EP4078463A2 - Verfahren zur bestimmung und korrektur des maschinenzustands einer werkzeugmaschine und diagnosesystem - Google Patents
Verfahren zur bestimmung und korrektur des maschinenzustands einer werkzeugmaschine und diagnosesystemInfo
- Publication number
- EP4078463A2 EP4078463A2 EP20829778.8A EP20829778A EP4078463A2 EP 4078463 A2 EP4078463 A2 EP 4078463A2 EP 20829778 A EP20829778 A EP 20829778A EP 4078463 A2 EP4078463 A2 EP 4078463A2
- Authority
- EP
- European Patent Office
- Prior art keywords
- machine
- parameters
- image
- module
- machine tool
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/18—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form
- G05B19/406—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form characterised by monitoring or safety
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/18—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form
- G05B19/404—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form characterised by control arrangements for compensation, e.g. for backlash, overshoot, tool offset, tool wear, temperature, machine construction errors, load, inertia
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
- B23K—SOLDERING OR UNSOLDERING; WELDING; CLADDING OR PLATING BY SOLDERING OR WELDING; CUTTING BY APPLYING HEAT LOCALLY, e.g. FLAME CUTTING; WORKING BY LASER BEAM
- B23K31/00—Processes relevant to this subclass, specially adapted for particular articles or purposes, but not covered by only one of the preceding main groups
- B23K31/02—Processes relevant to this subclass, specially adapted for particular articles or purposes, but not covered by only one of the preceding main groups relating to soldering or welding
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
- B23K—SOLDERING OR UNSOLDERING; WELDING; CLADDING OR PLATING BY SOLDERING OR WELDING; CUTTING BY APPLYING HEAT LOCALLY, e.g. FLAME CUTTING; WORKING BY LASER BEAM
- B23K31/00—Processes relevant to this subclass, specially adapted for particular articles or purposes, but not covered by only one of the preceding main groups
- B23K31/12—Processes relevant to this subclass, specially adapted for particular articles or purposes, but not covered by only one of the preceding main groups relating to investigating the properties, e.g. the weldability, of materials
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
- G05B23/0283—Predictive maintenance, e.g. involving the monitoring of a system and, based on the monitoring results, taking decisions on the maintenance schedule of the monitored system; Estimating remaining useful life [RUL]
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/36—Nc in input of data, input key till input tape
- G05B2219/36199—Laser cutting
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/50—Machine tool, machine tool null till machine tool work handling
- G05B2219/50064—Camera inspects workpiece for errors, correction of workpiece at desired position
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/50—Machine tool, machine tool null till machine tool work handling
- G05B2219/50297—Compensation of positioning error due to a-axis, b-axis tool rotation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
Definitions
- the present invention relates to a method for determining and correcting the machine status of a machine tool, in particular a laser cutting machine, and a diagnostic system.
- the machine condition of a machine tool is a frequent cause of inadequate surface quality during processing, especially during laser cutting. In laser cutting processes, this leads to poor quality at the cutting edge of the workpiece.
- the machine status depends on the status of the individual components.
- the protective gas of a laser cutting machine can influence the focus position and the nozzle of a laser cutting machine can influence the gas dynamics.
- the individual components overlap in their effect on the overall condition of the machine. In practice, therefore, a quality defect at the cut edge cannot be traced back to one or more specific individual components by skilled workers.
- a standardized manual maintenance program with at least 13 individual steps that build on one another has to be carried out on suspicion.
- the invention is based on the object of providing a method and a diagnostic system by means of which a defective machine condition can be determined and corrected in a simple and rapid manner.
- the object is achieved by a method according to claim 1 and a diagnostic system according to claim 4.
- the subclaims reproduce preferred embodiments.
- the object is thus achieved according to the invention by a method for determining and correcting the machine status of a machine tool, in particular one
- the output according to the invention of a maintenance instruction for correcting the machine status can relate to one or more specific individual components of the machine. This eliminates the need for maintenance of the entire machine in the form of many individual checks for suspicion, which is particularly time-saving and cost-effective.
- the sequence of the specified process steps is particularly advantageous with regard to a fast process sequence, but is not to be understood as conclusive. A changed order is also conceivable.
- the invention thus relates to a method for determining and correcting the defective machine state and / or at least one defective component state of a machine tool.
- an image of the surface created by the machine tool, in particular the cutting edge is provided.
- the surface image is provided in particular in the form of a photograph, particularly preferably in the form of a digital color photograph.
- a surface mapping is understood to mean only the mapping of the machined workpiece surface. If, in addition to the surface image, an image also contains an image of the surroundings of the workpiece, it is provided that the image is reduced to the surface image. In other words, the surface image is cut out from an overall image with an image of the surroundings.
- a further method step can be provided for this purpose. Particularly preferably, the surface image is cut free during the analysis of the surface image. As a result, the method can be carried out particularly quickly and easily.
- the method has at least one, in particular several, data aggregation routines.
- a data aggregation routine can be designed to aggregate several “determined data” into a new data packet.
- the new data packet can have one or more numbers or vectors.
- the new data packet can be made available in full or in part to further data aggregation routines as “determined data”.
- "Determined data” can be, for example, machine parameters, material parameters, machining parameters or data packets made available by one of the data aggregation routines.
- a method is particularly preferably designed in the form of an algorithm with several connected data aggregation routines. In particular, several hundred, in particular several thousands of such data aggregation routines can be linked together. This significantly improves the quality and speed of the process.
- the method can have a function with weighted variables.
- One, in particular several, particularly preferably all, data aggregation routines can be designed to combine several “determined data” in each case with a weighted variable, in particular to multiply, and thus to convert the “determined data” into “combined data” and then to aggregate the “combined data” into a new data package, in particular to add them.
- the method can be run through with data, in particular machine parameters, material parameters and / or machining parameters, the relationship between which is known in each case.
- the features of the machine and machining parameters as well as the cut edge features can themselves be data packets, in particular several structured data, in particular data vectors or data arrays, which themselves again "determined data”, e.g. for the method, in particular for the data aggregation routines of the Procedure.
- the surface mapping is analyzed by means of the data aggregation routine, in particular a convolutional neural network (CNN).
- the data aggregation routine first determines the relevant mapping area of the provided surface mapping. This ensures that image areas with low quality or fuzzy image areas are recognized by the data aggregation routine before the analysis and, if necessary, are interpreted as subordinate in the analysis, in particular excluded from the analysis. This can further improve the quality and the speed of the method.
- CNN convolutional neural network
- the data aggregation routine analyzes the surface with regard to the surface quality, in particular the surface structure, and determines the Surface quality underlying actual machine parameters of the machine tool.
- Actual machine parameters are the actually effective machine settings of the machine tool that lead to the depicted workpiece surface. For example, the laser power actually arriving on the sheet metal during the cutting process of a laser cutting machine can deviate from the set laser power in the faulty state of the laser cutting machine. The actual machine parameters therefore correspond to the set machine parameters in an error-free, in particular very good, machine condition.
- the machine parameters can include the machining parameters (referred to as “process parameters” in some prior art documents), such as the focus position, the feed and / or the gas pressure, of the machine tool, since these also infer the status of individual components to let.
- process parameters such as the focus position, the feed and / or the gas pressure, of the machine tool, since these also infer the status of individual components to let.
- the list is not to be understood as exhaustive.
- a changed feed rate can indicate a defect in the drive.
- any machine settings that have an influence on the cutting edge surface are to be understood as machine parameters.
- the set machine parameters of the machine tool are provided and compared with the actual machine parameters by the data aggregation routine.
- the actual and set machine parameters are compared.
- the data aggregation routine uses this comparison to determine the difference between actual and set machine parameters, or the difference between the optimal machine status and the actual machine status.
- the machine status can be determined by the status of only one individual component of the machine tool.
- the difference can be in a single value, in several values and / or in multi-dimensional comparisons (tables, graphs, etc.).
- the difference can take place in the form of a list of the machine components that are relevant for the creation of the cutting surface, in particular all of the machine components, with a representation of the probability of errors. This makes it particularly easy to understand the derived maintenance instructions.
- the determined states of the individual components and the resulting machine state are then used to output at least one specific maintenance instruction in order to correct the defective machine state.
- No optimization of the set cutting parameters of the machine tool is proposed here, which is intended to improve the cutting edge quality based on a current machine state that is assumed to be optimal, but rather an optimization of the machine state assuming optimally set cutting parameters.
- the specific individual component can be serviced particularly advantageously by a specialist without in-depth knowledge of the machine. There is no need for complex machine maintenance.
- the set machine parameters are determined from an image, in particular a photo, of an operator unit of the machine tool.
- the set machine parameters required for the method can be made available particularly easily by the user in the case of diagnosis.
- the mapping is evaluated by means of the data aggregation routine and the set machine parameters are determined automatically.
- a comparison between the image and the determined set machine parameters is checked by a specialist.
- the set machine parameters are transmitted via a machine interface and / or an automatic Image creation of the user interface are provided.
- the set machine parameters can be determined automatically in the event of a diagnosis.
- a further development of the method is also preferred in which at least one process parameter determined by a process sensor system is provided.
- the at least one process parameter can be provided via a sensor interface in the form of a variable and / or in the form of an image, in particular a photo, which is evaluated by a data aggregation routine.
- the process sensors are used to monitor and record the machining process by the machine tool.
- the process sensor system has at least one sensor for determining a process-relevant variable, for example a forward speed, process temperature, process gas pressure, etc. The list is only intended as an example and is not intended to be conclusive.
- a diagnostic system for carrying out the method according to the invention with an image generator module, a data processing module with a data aggregation routine, a reference module, an evaluation module and an output module
- the image generator module being designed to display an image , in particular to provide a photo of a surface processed by the machine tool and wherein the data aggregation routine is designed to evaluate the image of the processed surface with regard to the surface quality and to determine actual machine parameters
- the reference module is designed to provide set machine parameters
- the evaluation module is designed to determine the machine status, in particular the component statuses, on the basis of the actual machine parameters and the set machine parameters, and wherein a maintenance instruction based on the machine status can be provided via the output module.
- the image generator module comprises a central buffer and at least one imaging device, in particular a camera, particularly preferably a smartphone camera.
- the at least one imaging device can be designed to be mobile and / or fixed to a machine tool.
- the image generator module comprises at least one machine tool-fixed imaging device and a variable imaging device.
- the at least one imaging device is designed to store images on a central buffer store.
- the at least one imaging device can be connected to the data memory via a permanent or temporary data transmission connection, in particular wirelessly. In particular, it can be provided to carry out the data transmission via a smartphone application. This enables access to the central buffer store to be made possible in a particularly simple manner.
- the central buffer can have at least one, in particular several, digital storage units.
- the central buffer is designed to manage the digital storage units and the data storage.
- the central buffer can transmit storage instructions, in particular data designations, to the image generator module.
- the central buffer can be used centrally for several diagnostic systems in a particularly advantageous manner.
- the image generator module can be designed to carry out a machine and / or diagnostic assignment of the stored images, in particular after receiving a storage instruction from the central buffer.
- images transmitted by the image generator module can be stored in a particularly structured manner on the central buffer memory.
- the reference module is designed to determine the set machine parameters directly from the machine tool.
- the reference module is designed for communication with the machine tool. Communication can Be designed via a permanent and / or intermittent data transmission between the machine tool and the reference module.
- the reference module is designed to determine the set machine parameters from an image, in particular a photo, of the operator unit of the machine tool. This enables fast data acquisition and / or data transmission of the set machine parameters to the reference module and avoids errors in the manual data transmission from the operator unit to the reference module.
- the diagnostic system can have a machine tool fixed and / or a mobile imaging device.
- An embodiment is particularly preferred in which the imaging of the operator unit of the machine tool is provided by the imaging module, in particular the imaging device for imaging the processed surface.
- the same imaging device can be used in a particularly simple manner to transmit the image of the cut edge and the operator unit.
- the images transmitted by the image generator module are evaluated directly by the data aggregation routine.
- the reference module can therefore particularly easily access information that has already been evaluated.
- the diagnostic system has a central intermediate memory which is designed to store and provide all parameters relevant to the method.
- the provided actual machine parameters, the set machine parameters, process parameters and all determined data and information (process-relevant parameters) are stored on the central buffer under an individual diagnostic identifier, in particular with machine tool and user identifier.
- the image generator module has a smartphone and / or a camera that is fixed to the machine tool. This enables the processed surface and / or the set machine parameters to be recorded particularly quickly.
- the evaluation module is spatially spaced from the machine tool.
- a spacing of the evaluation module from the machine tool is understood here to mean a spatially wide distance between the evaluation module and the machine tool.
- a connection is preferably established via a data network, in particular via the Internet.
- FIG. 2 shows an embodiment of a diagnostic system according to the invention in a schematic representation.
- 1 shows the method 10 according to the invention in a schematic representation.
- a machining step 12 a workpiece 16 is machined by a machine tool 14 in accordance with machine parameters 18 set on the machine tool 14.
- the set machine parameters 18 also include the machining parameters, in particular the cutting parameters, of the machine tool for the machining step 12.
- the machined workpiece 16 has a machined surface 20.
- the processed surface 20 is imaged, in particular photographed, in a subsequent imaging step 22 by an imaging device 24, in particular a camera, preferably arranged fixed to the machine tool, and an image 26 of the processed surface 20 of the workpiece 16 is created.
- an imaging device 24 in particular a camera, preferably arranged fixed to the machine tool, and an image 26 of the processed surface 20 of the workpiece 16 is created.
- the created image 26 is divided into sub-areas 30.
- the sub-areas 30 are categorized according to irrelevant and relevant sub-areas 30.
- Irrelevant subregions 30 are characterized, for example, by the workpiece environment that is also depicted or by the blurred representation of the processed surface 20.
- Relevant partial areas have, for example, good resolution and quality in the image of the processed surface 20.
- FIG. 2 shows, by way of example, four partial areas 30 which, however, are only to be understood as examples with regard to their number and section dimensions. A division of the sub-areas 30 into further, for example partially relevant, categories is also conceivable.
- the processed surface 20 is analyzed by a data aggregation routine 34, in particular a convolutional neural network (CNN).
- a data aggregation routine 34 in particular a convolutional neural network (CNN).
- CNN convolutional neural network
- a comparison step 38 the set machine parameters 18 are compared with the actual machine parameters 36 and the difference is determined.
- the difference allows conclusions to be drawn about the nature of the individual components of the machine tool 14 and results in the output of a maintenance instruction 40 for correcting the machine condition.
- the machine parameters 18 set on an operator unit 42 of the machine tool 14 are made available to the method 10 via a data transmission 44. This takes place, for example, through manual input and / or through a data connection between machine tool 14 and a central buffer store 46.
- the central buffer 46 also serves to store the image 26 of the processed surface 20, the determined actual machine parameters 36 and possible process parameters 48.
- the process parameters 48 are created via a process sensor system 50 during the processing step 12.
- the process sensor system 50 is used to monitor the machining step 12 and the machine tool 14 and records the prevailing conditions.
- the diagnostic system 100 has an image generator module 110 with an imaging device 24, software and a central buffer 46.
- the imaging device 24 in the form of a smartphone is used to create the image 26 of the processed surface 20 expensive equipping of the machine tool 14 with an imaging device 24, for example in the form of a camera fixed to the machine tool (see FIG. 1), can be dispensed with.
- the software of the image generator module 110 is designed to establish a data connection to the central buffer store 46 and / or to a data processing module 120. This enables, on the one hand, the storage of the image 26 on the central buffer store 46 and, on the other hand, the further processing of the image 26 in the data processing module 120.
- the data processing module 120 comprises the data aggregation routine 34 and is used to subdivide the image 26 of the processed surface 20 into sub-areas 30 (see FIG. 1) and to analyze the image 26 of the processed surface 20. Results of the analysis and the creation of the sub-areas 30 (see FIG. 1) are stored on a central buffer 46 connected to the data processing module 120 by means of a data transmission 44.
- Set machine parameters 18 are transmitted to the diagnostic system 100 via a data transmission 44 between a reference module 130 and the operator unit 42 and stored in the central buffer 46.
- An evaluation module 140 determines the, in particular multi-dimensional, difference between the set machine parameters 18 (see FIG. 1) and the actual machine parameters 36 (see FIG. 1) as well as the machine status of the machine tool 14.
- a maintenance instruction 40 (see FIG . 1) for maintenance of the machine tool 14 via an output module 150 to an output unit accessible for a skilled worker entrusted with the maintenance of the machine tool 14, in particular the operator unit 42 and / or the smartphone of the imaging module 110.
- the invention relates to a method 10 for determining and correcting the defective machine state and / or at least one defective component state of a machine tool 14, the state determination by means of image 26 and analysis 32 of a created cutting edge 20 and comparison 38 being set Machine parameters 18 is determined and the correction is carried out by a maintenance instruction 40 based on the machine condition for maintenance of the machine tool 14.
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- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Human Computer Interaction (AREA)
- Manufacturing & Machinery (AREA)
- Mechanical Engineering (AREA)
- Numerical Control (AREA)
- Laser Beam Processing (AREA)
Description
Claims
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
DE102019220485.6A DE102019220485A1 (de) | 2019-12-20 | 2019-12-20 | Verfahren zur Bestimmung und Korrektur des Maschinenzustands einer Werkzeugmaschine und Diagnosesystem |
PCT/EP2020/087168 WO2021123268A2 (de) | 2019-12-20 | 2020-12-18 | Verfahren zur bestimmung und korrektur des maschinenzustands einer werkzeugmaschine und diagnosesystem |
Publications (1)
Publication Number | Publication Date |
---|---|
EP4078463A2 true EP4078463A2 (de) | 2022-10-26 |
Family
ID=74175775
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
EP20829778.8A Pending EP4078463A2 (de) | 2019-12-20 | 2020-12-18 | Verfahren zur bestimmung und korrektur des maschinenzustands einer werkzeugmaschine und diagnosesystem |
Country Status (6)
Country | Link |
---|---|
US (1) | US20220317654A1 (de) |
EP (1) | EP4078463A2 (de) |
JP (1) | JP7485767B2 (de) |
CN (1) | CN114868134A (de) |
DE (1) | DE102019220485A1 (de) |
WO (1) | WO2021123268A2 (de) |
Family Cites Families (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
DE10311822A1 (de) * | 2003-03-13 | 2004-10-07 | Ekra Eduard Kraft Gmbh Maschinenfabrik | Verfahren und Vorrichtung zur Kontrolle oder Beeinflussung des Druckprozesses beim Lotpastendruck |
CA2743522C (en) | 2008-11-21 | 2015-05-26 | Precitec Itm Gmbh | Method and device for monitoring a laser processing operation to be performed on a workpiece, and laser processing head having such a device |
WO2012000650A1 (en) | 2010-06-28 | 2012-01-05 | Precitec Kg | A method for classifying a multitude of images recorded by a camera observing a processing area and laser material processing head using the same |
DE102012203752A1 (de) * | 2012-03-09 | 2013-09-12 | Homag Holzbearbeitungssysteme Gmbh | Verfahren zum Manipulieren einer Bearbeitungsmaschine |
DE102014212682A1 (de) | 2014-07-01 | 2016-01-07 | Trumpf Werkzeugmaschinen Gmbh + Co. Kg | Verfahren und Vorrichtung zum Bestimmen einer Werkstoffart und/oder einer Oberflächenbeschaffenheit eines Werkstücks |
JP6625914B2 (ja) | 2016-03-17 | 2019-12-25 | ファナック株式会社 | 機械学習装置、レーザ加工システムおよび機械学習方法 |
-
2019
- 2019-12-20 DE DE102019220485.6A patent/DE102019220485A1/de active Pending
-
2020
- 2020-12-18 CN CN202080088757.4A patent/CN114868134A/zh active Pending
- 2020-12-18 EP EP20829778.8A patent/EP4078463A2/de active Pending
- 2020-12-18 JP JP2022537261A patent/JP7485767B2/ja active Active
- 2020-12-18 WO PCT/EP2020/087168 patent/WO2021123268A2/de unknown
-
2022
- 2022-06-21 US US17/844,905 patent/US20220317654A1/en active Pending
Also Published As
Publication number | Publication date |
---|---|
WO2021123268A2 (de) | 2021-06-24 |
JP2023507179A (ja) | 2023-02-21 |
DE102019220485A1 (de) | 2021-06-24 |
US20220317654A1 (en) | 2022-10-06 |
JP7485767B2 (ja) | 2024-05-16 |
CN114868134A (zh) | 2022-08-05 |
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