WO2011083087A1 - Procédé de traitement de pièces de fabrication au moyen d'une tête de traitement cognitive et tête de traitement cognitive utilisant ce procédé - Google Patents

Procédé de traitement de pièces de fabrication au moyen d'une tête de traitement cognitive et tête de traitement cognitive utilisant ce procédé Download PDF

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Publication number
WO2011083087A1
WO2011083087A1 PCT/EP2011/000044 EP2011000044W WO2011083087A1 WO 2011083087 A1 WO2011083087 A1 WO 2011083087A1 EP 2011000044 W EP2011000044 W EP 2011000044W WO 2011083087 A1 WO2011083087 A1 WO 2011083087A1
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WO
WIPO (PCT)
Prior art keywords
processing
initial
learning
cognitive
workpiece type
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PCT/EP2011/000044
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English (en)
Inventor
Stork Genannt Wersborg Ingo
Original Assignee
Precitec Kg
Precitec Itm Gmbh
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.)
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Publication date
Application filed by Precitec Kg, Precitec Itm Gmbh filed Critical Precitec Kg
Priority to DE112011100192T priority Critical patent/DE112011100192T5/de
Publication of WO2011083087A1 publication Critical patent/WO2011083087A1/fr

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Classifications

    • 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
    • B23K26/02—Positioning or observing the workpiece, e.g. with respect to the point of impact; Aligning, aiming or focusing the laser beam
    • B23K26/03—Observing, e.g. monitoring, the workpiece
    • B23K26/032—Observing, e.g. monitoring, the workpiece using optical means
    • 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
    • B23K26/02—Positioning or observing the workpiece, e.g. with respect to the point of impact; Aligning, aiming or focusing the laser beam
    • B23K26/06—Shaping the laser beam, e.g. by masks or multi-focusing
    • B23K26/062—Shaping the laser beam, e.g. by masks or multi-focusing by direct control of the laser beam
    • B23K26/0626—Energy control of the laser beam
    • G—PHYSICS
    • G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
    • G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
    • G09B19/00—Teaching not covered by other main groups of this subclass

Definitions

  • the present invention relates to a method for processing workpieces by means of a cogni- tive processing head and a cognitive processing head using the same.
  • the present invention is directed to transferring inspirations from natural cognition into realtime process control for industrial production systems.
  • This object is solved by a method for processing workpieces by means of a cognitive processing head according to claim 1 and by a cognitive processing head according to claim 15. Further advantages, refinements and embodiments of the invention are described in the respective sub-claims. In many cases, if a production system is installed or reconfigured, qualified professionals are present.
  • the technical cognitive architecture according to the present invention incorporates the possibility to first learn from a human expert and then to learn without supervision or to make decisions on its own.
  • the architecture of the present invention could be applied to problems of laser material processing or robotic welding with laser beams.
  • Laser beam welding is a suitable showcase for production processes which require a high level of precision, using many individually weak sensors for data inputs, and requiring quick adaptation to complex system configurations and real-time process control.
  • the present invention aims to offer a data analysis concept that will satisfy the need for different production scenarios, in particular in view of adaptive and autonomous laser material processing.
  • Many other architectures and frameworks towards cognitive technical systems and machine learning approaches capable of controlling production systems have been demonstrated.
  • the present invention provides a method for processing work- pieces by means of a cognitive machining or processing head, comprising the steps of performing an initial machining or processing process on a workpiece of an initial workpiece type by acting on learned knowledge; and making an autonomous decision in a previously unknown processing situation for adapting the learned knowledge to a secondary machining or processing process on a workpiece of a secondary workpiece type.
  • the present invention provides a method for processing work- pieces by means of a cognitive machining or processing head, cognitive laser machining head, or cognitive laser processing head, comprising the steps of abstracting relevant sensor information concerning or related to an initial machining or processing process on a workpiece of an initial workpiece type; learning from a human expert how to control the initial machining or processing process on a workpiece of the initial workpiece type; performing an initial machining or processing process on a workpiece of the initial workpiece type by acting on learned knowledge; and making an autonomous decision in a previously unknown processing situation for adapting the learned knowledge to a secondary machining or processing process on a work- piece of a secondary workpiece type.
  • the processing of the workpieces comprises a laser welding or cutting process.
  • the abstracting of relevant sensor information may preferably comprise an initial test run, in which an actuator parameter is altered and the sensor information is processed by a feature extraction technique to embed the sensor data input to a learned feature space.
  • the relevant sensor information comprises information of sensors includ- ing a high-speed camera, sensors for solid-borne and air-borne acoustics, and three photodiodes recording different process emissions in different wavelengths.
  • the feature extraction technique comprises a linear or non-linear dimensionality reduction processing of the sensor data input. It is preferred that the learning from a human expert how to control an initial machining or processing process comprises controlling of actuators including a linear drive or robotic positioning actuator or an actuator for setting a laser power.
  • learning from a human expert comprises classification techniques such as Support Vector Machines, Artifi- cial Neural Networks, and Fuzzy k-Nearest Neighbour.
  • performing an initial machining process comprises a combination of a PID-control with classification results.
  • performing an initial machining or processing process comprises performing laser power controlled overlap welds, in which too large gaps between two sheets to be joint are monitored.
  • making an autonomous decision includes a novelty check on the basis of the trained data.
  • a further test action is performed to classify the secondary workpiece type with the previously trained features by means of an supervised or unsupervised learning procedure.
  • the secondary workpiece type is preferably related to a different material thickness of the workpiece.
  • the supervised learning procedure includes reinforcement learning.
  • the object of the present invention is further solved by a cognitive machining or processing head, cognitive laser machining head, or cognitive laser processing head, which comprises a control unit being adapted to perform a method of the present invention.
  • the machining or processing head of the present invention preferably comprises a laser optic for laser processing of workpieces, actuators including linear drive or robotic positioning actuators, an actuator for setting a laser power, and sensors including a high-speed camera, sensors for solid-borne and air-borne acoustics, and three photodiodes recording different process emissions in different wavelengths.
  • FIG. 1 illustrates an architecture for real-time adaptive process control of actuators in industrial production systems according to the present invention
  • FIG. 2 shows an architecture design for real-time adaptive process control of laser welding according to the present invention
  • FIG. 3 A shows an In-process image taken with a co-axial camera
  • FIG. 3B shows an image of a laser welding process with acoustic sensors
  • FIG. 4 shows a diagram of features and probability related to a gap between two sheets of stainless steel
  • FIG. 5 shows a diagram related to a probability of a gap between two sheets of stainless steel for a workpiece with a number 151, wherein the real gap is indicated;
  • FIG. 6 shows a diagram related to a mapping of features from a reference workpiece onto workpieces of different thicknesses, wherein the shaded zone indicates the usual laser welding process operation area.
  • Memory coding units have been identified in the hippocampus of mice brains. Events of cognitive importance are not memorized in every detail but in a selective representation. The brain seems to abstract information by decoding it with activation patterns. Sensations then create those activation patterns enabling the mouse to identify previously learned situations. Furthermore, these characteristics not only appear to exist and be alike between individual mice, but also between different species. It has been shown in human brain activity that patterns of neural activation are associated with thinking in different semantic categories. A functional magnetic resonance imaging combination with a computational model, incorporating dimensionality reduction and classification techniques, is described that can display if a test person thinks of previously memorized objects, such as tools.
  • this method can visualize if a different test person is thinking of a hammer or a screw driver. Groups of nerve cells in different combinations appear to react to certain events or thoughts encoded by specific patterns. It seems as if natural cognition is organized with abstracted information or the mentioned patterns representing real-world events in combination with a categorization. Information from the real world is reduced to signals with patterns enabling the brain to distinguish and recognize events. These cognitive capabilities empower humans and other natural cognitive organisms not only to learn a lot but also to react fast. This is also highly desirable in technical systems.
  • a natural cognitive capability is to abstract relevant information from a greater magnitude and to differentiate categories within this information. Transferring this concept from natural cognition to the world of mathematical data analysis, a combination of data reduction techniques and classification methods can possibly be used to achieve something with similar behaviour.
  • many manufacturing processes can be con- sidered as black box model, focusing on the in and outs of the box rather than what happens inside.
  • the connections to the black box for production systems often are sensors and actuators. Sensors such as cameras, microphones, tactile sensors, and many more are provided to monitor production processes.
  • the systems need actuators such as linear drives or robotic positioning to interact with the real world. For every production process these actuators have to be parameterized.
  • a diagram illustrates an architecture or method of the present invention being suitable for adaptive process control.
  • the diagram describes the unit communication and information processing steps.
  • the architecture or method of the present invention may be designed for four modes of usage or method steps: first, abstracting relevant information; second, receiving feedback from a human expert how to control processes with other words supervised learning; third acting on learned knowledge; and fourth, controlling autonomously processes in previously unknown situations.
  • first, abstracting relevant information is discussed in detail.
  • natural human cognition we abstract or absorb information from all that we hear, feel, and see. So in general we only remember the most interesting things.
  • a technical cognitive system may also abstract relevant information from a production process.
  • Finding a useful feature space representation is critical, because the system will only be able to recognize or react to changes of the features.
  • the way of feature selection may vary for different production processes, in the last section, a best practice experimental experience for laser beam welding is demonstrated.
  • the step of supervised learning will be discussed.
  • In natural human cognition for instance in childhood, we often learn from our parents to manage complex tasks.
  • a machine should have the possibility to learn its task initially from a human expert.
  • a qualified human supervisor is usually present when the production system is being installed or configured.
  • the architecture of the present invention uses hu- man-machine communication to receive feedback from an expert, for instance, a graphical user interface. As mentioned above, in this architecture at least one test action per actuator or a test run is needed in an initial learning mode.
  • the robot executes one actuator from minimum to maximum output and the sensor data input is stored.
  • an expert gives feedback as to whether the robot executed the actuator correctly or whether its action was unsuccessful or undesirable.
  • the feedback may have many different categories, so kinds of failures and exit strategies may be defined.
  • a classification technique may then collect the features with the corresponding supervisory feedback. Together with lookup tables the classifier will serve as knowledge and planning repository and for classification of the current system state.
  • the Support Vector Machines, Fuzzy k-Nearest Neighbour, and Artificial Neural Networks will be discussed later as classification techniques. The more the human expert teaches the machine the likelier the system will achieve the desired goal. For cost saving the necessary human supervisor time should be minimized.
  • the architecture may use after the first test run its learned knowledge to give itself feedback, as explained in the unsuper- vised learning paragraph.
  • the step of adaptive process control should be discussed. To realize a robust process control in an industrial production process a fast closed-loop control is often required. If you think of a joining or cutting process, the loop should be completed at least once before the interaction zone has left the processing area. In a laser power controlled welding process for instance, a real-time process control implies that the laser focal spot should not have left the previous interaction zone, where the control-loop was started from.
  • the advantage of the architecture of the present invention is that the use of features instead of raw sensor data permits to complete control-loops faster while the loss of information is minimized.
  • any kind of controller design may be implemented, which fits to the classification output.
  • a simple version would be to have two possible classification output values: too much and too low.
  • a PID controller could adjust a parameter of the system's actuators, as it will be the case in the last section.
  • the architecture abstracts information which is reduced in data vol- ume. What has been called 'activation patterns' in the section concerning the inspiration from natural cognition, could also be understood as features representing sensory events. Using, for instance, dimensionality reduction, a lower dimensional feature calculated from the training events would indicate if the system has experienced a certain event.
  • a classification with Support Vector Machines can categorize and distinguish the events. After an initial feature calculation, the presence of features in sensory data inputs can be processed a lot faster than processing raw sensory data.
  • a combination of data reduction and classification techniques is promising for process control because it enables it to act fast.
  • the architecture of the present invention proposed for cognitive technical systems enables several cognitive capabilities, such as obtaining relevant information, learning from a hu- man expert and reacting to new situations based on previously learned knowledge.
  • This architecture may be used for different kinds of systems that need to control one or several actuators on the input of a high amount of sensor data.
  • the learning and reacting capabilities seem to be limited; however, the architecture is very robust in terms of data acquisition; it is easy to use and can be realized for fast computing up to real-time closed-loop control of complex systems such as the application shown in the following section.
  • a typical procedure for production systems is that a unit of an assembly line must first be configured and then be monitored for quality assurance. This is also the case for laser welding.
  • materials are processed with the use of laser light, a high degree of precision is necessary.
  • welding with laser beams is hard to observe because of strong radiation and process emissions.
  • many different sensors are used for monitoring activities.
  • these processes are usually initially configured with many manual trials, resulting in high costs in labour and machinery. All process parameters are kept constant, because change would result in high recalibration costs and may cause production to stop.
  • a cognitive system for laser material processing capable of reacting appropriately to changes would be of great help and an economic upgrade.
  • FIG. 2 An approach for a cognitive system for laser material processing following the architecture of the present invention is shown in figure 2.
  • the data processing is structured within this architecture.
  • the following sensors are used: a high-speed camera, sensors for solid-borne and air-borne acoustics, and three photodiodes recording different process emissions in different wavelengths, as demonstrated in figure 3A and 3B.
  • the cognitive capabilities under evaluation can be separated into four categories: first, abstracting relevant information; second, learning from a human expert how to control; third, acting on learned knowledge; and fourth, autonomous decision-making in previously unknown situations.
  • All of the data from the applied online sensor system can be represented by a 26.603 dimensional vector, which can be reduced to only 33 dimensions, representing the relevant process-information by combining different dimensionality reduction techniques.
  • Laser material processing systems are usually set up and their processes configured by human experts.
  • the discussed architecture may simplify and accelerate this process.
  • a test action such as a laser power ramp for welding a human expert points out
  • a graphical user interface displaying the workpiece
  • how the processing result would be classified for the different workpiece areas For instance, the expert may mark a poor weld with not enough laser power, a good weld, and a poor weld with too much laser power applied.
  • Our system stores this information together with the extracted characteristics described above with a classification technique.
  • classification techniques have been evaluated and have been found to be suitable embodiments of the present invention, such as Support Vector Machines, Artificial Neural Networks, and Fuzzy k-Nearest Neighbour. All of the named classifiers achieved good results; the extracted characteristics seem to be separable from each other for many different process setups.
  • the combination of feature extraction and classification proofed to be a reliable monitoring tool for stainless steel welding, for example soiled areas on workpieces or variation in material thickness may be monitored. Even previously hard-to-detect faults can be monitored, such as a too large gaps in between two sheets to be joined, as shown in figures 4 and 5.
  • the trained feature sets are now identified earlier or later on the workpiece with different material thickness.
  • the results show that the mapping of charac- teristics seems to work reliably, because the mapping points out the necessary laser power adjustments.
  • the experimental results indicate that the system of the present invention is capable of making a judgment and adjusting the applied laser power for receiving the desired welds accordingly. Further techniques for this step could be employed, such as re- inforcement learning in order to increase high-level adaptivity.
  • an architecture of the present invention is presented, which is suitable to different process control task within industrial production.
  • the design inspired by natural cognition is applied to laser welding process control problems.
  • the method and the apparatus of the present invention provide cognitive capabilities for managing multiple sensors and actuators.
  • the system has been able to learn from a human expert how to weld materials, make decisions in previously not trained situations, improve monitoring and process control with 500 Hz and greater.
  • the system classifies the incoming dimensionality reduced sensor data by calculating the class probability of the existing data entries or features. This step provides a similarity probability within the feature space. It therefore determines the process status and its probability of being in a desired state. The system optimizes this proba- bility of being in a desired state. The good class probability can be taken as set value for a controller such as PID, which tries to achieve 100%. Further human expert feedback can increase the labeled data stored within the classifier. Within this step the data of the current machining or processing process is labeled with the human expert feedback and is additionally stored within the classifier. This allows to support the machine if it cannot get into a good class system state or to fine-tune the performance.
  • a number of different methods such as Kernel Principal Component Analysis (PCA), or Artificial Neural Networks, or Support Vector Machines and similar classifiers allow the machine to do a novelty check.
  • PCA Kernel Principal Component Analysis
  • Artificial Neural Networks or Support Vector Machines and similar classifiers allow the machine to do a novelty check.
  • the novelty check the system knows if the system state exists within the learned data entries. It may be realized with another classification. It can actually determine if the system's state is similar to the previously learned dimensionality reduced sensor data. For instance all labeled data entries within the feature space are labeled for another classification as known data entries.
  • Support Vector Machine classification the known data entries may be one class and the feature space origin another class. The Support Vector Machine then calculates the probability if the system's state is known or similar to previously learned data entries.
  • the system's state may be considered as new or unknown.
  • the novelty check provides safety, because the system executes the desired action of a human expert only on learned knowledge. In unknown states the system acts on similarities, which may not be appropriate for the desired machining process.
  • This novelty check can be used as another monitoring signal for instance.
  • the human expert can trust all machining processes, where the novelty check has not been positive.
  • the system can either ask for additional human expert feedback or it can also start a self-learning process such as reinforcement learning or workpiece mapping.
  • Other sensor data feedback may be used as reward function for a reinforcement learning agent.
  • Workpiece mapping describes a process where dimensionality reduced sensor data is gained from a new machining process and labeled by feature similarity of previous features.
  • the present invention allows to perform an adap- tive process control on a black-box model. It is not necessary to preset a process control model, where parameters can be adapted.
  • the system can furthermore supervise itself using the novelty check and perform action upon it. It is especially suitable to processes with noisy and complex sensor data such as laser welding or laser cutting.

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  • Physics & Mathematics (AREA)
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  • Engineering & Computer Science (AREA)
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  • Business, Economics & Management (AREA)
  • Mechanical Engineering (AREA)
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Abstract

La présente invention porte sur un procédé de traitement de pièces de fabrication au moyen d'une tête de traitement cognitive, lequel procédé comprend les étapes de mise en œuvre d'un procédé de traitement initial du type initial de pièce de fabrication par action sur les connaissances apprises, et de prise d'une décision autonome dans une situation antérieurement inconnue afin d'adapter les connaissances apprises à un procédé de traitement secondaire d'un type secondaire de pièce de fabrication.
PCT/EP2011/000044 2010-01-08 2011-01-07 Procédé de traitement de pièces de fabrication au moyen d'une tête de traitement cognitive et tête de traitement cognitive utilisant ce procédé WO2011083087A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
DE112011100192T DE112011100192T5 (de) 2010-01-08 2011-01-07 Verfahren zum Bearbeiten von Werkstücken mittels eines kognitiven Bearbeitungskopfes und ein dieses verwendender Bearbeitungskopf

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
EP10000110.6 2010-01-08
EP10000110 2010-01-08

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WO2011083087A1 true WO2011083087A1 (fr) 2011-07-14

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Cited By (6)

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EP2883647A1 (fr) 2013-12-12 2015-06-17 Bystronic Laser AG Procédé de configuration d'un dispositif d'usinage au laser
JP2017164801A (ja) * 2016-03-17 2017-09-21 ファナック株式会社 機械学習装置、レーザ加工システムおよび機械学習方法
US9937590B2 (en) 2010-07-22 2018-04-10 Bystronic Laser Ag Laser processing machine
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BE1025957B1 (fr) * 2018-01-26 2019-08-27 Laser Engineering Applications Méthode pour la détermination de paramètres d'usinage laser et dispositif d'usinage laser utilisant ladite méthode
US10643127B2 (en) * 2016-01-28 2020-05-05 Fanuc Corporation Machine learning apparatus for learning condition for starting laser machining, laser apparatus, and machine learning method

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DE102020107623A1 (de) 2020-03-19 2021-09-23 Trumpf Werkzeugmaschinen Gmbh + Co. Kg Computerimplementiertes verfahren zum erstellen von steuerungsdatensätzen, cad-cam-system und fertigungsanlage

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Cited By (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9937590B2 (en) 2010-07-22 2018-04-10 Bystronic Laser Ag Laser processing machine
CN109903648A (zh) * 2013-03-11 2019-06-18 林肯环球股份有限公司 使用虚拟现实焊接系统导入和分析外部数据
EP2883647A1 (fr) 2013-12-12 2015-06-17 Bystronic Laser AG Procédé de configuration d'un dispositif d'usinage au laser
US9839975B2 (en) 2013-12-12 2017-12-12 Bystronic Laser Ag Method for configuring a laser machining machine
US10643127B2 (en) * 2016-01-28 2020-05-05 Fanuc Corporation Machine learning apparatus for learning condition for starting laser machining, laser apparatus, and machine learning method
CN107199397A (zh) * 2016-03-17 2017-09-26 发那科株式会社 机器学习装置、激光加工系统以及机器学习方法
JP2017164801A (ja) * 2016-03-17 2017-09-21 ファナック株式会社 機械学習装置、レーザ加工システムおよび機械学習方法
US10664767B2 (en) 2016-03-17 2020-05-26 Fanuc Corporation Machine learning apparatus, laser machining system and machine learning method
CN107199397B (zh) * 2016-03-17 2021-08-24 发那科株式会社 机器学习装置、激光加工系统以及机器学习方法
BE1025957B1 (fr) * 2018-01-26 2019-08-27 Laser Engineering Applications Méthode pour la détermination de paramètres d'usinage laser et dispositif d'usinage laser utilisant ladite méthode
WO2019145515A3 (fr) * 2018-01-26 2019-10-24 Laser Engineering Applications Methode pour la simulation d'usinages laser, systeme d'usinage laser ayant des moyens pour mettre en oeuvre ladite methode, et programme d'ordinateur pour implementer une telle methode
WO2019145513A3 (fr) * 2018-01-26 2020-01-23 Laser Engineering Applications Méthode pour la détermination de paramètres d'usinage laser et dispositif d'usinage laser utilisant ladite méthode
US11928401B2 (en) 2018-01-26 2024-03-12 Laser Engineering Applications Laser machining simulation method, laser machining system having means for implementing the method, and computer program for implementing this method

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DE202011110730U1 (de) 2016-01-26

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