Quality assurance in additive manufacturing

Abstract

A method for the additive manufacturing of a three-dimensional object using a device comprises a preparation unit for at least one plasticizable material, at least one print head with at least one dispensing nozzle, a build platform movable along several axes by a motor, sensors for recording physical values ​​of the motors, a control unit, and a human-machine interface. The method includes sensor data evaluation and controlled motor operation, whereby plasticized material is deposited from the dispensing nozzle onto the build platform, and the build platform is moved to produce the object layer by layer.The procedure further includes a comparison of previously determined statistical data (10, 20, 30) of the motors (200, 201, 202) with process data (40) determined during the manufacture of the item for the purpose of determining deviations, wherein the statistical data (10, 20, 30) and the process data (40) include the recorded physical values ​​of the sensors, and a deviation evaluation of the statistical data and the process data for the detection of anomalies of the item to be manufactured, as well as evaluation-dependent operator communication for process control.

Description

Field of the invention

[0001] 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 13. State of the art

[0002] Processes for the additive manufacturing of three-dimensional objects 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.

[0003] 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, the first sensor being 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, the second sensor being 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.

[0004] 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 property based on the control data, it is subsequently determined whether a problem exists within the device. 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.

[0005] 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.

[0006] The solutions disclosed in the prior art thus 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 detect anomalies of objects to be manufactured in the form of deviations from the process or object model. Summary of the invention

[0007] 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. Description of the invention

[0008] 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 regulating unit and a human-machine interface for an operator of the device,wherein the method comprises an evaluation of sensor data from the sensors and a controlled control of the motors and, if necessary, of the 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 construction platform and wherein the construction platform is moved in order 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, whereby the statistical data and the process data include the physical values ​​recorded by the sensors, - an evaluation of the deviations of the statistical data and the process data in the control and regulation unit to detect anomalies of the object to be manufactured and - evaluation-dependent communication between the control and regulation unit and an artificial intelligence and / or via the human-machine interface with the operator to control the process.

[0009] Secondly, the object is achieved by a device having the features of claim 13, which is designed to carry out the method.

[0010] Advantageous further developments are the subject of the dependent patent claims.

[0011] In a preferred embodiment of the method which facilitates the acquisition of relevant process data, the physical values ​​advantageously 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.

[0012] 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.

[0013] In addition, the previously determined statistical data can preferably 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.

[0014] 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.

[0015] 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.

[0016] 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.

[0017] In order to enable the operator to more easily detect the anomaly, a visualization of the process data is advantageously provided for direct quality control by the operator.

[0018] 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.

[0019] In order to advantageously facilitate the control and regulation of the construction platform, the construction platform is preferably moved by means of linear axes or a multi-unit robot.

[0020] Preferably, the human-machine interface and / or the control and regulation unit learns from the operator's inputs by means of artificial intelligence and / or issues recommendations for action to the operator in order to advantageously improve the outcome of interventions.

[0021] Likewise, in the control and regulation unit, the statistical data can be preferentially improved by using the process data using artificial intelligence to further increase the accuracy of the anomaly determination.

[0022] 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.

[0023] 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.

[0024] 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. Short description of the characters

[0025] The invention will now be explained in more detail using an exemplary embodiment. Shown are: Fig. 1 a flow chart of the procedure, Fig. 2 a possible visual representation of the manufacturing process on a human-machine interface, Fig. 3 a schematic representation of a device for additive manufacturing. Description of preferred embodiments

[0026] Before describing the invention in detail, it should be noted that it is not limited to the specific components of the device and the specific method steps, as these components and methods may vary. The terms used herein are intended solely to describe particular embodiments and are not intended to be limiting. Furthermore, when the singular or indefinite articles are used in the description or claims, this also refers to the plural of these elements, unless the overall context clearly indicates otherwise.

[0027] Fig. Figure 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. However, it can also be obtained from repeated real and / or simulated production of identical objects, which improves the accuracy of the method.

[0028] 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 discharge 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 detecting 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.

[0029] 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.

[0030] In the further course of the method, a comparison Δ 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 to determine deviations, wherein the statistical data 10, 20, 30 and the process data 40 comprise the recorded physical values ​​of the sensors 260. 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, and finally, an evaluation-dependent communication takes place between the control and regulation unit 270 and an artificial intelligence AI and / or via the human-machine interface 280 with the operator to control the method. 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.

[0031] 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, e.g. at each x, y, z coordinate point, mechanical resistance data from 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 mechanical resistance, experience a change in mechanical resistance during travel. 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.

[0032] Various error cases 60 - 63, e.g., error case 1, error case 2, ... error case n, ... error case n+i can 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 visualized 90 and output at the human-machine interface 280. The components belonging to the human-machine interface are in Fig. 1 shown in dash-dotted lines.

[0033] 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 also be equipped with a touchscreen function instead of or in addition to this.

[0034] 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.

[0035] 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, include a comparison Δ 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 production of the object 290 in the control and regulation unit 270, wherein the statistical data 10, 20, 30 and the further process data 70 include 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 with a simultaneous increase in the process pressure, 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.

[0036] For the data preparation of 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 build parameters and / or build strategy, machine instructions can be generated that 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 layer and / or per build time and can include the following expected information: build times, shift times, material consumption, speeds, energy consumption, current intensities, contour lengths, and fill volumes.

[0037] During the manufacturing process, additional 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 build chamber or the preparation unit 230, particularly in the form of a plasticizing unit), process pressures, and electrical signals.

[0038] 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.

[0039] 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).

[0040] 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.

[0041] 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.

[0042] 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 further process data 70, such as image data 80.

[0043] In this way, process errors and process interruptions can be avoided through timely detection and intervention. Even small deviations do not necessarily reduce component quality, as long as the deviations remain within acceptable limits.

[0044] 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.

[0045] In order to increase the application possibilities and in turn improve the accuracy of the anomaly determination, the visualization 90 can be carried out on 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 carried out by means of colored markings and / or diagrams and / or camera images 110, wherein the object 290 is shown in a 2D or 3D Fig. can be displayed. Measured variables can, for example, be displayed 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.

[0046] 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.

[0047] 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 (AI) and / or issues recommended actions to the operator. This allows recorded parameters to be traced back to specific error cases, enabling inexperienced operators to perform simple and quick troubleshooting.

[0048] 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 by using artificial intelligence AI by using the process data 40, 70.

[0049] 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.

[0050] 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 regulation unit 270 and a human-machine interface 280 for an operator of the device.

[0051] 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 includes an artificial intelligence AI.

[0052] 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. List of reference symbols 10 process parameters 20 Process prediction 30 Process model 40 process data 50 Assessment of deviations 60 error cases 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 Track 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 controls 200, 201, 202 engines 210 print head 220 discharge nozzle 230 processing unit 240, 241, 242 axes 250 construction platform 260 sensors 270 Control and regulation unit 280 Human-Machine Interface 290 Item 300 Camera 310 Interferometer 320 robots AI Artificial Intelligence P Process data value Δ Comparison of statistical data and process data QUOTES CONTAINED IN THE DESCRIPTION

[0000] This list of documents submitted by the applicant was generated automatically and is included solely for the convenience of the reader. This list is not part of the German patent or utility model application. The DPMA assumes no liability for any errors or omissions. Cited patent literature

[0000] WO 2021 / 021469 A1

[0003] EP 3 921 166 A1

[0004] EP 4 104 955 A1

[0005] EP 4 094 867 A1

[0005]

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 by that the procedure continues to include: - a comparison (Δ) 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), - 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 (AI) and / or via the human-machine interface (280) with the operator for controlling the process. [2] Method according to claim 1, characterized bythat 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 by that it comprises a comparison (Δ) 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 by that the previously determined statistical data (10, 20, 30) are obtained from process data (40, 70) of repeated real and / or simulated production of identical objects (290). [5] Method according to one of the preceding claims, characterized by that the statistical data (10, 20, 30) include 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) include shift times, material consumption, temperatures, process pressures and electrical signals which are recorded with further sensors (260) of the device. [6] Method according to one of the preceding claims, characterized by that, in order to evaluate deviations, additional process data (40, 70) are considered in relation to each other. [7] Method according to one of the preceding claims, characterized bythat 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 that the method comprises determining 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. [8] Method according to claim 7, characterized by that it comprises a visualization (90) of the process data (40, 70) for direct quality control by the operator. [9] Method according to claim 8, characterized bythat the visualization (90) takes place at the human-machine interface (280) and / or at an external computer and / or a production analysis and management tool and that the visualization (90) takes place 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). [10] Method according to one of the preceding claims, characterized by that the human-machine interface (280) and / or the control and regulation unit (270) learns from the operator's inputs by means of artificial intelligence (AI) and / or issues recommendations for action to the operator. [11] Method according to one of the preceding claims, characterized by that in the control and regulation unit (270) the statistical data (10, 20, 30) are improved by using the process data (40, 70) by means of artificial intelligence (AI). [12] Method according to one of the preceding claims, characterized by 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. [13] 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 by that it is arranged to carry out the method according to one of claims 1 to 12. [14] Device according to claim 13, characterized bythat the human-machine interface (280) and / or the control and regulation unit (270) contains an artificial intelligence (AI).

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