JP2023514753A - 電気化学電池、特に燃料電池のバイポーラプレートを検査するための方法および検査装置 - Google Patents
電気化学電池、特に燃料電池のバイポーラプレートを検査するための方法および検査装置 Download PDFInfo
- Publication number
- JP2023514753A JP2023514753A JP2022550973A JP2022550973A JP2023514753A JP 2023514753 A JP2023514753 A JP 2023514753A JP 2022550973 A JP2022550973 A JP 2022550973A JP 2022550973 A JP2022550973 A JP 2022550973A JP 2023514753 A JP2023514753 A JP 2023514753A
- Authority
- JP
- Japan
- Prior art keywords
- plate
- inspection
- evaluation system
- suspect
- bipolar
- 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.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 39
- 239000000446 fuel Substances 0.000 title claims abstract description 32
- 238000012360 testing method Methods 0.000 title claims abstract description 19
- 238000007689 inspection Methods 0.000 claims abstract description 79
- 238000011156 evaluation Methods 0.000 claims abstract description 59
- 238000013507 mapping Methods 0.000 claims abstract description 43
- 230000007547 defect Effects 0.000 claims abstract description 40
- 238000012545 processing Methods 0.000 claims abstract description 16
- 238000004519 manufacturing process Methods 0.000 claims description 30
- 238000013528 artificial neural network Methods 0.000 claims description 29
- 238000003466 welding Methods 0.000 claims description 23
- 230000002950 deficient Effects 0.000 claims description 15
- 238000004886 process control Methods 0.000 claims description 11
- 238000012549 training Methods 0.000 claims description 10
- 230000002787 reinforcement Effects 0.000 claims description 9
- 239000002184 metal Substances 0.000 claims description 6
- 229910052751 metal Inorganic materials 0.000 claims description 6
- 230000001066 destructive effect Effects 0.000 claims description 4
- 239000006249 magnetic particle Substances 0.000 claims description 4
- 238000004891 communication Methods 0.000 claims description 2
- 238000004080 punching Methods 0.000 claims description 2
- 150000002739 metals Chemical class 0.000 claims 1
- 238000009659 non-destructive testing Methods 0.000 claims 1
- 210000004027 cell Anatomy 0.000 description 32
- 238000009826 distribution Methods 0.000 description 9
- 230000002159 abnormal effect Effects 0.000 description 8
- 239000007789 gas Substances 0.000 description 8
- 238000013135 deep learning Methods 0.000 description 6
- 238000010801 machine learning Methods 0.000 description 6
- 238000011179 visual inspection Methods 0.000 description 6
- 239000005518 polymer electrolyte Substances 0.000 description 4
- 230000015572 biosynthetic process Effects 0.000 description 3
- 230000006378 damage Effects 0.000 description 3
- 230000006978 adaptation Effects 0.000 description 2
- 238000011511 automated evaluation Methods 0.000 description 2
- 238000002844 melting Methods 0.000 description 2
- 230000008018 melting Effects 0.000 description 2
- 239000012528 membrane Substances 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- UFHFLCQGNIYNRP-UHFFFAOYSA-N Hydrogen Chemical compound [H][H] UFHFLCQGNIYNRP-UHFFFAOYSA-N 0.000 description 1
- 230000000712 assembly Effects 0.000 description 1
- 238000000429 assembly Methods 0.000 description 1
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 description 1
- 210000003850 cellular structure Anatomy 0.000 description 1
- 239000007795 chemical reaction product Substances 0.000 description 1
- 239000003086 colorant Substances 0.000 description 1
- 239000002826 coolant Substances 0.000 description 1
- 238000001816 cooling Methods 0.000 description 1
- 238000000280 densification Methods 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 239000003792 electrolyte Substances 0.000 description 1
- 230000002349 favourable effect Effects 0.000 description 1
- 239000002737 fuel gas Substances 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 239000001257 hydrogen Substances 0.000 description 1
- 229910052739 hydrogen Inorganic materials 0.000 description 1
- 238000005286 illumination Methods 0.000 description 1
- 239000013067 intermediate product Substances 0.000 description 1
- 238000002955 isolation Methods 0.000 description 1
- 230000001795 light effect Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 239000001301 oxygen Substances 0.000 description 1
- 229910052760 oxygen Inorganic materials 0.000 description 1
- 230000035699 permeability Effects 0.000 description 1
- 229920005597 polymer membrane Polymers 0.000 description 1
- 230000002441 reversible effect Effects 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 238000007789 sealing Methods 0.000 description 1
- 230000026676 system process Effects 0.000 description 1
Images
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
-
- 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
- G01N21/8851—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
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Health & Medical Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Theoretical Computer Science (AREA)
- Evolutionary Computation (AREA)
- Artificial Intelligence (AREA)
- Signal Processing (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Chemical Kinetics & Catalysis (AREA)
- Electrochemistry (AREA)
- Quality & Reliability (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- Software Systems (AREA)
- General Chemical & Material Sciences (AREA)
- Sustainable Energy (AREA)
- Manufacturing & Machinery (AREA)
- Sustainable Development (AREA)
- Acoustics & Sound (AREA)
- Fuel Cell (AREA)
- Mechanical Engineering (AREA)
- Investigating Materials By The Use Of Optical Means Adapted For Particular Applications (AREA)
- Secondary Cells (AREA)
- Battery Electrode And Active Subsutance (AREA)
- Image Analysis (AREA)
Abstract
Description
12 マッピング
14 バイポーラプレート
16 一方のカテゴリ
18 他方のカテゴリ
20 ニューラルネットワーク
22 詳細検査
24 検査装置
26 カメラ
28 起こり得る欠陥
Claims (10)
- 電気化学電池、特に燃料電池のバイポーラプレートを検査するための方法であって、バイポーラプレート(14)の表面のマッピング(12)が作成され、
前記マッピング(12)は、起こり得る欠陥(28)について自動画像処理支援評価システムによって試験され、
前記評価システムが、試験されたバイポーラプレート(14)を潜在的欠陥の疑念があるプレートとして識別した場合、前記疑念があるプレートの、潜在的欠陥として識別された領域の詳細検査(22)が実施され、
前記評価システムは、疑念があるプレートを識別するために、非欠陥バイポーラプレート(14)および欠陥バイポーラプレート(14)から作成されるバイポーラプレート(14)の複数のマッピング(12)に基づいて訓練されるニューラルネットワーク(20)を有し、
前記ニューラルネットワーク(20)は、前記詳細検査(22)の結果を用いて継続的に訓練され、前記ニューラルネットワーク(20)は、強化学習を実施する、方法。 - 前記詳細検査(22)において、少なくとも1つの非破壊検査は、前記疑念があるプレートについて、前記潜在的欠陥として識別された領域で実施される、請求項1に記載の方法。
- 非破壊検査として、浸透探傷試験および/または磁粉探傷試験および/または超音波検査および/または放射線透過検査および/または渦電流試験は、前記疑念があるプレートについて、前記潜在的欠陥として識別された領域で実施される、請求項2に記載の方法。
- 前記バイポーラプレート(14)を製造するために、2枚以上のシートメタルの溶接が行われ、前記溶接中、前記溶接時に発生する溶接シームの複数のマッピング(12)が作成され、評価される、請求項1から3のいずれか一項に記載の方法。
- 前記バイポーラプレート(14)を製造するために、前記溶接後、前記溶接シーム全体のマッピング(12)が作成され、評価される、請求項1から4のいずれか一項に記載の方法。
- 前記評価システムによる前記評価の結果を用いて、前記溶接のプロセス制御の適合が行われる、請求項4または5に記載の方法。
- 請求項1から6のいずれか一項に記載の方法を実施するための検査装置であって、
前記バイポーラプレート(14)の表面の少なくとも1つのマッピング(12)を作成するためのカメラ(26)と、
起こり得る欠陥(28)について前記マッピング(12)を試験するための自動画像処理支援評価システムと、
前記評価システムによって潜在的欠陥の疑念があるプレートとして識別されたバイポーラプレートを詳細検査ステーションに誘導するための、前記評価システムによって操作可能な切替器と、を備え、
前記評価システムは、疑念があるプレートを識別するために、非欠陥バイポーラプレート(14)および欠陥バイポーラプレート(14)から作成されるバイポーラプレート(14)の複数のマッピング(12)に基づいて訓練されるニューラルネットワーク(20)を有し、前記評価システムは、前記詳細検査(22)の結果を保存するための、前記ニューラルネットワーク(20)と連結された、前記詳細検査ステーションと通信するインターフェースを、前記詳細検査(22)の前記結果を用いる前記ニューラルネットワーク(20)の継続訓練の目的で有する、検査装置。 - 評価ユニットは、前記バイポーラプレート(14)を製造するための少なくとも1つの生産ユニット、特に溶接装置および/または打ち抜き装置および/または成形装置と連結可能な少なくとも1つの出力ポートを、前記評価システムによる評価の結果に応じて、前記生産ユニットのプロセス制御を適合するために有することを特徴とする、請求項7に記載の検査装置。
- 前記少なくとも1つの生産ユニットは、溶接装置および/または打ち抜き装置および/または成形装置であることを特徴とする、請求項8に記載の検査装置。
- 前記詳細検査ステーションは、前記疑念があるプレートの非破壊検査のための、特に浸透探傷試験および/または磁粉探傷試験および/または超音波検査および/または放射線透過検査および/または渦電流試験のための、少なくとも1つの検査機器を有することを特徴とする、請求項7から9のいずれか一項に記載の検査装置。
Applications Claiming Priority (3)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
DE102020107779.3 | 2020-03-20 | ||
DE102020107779.3A DE102020107779A1 (de) | 2020-03-20 | 2020-03-20 | Verfahren und Prüfanlage zum Prüfen einer Bipolarplatte einer Brennstoffzelle |
PCT/DE2021/100195 WO2021185404A1 (de) | 2020-03-20 | 2021-02-26 | Verfahren und prüfanlage zum prüfen einer bipolarplatte einer elektrochemischen zelle, insbesondere einer brennstoffzelle |
Publications (2)
Publication Number | Publication Date |
---|---|
JP2023514753A true JP2023514753A (ja) | 2023-04-07 |
JP7438382B2 JP7438382B2 (ja) | 2024-02-26 |
Family
ID=75438539
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
JP2022550973A Active JP7438382B2 (ja) | 2020-03-20 | 2021-02-26 | 電気化学電池、特に燃料電池のバイポーラプレートを検査するための方法および検査装置 |
Country Status (6)
Country | Link |
---|---|
EP (1) | EP4122042A1 (ja) |
JP (1) | JP7438382B2 (ja) |
KR (1) | KR20220130755A (ja) |
CN (1) | CN115088125A (ja) |
DE (1) | DE102020107779A1 (ja) |
WO (1) | WO2021185404A1 (ja) |
Families Citing this family (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN114267844B (zh) * | 2021-11-09 | 2024-02-27 | 深圳市氢瑞燃料电池科技有限公司 | 一种燃料电池极板生产的系统与方法 |
KR20230110189A (ko) * | 2022-01-14 | 2023-07-21 | 주식회사 엘지에너지솔루션 | 모니터링 장치 및 그것의 동작 방법 |
CN114577816A (zh) * | 2022-01-18 | 2022-06-03 | 广州超音速自动化科技股份有限公司 | 一种氢燃料双极板检测方法 |
DE102022109188B3 (de) | 2022-04-14 | 2023-08-03 | Schaeffler Technologies AG & Co. KG | Brennstoffzellenstapel und Verfahren zur Montage eines Brennstoffzellenstapels |
WO2024049194A1 (ko) * | 2022-08-31 | 2024-03-07 | 주식회사 엘지에너지솔루션 | 인공지능 모델 기반의 이상 진단 방법, 이를 이용한 이상 진단 장치 및 공장 모니터링 시스템 |
Family Cites Families (25)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
DE3809221A1 (de) | 1988-03-18 | 1989-09-28 | Roth Electric Gmbh | Verfahren zum detektieren von fehlstellen an pressteilen oder anderen werkstuecken und vorrichtung zur durchfuehrung des verfahrens |
JP2968442B2 (ja) * | 1994-09-26 | 1999-10-25 | 川崎重工業株式会社 | 溶接欠陥の評価システム |
JP3936095B2 (ja) | 1999-03-31 | 2007-06-27 | 株式会社東芝 | 燃料電池 |
US7179553B2 (en) | 2002-09-06 | 2007-02-20 | General Motors Corporation | Method for detecting electrical defects in membrane electrode assemblies |
JP2007018176A (ja) | 2005-07-06 | 2007-01-25 | Sharp Corp | 学習装置、学習方法、学習プログラム、記録媒体、パターン認識装置およびパターン認識方法 |
JP5005218B2 (ja) | 2005-12-28 | 2012-08-22 | 愛知機械工業株式会社 | 検査装置および検査方法 |
DE102009059765A1 (de) | 2009-12-21 | 2011-06-22 | Daimler AG, 70327 | Verfahren zur Herstellung einer Bipolarplatte |
US9784625B2 (en) | 2010-11-30 | 2017-10-10 | Bloom Energy Corporation | Flaw detection method and apparatus for fuel cell components |
JP2013167596A (ja) * | 2012-02-17 | 2013-08-29 | Honda Motor Co Ltd | 欠陥検査装置、欠陥検査方法及びプログラム |
JP6237263B2 (ja) | 2014-01-24 | 2017-11-29 | 日産自動車株式会社 | 燃料電池の製造方法 |
CN104833925A (zh) * | 2015-05-07 | 2015-08-12 | 昆山弗尔赛能源有限公司 | 一种基于机器视觉的燃料电池双极板检测方法及系统 |
DE102015221697B3 (de) | 2015-11-05 | 2017-02-23 | Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V. | Anordnung zur Bestimmung der Oberflächenbeschaffenheit von Bauteiloberflächen |
DE102016211449A1 (de) | 2016-06-27 | 2017-12-28 | Bayerische Motoren Werke Aktiengesellschaft | Prüfsystem und Verfahren zum Prüfen von Bauteilen |
WO2018132321A1 (en) * | 2017-01-10 | 2018-07-19 | Kla-Tencor Corporation | Diagnostic methods for the classifiers and the defects captured by optical tools |
JP7021728B2 (ja) | 2017-05-12 | 2022-02-17 | 株式会社神戸製鋼所 | 自動溶接システム、溶接制御方法、及び機械学習モデル |
US10809635B2 (en) * | 2017-11-20 | 2020-10-20 | Taiwan Semiconductor Manufacturing Company, Ltd. | Defect inspection method and defect inspection system |
CN108320278A (zh) * | 2018-01-09 | 2018-07-24 | 北京百度网讯科技有限公司 | 产品缺陷检测定位方法、装置、设备及计算机可读介质 |
US11069030B2 (en) * | 2018-03-22 | 2021-07-20 | Adobe, Inc. | Aesthetics-guided image enhancement |
JP6990610B2 (ja) | 2018-03-23 | 2022-02-03 | 本田技研工業株式会社 | 燃料電池の電流リーク検査方法 |
CN108631727B (zh) * | 2018-03-26 | 2019-08-09 | 河北工业大学 | 一种基于卷积神经网络的太阳能电池板缺陷识别方法 |
US10769770B2 (en) * | 2018-05-07 | 2020-09-08 | Cummins Enterprise Llc | Quality monitoring system and quality monitoring method for fuel cell manufacturing line and quality monitoring system for manufacturing line |
DE102018214307A1 (de) * | 2018-08-23 | 2020-02-27 | Friedrich-Alexander-Universität Erlangen-Nürnberg | System und Verfahren zur Qualitätsprüfung bei der Herstellung von Einzelteilen |
CN109598721B (zh) * | 2018-12-10 | 2021-08-31 | 广州市易鸿智能装备有限公司 | 电池极片的缺陷检测方法、装置、检测设备和存储介质 |
CN110132237A (zh) * | 2019-05-05 | 2019-08-16 | 四川省地质工程勘察院 | 一种城市地面变形灾害早期识别的方法 |
CN110473806A (zh) * | 2019-07-13 | 2019-11-19 | 河北工业大学 | 光伏电池分拣的深度学习识别与控制方法及装置 |
-
2020
- 2020-03-20 DE DE102020107779.3A patent/DE102020107779A1/de active Pending
-
2021
- 2021-02-26 KR KR1020227028665A patent/KR20220130755A/ko unknown
- 2021-02-26 WO PCT/DE2021/100195 patent/WO2021185404A1/de active Application Filing
- 2021-02-26 CN CN202180013804.3A patent/CN115088125A/zh active Pending
- 2021-02-26 EP EP21717314.5A patent/EP4122042A1/de active Pending
- 2021-02-26 JP JP2022550973A patent/JP7438382B2/ja active Active
Also Published As
Publication number | Publication date |
---|---|
EP4122042A1 (de) | 2023-01-25 |
JP7438382B2 (ja) | 2024-02-26 |
CN115088125A (zh) | 2022-09-20 |
WO2021185404A1 (de) | 2021-09-23 |
DE102020107779A1 (de) | 2021-09-23 |
KR20220130755A (ko) | 2022-09-27 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
JP7438382B2 (ja) | 電気化学電池、特に燃料電池のバイポーラプレートを検査するための方法および検査装置 | |
US9683949B2 (en) | Non-destructive quantitative weld quality measurement using radiographic imaging | |
WO2013155135A1 (en) | Flaw detection method and apparatus for fuel cell components | |
CN116500086B (zh) | 一种基于深度学习的铜复铝散热底板生产评价方法及系统 | |
McGovern et al. | A review of research needs in nondestructive evaluation for quality verification in electric vehicle lithium-ion battery cell manufacturing | |
CN111735849B (zh) | 一种电路板焊点质量的阀值筛选法和红外检测法 | |
CN112975215A (zh) | 用于表征焊缝的方法 | |
CN114119595A (zh) | 一种基于集成深度学习的gmaw焊接质量在线监测及评价方法 | |
JP7404590B2 (ja) | バッテリーパックの診断方法 | |
JP2023124755A (ja) | 溶接品質を分析するためのシステムおよび方法 | |
KR20220046824A (ko) | 리튬 이차 전지의 용접부 검사 방법 | |
US20050237067A1 (en) | Arrangement and method for detection and localization of short circuits in membrane electrode arrangements | |
WO2017159709A1 (ja) | 検査装置 | |
Malge et al. | A survey: Automated visual pcb inspection algorithm | |
KR20200144938A (ko) | 연료전지 인라인 검사 방법 | |
Weiss et al. | A holistic approach for an intelligent laser beam welding architecture using machine learning for the welding of metallic bipolar plates for polymer electrolyte membrane fuel cells | |
CN113538325B (zh) | 评估点焊完整性的系统和方法 | |
JP2004191328A (ja) | 積層型ガスセンサ素子の検査方法 | |
JPH0850900A (ja) | 診断機能付電池製造装置 | |
Tantrapiwat | Spot welding defect detection using synthetic image dataset on convolutional neural networks | |
KR20230142009A (ko) | 금속분리판의 외관 검사 장치 및 방법 | |
US20230368367A1 (en) | Battery component inspection based on optical and thermal imaging | |
US20230327222A1 (en) | Inspection method for lithium secondary battery | |
TWI724288B (zh) | 焊錫製程方法 | |
Rohkohl et al. | How To Develop a NDT Method For Weld Inspection in Battery Cell Manufacturing Using Deep Learning |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
A621 | Written request for application examination |
Free format text: JAPANESE INTERMEDIATE CODE: A621 Effective date: 20220824 |
|
A977 | Report on retrieval |
Free format text: JAPANESE INTERMEDIATE CODE: A971007 Effective date: 20230823 |
|
A131 | Notification of reasons for refusal |
Free format text: JAPANESE INTERMEDIATE CODE: A131 Effective date: 20230829 |
|
A521 | Request for written amendment filed |
Free format text: JAPANESE INTERMEDIATE CODE: A523 Effective date: 20231122 |
|
TRDD | Decision of grant or rejection written | ||
A01 | Written decision to grant a patent or to grant a registration (utility model) |
Free format text: JAPANESE INTERMEDIATE CODE: A01 Effective date: 20240115 |
|
A61 | First payment of annual fees (during grant procedure) |
Free format text: JAPANESE INTERMEDIATE CODE: A61 Effective date: 20240213 |
|
R150 | Certificate of patent or registration of utility model |
Ref document number: 7438382 Country of ref document: JP Free format text: JAPANESE INTERMEDIATE CODE: R150 |