Method for arranging support structures for an additive manufacturing process for a dental restoration

Description

[0001] The present invention relates to a method for arranging support structures for an additive manufacturing process on a dental restoration and a computer program for carrying out the method.

[0002] Current CAM-AM-SW products generate support structures using general algorithms that do not follow dental-specific approaches.

[0003] US 2021 / 386519 A1 discloses a method for arranging a support structure for an additive manufacturing process on a dental restoration, wherein the support structure is arranged on the ridge line region of the digital model of the dental restoration.

[0004] It is the technical object of the present invention to provide an automated process for producing support structures in which the support structures engage in dentally non-critical areas of a dental restoration and dentally functional surfaces remain intact.

[0005] This technical problem is solved by the subject matter according to the independent claims. Technically advantageous embodiments are the subject matter of the dependent claims, the description, and the drawings.

[0006] According to a first aspect, the technical problem is solved by a method for arranging support structures for an additive manufacturing process on a dental restoration, comprising the steps of capturing a ridge line in a digital model of the dental restoration; and adding digital support structures to the digital model for supporting the dental restoration in the additive manufacturing process along the ridge line. The ridge line of a differentiable surface is characterized in that the gradient is perpendicular to one of the two main curvature directions. The curvature in this direction is negative. The method can be used to generate an optimized support structure for bottom-up stereolithography. The method implements an automated process for generating support structures for bottom-up stereolithography depending on the component in the 3D printing process.The support structure is created in such a way that it engages the dental restoration in areas that are not critical from a dental perspective, while leaving functional surfaces intact. This ensures a stable construction process and minimizes the effort required for rework. This is achieved by distributing the support structures along the ridge line. For this process of generating the ridge line support structures, the ridge lines are automatically detected and generated.

[0007] In a technically advantageous embodiment of the method, the digital model is rotated to a predetermined orientation before capturing the ridge line. This provides the technical advantage, for example, of being able to identify suitable ridge lines.

[0008] In another technically advantageous embodiment of the method, a type of dental restoration is determined based on the digital model before the ridge line is captured. The dental restoration can be, for example, a "bridge," "splint," "partial denture," or "full denture." The type of dental restoration can be determined, for example, based on the digital model. A self-learning algorithm, such as a neural network, can be used for this purpose, which can automatically assign a type to a digital model. This achieves the technical advantage, for example, that the ridge line can be captured depending on the type of dental restoration.

[0009] In another technically advantageous embodiment of the method, a predefined orientation is determined based on the type of dental restoration. Depending on the type of dental restoration, the digital model can be rotated to a predefined orientation. This provides the technical advantage, for example, of allowing the ridge line to be determined more clearly. Furthermore, the dental restoration can be manufactured in a suitable orientation and supported with support structures.

[0010] In another technically advantageous embodiment of the method, the ridge line is determined using a gradient method. This achieves the technical advantage, for example, that the ridge line can be determined easily.

[0011] In another technically advantageous embodiment of the method, a steepest descent on a surface of the digital model is determined based on a local gradient on a surface of the digital model. This achieves the technical advantage, for example, that the ridge line can be determined with just a few computational steps.

[0012] In another technically advantageous embodiment of the method, the ridge line is determined by detecting local minima in a layer of the digital model. This also provides the technical advantage, for example, of easily determining points along the ridge line.

[0013] In another technically advantageous embodiment of the method, the minima are determined in successive layers of the digital model. This achieves the technical advantage, for example, of being able to determine the ridge line layer by layer.

[0014] In another technically advantageous embodiment, the ridge line is determined using a self-learning algorithm or a watershed method. This achieves the technical advantage, for example, that the determination of the ridge line can be improved through training examples.

[0015] In a further technically advantageous embodiment of the method, the mutual spacing of the support structures along the ridge line is determined based on the gradient of the ridge line. The greater the gradient of the ridge line, the greater the spacing between the support structures along the ridge line. This achieves the technical advantage, for example, that the density of support structures can be adjusted depending on the spatial requirements.

[0016] In another technically advantageous embodiment of the method, the support structures are arranged at regular or irregular intervals along the ridge line. This achieves the technical advantage, for example, that the ridge line can be supported evenly or unevenly.

[0017] In a further technically advantageous embodiment of the method, the support structures are arranged within a predetermined distance from the ridge line. This achieves the technical advantage, for example, of increasing flexibility in the arrangement of support structures and leaving the ridge line unaffected by supports in critical areas.

[0018] In another technically advantageous embodiment of the method, the dental restoration with the support structures is manufactured using an additive manufacturing process. This offers the technical advantage, for example, of easily producing the digital model.

[0019] In another technically advantageous embodiment of the method, the additive manufacturing process is a stereolithography process. This achieves the technical advantage, for example, of using a particularly suitable manufacturing process.

[0020] According to a second aspect, the technical problem is solved by a computer program with instructions that, when executed by a computer, cause the computer to carry out the method according to the first aspect. The computer program can be executed on a manufacturing device.

[0021] Embodiments of the invention are illustrated in the drawings and are described in more detail below.

[0022] They show: Fig. 1 shows a view of a digital model of a dental restoration with a ridge line; Fig. 2 shows a schematic view of a gradient method; Fig. 3 shows a three-dimensional representation of the optimal starting points; Fig. 4 shows a schematic view of detecting a ridge line; Fig. 5 shows several cross-sectional views through the digital model of a dental restoration; Fig. 6 shows another cross-sectional view through the digital model of a dental restoration; Fig. 7 shows a view of a digital model of a dental restoration with added support structures; and Fig. 8 shows a block diagram of a method for arranging support structures on a dental restoration.

[0023] Fig. 1 shows a view of a digital model 101 of a denture base as a dental restoration 100 with a ridge line 103. The dental restoration 100 can generally also be a bridge, a splint or a partial or full denture.

[0024] The digital model 101 of the dental restoration 100 represents the spatial shape of a dental restoration 100 to be manufactured. The digital model 101 may include a data set in which the three-dimensional coordinates of the surface of the digital model 101 and other properties are stored. The dental restoration 100 may be manufactured using an additive manufacturing process based on the digital model 101, such as a 3D printing process.

[0025] For example, the dental restoration 100 is built layer by layer using a stereolithography process. Support structures are used to support the dental restoration 100 to be manufactured on a build platform of the manufacturing device. After manufacturing, the support structures are removed from the dental restoration 100.

[0026] The support structures extend between the build platform and the dental restoration 100. These support the dental restoration 100 on the build platform and transfer the separation forces to the build platform during manufacturing.

[0027] A ridge line 103 can be identified in the dental restoration 100 to be fabricated. The ridge line 103 is characterized by the fact that the gradient is perpendicular to one of the two main curvature directions of the surface.

[0028] The automatic generation of support structures 105 along the ridge line 103 represents an advantageous method for minimizing the support of functional surfaces in the component in certain cases. To achieve effective support along the ridge line 103, the corresponding component can be oriented in a predefined manner.

[0029] Conventional methods are not suitable for the targeted support of ridge lines 103, since the ridge lines 103 often do not exceed a defined limit angle. At the same time, other areas of the digital model 101 whose support is undesirable may have such a high angle that they are supported. To improve this situation, the ridge lines 103 are captured in the digital model 101 and then specifically supported with support structures.

[0030] Fig. 2 shows a schematic view of a gradient method for detecting ridge lines 103. One possible way to detect the ridge line 103 can be the use of an optimization approach, such as a gradient method (gradient descent algorithm). This involves gradually determining the steepest descent based on a local gradient on the surface of the digital model 101, and performing a step in the direction of the steepest descent. This is repeated with decreasing steps until no more descent occurs or a predetermined number of steps have been performed.

[0031] This method evaluates the local neighborhood of a starting point, which is used to determine the next step. Starting at a high starting point 107 on the surface, the algorithm proceeds step by step to a local minimum. A simple example in a 1D function is shown on the left. A more complex example on a height map is shown on the right.

[0032] Fig. 3 shows a schematic view of the detection of a ridge line 103. The result shows not only the local minimum found, but the entire path traveled. With an appropriately selected starting point 107 on the digital model 101, the gradient method follows the ridge line 103, since the steepest descent occurs along this line. Therefore, the starting points 107 are initially selected accordingly in the gradient method. Since the steps in the gradient method become increasingly smaller, the resulting path for the arrangement of the support structures is re-scanned. The digital model 101 of a denture base is shown on the left. The starting points 107 for the gradient method and the determined ridge line 103 are shown in the middle. The right side also shows local minima and a center line 109 along the palate area.

[0033] Fig. 4 shows a three-dimensional representation of the optimal starting points 107 in boundary regions 123. The determination of the starting point 107 for the gradient descent method can be performed through user interaction or automatically. During user interaction, a user clicks on the model surface to manually select a starting point 107. Alternatively, automation can be performed, for example, by searching for the global minima in the Y direction at defined boundary regions (in the X direction) of the digital model 101. To do this, the digital model 101 is aligned so that the local XY axes are identical to those of the build space. Then, boundary regions are defined, such as 20% of the span in the X direction with the boundary line 121. For each boundary region, the global minimum in the Y direction is searched for; this point on the surface represents a starting point 107. The boundary of the boundary regions is represented as boundary line 121.

[0034] The same process for determining starting points can be used for the approach using local minima in layered data. For each starting point, the procedure begins at the layer where the global minimum was found or at the layer where the user-selected starting point lies.

[0035] Fig. 5 shows several cross-sectional views through the digital model 101 of a dental restoration 100. The ridge line 103 can also be detected using local minima 117 in slice data. This slice data is given by sections through the digital model 101. The slice data is obtained at different heights in the digital model 101. If the orientation of the dental restoration 100 about the Z-axis is known, the positions of the ridge line 103 can be found by analyzing the local minima 117 in a slice. The ridge line 103 is determined by detecting and arranging local minima in slices of the digital model 101 that are parallel to the build plane.

[0036] The dental restoration 100 is aligned so that its local XY axes are identical to the axis of the construction space. Points 111 represent local minima 117 at the ridge line 103. Points 113, on the other hand, are local minima located in the palate area and should be excluded.

[0037] In the example shown, the minima 117 along the ridge line 103 can be easily identified by sorting along the x-axis. These are the minima 117 at points 111 with the minimum and maximum x-coordinates. For digital models 101 of other restorations, the slice data can be filtered to ignore minima 117 in areas of high frequency and / or low amplitude.

[0038] Fig. 6 shows a further cross-sectional view through the digital model 101 of a dental restoration 100. For easier detection of the minima 117, the areas of the layer data can be transformed into lines 115, for example by means of skeletonization.

[0039] Fig. 7 shows a view of a digital model 101 of a dental restoration 100 with added support structures 105. With the support structures 105, the digital model 101 is supported along the ridge line 103 on the build platform 119.

[0040] Fig. 8shows a block diagram of a method for arranging support structures 105 for an additive manufacturing process on the dental restoration 100. In step S101, the ridge line 103 of the digital model 101 of the dental restoration 100 is first detected. Subsequently, in step S102, the digital support structures 105 are added to the digital model 101 along the ridge line 103 to support the dental restoration 100 in the additive manufacturing process. The spacing of a grid of the support structures in the build plane or the build platform can be constant.

[0041] The support structures 105 and their contact points on the dental restoration 100 can be determined using the layer data based on bitmaps or a vector representation. The support structures 105 are determined individually for each dental restoration 100. The dental restorations 100 should be present in a component-specific or indication-specific orientation in the coordinate system. To align the dental restorations 100, the dental restorations 100 can be recognized based on their spatial shape in special software and then automatically oriented. This allows the selected algorithm to identify the correct degree lines for the supports more quickly.

[0042] For example, the occlusal side faces away from the build platform, i.e., it is distal to the build platform. The labial side faces in the +X or -X direction. The component is then positioned at an angle of 30° to 90° so that the occlusal side faces in the -X direction.

[0043] First, the local minima 117 of the dental restoration 100 are determined, which are always provided with support structures 105. To generate support structures 105 along the ridge line 103, the dental restoration 100 is then sliced, and raster graphics (bitmaps) are generated from the individual layers. The respective layers are scanned in the XY plane in the Z direction from the bottom, i.e., the surface of the build platform, upwards, i.e., away from the surface of the build platform. Isolated cross-sectional areas with subsequent cross-sectional areas are identified in the following layer as local minima 117. The cross-sectional areas of the dental restoration 100 are examined for local minima 117 in the layer opposite to the X direction (i.e., in the -X direction). From the opposite direction, these local minima 117 are interpreted as peaks in the X direction.

[0044] Now, the local minima found in the X direction can be assigned corresponding XYZ coordinates based on the known layer spacing. These coordinates indicate the contact point of the ridge line 103 on the surface of the dental restoration 100. The ridge line 103 is detected by connecting the found and directly adjacent points of the local minima 117 across all layers. The ridge line 103 is thus generated by connecting the nearby minima in the successive layers. If the local minima 117 found in this way are provided with support structures 105 in the X direction, a curtain of support structures 105 is created.

[0045] A peak-finder algorithm can also be applied to the bitmap data, which finds corresponding peaks as minima in the bitmaps. In this case, a distinction can be made between larger (major) and smaller (minor) peaks. Otherwise, the ridge line 103 can also be detected using a self-learning algorithm that has learned the course of the ridge line 103 from a large number of training examples. The training examples are formed by digital models 101 of dental restorations 100, in which the course of the ridge line 103 is predetermined. The ridge line 103 can also be detected using a watershed method. In this case, for example, the segmentation of a topographical surface of a grayscale image using watersheds corresponds to the division of the image plane into disjoint collecting basins.

[0046] If contour lines are placed in the digital model along the XY plane (outer contours in the layers), the connection of the convex vertices in the neighboring contour lines results in the ridge line 103 and the connection of the concave vertices in the neighboring contour lines results in the valley line.

[0047] If discrete support structures 105 are to be placed, the mutual distance between the support structures 105 along the ridge line 103 can be determined depending on the gradient of the ridge line; the steeper the gradient, the greater the distance along the ridge line 103. The support structures 105 can also be arranged at a predetermined distance laterally to the ridge line 103, ie in a strip along the ridge line 103.

[0048] By creating support structures 105 along the ridge line 103 in this way, other areas (except for locations with local minima and critical overhangs) are kept largely free of support structures 105. The dental restoration 100 is optimally supported, and a dental technician has minimal effort for post-processing.

[0049] Additionally, the ridge line 103 can be smoothed in the digital model 101. This is permitted, for example, in a limited range below the pixel / voxel resolution of the 3D printer, since the shape of the dental restoration 100 is preserved.

[0050] The method automatically identifies the folds of the dental restoration 100 and arranges support structures 105 along the ridge line 103. For this purpose, the dental restoration 100 can be previously assigned to a type for which support structure generation along a ridge line 103 can be efficiently used. The dental restoration can, for example, be of the type "bridge," "splint," "partial denture," or "full denture."

[0051] The method for arranging support structures 105 on the dental restoration 100 can be performed on a computer with a processor and a digital memory. In this case, the processor processes a computer program stored in the digital memory along with additional data. The digital model 101 of the dental restoration 100 is also stored in the computer's memory. The computer can also be part of a manufacturing facility, such as a 3D printer.

[0052] All features explained and shown in connection with individual embodiments of the invention can be provided in different combinations in the subject matter according to the invention in order to simultaneously realize their advantageous effects.

[0053] All method steps can be implemented by devices suitable for performing the respective method step. All functions performed by physical features can be a method step of a method.

[0054] The scope of the present invention is given by the claims and is not limited by the features explained in the description or shown in the figures. LIST OF REFERENCE SYMBOLS

[0055] 100Dental Restoration 101Digital Model 103Ridge Line 105Digital Support Structures 107Starting Point 109Centerline 111Point 113Point 115Line 117Minimum 119Building Platform 121Boundary Line 123Marginal Areas

Claims

1. A method for arranging support structures (105) for an additive manufacturing process on a dental restoration (100), comprising the steps of: - detecting (S101) a flash line (103) in a digital model (101) of the dental restoration (100); and - adding (S102) digital support structures (105) to the digital model (101) for supporting the dental restoration (100) in the additive manufacturing process along the flash line (103).

2. The method according to claim 1, wherein the digital model (101) is rotated to a predetermined orientation prior to detecting the flash line (103).

3. The method according to any one of the preceding claims, wherein a type of dental restoration (100) is determined based on the digital model (101) prior to detecting the flash line (103).

4. The method according to claim 3, wherein a predetermined orientation is determined based on the type of dental restoration (100).

5. The method according to any one of the preceding claims, wherein the flash line (103) is determined by a gradient method.

6. The method according to claim 5, wherein a steepest descent on a surface of the digital model (101) is established based on a local gradient on a surface of the digital model (101).

7. The method according to any one of the preceding claims, wherein the flash line (103) is determined by detecting local minima (117) in a layer of the digital model (101).

8. The method according to claim 7, wherein the minima (117) are determined in successive layers of the digital model (101).

9. The method according to any one of the preceding claims, wherein the flash line (103) is determined using a selflearning algorithm or a watershed method.

10. The method according to any one of the preceding claims, wherein a mutual distance of the support structures (105) along the flash line (103) is determined based on a slope of the flash line (103).

11. The method according to any one of the preceding claims, wherein the support structures (105) are arranged at regular or irregular distances along the flash line (103).

12. The method according to any one of the preceding claims, wherein the support structures (105) are arranged within a predetermined distance from the flash line (103).

13. The method according to any one of the preceding claims, wherein the dental restoration (100) with the support structures (105) is produced in an additive manufacturing process.

14. The method according to claim 13, wherein the additive manufacturing process is a stereolithography process.

15. A computer program, comprising instructions which, when the computer program is executed by a computer, cause the computer to execute the method of any one of claims 1 to 14.