiple measurements of a single generated component, and by enabling the comparison of measurements on different similar components when the same geometry is printed multiple times.

[0113] This comparison is made possible by storing the determined deviations from points P of the target geometry of the 3D structure. In this way, the target geometry of the 3D structure serves as a common reference point for the various determined deviations, making them comparable, since deviations determined for multiple generated 3D structures are always assigned to or stored at the same point P on the target geometry of the 3D structure.

[0114] Through the completion process, i.e., the generation of further deviation data for unscanned areas, it is possible to process a comparable number of measurement points for all measurements, which qualitatively improves both the possibility of the comparison mentioned above and the process of the described deformation.

[0115] Another advantage of this method is that initial and subsequent deviations from various measurements of a generated 3D structure can be stored relative to corresponding points P of the data representing the target geometry of the 3D structure. This allows for the merging of different measurements of a single 3D structure. In this way, areas of the 3D structure on which the generated 3D structure stands or rests during an initial three-dimensional scan can be identified.

[0116] Furthermore, it is possible to generate multiple 3D structures and to determine the completed deviation data of all generated 3D structures in the manner described, in order to distinguish between deterministic and non-deterministic distortions using statistical methods.

[0117] The present procedure also envisages the three-dimensional measurement of a 3D structure subsequently generated using corrected 3D printing data. This measurement aims to determine initial, further, and ultimately complete deviations between the target geometry of the 3D structure to be generated and the actual geometry of the subsequently generated 3D structure. The resulting complete deviation data is then used to deform the already corrected 3D printing data.

[0118] This results in a further improvement in dimensional accuracy. This process can be iterated. One possibility is to perform the described iteration not immediately after each generated 3D structure, but with a time interval or only after the generation of a predetermined number of 3D structures, in order to counteract any mechanical changes in the 3D printer or downstream processes. Alternatively, the need for a further iteration can be identified by evaluating the dimensional accuracy of the generated 3D structures over time.

[0119] This allows for iterative refinement of accuracy and dimensional stability, as well as enabling a self-regulating adjustment process in series production.

[0120] The features and advantages of this invention explained above can be better understood and evaluated after careful study of the following detailed description of the preferred, non-restrictive exemplary embodiments of the invention with the accompanying drawings, which show: Fig. 1: an exemplary flowchart for the method according to the invention, Fig. 2: an exemplary flowchart for the process of completing the missing deviation data belonging to the unscanned areas of the surface of the generated 3D structure, Fig. 3: an exemplary flowchart for a smoothing step, Fig. 4: a 3D structure to be generated by the 3D printing process, i.e., a target geometry of the 3D structure, Fig. 5: a 3D structure generated by the 3D printing process, i.e., an actual geometry of the 3D structure, Fig. 6: a model of the data of the scanned actual geometry of the 3D structure provided in the measurement step, Fig. 7: a cross-sectional view through the structure shown in the Fig. 6 The model shown, or the data of the scanned actual geometry, Fig. 8: a visualization of projection step 9, in which the data of the actual geometry and the data of the target geometry are projected onto each other and initial deviation data are generated, Fig. 9: an exemplary determination of further deviations for the unscanned areas, Fig. 10: a process of determining further deviations based on known initial deviations, Fig. 11: a continuation of the determination of further deviations in a further series, Fig. 12: a completed generation of deviations for the entire unscanned area, Figs. 13a, 13b: a partial process of deformation and the generation of the corrected 3D printing data taking place in one deformation step, and Fig. 14: a representation of a complete deformation or complete generation of the corrected 3D printing data.

[0121] In the Fig. 1 An exemplary flowchart for the method according to the invention is shown.

[0122] The process begins in provision step 1 with the provision of the 3D printing data, which consists of data for the desired target geometry of a 3D structure to be produced. Such 3D printing data can be provided, for example, by a computer-aided design system.

[0123] In conversion step 2, this 3D printing data, i.e., the data of the target geometry 30 of the 3D structure, is converted once into sub-surfaces, such as small triangular sub-surfaces that describe the outer contours of the 3D structure 30 to be produced. A triangulation thus takes place, in which the surface of the target geometry 30 of the 3D structure to be produced is completely covered, for example, by means of triangular sub-surfaces.

[0124] For each of these triangular sub-areas, the corresponding surface normal is determined, and based on this, the corresponding vertex normal is calculated for each vertex. These values ​​are then used in the subsequent process. The data of the target geometry 30 of the 3D structure, presented in this transformed form, are required for the later determination of deviations between corresponding points, that is, a point P' 36 on the surface of the actual geometry 31 of the 3D structure and its corresponding point P 37 on the surface of the target geometry 30 of the 3D structure. For example, in the case of triangular sub-areas, the points P 1 , P 2 , P 3 , ...,P n are arranged at the vertices of the triangular sub-areas, and the generated vertex normals pass through these points P 1 , P 2 , P 3 , ...,P n.

[0125] In the first slicer step 3, the 3D printing data of the target geometry 30 of the 3D structure is converted into specific instructions for generating individual layers in the 3D printing process. The first layer data 4 of the target geometry 30 of the 3D structure is then available.

[0126] In a first printing step 5, the 3D structure is generated using the 3D printing process with the provided layer data 4 in a 3D printer.

[0127] After the 3D structure has been created in the 3D printer 31, it is removed from the printer's print bed and cleaned in post-processing step 6. This post-processing step 6 can also include the processes of curing, post-processing, and transporting the 3D structure 31. Post-processing includes, for example, the removal of support structures and cleaning of the created 3D structure 31.

[0128] The generated 3D structure 31 is measured three-dimensionally in measurement step 7. This measurement process can be carried out, for example, using a suitable laser scanning device (3D scanning device). The result of such a measurement is three-dimensional data 34 of the actual geometry of the generated 3D structure 31, which in practice is usually incomplete. This data is incomplete because data for unscanned areas, caused, for example, by undercuts, is missing. Such unscanned areas 35 arise in measurement step 7 when parts of the surface of the generated actual structure 31 cannot be reached by the sensors of the 3D scanning device or are "visible" to the 3D scanning device. The data 34 of the scanned actual geometry 31 correspond to the model 34, by means of which the data 34 can be optically represented and thus illustrated.

[0129] Optionally, a further first printing step 5, a further post-processing step 6 and a further measurement step 7 can be carried out to create and measure more than one 3D structure 31 before the process is continued.

[0130] As a result of surveying step 7, data 34 of the scanned actual geometry of the generated 3D structure 31 are available as a measurement point cloud 8. These three-dimensional data of the measurement point cloud 8 are assigned, for example, to vertices P1, P2, P3, ..., Pn of the sub-surfaces, which were used in the process of triangulating the surface of the target structure to be generated in order to describe the outer contours of the 3D structure to be generated.

[0131] In projection step 9, the data of the actual geometry 31 of the generated 3D structure and the data of the target geometry 30 of the generated 3D structure are projected onto each other and compared. This allows deviations between the actual geometry 31 and the target geometry 30 of the 3D structure to be determined point by point using corresponding points P37 and P'36, which lie within the scanned areas. Points P1, P2, P3, ..., Pn37 each lie on the surface of the target geometry 30 of the 3D structure, and the corresponding points P1', P2', P3', ..., Pk'36 each lie on the surface of the actual geometry 31 of the 3D structure. Here, the relationship k < n applies, which means that the number of measurement points recorded during a measurement, which can be assigned to points P' 36 on the actual geometry 31, is less than the number of points P 37 on the target geometry 30.

[0132] These initial deviations 28, or deviation data 28, thus represent the respective distances between a point P 37 and the corresponding point P' 36 in the scanned areas. These deviation data 28, along with their determined values, are stored for the respective points P 1 , P 2 , P 3 , ..., P n, which lie on the surface of the target geometry 30 of the 3D structure.

[0133] Since the data 34 of the scanned actual geometry 31 of the generated 3D structure are incomplete in the three-dimensional scan, i.e., they contain unscanned areas 35, second deviations 29 or second deviation data 29 for the unscanned areas 35 are generated or interpolated according to the procedure in the subsequent completion step 10. In this procedure-based completion step 10, the first deviations 28 determined in projection step 9 in the scanned areas are used, in particular the first deviations 28 determined between corresponding points P 37 and P' 36, which lie at the edges of the unscanned areas 35.

[0134] After completion step 10, complete deviation data is available, which includes all deviation data 28 and 29 between the 3D structure 30 to be generated and the generated 3D structure 31, i.e. both the first deviation data 28 determined in the scanned areas and the second deviation data 29 determined in the unscanned areas 35.

[0135] This completion step 10 can optionally be followed by a smoothing step 11, in which the now complete deviation data, i.e., the first deviation data 28 of the scanned areas and the second deviation data 29 of the unscanned areas, are smoothed or post-processed. For this purpose, for example, a deviation 28 or 29 at any point P is corrected with a weighted average for this deviation 28 or 29.

[0136] Below are smoothed complete deviation data 12, which describe all differences or deviations between the target geometry 30 of the 3D structure and the actual geometry 31 of the 3D structure for all areas of the surface of the actual geometry 31 of the 3D structure.

[0137] Optionally, in the following step, the detection of deterministic distortions can be carried out based on an evaluation or analysis of multiple results from multiple three-dimensional scans of multiple generated 3D structures. In this way, for example, mean values ​​of deviations occurring at points on the surface of the 3D structure can be determined.

[0138] As a result of the optional detection 13, either the complete deviation data determined in step 12 or further corrected complete deviation data 14 are available, which are used in the further course of the procedure.

[0139] In deformation step 15, corrected 3D printing data is generated, which includes the deformations or deviations of the actual geometry 31 of the generated 3D structure compared to the specified target geometry 30 of the 3D structure to be generated. The deformation of the existing target model of the 3D structure is carried out in the opposite direction to the determined deviations in order to eliminate or at least minimize the deterministic distortions that occur when generating another 3D structure based on this corrected 3D printing data 38. "In the opposite direction to the determined deviations 28, 29" means that a determined excess of, for example, 0.2 mm at a specific point P1 37 on the surface of the 3D structure is corrected by an undersize of 0.2 mm for this specific point P1 37. During this deformation, points P1, P2, P3, ...,P n of the data of the target geometry 30 of the 3D structure as a function of the deviation 28, 29 determined for the respective point P 1 , P 2 , P 3 , ...,P n in a direction opposite to the determined deviation 28, 29 and shifted with the amount of the determined deviation 28, 29.

[0140] The 3D printing data generated in deformation step 15 are subsequently available as deformation data 16, which represent the corrected 3D printing data 38.

[0141] In the subsequent second slicer step 17, the deformation data 16 or the corrected 3D printing data 38 of the 3D structure are converted into specific instructions for generating individual layers in the 3D printing process. Further layer data 18 for generating another 3D structure is then available.

[0142] In a further printing step 19, another 3D structure is created using the 3D printing process with the provided additional layer data 18 in a 3D printer.

[0143] After the creation of another 3D structure in the further printing step 19, the process can be repeated and is continued in the measurement step 7.

[0144] Alternatively, the process flow may include an alternative deformation step in which 20 corrected 3D printing data are generated for the individual layers, which contain the deformations or deviations of the actual geometry 31 of the generated 3D structure compared to the specified target geometry 30 of the 3D structure to be generated.

[0145] The smoothed complete deviation data 12 or the corrected deviation data 14 are used, for example, to create so-called layer deformation data 21 from the existing first layer data 4 using the known deviations 28, 29 to the respective layers.

[0146] The deformation of the first layer data 4 is also carried out in the opposite direction to the determined deviations 28, 29 in order to eliminate or at least minimize the deterministic distortions that occur as a result of generating a further 3D structure on the basis of these corrected 3D printing data 38.

[0147] The layer deformation data 21 are transferred to second layer data 18 in the further process flow and another 3D structure is created in a further printing step 19.

[0148] The Fig. 2 shows an exemplary flowchart for the process of completing the missing further deviation data 29 belonging to the unscanned areas 35 of the surface of the generated 3D structure 31 in a completion step 10 using the data target geometry 30 of the 3D structure as well as the already determined first deviations 28 according to the present procedure.

[0149] The input data 22 for completion step 10 are surface data of the target geometry 30 of the 3D structure, which were already processed in conversion step 2 such that the surface of the target geometry 30 of the 3D structure was recreated or triangulated using small sub-areas, such as triangles, with points P1, P2, P3, ..., Pn assigned to the vertices of the triangles. The input data 22 also includes the first deviation values ​​or first deviation data 28 for the scanned areas, which were already determined in projection step 9.

[0150] Starting from the already determined initial deviation data 28 for the scanned areas, in particular the determined deviation data 28 for points P1, P2, P3, ..., Pn37 at the edges of the unscanned areas 35, an interpolation 23 is performed, in which further deviation data 29 for points P1, P2, P3, ..., Pn37, which lie in an unscanned area 35, are determined step by step. This interpolation process 23 uses, for example, the initial deviation data 28 known for two points P137 and P237, which lie in the scanned area of ​​a triangular sub-area, to determine a further deviation value 29 for point P337, which lies in the unscanned area 35. In this example, a further deviation value for point P 3 37 can be determined by determining an arithmetic mean from the first deviations 28 known for points P 3 37 and P 2 37 in the interpolation step 23.

[0151] This interpolation 23 is continued, for example, for adjacent triangular sub-areas with their points P1, P2, P3, ..., Pn37 along the edge of the unscanned area 35, until, for example, the corresponding further deviation values ​​29 have been determined for all points P1, P2, P3, ..., Pn37 located near the edge of the unscanned area 35 in a first row. The further deviation values ​​or further deviation data 29 determined in this way are also stored with their magnitudes for the corresponding points P1, P2, P3, ..., Pn37 of the data of the target geometry 30 of the 3D structure.

[0152] The interpolation 23 can then be performed for points P1, P2, P3, ..., Pn37 in an imaginary second row, which are located at a greater distance from the edge of the unscanned area 35, and so on. Using this interpolation 23, the further deviation data 29 for points P1, P2, P3, ..., Pn37 in the unscanned areas 35 are completed from the outside inwards, until corresponding further deviation data 29 have been generated for all points P1, P2, P3, ..., Pn37 located in the unscanned area 35.

[0153] The further deviation data 29 generated in this way for points P 1 , P 2 , P 3 , ...,P n 37 in the unstressed areas 35 are the initial data 24 of the completion step 10.

[0154] The Fig. 3 Figure 1 shows an exemplary procedure for smoothing step 11. When determining deviations 28 in the scanned areas or when determining further deviations 29 in the unscanned areas 35 or at the transitions between a scanned area and an unscanned area 35, abrupt changes in the values ​​of the deviation data 28, 29 for neighboring points P may occur.

[0155] The smoothing input data 25 available to the optional smoothing step 11 are the complete deviation data for points P 1 , P 2 , P 3 , ...,P n 37 in the scanned and unscanned areas 35 of the surface of the target geometry 30 of the 3D structure available after the completion step 10.

[0156] In this smoothing step 11, the steps that would otherwise occur in a subsequent deformation step 15 are reduced by reducing such abrupt deviations between values ​​of neighboring deviation data 28, 29 by correcting the deviation values ​​28, 29 determined for the corresponding points P 1 , P 2 , P 3 , ...,P n 37 by means of weighted averaging 26 for the corresponding deviation value.

[0157] For this purpose, a weighted mean value for a point P 1 37 is determined, for example, by processing the deviation value 28 or 29 for point P 1 37 itself and several deviation values ​​28, 29 for the adjacent points P 2 , P 3 , P 4 , ..., P n that immediately surround point P 1 37 when calculating the weighted mean value 26. When using triangular sub-areas, at least the deviation values ​​28, 29 of three adjacent points P 2 , P 3 and P 4 and the deviation value of point P 1 37 itself are used in the weighted mean value calculation 26.

[0158] This smoothing step 11 is performed for a defined number of points P1, P2, P3, ..., Pn37, for defined areas, or for all points P1, P2, P3, ..., Pn37 on the surface of the target geometry 30 of the 3D structure. As a result of this smoothing step 11, smoothed or corrected deviation values ​​for points P1, P2, P3, ..., Pn37 have been generated, which are the smoothing output data 27. These smoothing output data 27 are then converted into the smoothed complete deviation data 12 in the subsequent process.

[0159] The Fig. 4 Figure 1 shows a 3D structure 30 to be produced using the 3D printing process, i.e., a target geometry 30 of the 3D structure to be produced. Data for this target geometry 30 of the 3D structure is available, which, for example, in a first slicer step 3, is converted into specific instructions for the production of individual layers in the 3D printing process, i.e., first layer data 4 of the target geometry 30 of the 3D structure.

[0160] In a subsequent first printing step 5, the 3D structure is created using the 3D printing process in a 3D printer with the provided layer data 4. For better understanding, the following are explained: Fig. 4 as well as the following figures, the process steps from the Fig. 1 named with their reference numbers, although these procedural steps originate from the Fig. 1 in the Fig. 4 as well as are not shown in the following figures.

[0161] The Fig. 5 Figure 1 shows a 3D structure 31 produced using the 3D printing process, i.e., the actual geometry 31 of the 3D structure, for example, after the first printing step 5, curing, and post-processing step 6. This generated 3D structure 31 corresponds to the data of the actual geometry 31 of the 3D structure. For the sake of simplicity, a cuboid body was chosen as the 3D structure. In practice, such 3D structures 31 are designed differently. Even though unscanned areas do not occur during the 3D scan of such a cuboid body, this is assumed here as an example to illustrate the present method in a clear and concise manner.

[0162] The one in Fig. 5 The generated 3D structure 31 shown exhibits two deviations 32, 33 from the specified target geometry 30. In this example, these deviations are a concave deviation 32 and a convex deviation 33, which are shown on a surface of the generated 3D structure 31.

[0163] The one in Fig. 5 The generated 3D structure 31 shown is, for example, measured three-dimensionally in surveying step 7. A model 34 of the data 34 of the scanned actual geometry 31 of the 3D structure provided in this surveying step 7 is in the Fig. 6 This model 34 consists of the measurement point cloud 8 or data for points P' 1 , P' 2 , P' 3 , ..., P' k 36, which depict the actual geometry 31 of the 3D structure. In the representation of the Fig. 6 Only a few points P' 1 , P' 2 , P' 3 , ..., P' k 36 on the actual geometry 31 of the 3D structure are shown as examples.

[0164] The presentation is exemplary in the Fig. 6 An unscanned area 35 is shown. In this unscanned area 35, no three-dimensional data for points P' 1 , P' 2 , P' 3 , ..., P' k 36 on the surface of the generated 3D structure 31 were determined in surveying step 7, for example because such an area 35 was not "visible" to the sensors of the 3D scanning device.

[0165] The Fig. 7 shows a cross-sectional view through the in the Fig. 6 The model 34 shown, or rather the data of the scanned actual geometry 34 along a section line AA. Fig. 7 shows that in the unscanned area 35, no data on the surface course or outer contour of model 34 is available. In the Fig. 7 The concave deviation 32 is shown completely and the convex deviation 33 at least partially.

[0166] The Fig. 8 This visualizes projection step 9, in which the data of the actual geometry 31 of the generated 3D structure and the data of the target geometry 30 of the 3D structure to be generated are projected onto each other and compared. In projection step 9, initial deviations 28 between the actual geometry 31 of the 3D structure and the target geometry 30 of the 3D structure are determined at corresponding points. The data of the target geometry 30 are represented by a dashed line, while the data of the actual geometry 31 are represented by a solid line. Furthermore, the Fig. 8 For example, an unscanned area 35.

[0167] In projection step 9, the first deviations 28 between corresponding points P 37 and P' 36, which lie in the scanned areas 35, are determined. Here, points P 1 , P 2 , P 3 , ...,P n 37 each lie on the surface of the target geometry 30 of the 3D structure, and the corresponding points P' 1 , P' 2 , P' 3 , ..., P' k 36 each lie on the surface of the actual geometry 31 of the 3D structure. This is in the Fig. 8 An example of a first deviation 28 between point P 1 37 and point P' 1 36 is shown, which lies in the area of ​​the concave deviation 32.

[0168] The problem to be solved by the present procedure is that no initial deviations 28 between corresponding points P 37 and P' 36 can be determined in the unscanned areas 35.

[0169] The determined first deviations 28, or deviation data 28, represent only the respective distances between, for example, a point P 1 37 and the corresponding point P' 1 36 in the scanned areas. The determined first deviations 28 are stored with their determined values ​​for the respective points P 1 , P 2 , P 3 , ...,P n 37, which lie on the surface target geometry 30 of the 3D structure.

[0170] The Fig. 9 shows an exemplary determination of further deviations 29 for the unscanned areas 35.

[0171] The Fig. 9 shows a section of a transition from a scanned area to an unscanned area 35 in a zoomed view.

[0172] A prerequisite for determining further deviations 29 is that the surface of the target component is already in a triangulated form. As described in conversion step 2, the surface of the target geometry 30 of the 3D structure to be generated was completely covered, for example, by means of triangular sub-surfaces. Thus, data on the neighborhood relationships of points P1, P2, P3, ..., Pn 37 at the vertices of the triangular sub-surfaces are available, which are used subsequently.

[0173] Triangular sub-areas or triangles are determined on the surface of the target geometry 30, which lie at the edge of the unscanned area 35 and for which initial deviation data 28 have already been determined at two corners in projection step 9. In the example of the Fig. 9 This is the first triangular sub-area with vertices P1, P2, P3, for which initial deviations 28 to vertices P1 and P2 are known, since two initial deviations 28 between corresponding points P1 37 and P'1 36 as well as P2 37 and P'2 36 could be determined. However, determining deviations between corresponding points P37 and P'36 is not possible in the unscanned areas 35, as data for points P'36 are missing in these areas 35.

[0174] When determining or ascertaining further deviations 29, it is assumed that the values ​​of the first deviations 28 at the edges of the unscanned area 35 extend into the unscanned area with comparable values ​​for the further deviations 29. In the example, the further deviation 29 for the vertex P 3 of the first triangular sub-area can be determined by calculating an arithmetic mean from the first deviations 28 known for points P 1 and P 2. The determined first and further deviations 28 and 29 for points P 1, P 2, and P 3 have a value for the deviation, with the directions of the deviations 28 and 29 extending along the vertex normals belonging to point P 1, P 2, or P 3, which are shown in the representation of the Fig. 9 are not shown.

[0175] Determining a further deviation 29, for example for the corner point P 3, by means of an arithmetic mean calculation of the first deviations 28 known for points P 1 and P 2 is exemplary. In practice, in addition to averaging, other calculation rules can be defined, which, for example, take into account more complex partial area constellations comprising several points P, priority weightings in the case of strongly differing first deviations, or the degree of uncertainty of a particular further deviation due to the distance to a determined first deviation.

[0176] After determining the further deviation 29 for corner point P 3, for example, another triangular sub-area is determined on the surface of the target geometry 30, which lies at the edge of the unscanned area 35 and for which initial deviation data 28 have already been determined at two corners in projection step 9. In the example of the Fig. 9 This is the second triangular sub-area with vertices P2, P4, P5, for which initial deviations 28 to vertices P2 and P4 are known, since two initial deviations 28 between the corresponding points P2 37 and P'2 36 as well as P4 37 and P'4 36 could be determined. The determination of the further deviation 29 for vertex P5 of the second triangular sub-area is again carried out by way of example by means of an arithmetic mean calculation of the initial deviations 28 known for points P2 and P4.

[0177] Such a determination of further deviations 29 for corresponding vertices P 6 , P 7 , P 8 , ..., P n of further triangular sub-areas can, for example, be continued along the edge of the unscanned area 35 until an imaginary first row of assumed triangular sub-areas has been created along the entire edge area. This process of determining further deviations 29 based on known first deviations 28 for corresponding vertices or points P 37 on the surface of the target geometry 30 of the 3D structure to be generated is described in the Fig. 10 illustrated by example. The illustration of the Fig. 10 The triangular sub-areas shown are depicted in a representation using Model 34 to illustrate the iterative process of determining further deviations 29. As already described, the triangular sub-areas are those generated during the triangulation of the surface of the target geometry 30.

[0178] For example, if a first series of further deviations 29 was detected near the edge of the unscanned area 35, the detection of further deviations 29 can be continued in an area that is further away from the edge of the unscanned area 35, for example in a second series.

[0179] This continuation of the investigation of further deviations 29 is carried out by means of the in the Fig. 9 The third triangular sub-area depicted, with vertices P3, P5, and P6, is described. In this third triangular sub-area, further deviations 29 from vertices P3 and P5, as described above, have been determined. Therefore, the further deviation 29 from vertex P6 can be determined in the manner described above.

[0180] This continuation of the determination of further deviations 29 in a further imaginary series, wherein this further series is further away from the edge of the unscanned area 35 than the first series, is in the Fig. 11 illustrated by example. The illustration of the Fig. 11 The triangular sub-areas shown are depicted in a representation with model 34 for illustration purposes, in order to visualize the iterative process of determining further deviations 29 in a further series.

[0181] The process of determining further deviations 29 in the unscanned area 35 is continued iteratively until further deviations 29 have been generated, for example, for the entire unscanned area 35, as exemplified in the Fig. 12 is visualized.

[0182] As it is in the Fig. 11 and 12 As can be seen, the procedural determination of further deviations 29 is carried out independently of the surface profile of the generated 3D structure 31, which represents a particular advantage of the procedure.

[0183] In the Fig. 13a und 13b The process of deformation and the generation of the corrected 3D printing data, which takes place in deformation step 15, is illustrated by means of two excerpted images.

[0184] In the Fig. 13a is an excerpt from the Fig. 8 reproduced, which has already been explained that deviations between the actual geometry 31 of the 3D structure and the target geometry 30 of the 3D structure are determined at corresponding points, whereby these first deviations 28 are determined between corresponding points P 1 , P 2 , P 3 , ...,P n 37 and P' 1 , P' 2 , P' 3 , ..., P' k 36.

[0185] This is an exemplary illustration of the Fig. 13a An example of a first deviation 28 is shown between point P 1 37 and point P' 1 36, which lies in the area of ​​the concave deviation 32. The determined first deviations 28, or deviation data 28, represent only the respective distances between a point P 37 and the associated point P' 36 in the scanned areas and are stored with their determined value for the respective points P 1 , P 2 , P 3 , ...,P n 37, which lie on the surface target geometry 30 of the 3D structure.

[0186] In the process of deformation, which occurs in the Fig. 13b As shown, points P1, P2, P3, ..., Pn are shifted in the opposite direction to the determined deviation 28 or 29 and by an amount of the stored value of the deviation 28 or 29 that depends on the deviation 28 or 29. In this example, the Fig. 13 For example, the first deviation determined, 28, had a value of -0.2 mm and was directed into the 3D structure, which corresponds to an undersize of 0.2 mm.

[0187] In the example, point P1 is thus moved in the opposite direction to generate the corrected 3D printing data 38, away from the 3D structure, and shifted outwards by the amount of 0.2 mm determined for this first deviation 28 from the surface of the target geometry 30 of the 3D structure, as shown in the Fig. 13b This is exemplified by the double arrows 39 depicting the deformation.

[0188] The Fig. 14 Figure 38 shows an exemplary complete deformation or complete generation of the corrected 3D printing data 38, which differ from the original data of the target geometry 30, in order to reduce or eliminate deterministic distortions when printing another 3D structure with the corrected 3D printing data 38.

[0189] This deformation and the generation of the corrected 3D printing data 38 takes place in the scanned areas using the first deviations 28 and in the unscanned areas 35 using the determined further deviations 29. In this way, the deformation can take place over the entire surface of the target geometry 30 of the 3D structure. The deformation process is also in the Fig. 14 This is exemplified by means of the double arrows 39.

[0190] In a first alternative, only a part of the value determined for the deviations can be used in this deformation, for example 50%, 75% or 90% of the value of a deviation to a point P 37.

[0191] Alternatively, in this deformation, the determined value of the deviation can be multiplied by a factor, resulting in a deformation of, for example, 110%, 125% or 150% of the determined amount of a first or further deviation to a point P 37. LISTE DER BEZUGSZEICHEN

[0192] 1 Provisioning step 2 Conversion step 3 First slicer step 4 First layer data 5 First printing step 6 Post-processing step 7 Measurement step 8 Measurement point cloud 9 Projection step 10 Completion step 11 Smoothing step 12 Smoothed complete deviation data 13 Detection 14 Corrected complete deviation data 15 Deformation step 16 Deformation data 17 Second slicer step 18 Second layer data 19 Further printing step 20 Alternative deformation step 21 Layer deformation data 22 Input data 23 Interpolation 24 Output data 25 Smoothing input data 26 Weighted averaging 27 Smoothing output data 28 First deviations / first deviation data 29 Further deviations / second deviation data 30 3D structure to be generated / target geometry of the 3D structure 31 Generated 3D structure / Actual geometry of the 3D structure 32 Concave deviation 33 Convex deviation 34 Data of the scanned actual geometry / Model 35 Unscanned area 36 Point on actual geometry of the3D structure / P' 37Point on target geometry of the 3D structure / P 38corrected 3D printing data 39Double arrow / deformation

Claims

1. Method for producing a 3D structure in a 3D printing process, in which, for a 3D structure (30) to be created from 3D print data, which are data regarding a target geometry (30) of the 3D structure, first layer data (4) for the individual layers of the 3D structure (30) to be created are provided and are used to control the creation of the 3D structure in a 3D printing process, • wherein a created 3D structure (31) is measured in three dimensions, wherein in practice incomplete three-dimensional data regarding a scanned actual geometry (31) of the 3D structure of a surface of the created 3D structure (31) are generated, these being mapped in a model (34) having one or more unscanned regions (35), • wherein first deviations (28) between corresponding points P (37) and P' (36) in the scanned regions are determined in a projection step (9), wherein the point P (37) is respectively located on a surface of the target geometry (30) of the 3D structure and the associated point P' (36) is respectively located on the model (34) of the surface of the scanned actual geometry (31) of the 3D structure, • wherein these ascertained first deviations (28) are stored, with their value, as deviation data in relation to associated points P1, P2, P3, ..., Pn (37) of the data regarding the target geometry (30) of the 3D structure, • wherein the further deviations (29) belonging to the unscanned regions (35) of the surface of the created 3D structure (31) are completed, in a completion step (10), using the data regarding the target geometry (30) of the 3D structure and the already ascertained first deviation data (28), and in that the generated further deviation data (29) in relation to associated points P1, P2, P3, ..., Pn (37) of the data regarding the target geometry (30) of the 3D structure are stored with their value, and in that deviation data completed in this way are generated, • wherein corrected 3D print data (38) are generated in a deformation step (15), in which points P1, P2, P3, ..., Pn (37) of the data regarding the target geometry (30) of the 3D structure are displaced by a magnitude, dependent on the first or further deviation (28, 29), of the stored value in a direction opposite the ascertained first or further deviation (28, 29) on the basis of the stored first or further deviation (28, 29) ascertained in relation to the respective point P1, P2, P3, ...,Pn (37), and • wherein these corrected 3D print data (38) are used to control the creation of subsequent 3D structures in a 3D printing process.

2. Method according to Claim 1, characterized in that unscanned regions (35) are regions of the surface of the created 3D structure (31) in relation to which no data were able to be generated in the model (34) during the three-dimensional measurement.

3. Method according to either of Claims 1 and 2, characterized in that the surface of the target geometry (30) of the 3D structure to be created is reproduced, that is to say triangulated, by way of multiple partial surfaces each having multiple corners in a conversion step (2), wherein points P1, P2, P3, ..., Pn (37) are assigned to the corners of the partial surfaces.

4. Method according to one of Claims 1 to 3, characterized in that, in the completion step (10), further deviations (29) in relation to points P1, P2, P3, ..., Pn (37) are ascertained in a first step by generating a further deviation (29) in relation to a point P1, P2, P3, ...,Pn (37) in the unscanned region (35) from multiple ascertained first deviations (28) in relation to points P1, P2, P3, ...,Pn (37) at the edges of the unscanned regions (35) by way of a function, and in that, in a subsequent step, a further deviation (29) in relation to a point P1, P2, P3, ..., Pn (37) in the unscanned region (35) is ascertained from multiple ascertained first deviations (28) or further deviations (29) in relation to points P1, P2, P3, ..., Pn (37) by way of the function.

5. Method according to Claim 4, characterized in that a function is arithmetic averaging, area-weighted averaging, angle-weighted averaging or distance-weighted averaging.

6. Method according to one of Claims 1 to 5, characterized in that the ascertaining of further deviations (29) in relation to points P1, P2, P3, ..., Pn (37) in the unscanned region (35) begins at the edge of the unscanned region (35) and is continued in the direction of an assumed centre of the unscanned region (35).

7. Method according to one of Claims 1 to 6, characterized in that, in the deformation step (15), points P1, P2, P3, ..., Pn (37) are displaced in each case along a corner normal running through the respective point P1, P2, P3, ..., Pn (37).

8. Method according to one of Claims 1 to 7, characterized in that, after the generation of the further deviations (29) in the completion step (10), in a smoothing step (11), the magnitudes, stored in relation to the points P1, P2, P3, ..., Pn (37), of the first and further deviations (28, 29) are smoothed such that in each case weighted averaging is carried out for each point P1, P2, P3, ...,Pn (37) or selected points P1, P2, P3, ...,Pn (37) and that smoothed complete deviation data (12) are thereby generated.

9. Method according to one of Claims 1 to 8, characterized in that, in the deformation step (15), the points P1, P2, P3, ...,Pn (37) of the data regarding the target geometry (30) of the 3D structure are deformed for selected points P1, P2, P3, ...,Pn (37) or for all points P1, P2, P3, ...,Pn (37).

10. Method according to one of Claims 1 to 9, characterized in that, in the deformation step (15), the points P1, P2, P3, ...,Pn (37) of the data regarding the target geometry (30) of the 3D structure are displaced by a product given by a magnitude, stored in relation to a first or further deviation (28, 29), as a first factor and a second factor in the range of 0.3 to 1.7.

11. Method according to one of Claims 1 to 10, characterized in that a 3D structure subsequently created by way of corrected 3D print data (38) is measured in three dimensions in order, in accordance with the method, again to ascertain first and further deviations (28, 29) between the target geometry (30) of the 3D structure to be created and the actual geometry (31) of the subsequently created 3D structure, wherein subsequently, in a deformation step, the already corrected 3D print data (38) are deformed at least partially by way of the first and further deviations (28, 29), wherein further-corrected 3D print data are generated by way of which the creation of a 3D structure to be created subsequently in the 3D printing process is controlled.