Method of providing a dataset for the additive manufacture and corresponding quality control method

Abstract

A method of providing a dataset for additive manufacturing includes collecting a first type of data for the dataset during the additive buildup of a at least one layer of a component to be manufactured, evaluating of the structural quality of the layer by the first type of data, modifying the first type of data in that fractions of the data representing an insufficient structural quality of the layer are deleted from the first type of data, and superimposing second type of data, to the first type of data, wherein the second type of data is suitable to support a validation of the structural quality of the as-manufactured component.

Claims

1. A method of providing a dataset for additive manufacturing comprising: collecting a first type of data for the dataset during an additive buildup of at least one layer of a component to be manufactured, evaluating a structural quality of the layer by means of the first type of data, modifying the first type of data in that fractions of the data representing an insufficient structural quality of the layer are deleted from the first type of data, wherein an algorithm captures regions of unsolidified or erroneously solidified powder in a powder bed in each layer for the component, superimposing a second type of data, to the modified first type of data, wherein the second type of data is suitable to support a validation of the structural quality of an as-manufactured component, and superimposing a third type of collected data on the modified first type of data, wherein the second type of data and/or the third type of data are toggleable on and off in a data processing device to validate the structural quality of the as-manufactured layer of the component.

2. The method according to claim 1, wherein the first type of data comprises optical or image data, microscopical data, CAD-data including geometrical information, CAM-data and/or numerical control data of or for the layer.

3. The method according to claim 1, wherein the second type of data and/or a further type of data comprises temperature, pressure or gas flow information, or information about beam properties or about a melt pool or powder bed, collected or read out from a manufacturing device or a further sensor devices of or for the layer.

4. The method according to claim 1, wherein the dataset is a graphical and/or a tomographical 3D-data set of the as-manufactured component.

5. The method according to claim 1, wherein a coloured, textured or otherwise graphically enhanced 3D representation of the dataset is generated.

6. The method according to claim 1, wherein the different and/or superimposed types of data are subjected to correlation algorithms, and/or with the aid of mathematical models, and wherein correlated information is in turn put into machine learning algorithms.

7. A quality control method for the additive manufacture of a component comprising: providing a dataset according to the method of claim 1; and validating the structural quality of the as-manufactured component based on the provided dataset.

8. The method according to claim 1, further comprising: additively manufacturing the component.

9. The method according to claim 1, further comprising: validating the structural quality of the as-manufactured component based on the dataset.

10. A method of additive manufacturing a component, applying the method of providing the dataset according to claim 1.

11. A method of additive manufacturing a component, applying the quality control method according to claim 7.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

(1) Further features, expediencies and advantages refinements become apparent from the following description of the exemplary embodiment in connection with the Figures.

(2) FIG. 1 shows a simplified flowchart, illustrating method steps of the presented method.

(3) FIG. 2 shows a schematic pictogram of the idea of the presented method.

(4) FIG. 3 indicates an additive manufacturing device in a schematic.

(5) Like elements, elements of the same kind and identically acting elements may be provided with the same reference numerals in the Figures.

DETAILED DESCRIPTION OF INVENTION

(6) FIG. 1 shows particularly flowchart of the following steps by which a dataset (cf. DS below) is provided:

(7) Step 1 (S1): Each layer of a layerwise additive manufacturing process (LAM) is documented by means of photography (first type of data, cf. I1 below), most likely by a camera with more or less high dimensional resolution, located in the chamber roof, i.e. in the upper part of a build space BS as shown in FIG. 3, for example. This gives the possibility to create a three dimensional (data) model if the spacing in z-direction is identical to the layer thickness of the CAM-file that was sent to the 3D-printer or AM-manufacturing device. The step S1 may relate to the step of collecting the first type of data, as described above.

(8) Step 2 (S2) being optional (cf. dashed contour): An algorithm e.g. identifies or evaluates in each picture what parts of each picture are unaffected (unsolidified) in the powder bed (or fluid in case of stereo-lithography as AM-process). In this way, the algorithm, which may be known to a skilled person, also evaluates structural quality of the respective layer. This identification of unsolidified powder bed sections advantageously takes place directly after the taking of each individual picture (in-line monitoring). If step S2 is omitted, then the focus of the analytics is not solely directed to the part to be printed, but can also be used for the inclusion of powder bed related phenomena into the analytics work.

(9) Step 3 (S3): The powder bed sections of each picture are deducted or deleted from the pixel-representation of each picture. This deletion of unsolidified powder bed sections from each picture takes place directly after the taking of each individual picture. Step S3 may be a real-time calculation and further relate to the step of modifying the first type of data, as described above.

(10) Step 4 (S4): Onto each of the prepared pictures from steps 1 to 3, one or more additional sensor data (second and/or third type of data, cf. I2, I3 below) types are collected and/or superimposed in graphical form. As an example, one could imagine temperature readings from an additional infra-red camera. By doing so, additional sensor data can be visualized not only in the correct temporal correlation (“so and so many seconds into the print”) but also directly in the correct spatial order: E.g., In layer 2333 of the buildup for the component, a change from sensor data X1 (see below) occurred, showing that the approved tolerance band for X1 (=“good process”) was abandoned—that is why the build crashed in the layer 2334.”

(11) Step 5 (S5) being optional: Toggling on and off of one or several or all but one additional sensor data sets gives the precondition for a deeper understanding to the human AM-technician or operator.

(12) Step 6 (S6) being optional: A colored, textured or in other form graphically enhanced 3D-representation of the combination of the layered photographic evidences of the build plus one or several additional sensor data sets in graphical form superimposed onto the right layer picture can then be used for virtual “fly-throughs”, study or evaluation of the dataset. This way of making accessible complex correlations has shown to be effective for deep material or structural insights.

(13) Step 7 (S7) being optional: The identified correlations from step 5 and 6 can then be described in mathematical models and algorithms.

(14) Step 8 (S8) being optional: The algorithms and mathematical models as described above can then be used for machine learning exercises.

(15) The present dataset may be in the “.jz”-format and provide all information relevant to reproducibly and comprehensively describe an AM manufacturing process. Said dataset may as well be or comprise CAM-data.

(16) The described method steps may as well be applied in the presented quality control method and/or the method of additively manufacturing of a component (not explicitly indicated in the Figures).

(17) The pictogram of FIG. 2 shows a representation or dataset identifying characteristics of an AM-printing process, i.e. the dynamic parameters describing a transfer function of the subsystem represented by the printing process.

(18) F.sub.0(t) (see below) may represent or comprise the following parameters: Powder properties, inert gas flow, laser properties etc. as a function of time.

(19) Δd(t) may on the other hand describe the deviation of the component, concerning imperfections included in the manufacture, thus a geometrical integrity check inspection.

(20) The provided dataset described herein or a corresponding tool, or processing device may further enable an operator, to use said tool or device (cf. numeral 100 in FIG. 3) without specific physical or chemical knowledge.

(21) The operator shall further be enabled to online influence the manufacturing process (adaptive process), e.g. to increase and decrease of building speed depending of complexity and analyze the component or a specific layer thereof in terms of geometry, dimensions, surface integrity, and also to report data or finding of specific interest.

(22) The parameters as shown in the pictogram of FIG. 2 may be characterized as follows: X(t): The relative displacement between Laser and CAD model. F(t): The instantaneous value and F.sub.0(t)—The nominal value Δd(t): Total deviation of the relative displacement X(t)

(23) P(t) and Pd(t) are disturbances as thermal dilation, variation of parameter building speed, thickness etc.

(24) The parameter ΔF may denote a correlation factor, as shown in FIG. 2, such as: the building speed, the argon or inertial gas feed/speed, information concerning the CAD model, melt pool or its dynamics, the powder bed, the optical and/or microscopical picture or image of an as-manufactured layer, the layer thickness, the recoater feed/speed and/or any information concerning the manufacturing execution system (MES), for example.

(25) Said correlation factor may as well denote or describe information concerning to a post-manufacture (structural and/or chemical) analysis, such as computed tomography, electron microscopy or further means.

(26) The parameter Δd(t) may denote differences of the as-manufactured component or its structure as compared to a corresponding CAD-model.

(27) The presented method of providing the dataset may be method of visualising, controlling or monitoring properties of an AM-manufactured component. The presented process may be an adaptive and/or interactive process, by means of which the manufacturing buildup of the component can be supervised and/or online controlled. E.g., when a certain threshold or tolerance of imperfection, more particularly rupture in the structure of the component, is exceeded, process parameters may be adapted online, i.e. during the buildup of the component.

(28) The presented method allows using this method or tool (when implemented in a data processing device) without specific knowledge concerning the structure or the materials of the as-manufactured component itself.

(29) Further, all information necessary may be shown on the screen, such as a touchscreen.

(30) There is also the possibility to turn and touch the component in the model.

(31) Further, process parameters may be read or edited inside as well as outside of the structure to be manufactured.

(32) FIG. 3 indicates an additive manufacturing device 100 in a schematic way. The device comprises or provides a three-dimensional build space BS (cf. coordinate system with translational dimensions x, y, and z on the left). In the build space BS, a component 10 is arranged, advantageously in a state in which it is actually being manufactured by an additive process, such as selective laser melting out of a powder bed PB. Thereby, the component 10 is manufactured by a plurality of layers L.sub.N as indicated. The number of layers can easily reach the number of one thousand or even ten thousand or more.

(33) In the upper right corner of the device 100, a display 20, particularly comprising a touchscreen TS, is shown, showing a digital (image) data of the component 10.

(34) Information or data of the presented dataset may be edited, modified, scrolled-through, varied or changed, e.g. with regard to its values and orientation by means of the touchscreen TS for obtaining knowledge on the as-built structure of the component 10. The image of the component 10 may be part of the dataset DS or data structure. Synonymously with the dataset DS, a digital twin DT for or of the component 10 may be referenced. Said dataset DS is—in other words—advantageously a visual, graphical and/or tomographical 3D dataset of the component 10.

(35) As on the left in the physical component 10 in the build space BS, the component (or digital model thereof) on the right side of FIG. 3 is subdivided in layers L.sub.N.

(36) According to the present invention, each layer comprises a set of subdata or information I1, I2, I3 up to IN. Said data or information may relate to optical or image data, microscopic data, CAD-data, such as geometrical information, CAM-data and/or numerical control data and/or information of temperature, pressure, gas flow or about the melt pool a powder bed of or for the layer. I1, I2, I3 up to IN may as well relate to any information as described herein with regard to FIG. 2.

(37) For example I1 may denote optical image data of an as-manufactured layer. Alternatively, micrographs or other microscopical image information may be comprised by I1. Advantageously, one image is recorded per layer L.sub.N during the additive buildup of the component 10, so that, for the whole component, N images are collected and stored only for subdata I1, e.g. in a data processing device. Reference I2 may e.g. denote temperature or pressure data any further data as mentioned above.

(38) At least the data I1 and I2 are then, according to the present invention, advantageously superimposed as indicated by the bubble in the graphical display 20, such that the inventive advantages can be exploited.

(39) The mentioned subdata I1, I2, IN may further be subjected to correlation algorithms with the aid of mathematical methods. Thereby, correlated information or further collected data may in turn be exposed to machine learning algorithms as to allow for most expedient and authentic investigation of process and material properties by means of the “digital twin” of the dataset DS provided by the present invention.

(40) The number of parameters accordingly describing or (comprehensively) characterizing a layer for a structurally sophisticated component 10 may easily exceed the number of 100. Just to give further examples of the mentioned values I1, I2 to IN, said information or pieces of information may relate to: Layer thickness, melt pool geometry, heat impact per volume or area unit, laser wavelength, hatching distance, i.e. distance of adjacent scanning lines, beam speed, geometry of beam spot, beam angle, type of purge gas, flow rate of purge gas, flow rate of possible exhaustion gas, states of gas valves, set ambient pressure prior to or during build job, state of base material, i.e. the quality, and many more.

(41) By means of the parameters, values or information that can be assigned to each layer as described, it becomes apparent that a comprehensively quality control means of the component 10 can also be provided by the dataset by means of which “quality”, as e.g. mechanical structure and chemical composition can also be controlled retroactively when scanning or scrolling through the different layers L.sub.N of the dataset DS after the complete manufacture of the component 10. As single pieces of information (cf. e.g. I3, IN) may be deactivated or toggled on and off in the dataset DS, the presented method or dataset further allows for analytical process improvements.

(42) The scope of protection of the invention is not limited to the examples given hereinabove. The invention is embodied in each novel characteristic and each combination of characteristics, which particularly includes every combination of any features which are stated in the claims, even if this feature or this combination of features is not explicitly stated in the claims or in the examples.