Quality assurance in additive manufacturing

Abstract

The invention relates to a method for additive manufacturing of a three-dimensional object comprising a device having a preparation unit for at least one plasticisable material, at least one printing head having at least one discharge nozzle, a construction platform that can be moved by motors along a plurality of axes, sensors for recording physical values of the motors, an open-loop and closed-loop control unit and a human-machine operator interface. The method comprises a sensor data evaluation and regulated control of the motors, wherein plasticised material from the discharge nozzle is deposited on the construction platform and the construction platform is moved in order to manufacture the object layer-by-layer. Previously determined statistical data (10, 20, 30) of the motors (200, 201, 202) are compared with process data (40) determined during the manufacture of the object, in order to determine a deviation, wherein the statistical data (10, 20, 30) and the process data (40) comprise the recorded physical values of the sensors. A deviation of the statistical data and the process data is evaluated in order to determine anomalies of the object to be manufactured, wherein an assessment-dependent communication takes place with an artificial intelligence and / or an operator for process control.

Description

[0001] Quality assurance in additive manufacturing

[0002] Description

[0003] Reference to related applications

[0004] The present application relates to and claims priority from German patent application 10 2023 119 616.2, filed on July 25, 2023, the disclosure content of which is hereby expressly incorporated in its entirety into the subject matter of the present application.

[0005] Field of the invention

[0006] The present invention relates to a quality-assuring method having the features of claim 1 for the additive production of a three-dimensional object using a device. Furthermore, the invention relates to a device operating according to the method having the features of claim 12.

[0007] State of the art

[0008] Processes for the additive manufacturing of a three-dimensional object are already known in which the manufacturing process is automatically monitored using camera surveillance. Monitoring is carried out using complex models and image processing algorithms. If anomalies occur, the manufacturing process is interrupted so that an operator can review the previous result and intervene if necessary.

[0009] WO 2021 / 021469 A1 describes a method and a device for quality assurance during the printing of at least one three-dimensional object, in which two process parameters that directly relate to the object to be produced are analyzed by means of two sensors.The method comprises (a) analyzing data collected by a first sensor to identify a first deviation from a first expected value, wherein the first sensor is configured to detect a first aspect of the printing of the at least one three-dimensional object, and (b) analyzing data collected by a second sensor to identify any second deviation from a second expected value, wherein the second sensor is configured to detect a second aspect of the printing of the at least one three-dimensional object, and (c) assessing the quality of the printing of the at least one three-dimensional object taking into account the first deviation and the second deviation. Thus, two sensor data are used that are directly related to the printing of the three-dimensional object, e.g.Temperature (object and environment), ambient pressure, contour of the object (camera), material density, material weight, flow properties, etc., in order to detect anomalies of the object in conjunction with correspondingly complex models and to change process data if necessary.

[0010] EP 3 921 166 A1 describes a 3D printing method with a controller for a fluid dispensing system, in particular a print head, in which the print head current is detected by a sensor and, in response to whether the measurement meets an expected characteristic, it is subsequently determined whether a problem exists in the device based on the control data. Thus, it is determined whether an anomaly exists within the print head, whereupon the printing process can be stopped or process data can be changed.

[0011] EP 4 104 955 A1 and EP 4 094 867 A1 disclose a method for the additive manufacturing of a component and an additive manufacturing device for carrying out a method. Specifically, this involves an arc wire deposition welding method and an arc wire deposition welding device, respectively, in which sensors measure the current and voltage at a print head electrode in conjunction with a welding wire. Furthermore, the component to be manufactured is monitored by a camera to detect its contour, structure, and quality defects. Additional sensors detect further process data, such as the temperature of the liquid phase, the feed rate of the print head, and the feed rate of the welding wire.From this data, a digital twin (model) of the object to be manufactured is created and compared with a stored model to detect anomalies, whereupon additive manufacturing can be stopped or process data can be changed.

[0012] EP 2 996 006 A1 discloses a method for monitoring a manufacturing and / or assembly process, which is observed in at least three steps with at least one sensor per step. From this, a monitoring result is derived, wherein the sensor can detect an expression of a feature detected by it. The method is preferably based on optical sensors and particularly preferably cameras that generate image signals as sensor signals. However, the sensors can also be devices that generate image signals as sensor signals, such as X-ray cameras, infrared cameras, thermography sensors, laser scanners, and the like. However, sensors can also be light barriers or tactile sensors that do not generate image signals. An observed feature can be the shape and / or dimension of an object processed in the corresponding step, wherein the corresponding shape or dimension represents the expression of this feature.Accordingly, the condition of at least one surface of a machined object can also be a feature, whereby the concrete surface condition can be the expression of this feature. Advantageously, a limited number of surface conditions can be present or predetermined. Furthermore, properties of the interior of an object can also be features, particularly if the sensor can provide information about the interior of the workpiece, such as an X-ray device or a radiographic device for transparent objects. The expression of this feature is then the concrete realization of the corresponding property in the interior of the object. The results determined over a predetermined period of time or over a large number of observation times can be categorized in order to derive information about the overall process. This can be the sum of identical feature expressions orthe same results concluded from this over an analysis period. Through the statistical evaluation of, for example, mean value, dispersion and trends of the results over time, further statements about continuous changes in the system can be made. The sensor signals of the observed steps are each compared with corresponding target sensor signals, which may have been defined, for example, in a teach-in mode. The result of this teach-in mode is concrete feature values ​​that have been linked to target sensor signals. The target sensor signals are, at least in some areas, each assigned to a value of at least one feature of the step that is observed by the sensor generating the corresponding sensor signal. From this comparison, the value of the at least one feature for new sensor sequences can be derived. These independently recognised values ​​are stored together with their properties, such as, for example, sensor location orThe sensor number, time and duration of the persistent characteristic, and the associated generic characteristic for the characteristic are stored in a characteristic memory. From this characteristic memory, the characteristics of the characteristics of several steps, together with their properties at at least one of the observation times, are compared with further characteristics of the corresponding characteristics with specific characteristics. A monitoring result is derived from the joint comparison of individual characteristics or the combination of several synchronous or specifically asynchronous characteristics. This result can be accumulated over a monitoring period and thus statistically evaluated or fed back into the production and / or assembly system in near real-time to assume a target state. US 2021 / 0308950 A1 discloses a method for the quality-assured additive manufacturing of a three-dimensional object using a device.The device comprises a processing unit for at least one plasticizable or plasticized material, at least one print head movable along two axes by means of at least two motors assigned to the axes, said print head having at least one discharge nozzle, a build platform movable along an axis by means of at least one motor assigned to the axis, sensors for detecting physical values ​​of the motor and, if necessary, of at least one print head and / or at least one processing unit, a first control and regulation unit for controlling the print head and the motor, and for evaluating the sensors, and a human-machine interface. The method comprises an evaluation of sensor data, a regulated control of the motors and, if necessary, of the print head and / or the processing unit, by the control and regulation unit.At least one plasticized material is deposited from the dispensing nozzle onto the build platform, with the build platform and print head being moved to produce the object layer by layer. The first control and regulation unit receives stored control codes and setting data with time information, including limit values ​​for the setting data, for controlling the motors, the print head, and the preparation unit from a second control and regulation unit. The first control and regulation unit checks the data supplied by the sensors for any exceedances or undershoots of the limit values, generates so-called quality data from this, including setting data with time information, status data such as sensor data, and any error data, and transmits this to the second control and regulation unit. The stored setting data can be changed according to an error that has occurred and causes a deviation between expected and actual process data.The second control unit outputs the quality data via the human-machine interface. The second control unit also includes further analysis of the quality data using information acquired over a longer period of time than the information used in the first control unit. Alternatively, using machine learning or artificial intelligence, patterns observed in the sensor data for quality defects are learned in advance by a learning model, and the model is used for determination after learning. The input to a learning model is not limited to the sensor data but can also include intended control code, the defect data, the setting and status information of three-dimensional manufacturing, image information, and 3D measurement data.

[0013] The solutions disclosed in the prior art therefore either check components of manufacturing devices using sensors for their process-compliant function or for deviations therefrom, or use complex models taking into account several process parameters that are detected by sensors to determine anomalies of objects to be manufactured in the form of deviations from the process or object model.

[0014] Summary of the invention

[0015] The invention is therefore based on the object of specifying a method for the additive production of a three-dimensional object and a device for carrying out the method, which enables the detection of an anomaly in an object to be produced without using several different process parameters and complex models.

[0016] Description of the invention

[0017] This object is firstly achieved by a method according to the features of patent claim 1. This is a method for the quality-assured additive production of a three-dimensional object by means of a device comprising a preparation unit for at least one plasticizable or plasticized material, at least one print head with at least one discharge nozzle, a construction platform movable along several axes by means of at least one motor assigned to each axis, sensors for detecting physical values ​​of the motors and, if necessary, at least one of the elements from the group comprising the at least one print head and the preparation unit,a control and regulation unit and a human-machine interface for an operator of the device. The method comprises an evaluation of sensor data from the sensors and a controlled activation of the motors and, if necessary, of at least one element from the group comprising the at least one print head and the processing unit by the control and regulation unit, wherein at least one plasticized material is deposited under pressure from the at least one discharge nozzle onto the build platform and wherein the build platform is moved to produce the object layer by layer, comprising the following steps: a comparison of previously determined statistical data of the motors with process data determined during the manufacture of the object in the control and regulation unit to determine deviations, wherein the statistical data and the process data comprise the recorded physical values ​​of the sensors and wherein the statistical data,consisting of process parameters and a process prediction, which form a process model that maps the manufacturing process of the object, obtained from process data of repeated real and / or simulated production of identical objects, an evaluation of the deviations of the statistical data and the process data in the control and regulation unit to detect anomalies in the object to be manufactured and an evaluation-dependent communication of the control and regulation unit with an artificial intelligence and / or via the human-machine interface with the operator to control the process, whereby the artificial intelligence, in conjunction with the control and regulation unit, automatically adapts the control of the process and / or issues recommendations for action to the operator in the event of a deviation of the process data from the statistical data.

[0018] Artificial intelligence advantageously supports the operator in avoiding malfunctions or incorrect settings, or in quickly identifying and correcting malfunctions based on stored and learned information, thus creating high-quality products. Countermeasures can be initiated during the respective process. The artificial intelligence draws on the training data but also continuously learns, possibly through interactive contact with the operator. If the artificial intelligence cannot or should not correct a malfunction independently, for example, because manual intervention by the operator is still required, it can guide the operator step by step.

[0019] The previously determined statistical data can be obtained from process data of repeated real and / or simulated production of identical objects in order to advantageously have information available that improves the accuracy of the anomaly determination.

[0020] Secondly, the object is achieved by a device having the features of claim 12, which is designed to carry out the method.

[0021] Advantageous further developments are the subject of the dependent patent claims.

[0022] In a preferred embodiment of the method which advantageously facilitates the acquisition of relevant process data, the physical values ​​comprise a motor current and / or a motor voltage and / or a dynamic electrical resistance determined from a motor current and a motor voltage and / or a mechanical resistance, i.e. physical values ​​which can be advantageously derived or determined in particular on the basis of the power of the motors.

[0023] In order to advantageously improve the accuracy of the method, the process data for determining deviations preferably comprise, in addition to the motor data of the axes of the build platform, a comparison of previously determined statistical data of the print head and / or the preparation unit with process data during the manufacture of the object in the control and regulation unit, wherein the statistical data and the process data comprise the recorded physical values ​​of the sensors.

[0024] Preferably, the statistical data includes construction times, shift times, material consumption, speeds, energy consumption, current intensities, contour lengths, and fill volumes, while the process data includes shift times, material consumption, temperatures, process pressures, and electrical signals recorded by additional sensors in the device. This also contributes advantageously to the accuracy of anomaly detection.

[0025] Preferably, in order to evaluate deviations, process data are additionally considered in relation to one another in order to advantageously obtain a data field that supports the assessment of anomalies.

[0026] The accuracy of anomaly determination is advantageously enhanced by the fact that the device preferably comprises additional internal measuring devices, e.g., optical ones, such as a triangulation sensor for distance measurement, in particular a laser-based triangulation sensor, and / or a camera and / or an interferometer, in particular a white-light interferometer, for generating additional process data, and the method preferably comprises determining the surface topography or surface quality using the additional process data from the triangulation sensor and / or the camera and / or the interferometer. Tactile measuring devices are also conceivable.

[0027] In order to advantageously enable the operator to more easily detect the anomaly, a visualization of the process data is preferably provided for direct quality control by the operator.

[0028] In order to advantageously increase the application possibilities and improve the accuracy of the anomaly determination, the visualization is preferably carried out at the human-machine interface and / or on an external computer and / or a production analysis and management tool, e.g. by means of colored markings and / or diagrams and / or camera images, whereby the object is represented in a 2D or 3D image.

[0029] To advantageously facilitate the control and regulation of the build platform, the build platform is preferably moved using linear axes or a multi-unit robot. Preferably, the human-machine interface and / or the control and regulation unit learns from the operator's inputs using artificial intelligence and / or provides recommended actions to the operator, thereby advantageously improving the outcome of interventions.

[0030] Likewise, in the control and regulation unit, the statistical data can be preferably improved by using artificial intelligence by using the process data in order to advantageously further increase the accuracy of the anomaly determination.

[0031] Productivity and quality can be advantageously improved by allowing the operator to set a deviation limit via the human-machine interface, preferably one which, if reached or exceeded, will interrupt the production of the object.

[0032] In an embodiment that advantageously facilitates the operability of the device and the executability of the method, the human-machine interface and / or the control and regulation unit preferably includes artificial intelligence.

[0033] The features listed individually in the patent claims can be combined with one another in a technologically meaningful manner and can be supplemented by explanatory facts from the description and by details from the figures, whereby further embodiments of the invention are shown.

[0034] Short description of the characters

[0035] The invention will now be explained in more detail using an exemplary embodiment. Shown are:

[0036] Fig. 1 is a flow chart of the process,

[0037] Fig. 2 a possible visual representation of the manufacturing process on a human-machine interface,

[0038] Fig.3 is a schematic representation of a device for additive manufacturing.

[0039] Detailed description of preferred embodiments

[0040] The invention will now be explained in more detail by way of example with reference to the accompanying drawings. However, the embodiments are only examples and are not intended to limit the inventive concept to a specific arrangement. Before describing the invention in detail, it should be pointed out that it is not limited to the specific components of the device or the specific method steps, since these components and methods can vary. The terms used herein are intended to describe particular embodiments only and are not used in a limiting sense. Furthermore, when the singular or indefinite article is used in the description or claims, this also refers to the plural of these elements, unless the overall context clearly indicates otherwise.

[0041] Fig. 1 shows the process principle. A process model 30 is created from process parameters 10 and a process prediction 20, which model depicts the manufacturing process of an object using a method for quality-assured additive manufacturing of the three-dimensional object 290. The process model 30 can be created based on data from a single simulation and / or production of an object. The accuracy of the process can be improved through repeated real and / or simulated production of identical objects.

[0042] These - hereinafter - statistical data 10, 20, 30 form the basis for a later determination and evaluation of anomalies of the object 290 to be manufactured. The object is manufactured by means of a device comprising a processing unit 230 for at least one plasticizable or plasticized material, at least one print head 210 with at least one dispensing nozzle 220, a construction platform 250 movable along several axes, such as x, y, and z coordinate axes, by means of at least one motor 200, 201, 202 assigned to each axis, sensors 260 for recording physical values ​​of the motors 200, 201, 202 and, if applicable, of the one print head 210 and / or the processing unit or other elements of the device. The construction platform can, however, also be located on a robot arm, for example, wherein the sensors can then be assigned to the parts / axes of the robot arm.Also provided are a control and regulation unit 270, which may be internal and / or external depending on the embodiment, and a human-machine interface 280, which may be internal and / or external depending on the embodiment, for an operator of the device.

[0043] The method comprises an evaluation of the sensor data and a controlled control of the motors 200, 201, 202 and, if necessary, at least one of the elements from the group comprising the at least one print head 210 and the processing unit 230 by the control and regulation unit 270, wherein at least one plasticized material is deposited, for example under pressure, from the discharge nozzle 220 onto the construction platform 250 and wherein the construction platform 250 and / or the discharge nozzle are moved relative to one another in order to produce the object 290 layer by layer.In the further course of the method, a comparison A of previously determined statistical data 10, 20, 30 of the motors 200, 201, 202 with process data 40 determined during the manufacture of the object is carried out in the control and regulation unit 270 in order to determine deviations, wherein the statistical data 10, 20, 30 and the process data 40 comprise the recorded physical values ​​of the sensors 260 and wherein the statistical data 10, 20, 30, consisting of process parameters 10 and a process prediction 20, which form a process model 30 which depicts the manufacturing process of the object 290, are obtained from process data 40 of repeated real and / or simulated manufacture of identical objects 290.

[0044] In a further step, an evaluation of the deviations 50 of the statistical data 10, 20, 30 and the process data 40 is carried out in the control and regulation unit 270 to determine anomalies of the object 290 to be manufactured. Finally, an evaluation-dependent communication takes place between the control and regulation unit 270 and an artificial intelligence K1 and / or via the human-machine interface 280 with the operator to control the process. In the event of a deviation of the process data 40 from the statistical data 10, 20, 30, the artificial intelligence, in conjunction with the control and regulation unit 270, automatically adapts the control of the process and / or issues recommended actions to the operator. The dashed-bordered blocks 70, 80, and 90 are optional blocks whose meaning will be discussed later. The corresponding structure of the device is shown in Fig. 3.

[0045] Artificial intelligence derives a problem solution and / or recommended action from a knowledge base that, for example, contains a large amount of expert knowledge, encompassing the knowledge and experience of a long-time machine operator, as well as process data, process parameters, procedures, quality parameters, and malfunctions that have been learned, learned, or recorded from past and current processes or cycles. A semantic database can contain additional or supplementary information from manuals, expert information, data sheets, etc.

[0046] Training data for the artificial intelligence Kl includes, for example, statistical values, process information, fault classes, context information, and process information (e.g., material). Quality parameters can also be learned, such as surface quality, dimensional accuracy, gloss, weight, underfill, overfill, local defects, surface topology (e.g., roughness), foreign material, foreign particles, and material carryover. The training data also includes other process-dependent parameters, such as material consumption, production time, or the like. For production time, the layer construction time and the total construction time, or even the production time, are considered and / or learned. In principle, additional parameters / criteria can also be considered and / or learned.

[0047] Through the knowledge base, artificial intelligence is able to derive its own conclusions and recommendations for action, generate new knowledge, and explain to the operator how the problem solution and recommended action were developed. The most important tasks of artificial intelligence and the expert system include: interpreting data by comparing statistical data 10, 20, 30 and process data 40; classifying events; identifying and reducing error causes; and eliminating critical conditions, for example, by modifying control data.

[0048] Stopping the process and / or interacting with the operator, providing dialog-oriented advice to the operator, predicting events based on deviations in the process data 40 and the statistical data 10, 20, 30.

[0049] When implementing artificial intelligence and expert systems, various models can be used, such as case-based, rule-based, or classification systems. Case-based systems search the knowledge base for cases similar to the current problem and apply the solution to the current problem. Rule-based systems work with predefined if-then rules and solve the problem by finding and applying the rules appropriate to the problem. In a classification system, artificial intelligence generates independent learning processes using decision trees, deriving hypotheses for new problems from given facts.

[0050] A typical process involving artificial intelligence might look like this:

[0051] A fault is detected

[0052] The disturbance is classified by a class model based on statistical values, process information, etc. (e.g. overfilling).

[0053] Fault class, context information and process information (e.g. material) are searched in a semantic database.

[0054] The database returns the most relevant sections. The database return, fault class, context information, process information, and statistical values ​​are passed to the chatbot / Large Language Model (LLM) in a prompt.

[0055] Based on this information, the LLM generates instructions for correcting the fault. If possible, the controller performs these steps independently (e.g., if only parameters need to be changed).

[0056] If this is not possible, for example because the operator has to intervene manually, the operator is guided through the process step by step.

[0057] The LLM and the database can run either on the machine's control system or in the cloud.

[0058] The coordinates of each material discharge are known from the statistical data 10, 20, 30 of the motors 200, 201, 202. At each point on the axes 240, 241, 242, for example at each x, y, z coordinate point, mechanical resistance data of the motors 200, 201, 202 can also be stored in the statistical data 10, 20, 30, which allows conclusions to be drawn about the correct amount of material deposited. Depending on the position and weight of the object 290 as well as the mechanical equipment of the machine, the axes 240, 241, 242 and thus their process data 40, e.g. in the form of a mechanical resistance, experience a change in mechanical resistance during the movement. For example, if the mechanical resistance during a movement of the axes (locally) differs from the statistical data, there may be overfilling of the component cross-section at this point. This can be noticeable across layers.The same applies to a reduction in mechanical resistance compared to the statistical data 10, 20, 30. This may indicate underfilling.

[0059] Various error cases 60-63, e.g., error case 1, error case 2, ... error case n, ... error case n+i, can thus be output via the human-machine interface 280. In addition to the error cases 60-63, statistical data 10, 20, 30 and process data 40 can also be output in a visualized form 90 at the human-machine interface 280. The components belonging to the human-machine interface are shown in dash-dotted lines in Fig. 1.

[0060] Fig. 2 shows an example of a human-machine interface 280 with output and input areas. In one area, the path 100 is shown in a specific layer. However, a camera image 110 can also be shown in another area, allowing the operator to directly inspect the discharged material. A third area shows a 3D representation 120 of the object, as well as the display of a specific layer in the object. In a fourth area, a deviation curve 130 of process data values ​​P, e.g., the specific current of the axes, from expected values ​​is shown. The operator can make inputs via a final operating area 140. The output areas can, however, also be equipped with a touchscreen function instead of or in addition to this.

[0061] The acquisition of relevant process data 40 can be facilitated if the physical values ​​include a motor current and / or a motor voltage and / or a dynamic electrical resistance determined from a motor current and a motor voltage and / or a mechanical resistance. The acquisition of currents and voltages can be easily performed, for example, using measuring resistors and corresponding analog-to-digital converters. An increase in mechanical resistance, for example, correlates with an increase in motor current, thus avoiding the more complex determination of the mechanical resistance.

[0062] To improve the accuracy of the method, the determination of deviations can, in addition to the motor data of the axes 240, 241, 242 of the build platform 250, comprise a comparison A of previously determined statistical data 10, 20, 30 of the print head 210 and / or the processing unit 230 with further process data 70 during the manufacture of the object 290 in the control and regulation unit 270, wherein the statistical data 10, 20, 30 and the further process data 70 comprise the recorded physical values ​​of the sensors 260. If, for example, the mechanical resistance or the current increases (locally) during a movement of the axes while the process pressure simultaneously increases, an overfilling of the component cross-section exists at this point.However, if there is no increase in the mechanical resistance or the current of the axes 240, 241, 242, but an increase in the process pressure due to a blocked nozzle outlet, the component may be underfilled because not enough material can be discharged.

[0063] For the data preparation of the statistical data 10, 20, 30, the 3D data of the object 290 to be manufactured can be broken down into layers and, depending on the selected construction parameters and / or construction strategy, machine instructions can be generated which specify how much material is deposited layer by layer along which paths and per layer. Even before the component is manufactured, data can be extracted from these machine instructions for process prediction. The data can be evaluated per shift and / or construction time and can include the following expected information: construction times, shift times, material consumption, speeds, energy consumption, current strengths, lengths of contours and volumes of fillings. During the manufacturing process, further process data 70 can be continuously recorded and analyzed. This data can include the following expected information: shift times, material consumption, temperatures (e.g.in the construction space or the processing unit 230, in particular in the form of a plasticizing unit), process pressures and electrical signals.

[0064] Considering the above-mentioned statistical data 10, 20, 30 and additional process data 70 can further improve the accuracy of anomaly determination. This can also be the case if, in particular, process data 40, 70 are considered in relation to one another.

[0065] Actual layer construction times may, for example, deviate from calculated times due to dosing processes taking too long, which may indicate insufficient material supply (e.g. trickling problems).

[0066] Material consumption is calculated based on the number of droplets. If this value deviates from the expected value, it could indicate a leak in the system (leakage flow through the locking ring, leakage between the nozzle and cylinder, etc.) caused by faulty components, material fatigue, or incorrect assembly. If this leak is located at the discharge nozzle 220 within the build chamber, it could lead to a reduction in part quality and even process termination.

[0067] A drop in process pressure may indicate underfilling due to insufficient material discharge. An increase in process pressure, in turn, may be caused by local overfilling of the component layer, but also by a partially blocked nozzle outlet caused by foreign matter in the material or insufficiently melted material. To determine the cause of the increase, additional sensors could be used to verify the cause.

[0068] To detect the anomaly even more precisely or to rule out errors, additional internal measuring devices for determining the surface topography and checking the surface quality, such as a triangulation sensor for distance measurement, in particular a laser-based triangulation sensor, and / or a camera 300 and / or an interferometer 310, in particular a white light interferometer, can be included in the device and the method. These provide additional process data 70, such as image data 80. In this way, process errors and process interruptions can be avoided through timely detection and intervention. Small deviations do not necessarily reduce the component quality as long as the deviations remain within the permissible limits.

[0069] In a further embodiment of the method that improves the accuracy of anomaly detection, this can include a visualization 90 of the process data 40, 70, including the additional process data 70 from the additional internal measuring devices, for direct quality control by the operator. After the production process, the process data 40, 70 can be visualized, e.g., shift-related, for quality control purposes in order to be able to select potentially critical components. The data can be displayed along the displayed machine path, e.g., in the form of a path 100.

[0070] To expand the possible applications and, in turn, improve the accuracy of anomaly determination, the visualization 90 can be performed at the human-machine interface 280 and / or on an external computer and / or a production analysis and management tool, and the visualization 90 can be performed using colored markings and / or diagrams and / or camera images 110, wherein the object 290 can be represented in a 2D or 3D image 120. Measured variables can be displayed, for example, in color gradations to quickly identify critical areas. For more detailed analysis, visualized and color-coded statistical data 10, 20, 30 can be displayed on a surface together with process data 40, 70, e.g., in diagram form, as well as the camera images 110 from the process. Each layer is displayed individually. A 3D representation 120 of the object 290 including the visualized process data 40, 70 would also be conceivable.

[0071] The detection of anomalies can also be improved if the build platform 250 is moved using linear axes or a multi-joint robot 320. The use of linear motors and robot arms with appropriate sensors is low-interference and highly accurate.

[0072] In an embodiment of the method that is advantageous for the operability of the device and the executability of the method, the human-machine interface 280 and / or the control and regulation unit 270 learns from the operator's inputs using artificial intelligence K1 and / or issues recommended actions to the operator. In this way, recorded parameters can be traced back to specific error cases, and inexperienced operators can be enabled to carry out simple and rapid troubleshooting. It also contributes to improving the accuracy of anomaly determination if the statistical data 10, 20, 30 are improved in the control and regulation unit 270 using artificial intelligence K1 by using the process data 40, 70.

[0073] Productivity and quality can also be improved if the operator can set a deviation limit using the human-machine interface 280. If this limit is reached or exceeded, the production of the object 290 is interrupted. In this case, the size of the permitted deviations (as a percentage) can be set by the operator using the control elements 140 or the touchscreen to either continue the process or abort it.

[0074] The device for carrying out the method for quality-assured additive manufacturing of a three-dimensional object 290 comprises, according to Fig. 3, a processing unit 230 for at least one plasticizable or plasticized material, at least one print head 210 with at least one discharge nozzle 220, a construction platform 250 movable in an x, y and z coordinate axis or along several axes 240, 241, 242 by means of at least one motor 200, 201, 202 assigned to each axis, sensors 260 for detecting physical values ​​of the motors 200, 201, 202 and optionally of the at least one print head 210 and the processing unit 230, an internal and / or external control and regulating unit 270 and a human-machine interface 280 for an operator of the device.

[0075] It is advantageous for the operability of the device and the executability of the method if the human-machine interface 280 and / or the control and regulation unit 270 contains an artificial intelligence Kl.

[0076] Furthermore, it is advantageous for the accuracy of the process if the build platform 250 is moved along the x, y, and z coordinate axes using linear axes. These can be driven by linear motors and contain corresponding sensors for current, voltage, and mechanical resistance.

[0077] It goes without saying that this description is susceptible to various modifications, changes, and adaptations within the scope of equivalents to the appended claims.

[0078] 10 Process parameters 20 Process prediction 30 Process model 40 Process data 50 Evaluation of deviations 60 Error case n 61 Error case n+1 62 Error case n+2 63 Error case n+i 70 Additional process data 80 Image data 90 Visualization 100 Path 110 Camera image 120 3D representation of the object, display of a specific layer 130 Deviation history of process data values ​​from expected values ​​140 Control elements 200, 201, 202 Motors 210 Print head 220 Discharge nozzle 230 Preparation unit 240, 241, 242 Axes 250 Build platform 260 Sensors 270 Control and regulation unit 280 Human-machine interface 290 Object 300 Camera 310 Interferometer 320 Robot

[0079] Kl Artificial Intelligence P Process data value A Comparison of statistical data and process data

Claims

Patent claims 1. Method for the quality-assured additive production of a three-dimensional object by means of a device comprising - a processing unit (230) for at least one plasticizable or plasticized material, - at least one print head (210) with at least one discharge nozzle (220), - a construction platform (250) movable along several axes (240, 241, 242) by means of at least one motor (200, 201, 202) assigned to each axis, - sensors (260) for detecting physical values ​​of the motors (200, 201, 202) and, if necessary, at least one of the elements from the group comprising the at least one print head (210) and the processing unit (230), - a control and regulation unit (270) and - a human-machine interface (280) for an operator of the device, - wherein the method comprises an evaluation of sensor data from the sensors (260) and a controlled control of the motors (200, 201, 202) and, if necessary, of the at least one element from the group comprising the at least one print head (210) and the processing unit (230) by the control and regulation unit (270), wherein at least one plasticized material from the at least one discharge nozzle (220) is deposited on the construction platform (250) and wherein the construction platform (250) is moved in order to produce the object (290) layer by layer, characterized in that the method further comprises: - a comparison (A) of previously determined statistical data (10, 20, 30) of the motors (200, 201, 202) with process data (40) determined during the manufacture of the object (290) in the control and regulation unit (270) to determine deviations, wherein the statistical data (10, 20, 30) and the process data (40) comprise the recorded physical values ​​of the sensors (260) and wherein the statistical data (10, 20, 30), consisting of process parameters (10) and a process prediction (20), which form a process model (30) that depicts the manufacturing process of the object (290), are obtained from process data (40) of repeated real and / or simulated manufacture of identical objects (290), - an evaluation of the deviations of the statistical data (10, 20, 30) and the process data (40) in the control and regulation unit (270) to detect anomalies of the object to be manufactured (290) and - an evaluation-dependent communication of the control and regulation unit (270) with an artificial intelligence (Kl) and / or via the human-machine interface (280) with the operator for controlling the method, wherein the artificial intelligence, in the event of a deviation of the process data (40) from the statistical data (10, 20, 30), Connection to the control and regulation unit (270) automatically adapts the control of the process and / or issues recommendations for action to the operator.

2. Method according to claim 1, characterized in that the physical values ​​comprise a motor current and / or a motor voltage and / or a dynamic electrical resistance determined from a motor current and a motor voltage and / or a mechanical resistance.

3. Method according to one of claims 1 or 2, characterized in that it comprises a comparison (A) of previously determined statistical data (10, 20, 30) of the print head (210) and / or the processing unit (230) with process data (40, 70) during the manufacture of the article (290) in the control and regulation unit (270) in order to determine deviations, wherein the statistical data and the process data comprise the recorded physical values ​​of the sensors (260).

4. Method according to one of the preceding claims, characterized in that the statistical data (10, 20, 30) comprise construction times, shift times, material consumption, speeds, energy consumption, current intensities, lengths of contours and volumes of fillings and that the process data (40, 70) comprise shift times, material consumption, temperatures, process pressures and electrical signals which are recorded with further sensors (260) of the device.

5. Method according to one of the preceding claims, characterized in that, in order to evaluate deviations, process data (40, 70) are additionally considered in relation to one another.

6. Method according to one of the preceding claims, characterized in that the device comprises additional internal optical measuring devices, such as a triangulation sensor for distance measurement, in particular a laser-based triangulation sensor, and / or a camera (300) and / or an interferometer (310), in particular a white light interferometer, for generating further process data (70), and in that the method comprises a determination of the surface topography or the surface quality by means of the further process data (70) of the triangulation sensor and / or the camera and / or the interferometer.

7. Method according to claim 6, characterized in that it comprises a visualization (90) of the process data (40, 70) for quality control by the operator, which (90) is displayed on the human-machine interface (280) and / or on an external computer and / or a production analysis and management tool and that the visualization (90) is carried out by means of colored markings and / or diagrams and / or camera images (110), wherein the object (290) is represented in a 2D or 3D image (120).

8. Method according to one of the preceding claims, characterized in that the human-machine interface (280) and / or the control and regulation unit (270) learns from operator inputs by means of artificial intelligence (Kl) and / or issues recommendations for action to the operator.

9. Method according to one of the preceding claims, characterized in that in the control and regulation unit (270) by means of artificial intelligence (Kl) the statistical data (10, 20, 30) are improved by using the process data (40, 70).

10. Method according to one of the preceding claims, characterized in that the operator can set a deviation limit by means of the human-machine interface (280), upon reaching or exceeding which the production of the article (290) is interrupted.

11. Device for the quality-assured additive production of a three-dimensional object (290) comprising a processing unit (230) for at least one plasticizable or plasticized material, at least one print head (210) with at least one discharge nozzle (220), a construction platform (250) movable along several axes (200, 201, 202) by means of at least one motor (240, 241, 242) assigned to each axis, sensors for detecting physical values ​​of the motors (240, 241, 242) and, if necessary, at least one of the elements from the group comprising the at least one print head (210) and the processing unit (230), a control and regulating unit (270) and a human-machine interface (280) for an operator of the device, characterized in that it is set up to carry out the method according to one of claims 1 to 10.

12. Device according to claim 11, characterized in that the human-machine interface (280) and / or the control and regulation unit (270) contains an artificial intelligence (Kl).

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