EP3482348A1 - Verfahren und einrichtung zur kategorisierung einer bruchfläche eines bauteils - Google Patents
Verfahren und einrichtung zur kategorisierung einer bruchfläche eines bauteilsInfo
- Publication number
- EP3482348A1 EP3482348A1 EP17755065.4A EP17755065A EP3482348A1 EP 3482348 A1 EP3482348 A1 EP 3482348A1 EP 17755065 A EP17755065 A EP 17755065A EP 3482348 A1 EP3482348 A1 EP 3482348A1
- Authority
- EP
- European Patent Office
- Prior art keywords
- image
- picture elements
- fracture surface
- digital image
- spatial distribution
- 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.)
- Withdrawn
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; 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
-
- 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
- G01N21/01—Arrangements or apparatus for facilitating the optical investigation
-
- 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
- G01N21/8803—Visual inspection
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/42—Global feature extraction by analysis of the whole pattern, e.g. using frequency domain transformations or autocorrelation
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/60—Type of objects
- G06V20/69—Microscopic objects, e.g. biological cells or cellular parts
- G06V20/695—Preprocessing, e.g. image segmentation
-
- 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
- G06—COMPUTING OR CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20021—Dividing image into blocks, subimages or windows
Definitions
- joining technology In the field of joining technology a variety of joining methods and joining means are known to connect different elements of components together. A well-known example of such a joining technique is gluing. However, a number of other joining techniques are known.
- the present invention is in the light of the prior art
- the task is to systematise the assessment of fractures in the area of joint surfaces and to be able to efficiently carry out these tests for a larger number of tests.
- the object is achieved with the features of the invention according to claim 1 by a method.
- the claim 14 relates to a method for testing the joint strength of a bonded joint surface and the claims 15 and 16 relate to a device for carrying out the method according to the invention.
- the invention relates to a method for categorizing a fracture surface of a component, in which a digital image of the fracture surface is generated, which assigns to the image a value or a value group or a vector or a matrix to each smallest resolvable image unit (pixel), and the digital image or one or more sections of the digital image is / are analyzed, whereby image elements and their spatial distribution are determined, wherein one or more regions of the digital image or one or more sections due to the spatial distribution of image elements, a category of Bruchmus- and, as far as several areas and / or several sections of the digital image have been analyzed, the information about the areas and / or sections of the digital image and the categories of fracture patterns associated therewith are linked to each other and due to these r Linking the fracture surface is assigned a category.
- Such a photographic image can take place both in the visible range of the light spectrum, as well as in the infrared or ultraviolet range, or it can include wavelength ranges outside of the human-visible range. Imaging techniques in the range of shorter or longer wavelengths of electromagnetic radiation or even corpuscular radiation may also be used. Moreover, digital imaging can also be provided by mechanical or capacitive scanning of the fracture surface or by other conceivable techniques that allow the assignment of parameters to the points of the fracture surface. As a result, the digital image may consist of a set of data associated with the fracture surface, corresponding to the given resolution to the smallest resolvable image units (eg, pixels) in the form of intensity, color, or phase values or other scalar quantities or vectors or matrices are assigned.
- the digital image may consist of a set of data associated with the fracture surface, corresponding to the given resolution to the smallest resolvable image units (eg, pixels) in the form of intensity, color, or phase values or other scalar quantities or vectors or matrices are assigned
- Image units are meaningfully combined to form picture elements in accordance with their assigned parameters, vectors or matrices.
- picture elements can be given even by the smallest image units (eg pixels) that can be resolved in the given technique. Often, however, several of these smallest resolvable image units are grouped together to form a pixel in the form of a spot or other appearance.
- the detected spatial distribution of patterns is compared with patterns stored in a data processing device and already categorized, and a category is assigned on the basis of the result of the comparison of the detected spatial distribution.
- the differences to the neighboring image units are first determined and evaluated on the basis of the given values, vectors or matrices for each given smallest image unit. Thereupon, in an analysis of a multiplicity of values / vectors / matrices of the image, if appropriate, clusters of similar image units are formed, the corresponding pixel parameters of adjacent image units (pixels) of a cluster having smaller differences than the differences to neighboring image units which do not belong to the respective cluster.
- picture elements can also provide that the detected spatial distribution of patterns or smallest homogenous image units determines borderlines between differently categorized subareas of the fracture surface and uses them in the categorization.
- the spatial distribution can be determined by various known types of image processing, for example by determining distances or differences of the properties / assigned values, quantities, vectors or matrices of respectively adjacent image elements, determining mean distances / differences or determining standard deviations thereof or by deviations of individual distances / Differences are evaluated by mean distances / differences. It is also possible to determine gradients of the density of picture elements or other similar parameters which, for example, statistically describe the spatial distribution. As a result, one or more parameters of the spatial distribution are determined, which are particularly useful for distinguishing different categories of fracture surfaces and demarcation sharp.
- a trainable data processing device in particular in the form of a self-learning system and / or a trainable computer-aided method, in particular in the form of a self-learning method, is used.
- experts may first categorize a number of fracture surfaces or faces of fracture surfaces based on given digital mappings, and the mappings and categorizations made may be incorporated into the system, i. H. the trainable data processing device is input.
- a self-learning system is then provided that takes up the categorization in connection with the digital images and sets up and deposits rules for the categorization itself. After a certain number of categorizations made by the experts, the data processing device itself can assign categories based on digital images.
- the category of a fractured surface can often only be assessed and categorized on the basis of the evaluation of partial surfaces due to the lack of a homogeneous appearance of the fracture surface.
- the detected spatial distribution of picture elements, in particular in the form of patterns, respectively different sub-areas of the fracture surface is assigned to each category.
- boundary lines are determined between differently categorized partial surfaces of the fracture surface and used in the categorization of the fracture surface.
- cutouts do not overlap one another and are adjacent to one another in the image directly and without spacing.
- Such a device may also be embedded in a more comprehensive device for testing the joint strength of a glued joint surface, which may additionally include a device for breaking a component in the region of the joint surface.
- FIG. 6 identifies the section of a fracture surface from FIG. 4
- FIG. 2 schematically shows a device for generating a digital image of a fracture surface 2, which comprises a camera 4 for taking a photographic digital image and various illumination sources 5, 6, 7.
- the various illumination sources 5, 6, 7 can be optional be used, with the image recording with only a single illumination source or with the help of ambient light is conceivable. It is also possible to take different pictures in which only one of the illumination sources 5, 6, 7 is active at a time. As a result, images can be obtained with different shadows, which can be linked together and charged to a high-contrast image.
- the single ones can be obtained with different shadows, which can be linked together and charged to a high-contrast image.
- FIG. 3 alternatively shows a device for generating a digital image of the fracture surface 2, which operates according to a scanning method, wherein a sensor 8 is moved in parallel along the fracture surface 2, as indicated by the double arrow 9.
- the sensor is guided on a rail 10 or on a plurality of mutually perpendicular rails in order to be able to scan or cover the entire fracture surface.
- the sampling can be capacitive with the application of an electrical voltage.
- the digital image can then be stored in a data processing device.
- FIG. 4 shows, by way of example, an illustration of a fracture surface 2 with different partial surfaces 11, 12, 13, 14, which have different forms of appearance due to the nature of the fracture and can therefore be processed separately for evaluation.
- the sub-areas can be determined after determining the spatial distribution of the picture elements to divide the image to be analyzed and to first perform an assessment or categorization of the individual sub-areas.
- V denotes a round cutout, which is shown enlarged in FIG.
- FIG. 5 shows in a section and enlarges a pattern of the smallest resolvable image units (pixels) 15, 16, 17 of the fracture surface.
- the achievable resolution in the image of the fracture surface essentially depends on the imaging system, for example the pixel density of the digital camera used or another imaging device.
- Be assigned color value It can be the smallest individual picture units however, as explained above, a vector or matrix or generally an n-tuple of scalars may be associated. This may be useful, for example, if a polarization value or a phase value is to be recorded in addition to a brightness value. In the example of FIG. 5, only intensity values are entered in the areas of the smallest picture units which are of the
- Value 1 to the value 4 range.
- the result of the first evaluation of the figure is that two clusters / picture elements can be formed from the smallest picture units, which are each grouped around an image unit with the value 4. From this central image unit with the value 4, the values decrease with increasing distance over 3 and 2 to the value 1. To this
- FIG. 7 different highly idealized distributions of picture elements are shown on a fracture surface on the partial surfaces 11, 12, 13, 14. It is typical that different spatial distributions of the image elements occur on a fracture surface in the region of partial surfaces, so that the fracture surface shows different categories of fractures in different partial surfaces.
- the determined parameters are compared with reference values from a database or from a data processing device.
- the determined parameters are assigned, on the basis of the comparison carried out, to certain predetermined parameter ranges, which in turn are assigned to specific categories of fracture surfaces.
- the areas that are identified by specific parameter areas can each be defined as a subarea. This results in a categorization for the fracture surface or partial surfaces of the fracture surface.
- a last step 44 the categorizations of the partial surfaces or sections are linked together and processed to categorize the entire fracture surface. This can then be displayed and / or saved.
- FIG. 9 shows a schematic structure for carrying out the method.
- the reference numeral 45 denotes a broken component with a fracture surface, which is imaged by a device 46 for generating a digital image. This can be given for example by a digital camera.
- the devices 47 and 48 can also be combined.
- FIG. 9 the communication of the device for determining the parameters of the spatial distribution 48 with a classification database 49 is described by a double arrow 50.
- the dashed double arrow 51 symbolizes the interaction between the training data sets stored in a training database 52 and the device 48.
- the classification may also be performed, for example, with other classification systems, such as the Support Vector Machine method, which in its simplest form in an n-dimensional space in which n-dimensional state vectors of the pixels are plotted, one plane between any two Sets picture elements that are to be separated by classification.
- Support Vector Machine method which in its simplest form in an n-dimensional space in which n-dimensional state vectors of the pixels are plotted, one plane between any two Sets picture elements that are to be separated by classification.
- This method can be made even more complex by allowing non-linear interfaces to be transformed by transforming the vectors into higher dimensional spaces.
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Health & Medical Sciences (AREA)
- Theoretical Computer Science (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Multimedia (AREA)
- Quality & Reliability (AREA)
- Biomedical Technology (AREA)
- Molecular Biology (AREA)
- Image Analysis (AREA)
- Investigating Materials By The Use Of Optical Means Adapted For Particular Applications (AREA)
Abstract
Description
Claims
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| DE102016212486.2A DE102016212486A1 (de) | 2016-07-08 | 2016-07-08 | Verfahren und Einrichtung zur Kategorisierung einer Bruchfläche eines Bauteils |
| PCT/EP2017/067148 WO2018007619A1 (de) | 2016-07-08 | 2017-07-07 | Verfahren und einrichtung zur kategorisierung einer bruchfläche eines bauteils |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| EP3482348A1 true EP3482348A1 (de) | 2019-05-15 |
Family
ID=59683494
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| EP17755065.4A Withdrawn EP3482348A1 (de) | 2016-07-08 | 2017-07-07 | Verfahren und einrichtung zur kategorisierung einer bruchfläche eines bauteils |
Country Status (4)
| Country | Link |
|---|---|
| US (1) | US10983043B2 (de) |
| EP (1) | EP3482348A1 (de) |
| DE (1) | DE102016212486A1 (de) |
| WO (1) | WO2018007619A1 (de) |
Families Citing this family (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111709905A (zh) * | 2020-03-27 | 2020-09-25 | 南京智慧基础设施技术研究院有限公司 | 一种基于图像检测裂缝的分析方法 |
| EP3904867B9 (de) * | 2020-04-29 | 2022-11-02 | voestalpine Stahl GmbH | Verfahren und vorrichtung zur bestimmung der bruchfläche einer probe |
| JP7450517B2 (ja) * | 2020-10-23 | 2024-03-15 | 株式会社荏原製作所 | 加工面判定装置、加工面判定プログラム、加工面判定方法、及び、加工システム |
| FR3123474B1 (fr) * | 2021-05-31 | 2024-10-25 | Safran Aircraft Engines | Procédé de classification d’un mode de rupture visible sur une image d’un faciès de rupture d’un assemblage collé |
| JP7741370B2 (ja) * | 2021-09-14 | 2025-09-18 | 日本製鉄株式会社 | 破断面特徴領域判定モデル生成装置、破断面特徴領域判定モデル生成方法、プログラム、破断面特徴領域判定装置、及び、破断面特徴領域判定方法 |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102014212511A1 (de) * | 2014-06-27 | 2015-12-31 | Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V. | Vorrichtungen und Verfahren zur Prüfung einer Oberflächengüte |
| AU2015392660B2 (en) * | 2015-04-27 | 2019-05-16 | Wuhan Optics Valley Zoyon Science And Technology Co., Ltd. | Stepwise-refinement pavement crack detection method |
| WO2017039475A1 (en) * | 2015-09-03 | 2017-03-09 | Schlumberger Technology Corporation | A computer-implemented method and a system for creating a three-dimensional mineral model of a sample of a heterogenerous medium |
-
2016
- 2016-07-08 DE DE102016212486.2A patent/DE102016212486A1/de not_active Ceased
-
2017
- 2017-07-07 WO PCT/EP2017/067148 patent/WO2018007619A1/de not_active Ceased
- 2017-07-07 EP EP17755065.4A patent/EP3482348A1/de not_active Withdrawn
- 2017-07-07 US US16/315,762 patent/US10983043B2/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| DE102016212486A1 (de) | 2018-01-11 |
| US10983043B2 (en) | 2021-04-20 |
| US20190242811A1 (en) | 2019-08-08 |
| WO2018007619A1 (de) | 2018-01-11 |
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