WO2024052264A1 - Examen de composants pour pannes au moyen d'un analyseur de composants - Google Patents

Examen de composants pour pannes au moyen d'un analyseur de composants Download PDF

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Publication number
WO2024052264A1
WO2024052264A1 PCT/EP2023/074145 EP2023074145W WO2024052264A1 WO 2024052264 A1 WO2024052264 A1 WO 2024052264A1 EP 2023074145 W EP2023074145 W EP 2023074145W WO 2024052264 A1 WO2024052264 A1 WO 2024052264A1
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WO
WIPO (PCT)
Prior art keywords
routine
component
data
routines
defects
Prior art date
Application number
PCT/EP2023/074145
Other languages
German (de)
English (en)
Inventor
Tobias Masiak
Georg Schneider
Patrick Trampert
Felix Schmidt
Original Assignee
Zf Friedrichshafen Ag
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Zf Friedrichshafen Ag filed Critical Zf Friedrichshafen Ag
Publication of WO2024052264A1 publication Critical patent/WO2024052264A1/fr

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    • 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
    • G06N3/084—Backpropagation, e.g. using gradient descent
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • 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
    • G06N3/09—Supervised learning

Definitions

  • the present invention relates to a method for examining components for defects using a component analyzer and a component analyzer.
  • a method for examining components for defects using a component analyzer comprising a Kl routine for object analysis and a Kl routine for detecting and classifying defects that are known to the component analyzer from data sets and/or a Kl routine for Detection of anomalies that are not known to the component analyzer from data sets, the Kl routines being fed with sensor data or processed sensor data relating to a component to be examined as input data and annotated data relating to the component to be examined as output data containing a result of the respective routine generate, wherein at least one Kl routine is trained with annotated output data of at least one of the other Kl routines; as well as
  • a component analyzer for examining components for defects with a holder in which the component is positioned during the examination; at least one sensor, in particular an optical sensor, to generate sensor data relating to a component to be examined; a control device to control the sensor such that it automatically generates the sensor data; an interface to a Kl module with at least one Kl routine for object analysis, at least one Kl routine for detecting and classifying defects that are known to the Kl module from data sets, and at least one Kl routine for detecting anomalies are not known to the Kl module from data sets, whereby the Kl routines of the Kl module Modules interact with each other; and with means for sorting examined components according to their examination results.
  • a sensor also known as a detector, (measurement or measuring) transducer or (measuring) probe, is a technical component that detects certain physical, chemical properties or states, e.g. B. temperature, humidity, pressure, speed, brightness, acceleration, pH value, ionic strength, electrochemical potential and / or the material nature of its environment can be recorded qualitatively or quantitatively as a measurement variable. These variables are recorded using physical or chemical effects and converted as sensor data into an electrical signal that can be further processed. Sensors include camera, lidar, radar, TOF and other sensor technologies, such as acoustic sensors.
  • Computer program products typically include a sequence of instructions that, when the program is loaded, causes the hardware to perform a specific procedure that results in a specific result.
  • Machine learning or artificial intelligence is a generic term for the “artificial” generation of knowledge from experience: an artificial system learns from examples and can generalize them after the learning phase has ended. To do this, machine learning algorithms build a model that is based on training data. This means that the examples are not simply memorized, but patterns and regularities are recognized in the learning data. The system can also assess unknown data (learning transfer) or fail to learn unknown data (overfitting).
  • a Kl routine is a computer program section that is implemented using artificial intelligence.
  • Object analysis refers to determining the properties of a component. This includes, for example, object classification, i.e. the division of components into different groups/classes according to their type and/or properties, the measurement of an object, or object recognition, i.e. the classification of components according to their type and/or properties.
  • object classification i.e. the division of components into different groups/classes according to their type and/or properties
  • object recognition i.e. the classification of components according to their type and/or properties.
  • Training manifests itself in a quantitative assessment of the distribution of training data.
  • An AI uses the training data to learn what distribution of sensor data is to be expected in the future.
  • Anomalies are defects that are not known to a Kl routine. These can, for example, be defects that occur rarely or are caused by a change in the manufacturing process. Anomaly detection can be achieved using statistical methods in conjunction with artificial intelligence by first determining a distribution of sensor data. If the distribution deviates from the expected distribution by more than a predetermined standard deviation factor, it may be useful to detect an anomaly.
  • the distribution of expected training data can be determined as a reference and/or patterns are determined in the data based on historical data, which can be classified at least into the normal classes through appropriate annotations and optionally classified as abnormal.
  • One way to train a Kl is error feedback. In the early training phase, a class makes mistakes. To correct the error, the triggers of the deviations (errors) between the generated assignment (actual output data) and the solution assignment (target output data) are traced back and controlling factors are changed in such a way that the assignment errors become smaller.
  • training data is data pairs consisting of input data that is to be processed by the KL and target output data that is to be determined by the KL.
  • the Kl is adjusted based on a comparison of target output data with the actual output data determined by the Kl, which results in a training effect.
  • An examination result can, for example, be the classification of a component into quality classes, such as good/not good, or a probability for the quality of a component.
  • a result space can contain any number of classes
  • the components of a component analyzer include hardware and/or software modules, comprising hardware and/or software modules for regulating and/or controlling industrial manufacturing processes.
  • the hardware modules include electronic units, integrated circuits, embedded systems, microcontrollers, multiprocessor systems-on-chip, central processors and/or hardware accelerators, for example graphics processors, data storage units and connectivity elements, for example WLAN modules, RFID modules, Bluetooth modules, NFC modules.
  • the Kl modules are executed as functional software in the cloud infrastructure.
  • the commands of the computer programs according to the invention include machine instructions, source text or object code written in assembly language, an object-oriented programming language, for example C++, or in a procedural programming language, for example C.
  • the computer programs are hardware-independent application programs that, for example, via a data carrier or a data carrier signal is provided using Software Over The Air technology.
  • the basic idea of the invention is a component analyzer or a method for examining components using several Kl routines, each of which is specialized for individual special tasks.
  • At least one Kl routine for object analysis there are at least one Kl routine for object analysis, at least one Kl routine for detecting and classifying defects that are known to the component analyzer from data sets, and / or at least one Kl routine for detecting anomalies that are not known to the component analyzer from data sets are.
  • the invention comprises at least two Kl modules
  • the Kl routines interact with each other, which means that the Kl routines learn from each other. For example, it can be provided that if the Kl routine detects a previously unknown anomaly, this deviation will also be recognized in the future by a Kl routine for detecting and classifying defects. Furthermore, it is provided that the Kl routines can process output data from other Kl routines. For example, it can be provided that a result of a Kl routine for object analysis is further processed by a Kl routine for detecting and classifying defects.
  • the Kl routines are fed with sensor data or processed sensor data relating to a component to be examined and generate annotated output data for these sensor data with conclusions that the respective Kl routine has determined.
  • the annotated data is at least partially checked by a human editor.
  • the component analyzer has a human-machine interface. It can be provided that a further Kl routine is provided for determining the reliability of output data and that the annotated data is presented to a human processor for checking, provided that the reliability determined for the annotated output data falls below a predetermined threshold value.
  • the output data of the Kl routine for detecting anomalies is generally checked by a human processor, provided that the output data contains recognized anomalies. This means that newly occurring defects can be checked and production coordinators can be informed about new defects that have arisen. The human editor can also decide whether other Kl routines are trained with the respective annotated source data.
  • a first examination result is generated by means of at least a first Kl routine regarding the quality of a component, while the component analyzer has access to the component and the component is prepared by the component analyzer for sorting according to quality classes or is sorted according to quality class.
  • the component analyzer decides on the quality of a component within the clock frequency.
  • the component analyzer can therefore also ensure or at least prepare for the sorting out of rejects.
  • a second examination result is generated using at least a second Kl routine regarding the quality of a component if the component was sorted into a positive quality class based on the first examination result, the second examination result being generated while the component analyzer does not has access to the component.
  • a batch containing components that were sorted into a positive quality class based on the first test result is retrieved if a second test result corresponding to a negative quality class was generated for at least one component of the batch.
  • optical sensor data relating to a component to be examined are transformed to a predetermined size and at least one Kl routine is fed with the transformed sensor data.
  • Image data with a fixed size is often easier to process by a Kl routine.
  • changes to image data to a fixed size were often not considered, as this often results in the loss of interpretability for human editors, as a transformation to a fixed size causes distortions, for example.
  • Kl routines can deal with distortions and distortions do not reduce the reliability of the results of a Kl routine.
  • the Kl routine for object analysis is fed with optical sensor data and the fed Kl routine generates image sections relating to relevant features of a component as output data. It is intended that the output data (annotated image sections) be annotated with annotations by the Kl routine for object analysis.
  • Kl routines which are downstream in the examination process of the Kl routines for object analysis, can use this result and, for example, assess the relevance of abnormalities in sensor data based on the object analysis.
  • At least one Kl routine for detecting and classifying defects and/or at least one Kl routine for detecting anomalies is fed with annotated image sections.
  • each of the Kl routines for detecting and classifying defects is provided, each of the Kl routines for detecting and classifying defects of a predetermined type and/or specialized in a predetermined location of defects is.
  • one Kl routine can be specialized for the detection of scratches and another Kl routine for the detection of breakage damage.
  • another Kl routine may inspect a coating, a weld, or other local features in a specific local area of the component.
  • a computer program product carries out the steps of a method according to the preceding description when the computer program product runs on a computer.
  • the computer program product causes an effect, namely the interaction of several Kl routines, which improves the reliability of the detection of rejects in a component analyzer.
  • Figure 1 is a schematic block diagram of an embodiment of the invention
  • Figure 2 shows a schematic principle sketch of an embodiment of the invention.
  • Figure 1 shows a schematic block diagram according to a method for examining components for errors using a component analyzer.
  • the method includes a Kl routine for object analysis K1, as well as a Kl routine for detecting and classifying defects K2 and a Kl routine for detecting anomalies K3.
  • the Kl routines K2 and K3 are downstream of the Kl routine K1.
  • the Kl module with the Kl routines K1, K2, K3 is fed with sensor data S relating to a component to be examined and from this generates output data containing annotated data relating to the component to be examined. Furthermore, the Kl routines K2 and K3 are fed with the annotated output data of the Kl routine K1.
  • At least one Kl routine is trained with annotated output data of at least one other Kl routine.
  • the training can take place during ongoing operations or on an event-related basis.
  • FIG. 2 shows a schematic block diagram of a component analyzer for examining components for errors.
  • the component analyzer comprises a receptacle 1 in which the component is positioned during the examination, at least one sensor 2 to generate sensor data relating to a component to be examined, a control device 3 to control the sensor in such a way that it automatically generates the sensor data, an interface 4 to a Kl module 5 with at least one Kl routine K1 for object analysis, at least one Kl routine K2 for detecting and classifying defects, and at least one Kl routine K3 for detecting anomalies.
  • the component analyzer has means 6 for sorting examined components according to their examination results.

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  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Health & Medical Sciences (AREA)
  • Computing Systems (AREA)
  • Biomedical Technology (AREA)
  • Biophysics (AREA)
  • Computational Linguistics (AREA)
  • Data Mining & Analysis (AREA)
  • Evolutionary Computation (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Molecular Biology (AREA)
  • Artificial Intelligence (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Health & Medical Sciences (AREA)
  • Automatic Analysis And Handling Materials Therefor (AREA)
  • Investigating Materials By The Use Of Optical Means Adapted For Particular Applications (AREA)

Abstract

L'invention concerne un procédé comprenant une routine d'IA (K1) pour l'analyse d'objets et une routine d'IA (K2) pour reconnaître et classifier des défauts connus de l'analyseur de composants à partir d'ensembles de données, et/ou une routine d'IA (K3) pour reconnaître des anomalies qui ne sont pas connues de l'analyseur de composants à partir d'ensembles de données, les routines d'IA étant alimentées avec des données de capteur ou des données de capteur traitées concernant un composant à examiner, en tant que données d'entrée, et générer, en tant que données de sortie, des données annotées concernant le composant à examiner, contenant un résultat de la routine en question, et au moins une routine d'IA étant entraînée à l'aide de données de sortie annotées à partir d'au moins l'une des autres routines d'IA.
PCT/EP2023/074145 2022-09-06 2023-09-04 Examen de composants pour pannes au moyen d'un analyseur de composants WO2024052264A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE102022209247.3A DE102022209247A1 (de) 2022-09-06 2022-09-06 Untersuchung von Bauteilen auf Fehler mittels eines Bauteilanalysators
DE102022209247.3 2022-09-06

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WO2024052264A1 true WO2024052264A1 (fr) 2024-03-14

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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102019110721A1 (de) * 2019-04-25 2020-10-29 Carl Zeiss Industrielle Messtechnik Gmbh Workflow zum trainieren eines klassifikators für die qualitätsprüfung in der messtechnik
US20210142456A1 (en) * 2019-11-12 2021-05-13 Bright Machines, Inc. Image Analysis System for Testing in Manufacturing
WO2021142475A1 (fr) * 2020-01-12 2021-07-15 Neurala, Inc. Systèmes et procédés permettant la reconnaissance et la détection d'anomalie au moyen de réseaux neuronaux profonds continus

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102019110721A1 (de) * 2019-04-25 2020-10-29 Carl Zeiss Industrielle Messtechnik Gmbh Workflow zum trainieren eines klassifikators für die qualitätsprüfung in der messtechnik
US20210142456A1 (en) * 2019-11-12 2021-05-13 Bright Machines, Inc. Image Analysis System for Testing in Manufacturing
WO2021142475A1 (fr) * 2020-01-12 2021-07-15 Neurala, Inc. Systèmes et procédés permettant la reconnaissance et la détection d'anomalie au moyen de réseaux neuronaux profonds continus

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