EP4533387A1 - Verfahren zur ermittlung eines qualitätsmerkmals einer schweissverbindung zwischen zwei leiterenden, verfahren zur bereitstellung eines trainingsdatensatzes, trainingsdatensatz, verfahren zum verschweissen von leiterenden sowie vorrichtung zur ermittlung eines qualitätsmerkmals einer schweissverbindung - Google Patents
Verfahren zur ermittlung eines qualitätsmerkmals einer schweissverbindung zwischen zwei leiterenden, verfahren zur bereitstellung eines trainingsdatensatzes, trainingsdatensatz, verfahren zum verschweissen von leiterenden sowie vorrichtung zur ermittlung eines qualitätsmerkmals einer schweissverbindungInfo
- Publication number
- EP4533387A1 EP4533387A1 EP23728736.2A EP23728736A EP4533387A1 EP 4533387 A1 EP4533387 A1 EP 4533387A1 EP 23728736 A EP23728736 A EP 23728736A EP 4533387 A1 EP4533387 A1 EP 4533387A1
- Authority
- EP
- European Patent Office
- Prior art keywords
- conductor ends
- depth information
- information
- determining
- quality feature
- 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
- 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
-
- 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
- B23K26/00—Working by laser beam, e.g. welding, cutting or boring
- B23K26/20—Bonding
- B23K26/21—Bonding by 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 any single one of main groups B23K1/00 - B23K28/00
- B23K31/12—Processes relevant to this subclass, specially adapted for particular articles or purposes, but not covered by any single one of main groups B23K1/00 - B23K28/00 relating to investigating the properties, e.g. the weldability, of materials
- B23K31/125—Weld quality monitoring
-
- 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
- G06T2207/10—Image acquisition modality
- G06T2207/10024—Color image
-
- 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
- G06T2207/10—Image acquisition modality
- G06T2207/10028—Range image; Depth image; 3D point clouds
-
- 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
- G06T2207/20081—Training; Learning
-
- 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
- G06T2207/20084—Artificial neural networks [ANN]
-
- 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
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30108—Industrial image inspection
-
- 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
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30108—Industrial image inspection
- G06T2207/30164—Workpiece; Machine component
Definitions
- Method for determining a quality feature of a welded connection between two conductor ends method for providing a training data set, training data set, method for welding conductor ends and device for determining a quality feature of a welded connection
- the invention relates to a method for determining a quality feature of a welded connection between two conductor ends.
- the invention also relates to a method for welding conductor ends.
- the invention further relates to a method for providing a training data set for a machine learning system for determining a quality feature of a welded connection between two conductor ends.
- Another subject of the invention is a training data set for a machine learning system for determining a quality feature of a welded connection between two conductor ends.
- Another subject is a method for welding conductor ends.
- the invention relates to a device for determining a quality feature of a welded connection.
- Such welded connections are typically formed between two conductor ends of hairpin elements of an electrical machine in order to join several hairpin elements, i.e. hairpin-shaped copper wires with a mostly rectangular cross section, to a winding of the electrical machine.
- An electrical machine usually includes a large number, often hundreds, of such welded connections.
- connection area i.e. the cross-sectional area available for the current flow from one conductor to the other conductor
- a connection area that is too small can result in an undesirably high ohmic resistance and negatively influence the efficiency of the electrical machine. It is therefore necessary to check the quality of the welded connections.
- a method widely used in the prior art involves determining the bonding surface using X-ray analysis. Here, the joined connection area of the two conductors is placed in a corresponding device and x-rayed, which involves increased expenditure on equipment and time.
- the task is to enable sensitive testing of welded connections between conductor ends with little time expenditure.
- a method for determining a quality feature of a welded connection between two conductor ends, in particular between two conductor ends of hairpin elements of an electrical machine, whereby at least intensity information of the image points and depth information of the image points are recorded for several image points of a detection area comprising the conductor ends, and wherein a quality feature, in particular a connection area or a pore volume, is determined as a function of the recorded intensity information and depth information.
- a further subject of the invention is a device for determining a quality feature of a welded connection between two conductor ends, in particular between two conductor ends of hairpin elements of an electrical machine, with an image capture device for capturing intensity information for several image points of a detection area comprising the conductor ends, with a depth information Detection device for detecting depth information of the image points, and with a processor unit for determining a quality feature, in particular a connection area or a pore volume, depending on the detected intensity information and depth information.
- intensity information and additional depth information are recorded for several pixels of a detection area. The combination of this information makes it possible to derive a quality feature of the welded connection between the conductor ends depending on this information.
- the process can be carried out alongside the actual welding process or shortly after the welding process has been completed, thereby enabling in-process quality monitoring with little time expenditure.
- the method step of determining the quality feature depending on the recorded intensity information and depth information is preferably carried out in a computer-implemented manner, for example by means of a processor device.
- the intensity information can in particular be image data from a 2D camera.
- the depth information is in particular information about the topological position of a surface of the welded connection in a direction perpendicular to the plane of the detection area.
- the conductor ends are preferably made of copper.
- the hairpin elements are particularly preferably made of copper. In this respect, it is preferably a copper-copper welded connection.
- a first acquisition of the intensity information and the depth information takes place before the formation of the welded joint and a second acquisition of the intensity information and the depth information takes place after the formation of the welded joint, and the determination of the quality feature as a function of the before formation and intensity information and depth information determined after the weld connection has been formed.
- the quality feature can be determined with improved accuracy. It is advantageous here if, in addition to the second recording of the intensity information and the depth information, the intensities of the process emissions are recorded after the weld connection has been formed.
- the Process emissions can be recorded in particular with detectors based on photodiodes for process-typical wavelengths.
- the depth information is determined using a triangulation device.
- the triangulation device can enable optical distance measurement and thereby provide depth information for the image points of the detection area.
- the triangulation device is preferably designed as a laser triangulation device.
- the laser triangulation device can include one or more line lasers.
- the depth information is determined using a device for optical coherence tomography or a grazing light device or a stereo camera.
- the intensity information is recorded using an intensity channel.
- the intensity information can form a grayscale image of the detection area.
- the intensity information can be recorded using several, in particular three, intensity channels for different colors.
- the intensity information can form several grayscale images for different colors, which in combination represent a colored image of the detection area.
- intensity channels can be provided for the colors red, yellow and blue.
- the quality feature is determined using a regression analysis.
- the regression analysis can be carried out using statistical analysis methods.
- the invention further relates to a training data set for a machine learning model for determining a quality feature of a welded connection between two conductor ends, comprising: intensity information from several image points of a detection area comprising the conductor ends and depth information of the image points.
- intensity information from several image points of a detection area comprising the conductor ends and depth information of the image points.
- information from time series formed from the intensities of the process emissions can be provided.
- a further subject of the invention is a method for welding conductor ends, in particular hairpin elements of an electrical machine, using a beam welding process, in particular a laser welding process, wherein - before welding a first pair of conductor ends - intensity information is provided for several image points of a detection area comprising the conductor ends of the pixels and depth information of the pixels are recorded, the conductor ends of the first pair being welded, wherein - after welding the first pair of conductor ends - intensity information of the pixels and depth information of the pixels are recorded for several pixels of a detection area comprising the conductor ends, depending on the recorded process emission intensity information, the intensity information of the image points and the depth information, a quality feature, in particular a connection surface or a pore volume, is determined, with the welding of a second pair of conductor ends following the first pair depending on the quality feature determined is controlled.
- the intensities of the process emissions can advantageously be recorded during welding.
- the method makes it possible to test welded connections between conductor ends with little expenditure of time and to set the creation of subsequent welded connections depending on the result of the test.
- Fig. 1 shows a schematic flow of an exemplary embodiment
- Fig. 4 shows another exemplary method for determining a
- the conductor ends 1 shown here are conductor ends 1 of hairpin elements of an electrical machine.
- the illustrations on the left show intensity information IR(XY), IG(X,Y), IB(X,Y) of pixels of a detection area that includes the conductor ends 1.
- the intensity information IR(X,Y), IG(X,Y), IB(X,Y) in the upper illustration was recorded before forming the weld and the intensity information IR(XY), IG(X,Y), IB( X,Y) in the illustration below were recorded after the weld joint 2 was formed.
- the intensity information IR(XY), IG(X,Y), IB(X,Y) can be captured by an image capture device, for example an optical camera.
- the intensity information IR(X,Y), IG(X,Y), IB(X,Y) was recorded with several, here three, intensity channels. These can, for example, correspond to the intensity of the colors red, yellow and blue in the detection area.
- depth information Z(x,y) of the image points was also recorded for the image points.
- Detecting the depth information Z(x,y) can be done using a depth information detection device. This is preferably designed as a triangulation device, in particular as a laser triangulation device.
- a depth information detection device can be used, which is designed as a device for optical coherence tomography or a grazing light device or a stereo camera.
- a quality feature in particular a connection surface or a Pore volume of weld connection 2 is determined.
- the quality feature is determined using a regression analysis.
- the quality feature is preferably determined using a machine learning system 3.
- the representation in Fig. 2 shows schematically a system for machine learning 3, which is designed as an artificial neural network.
- a training data set 4 is supplied to it, which includes intensity information from several pixels of a detection area encompassing the conductor ends and depth information of the pixels.
- the training data set 4 contains data for supervised training of a model necessary information about the fundamental truth, the so-called “ground truth”, for example the connection cross section and the number of pores and their volume.
- the training data set 4 contains both the intensity and depth information recorded before the weld connection is formed and the intensity and depth information recorded after the weld connection is formed.
- the training data set 4 according to FIG. 2 includes intensity information with three channels.
- the training data set includes 4 intensity information with exactly one intensity channel.
- FIG. 4 shows another system for machine learning 3, which is designed as an artificial neural network and is constructed from the network 6, consisting of LSTM and FCN layers, and from the network 7, consisting of CNN and FCN.
- the training data set 4 includes intensity information with, for example, an intensity channel, depth information and time series consisting of the intensities of the process emissions.
- intensity information with, for example, an intensity channel, depth information and time series consisting of the intensities of the process emissions.
- Three time series are shown as examples, corresponding to the intensities at three selected wavelengths 5.
- the model to be trained is structured in such a way that there is a branch specifically designed for time series regression, based for example on an LSTM-FCN (Neural Network consisting of Long Short-Term Memory Layers and Fully Connected Layers), and a branch for image regression , based for example on a CNN-FCN (Convolutional layers and fully connected layers). Both branches are based on the so-called
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Quality & Reliability (AREA)
- Mechanical Engineering (AREA)
- Optics & Photonics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Plasma & Fusion (AREA)
- Length Measuring Devices By Optical Means (AREA)
- Image Analysis (AREA)
Abstract
Description
Claims
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| DE102022113705.8A DE102022113705A1 (de) | 2022-05-31 | 2022-05-31 | Verfahren zur Ermittlung eines Qualitätsmerkmals einer Schweißverbindung zwischen zwei Leiterenden, Verfahren zur Bereitstellung eines Trainingsdatensatzes, Trainingsdatensatz, Verfahren zum Verschweißen von Leiterenden sowie Vorrichtung zur Ermittlung eines Qualitätsmerkmals einer Schweißverbindung |
| PCT/EP2023/063948 WO2023232600A1 (de) | 2022-05-31 | 2023-05-24 | Verfahren zur ermittlung eines qualitätsmerkmals einer schweissverbindung zwischen zwei leiterenden, verfahren zur bereitstellung eines trainingsdatensatzes, trainingsdatensatz, verfahren zum verschweissen von leiterenden sowie vorrichtung zur ermittlung eines qualitätsmerkmals einer schweissverbindung |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| EP4533387A1 true EP4533387A1 (de) | 2025-04-09 |
Family
ID=86692805
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| EP23728736.2A Pending EP4533387A1 (de) | 2022-05-31 | 2023-05-24 | Verfahren zur ermittlung eines qualitätsmerkmals einer schweissverbindung zwischen zwei leiterenden, verfahren zur bereitstellung eines trainingsdatensatzes, trainingsdatensatz, verfahren zum verschweissen von leiterenden sowie vorrichtung zur ermittlung eines qualitätsmerkmals einer schweissverbindung |
Country Status (4)
| Country | Link |
|---|---|
| US (1) | US20250348991A1 (de) |
| EP (1) | EP4533387A1 (de) |
| DE (1) | DE102022113705A1 (de) |
| WO (1) | WO2023232600A1 (de) |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130075371A1 (en) * | 2011-09-22 | 2013-03-28 | GM Global Technology Operations LLC | Non-destructive evaluation of welded joints of bar wound stator utilizing infrared and thermal methods |
| JP6187246B2 (ja) * | 2013-05-07 | 2017-08-30 | トヨタ自動車株式会社 | 溶接品質検査装置 |
-
2022
- 2022-05-31 DE DE102022113705.8A patent/DE102022113705A1/de active Pending
-
2023
- 2023-05-24 EP EP23728736.2A patent/EP4533387A1/de active Pending
- 2023-05-24 US US18/870,396 patent/US20250348991A1/en active Pending
- 2023-05-24 WO PCT/EP2023/063948 patent/WO2023232600A1/de not_active Ceased
Also Published As
| Publication number | Publication date |
|---|---|
| DE102022113705A1 (de) | 2023-11-30 |
| WO2023232600A1 (de) | 2023-12-07 |
| US20250348991A1 (en) | 2025-11-13 |
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