Classifications

• • H—ELECTRICITY
  • H01—ELECTRIC ELEMENTS
  • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
  • H01M8/00—Fuel cells; Manufacture thereof
  • H01M8/02—Details
  • H01M8/0202—Collectors; Separators, e.g. bipolar separators; Interconnectors
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
  • G01N21/84—Systems specially adapted for particular applications
  • G01N21/88—Investigating the presence of flaws or contamination
  • G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
• • 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
  • B23K31/125—Weld quality monitoring
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
  • G01N21/84—Systems specially adapted for particular applications
  • G01N21/88—Investigating the presence of flaws or contamination
  • G01N21/95—Investigating the presence of flaws or contamination characterised by the material or shape of the object to be examined
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N23/00—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00
  • G01N23/02—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00 by transmitting the radiation through the material
  • G01N23/06—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00 by transmitting the radiation through the material and measuring the absorption
  • G01N23/18—Investigating the presence of flaws defects or foreign matter
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N27/00—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means
  • G01N27/72—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating magnetic variables
  • G01N27/82—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating magnetic variables for investigating the presence of flaws
  • G01N27/83—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating magnetic variables for investigating the presence of flaws by investigating stray magnetic fields
  • G01N27/84—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating magnetic variables for investigating the presence of flaws by investigating stray magnetic fields by applying magnetic powder or magnetic ink
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
  • G01N29/04—Analysing solids
  • G01N29/043—Analysing solids in the interior, e.g. by shear waves
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
  • G01N29/44—Processing the detected response signal, e.g. electronic circuits specially adapted therefor
  • G01N29/4481—Neural networks
• • 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
• • 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
  • G06N3/08—Learning methods
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T7/00—Image analysis
  • G06T7/0002—Inspection of images, e.g. flaw detection
  • G06T7/0004—Industrial image inspection
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
  • G01N21/84—Systems specially adapted for particular applications
  • G01N21/88—Investigating the presence of flaws or contamination
  • G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
  • G01N2021/8854—Grading and classifying of flaws
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
  • G01N21/84—Systems specially adapted for particular applications
  • G01N21/88—Investigating the presence of flaws or contamination
  • G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
  • G01N2021/8883—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges involving the calculation of gauges, generating models
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
  • G01N21/84—Systems specially adapted for particular applications
  • G01N21/88—Investigating the presence of flaws or contamination
  • G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
  • G01N2021/8887—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges based on image processing techniques
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
  • G01N21/84—Systems specially adapted for particular applications
  • G01N21/88—Investigating the presence of flaws or contamination
  • G01N21/91—Investigating the presence of flaws or contamination using penetration of dyes, e.g. fluorescent ink
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N2223/00—Investigating materials by wave or particle radiation
  • G01N2223/60—Specific applications or type of materials
  • G01N2223/629—Specific applications or type of materials welds, bonds, sealing compounds
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N2223/00—Investigating materials by wave or particle radiation
  • G01N2223/60—Specific applications or type of materials
  • G01N2223/646—Specific applications or type of materials flaws, defects
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N2291/00—Indexing codes associated with group G01N29/00
  • G01N2291/04—Wave modes and trajectories
  • G01N2291/044—Internal reflections (echoes), e.g. on walls or defects
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N2291/00—Indexing codes associated with group G01N29/00
  • G01N2291/26—Scanned objects
  • G01N2291/267—Welds
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N2291/00—Indexing codes associated with group G01N29/00
  • G01N2291/26—Scanned objects
  • G01N2291/269—Various geometry objects
  • G01N2291/2697—Wafer or (micro)electronic parts
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T2207/00—Indexing scheme for image analysis or image enhancement
  • G06T2207/20—Special algorithmic details
  • G06T2207/20081—Training; Learning
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T2207/00—Indexing scheme for image analysis or image enhancement
  • G06T2207/20—Special algorithmic details
  • G06T2207/20084—Artificial neural networks [ANN]
• • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
  • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
  • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
  • Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
  • Y02E60/30—Hydrogen technology
  • Y02E60/50—Fuel cells

Definitions

• the invention relates to a method for testing a bipolar plate, in particular an electrochemical cell such as a fuel cell, and a test system provided for this purpose, with the aid of which a bipolar plate of an electrochemical cell such as a fuel cell can be examined for defects during manufacture.
• a fuel cell stack or stack typically includes a plurality of fuel cells in a stack configuration.
• the respective fuel cell comprises an electrolyte and electrodes, which are contacted via conductive plates.
• a polymer electrolyte fuel cell that is operated in the low temperature range, a polymer membrane-electrode unit is present.
• Electrically conductive bipolar plates mostly made of metal, are used to separate the individual polymer electrolyte fuel cells in a stack. They not only serve to make electrical contact with the electrodes and conduct the electrical current to the neighboring row, but also help to supply and distribute fuels and cooling media as well as to dissipate heat and reaction products.
• bipolar plates usually have gas distribution fields which, due to their structure, lead to an optimal distribution of the gaseous fuels, mostly hydrogen and oxygen, with regard to the membrane surfaces.
• the bipolar plate can be produced by two or more thin-walled metal sheets welded to one another, which form the desired flow paths, cooling channels and / or openings.
• US 2019/0340747 A1 discloses a quality monitoring system and method in the area of a fuel cell production line.
• US 2013/0230072 A1 describes a method and a device for error detection on fuel cell components, in particular bipolar plates.
• DE 10393237 B4 discloses a method for detecting electrical defects in membrane electrode arrangements.
• DE 3809221 A1 describes a method and a device for detecting defects, in particular cracks and / or constrictions, on pressed parts or other workpieces.
• DE102015221 697 B3 discloses an arrangement for determining the surface quality of component surfaces, in particular of defects.
• DE 102016211 449 A1 describes a test system with a portable test unit and a method for testing components.
• DE 102009059765 A1 discloses a method for positioning a bipolar plate.
• the bipolar plate is formed step by step from a first and a second plate, after each step an actual pattern of a large number of lines is generated by projection onto an intermediate product representing the respective step and recorded by means of a camera. It is then compared with a reference pattern.
• One embodiment relates to a method for testing a bipolar plate of an electrochemical cell, in particular a fuel cell, in which an image of a surface of a bipolar plate is created, the image is examined for possible defects by an automated image processing-assisted evaluation system and in the event that the evaluation system an examined bipolar plate is identified as a potentially defective suspect plate, a detailed examination of an area of the suspect plate identified as potentially defective is carried out, the evaluation system displaying images of bipolar plates using a large number of non-defective and defective bipolar plates has a trained neural network for identifying a suspect plate and the neural network is continuously trained with the results of the detailed check, the neural network performing reinforcement learning.
• a bipolar plate is understood to be a component of an electrochemical cell which provides the functions of an electrode and a flow guide element.
• This can be, for example, bipolar plates of fuel cells or electrolysers or electrode plates of redox flow cells.
• results of other test results that are relevant for the bipolar plate manufacture or process data for reinforcement learning are used as label or reward information.
• the detailed tests can be integrated 100% inline or randomly if the process time is slower. In this case, only the amount of weld seams checked in detail in the training data set of the machine learning process is taken into account, which grows in the course of the bipolar plate production and gains in sharpness over time.
• An essential manufacturing step in the manufacture of a fuel cell, in particular a polymer electrolyte fuel cell, is the manufacture of the bipolar plate, which takes place in particular by welding two or more metallic plates or sheets.
• the weld seam produced during welding can be easily checked by means of a visual inspection.
• This visual inspection is carried out automatically, however, in that the surface of the bipolar plate, in particular both flat sides of the bipolar plate, is recorded in the image with the aid of a camera and is fed to an image processing system of the automated evaluation system.
• the evaluation system can process the image and, for example, use optical parameters to identify certain shapes and / or colors and / or brightnesses and / or contrasts and check whether these are within certain target values.
• Such checking activities can be easily automated and implemented with the aid of evaluation software in the evaluation system.
• it can also be checked whether a weld seam runs in a correct position relative to, for example, media-carrying channels, forms or structures, for example in the area of a gas distribution field.
• the characteristics and structures of the welded bipolar plates can be identified in the image of the surface of the bipolar plate and used, for example, as a reference for the correct positioning of the weld seam.
• dimensional deviations of the plate and its structures in particular the three-dimensional structures in the area of a gas distribution field, the formation of the openings in the area of the plate ends and the gas distribution field and the like, can also be checked in order to identify and sort out irregularities and to take corrective action in the area of the respective method step that potentially generates the deviation.
• the image processing evaluation of the image does not make it possible, for example, to identify the correct execution of the characteristics or structures in the area of a gas distribution field with the required reliability, this can be due to a manufacturing error, so that such defects can also be detected with the help of the evaluation system the bipolar plate can be checked.
• the bipolar plates found to be potentially “not in order” by the evaluation system are subjected to a detailed inspection as suspect plates, in which an inspection is carried out with significantly more effort, if necessary.
• the examination effort can be reduced. For example, this means that an eddy current test is not carried out on every bipolar plate, but only on those suspect plates that were found to be suspicious during the automated visual inspection by the evaluation system.
• the bipolar plates assessed as harmless during the automated image processing can thus be processed further without a further detailed examination, while after the detailed examination only those suspect plates are sorted out as rejects that actually do not meet the intended criteria and those suspect plates that still meet the criteria applied and can be fed back for further processing. Separation of a suspect plate as reject, which is unnecessary for actual reasons, is avoided, so that unnecessary costs are avoided.
• the evaluation method used in the image processing of the image in the automated evaluation system allows the inspection quality to be increased when inspecting the fuel cell without significant additional effort, so that a cost-effective and reliable inspection of a fuel cell is made possible.
• At least one non-destructive test in particular penetrant test, magnetic particle test, ultrasonic test, radiographic test and / or eddy current test, is carried out in the detailed test on the suspect plate in the area identified as potentially defective. A destruction of the suspicion plate this can be avoided during the detailed inspection. In the event that the detailed check should show that the suspicion plate does meet the requirement profile, the suspicion plate can be returned to the subsequent manufacturing steps as a regular bipolar plate. Unnecessary costs in the production of the fuel cell can thereby be avoided. At the same time, in comparison to a purely visual inspection, a measurement of the conditions present inside the suspect plate can take place, whereby a particularly reliable inspection is achieved. The detailed test can be carried out automatically and / or by a test person trained for this purpose.
• the requirement profile can in particular relate to the tightness and position of a weld seam, the permeability and position of a structure in the gas distribution field, the arrangement of openings for the fuel gas supply, the design and position of seals and the like.
• the evaluation system has a neural network trained on the basis of a large number of non-defective and defective bipolar plates depicting images of bipolar plates for identifying a suspect plate.
• the neural network can be trained by machine learning so that the image evaluation of the images in the evaluation system can be improved. This makes it possible, in particular, to take into account a large number of different, possibly differently weighted parameters when evaluating the images.
• the weighting of the parameters is automatically adjusted during the training of the neural network in such a way that, when evaluating the image used for the training, it is already known in advance whether a bipolar plate with this type of appearance is OK or not not, the correct images will be rated as “okay” or “not okay”. This makes it possible to automatically recognize complex damage patterns even with complex geometries. This will improve the quality of the exam.
• the neural network is continuously trained with the results of the detailed check.
• the detailed examination of the suspect plates leads to an additional gain in knowledge when evaluating the bipolar plates, with additional training data being permanently fed back to the evaluation system.
• "Deep Learning” German: multilayered learning, deep learning or deep learning
• Machine learning is a self-adaptive algorithm. Deep learning is a subset of machine learning and uses a number of hierarchical layers or a hierarchy of concepts to carry out the machine learning process.
• two or more metal sheets are welded to produce the bipolar plate, with several images of a weld seam occurring during welding being created and evaluated and / or an image of the entire weld seam being created and evaluated after welding.
• the image evaluation in the evaluation system can already take place during the welding process, so that melt zones that arise when the areas to be welded are melted can be taken into account in the evaluation.
• the evaluation system preferably uses the results of the evaluation to adapt a process control of the welding.
• the evaluation system can classify the possible defect on the basis of the criterion used for this purpose. This makes it possible to assign certain identified defects to a certain classification and, if necessary, to certain causes. If the defect of the suspect plate assigned to a certain class of defects can be assigned to a faulty process control during welding, it is possible, by feedback of the results of the evaluation by the evaluation system with the process control of the welding, to adapt the process control so that such defects can be avoided in the future.
• the evaluation system can be part of a control loop for the process control of the welding, for example in order to carry out wear-related adaptations of control variables. As a result, manufacturing errors during welding can be recognized very early on and corrected automatically, so that unnecessary rejects and unnecessary costs are avoided.
• Another embodiment relates to a test system for testing a bipolar plate of a fuel cell by means of the method according to the invention, with a camera for creating at least one image of a surface of the bipolar plate, an automated image processing-supported evaluation system for examining the image for possible defects and a switch that can be operated by the evaluation system for derivation a bipolar plate identified by the evaluation system as a potentially defective suspect plate to a detailed inspection station.
• the test system is set up to carry out the method described above. As explained above with reference to the method, the test system is trained and further developed.
• the bipolar plates found to be potentially “not in order” by the evaluation system are subjected to a detailed inspection as suspect plates, in which an inspection is carried out with significantly more effort, if necessary. However, since only the suspect plates are subjected to the detailed inspection in the detailed inspection station, the inspection effort can be reduced.
• the bipolar plates assessed as harmless in the automated image processing in the evaluation system can thus be processed further without a further detailed check, while of the suspect plates after the detailed check only those are rejected as rejects that actually do not meet the intended criteria. Those suspect plates that still meet the criteria are sent back for further processing. An unnecessary separation of a suspect plate as scrap is avoided, so that unnecessary costs are avoided. Due to the evaluation method used in the image processing of the image in the automated evaluation system, the inspection can be The quality of the testing of the bipolar plate can be increased, so that a cost-effective and reliable test is made possible.
• the evaluation system of the test system has a neural network trained on the basis of a large number of non-defective and defective bipolar plates depicting images of bipolar plates for identifying a suspect plate, the evaluation system having an interface that is coupled to the neural network and communicates with the detailed test station for feeding in the results of the detailed test for the purpose of continuous training of the neural network with the results of the detailed test.
• the detailed inspection of the suspect plates in the detailed inspection station leads to an additional gain in knowledge when evaluating the bipolar plates, which is fed back to the evaluation system as training data via the interface of the evaluation system. This leads to reinforcement learning of the neural network, in particular as part of a deep learning structure with several intermediate layers of the neural network. As a result, the quality of the test will continue to improve over time.
• the evaluation unit has an output port that can be coupled to a production unit for producing the bipolar plate for adapting a process control of the production unit as a function of the results of the evaluation by the evaluation system.
• a production unit is, for example, a laser welding system, a punching system, a forming system and the like.
• the evaluation system can classify the possible defect on the basis of the criterion used for this purpose. This makes it possible to assign certain identified defects to a certain classification and, if necessary, to certain causes.
• the defect of the suspect plate assigned to a certain class of defects can be assigned to a faulty process control in the production unit, it is possible, by feeding back the results of the evaluation via the output port of the evaluation system to the production unit, to adapt the process control so that such defects can be avoided in the future.
• the evaluation system can be part of a control loop for the process control of the production unit, for example in order to make adjustments to control variables due to wear. Manufacturing defects can thus be identified very early on and corrected automatically so that unnecessary rejects and unnecessary costs are avoided.
• the detailed testing station preferably has at least one testing device for non-destructive testing, in particular for penetrant testing and / or magnetic particle testing and / or ultrasonic testing and / or radiographic testing and / or eddy current testing, of the suspect plate. Destruction of the suspicion plate during the detailed inspection can thus be avoided. In the event that the detailed inspection should show that the suspicion plate does meet the requirement profile, the suspicion plate can be returned to the subsequent positioning steps as a regular bipolar plate. This avoids unnecessary costs when setting up the components of a fuel cell. At the same time, in comparison to a purely visual inspection, a measurement of the conditions present inside the suspect plate can take place, whereby a particularly reliable inspection is achieved. The detailed inspection is carried out automatically, whereby an inspector can be dispensed with due to the constant improvement of the inspection quality.
• the method according to the invention and the test system according to the invention are particularly suitable for testing bipolar plates for use in a fuel cell, in particular a polymer electrolyte fuel cell, the bipolar plate being formed from two or more metal sheets.
• FIG. 2 a schematic perspective view of part of a test system for carrying out a test on a bipolar plate.
• images 12 are provided, which in particular show the surface of a bipolar plate 14 after a welding process.
• the images 12 are divided into two categories, for example, in which one category 16 is provided for images 12, which represent a bipolar plate 14 that is “OK”, and the other category 18 for Figures 12 are provided, which show a bipolar plate 14 that is “not in order” (NOK).
• the category 18 provided for defective bipolar plates 14 can have several sub-categories, to which different causes for different defect patterns are assigned.
• a neural network 20 is trained with the aid of the images 12 divided into categories 16, 18.
• the neural network 20 is then automatically assigned the rating “OK” or “NOK” in a fourth step during a manufacturing process for the fuel cell 14.
• the bipolar plates 14 found to be “in order” are fed to a further production step.
• the suspicion plates assessed as potentially “not in order” are subjected in a fifth step to a detailed check 22 in order to confirm or correct the assessment given by the neural network 20 as “NOK”.
• the detailed check 22 automatically produces a result in which, with a very high level of reliability, the neural network 20, at least as a case of doubt, still in the images 12 sorted under the “NOK” category as actually “NOK” or as “OK”. be classified. This corresponds to the state after the second step, in which training data were generated for the neural network 20.
• These data automatically generated as a result of the detailed check 22 are fed back to the neural network 20 as additional training data in order to achieve reinforcement learning of the neural network 20.
• the test system 24 can have a camera 26 which generates an image 12 of a surface of a, in particular welded, bipolar plate 14.
• the camera 26 can also generate a plurality of images 12 or even a film sequence.
• the bipolar plate 14 can be suitably illuminated for this purpose so that all relevant features can be seen in the illustration 12 and a possible defect 28 can be recognized by an evaluation system having the neural network 20 by means of an optical evaluation.
• dimensional deviations of the plate and its structures in particular the three-dimensional structures in the area of a gas distribution field, the formation of the openings in the area of the plate ends and the gas distribution field and the like, can also be checked in order to identify and sort out irregularities and to take corrective action in the area of the respective method step that potentially generates the deviation.

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• 2020
  • 2020-03-20 DE DE102020107779.3A patent/DE102020107779A1/de active Pending
• 2021
  • 2021-02-26 JP JP2022550973A patent/JP7438382B2/ja active Active
  • 2021-02-26 KR KR1020227028665A patent/KR20220130755A/ko unknown
  • 2021-02-26 EP EP21717314.5A patent/EP4122042A1/de active Pending
  • 2021-02-26 WO PCT/DE2021/100195 patent/WO2021185404A1/de active Application Filing
  • 2021-02-26 CN CN202180013804.3A patent/CN115088125A/zh active Pending

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