EP4256464A1 - Génération d'images de référence de supports de pièce vides - Google Patents

Génération d'images de référence de supports de pièce vides

Info

Publication number
EP4256464A1
EP4256464A1 EP21824315.2A EP21824315A EP4256464A1 EP 4256464 A1 EP4256464 A1 EP 4256464A1 EP 21824315 A EP21824315 A EP 21824315A EP 4256464 A1 EP4256464 A1 EP 4256464A1
Authority
EP
European Patent Office
Prior art keywords
algorithm
workpiece carrier
recording
workpiece
camera
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
Application number
EP21824315.2A
Other languages
German (de)
English (en)
Inventor
Willi POENITZ
Marc Teschner
Korbinian WEISS
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Trumpf Werkzeugmaschinen SE and Co KG
Original Assignee
Trumpf Werkzeugmaschinen SE and Co KG
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 Trumpf Werkzeugmaschinen SE and Co KG filed Critical Trumpf Werkzeugmaschinen SE and Co KG
Publication of EP4256464A1 publication Critical patent/EP4256464A1/fr
Pending legal-status Critical Current

Links

Classifications

    • 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
    • G06T7/001—Industrial image inspection using an image reference approach
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/25—Determination of region of interest [ROI] or a volume of interest [VOI]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/12—Edge-based segmentation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/194—Segmentation; Edge detection involving foreground-background segmentation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
    • 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
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30108—Industrial image inspection
    • G06T2207/30136—Metal
    • 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
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30108—Industrial image inspection
    • G06T2207/30164—Workpiece; Machine component
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V2201/00—Indexing scheme relating to image or video recognition or understanding
    • G06V2201/06—Recognition of objects for industrial automation

Definitions

  • the invention relates to a method for creating a reference recording of the unloaded state of a workpiece carrier.
  • the invention also relates to a device for carrying out such a method.
  • WO 2020/127797 A1 describes support for a user in the manual sorting of manufactured parts from a workpiece carrier in the form of a pallet.
  • Existing program data and image data recorded with a camera are used to achieve reliable recognition and booking of removed parts.
  • the problem here is recognizing when the workpiece carrier is free of workpieces, ie completely sorted, since the workpiece carrier has changed due to contamination and/or wear. It has become known from US 2016/0184945 A1 to detect contamination by a camera either detecting a color that lies outside of predetermined color ranges or by comparing it with an image of a new palette, ie the original palette. In addition to the camera, a weight sensor can be provided in order to detect contamination based on the (additional) weight of the contamination.
  • Process step A) is carried out by the camera, process steps B) and C) are preferably carried out by a computer with a memory connected to the camera.
  • the recording can be made with ultraviolet and/or infrared light.
  • the recording is preferably made with light in the visible range.
  • the method is preferably restarted when the workpiece carrier is recognized as being loaded in method step B).
  • the method can in particular be carried out continuously, so that a recording of the last unloaded workpiece carrier is continuously stored as a reference recording.
  • the algorithm is designed to distinguish whether an object in the recording is an object that characterizes the workpiece carrier as not being unloaded or whether it is an image area to be ignored. An image area is to be ignored, for example, in the case of contamination, a change in the workpiece carrier or in the case of an object under the workpiece carrier.
  • the algorithm can be designed to ignore or reject a detected object if its size and/or its position does not meet previously defined criteria.
  • Predefined criteria can be in the form of the width of the object (is the object too narrow for an object on the workpiece carrier?) or in the form of its proximity to the edge of the workpiece carrier (is the object too close to the edge of the workpiece carrier for an object?).
  • the size and/or the position of the object is preferably recognized by a Maximum Stable Extreme Regions (MSER) algorithm.
  • MSER Maximum Stable Extreme Regions
  • the MSER algorithm is preferably taken from an Open Computer Vision (Open CV) library.
  • Open CV Open Computer Vision
  • the algorithm is designed to recognize whether an object is located above or below webs of the workpiece carrier. If the object is above the bars, it is taken into account; if it is below, it is ignored.
  • the algorithm can be designed to recognize whether an object is only visible between a plurality of webs. In this case, it must be below the webs so that this object does not characterize the workpiece carrier as loaded.
  • the algorithm can perform the following method steps to recognize the ridges: b) creating an edge image from gradients in a first direction (e.g. X direction) and a second direction (e.g. Y direction) which runs perpendicular to the first direction; c) dividing the recording into sectors; d) calculating the average pixel value within a rectangle shifted in the first direction (convolution) in the sectors; e) defining ridge segments from maxima that are more than a specified distance from the nearest minimum in the second direction; f) Specifying ridges if a predefined proportion (e.g. more than 80%) of the ridge elements is within a range boundary.
  • a predefined proportion e.g. more than 80%
  • process step b) a) Performing a histogram equalization.
  • the following method step can be carried out: g) Determination of a web removal if there is a distance between two bars that is significantly larger than the distance between the other bars.
  • the condition of the workpiece carrier can be characterized in a particularly precise and comprehensible manner.
  • the (easily verifiable) information can be output to a user that a web of the workpiece carrier is missing.
  • the algorithm has a neural network.
  • the neural network can include a variety of weighted data aggregation routines.
  • the neural network can be trained to reliably recognize the unloaded state of the workpiece carrier.
  • the neural network can be trained by reference recordings that were generated by the method steps described here.
  • the neural network can be trained using reference recordings that were generated and stored using the method steps described above.
  • the neural network can replace one or more method steps of the algorithm described here.
  • a workpiece carrier in the form of a machine bed of a machine tool, in particular a laser cutting machine is preferably used.
  • the object according to the invention is also achieved by a device for carrying out a method described here, the device having a workpiece carrier, a camera, a computer connected to the camera and a memory, the algorithm being executable on the computer and the reference recording being stored on the memory is.
  • the device preferably has a machine tool, with a machine bed of the machine tool (“pallet”) being designed in the form of the workpiece carrier.
  • a machine bed of the machine tool (“pallet”) being designed in the form of the workpiece carrier.
  • the machine tool is particularly preferably designed in the form of a laser cutting machine.
  • FIG. 1 shows a perspective view of a device according to the invention for carrying out the method according to the invention, the device having a loaded workpiece carrier.
  • FIG. 2 shows a top view of the workpiece carrier according to FIG. 1 in a different loading state.
  • FIG. 4a shows a plan view of a workpiece carrier whose webs are to be recognized.
  • FIG. 4b shows a plan view of an evaluation of the workpiece carrier from FIG. 4a.
  • FIG. 4c shows a plan view of the workpiece carrier from FIG. 4a with identified webs.
  • 1 shows a device 10 with a machine tool 12.
  • the machine tool 12 is in the form of a laser cutting machine.
  • the device 10 has a camera 14 .
  • the camera 14 can be designed to record moving images (videos) and/or photos.
  • the camera 14 takes pictures of a workpiece carrier 16, in particular from a bird's eye view.
  • the workpiece carrier 16 is designed in the form of a machine bed of the machine tool 12 .
  • a workpiece 18 can be placed on the workpiece carrier 16 .
  • the workpiece carrier 16 is loaded.
  • the workpiece 18 is part of a metal sheet 20 that can be machined in the machine tool 12 .
  • the device 10 has a computer 22, in particular in the form of an industrial PC.
  • the computer 22 has a memory 24 .
  • Computer 22 and/or memory 24 can alternatively or additionally be provided externally, for example in a cloud.
  • the computer 22 is connected to the camera 14 .
  • An algorithm 26 is stored on the computer 22 .
  • Algorithm 26 is executed to evaluate images captured by camera 14 .
  • the computer 22 can be connected to a display 28, here in the form of a monitor, in order to display which workpieces 18 have already been removed from the workpiece carrier 16.
  • the workpiece carrier 16 To monitor the manufacturing process, it is important to know when the workpiece carrier 16 is unloaded, ie empty. However, recognizing this is not trivial, since the workpiece carrier 16 and its surroundings change over time. For example, the workpiece carrier 16 can be worn out over time and/or covered with slag. Chips and the like can accumulate under the workpiece carrier 16 .
  • FIG. 2 shows this by way of example. 2 shows a workpiece carrier 16 on which a workpiece 18 lies. This workpiece 18 leads to a loading of the workpiece carrier 16. However, an interfering object 30 is also visible in FIG. This Interfering object 30 (here in the form of an object lying under the workpiece carrier 16) must not result in the workpiece carrier 16 being regarded as loaded.
  • FIG. 3 schematically shows the method according to the invention, which facilitates the assessment of the charge state of the workpiece carrier 16 (see FIG. 2). The following process steps are carried out:
  • the method can be carried out continuously (arrow 38), so that there is always a current reference image of the empty workpiece carrier 16 (see FIG. 2), which can be used to determine the current loading status of the workpiece carrier 16 (see FIG. 2).
  • the algorithm 26 (see FIG. 1) is therefore designed to distinguish whether a workpiece 18 (see FIG. 2) is located on the workpiece carrier 16 or whether an object is an interfering object 30 (see FIG. 2) acts.
  • the algorithm 26 can have a maximally stable extreme regions (MSER) algorithm, which is designed in particular to assess whether an object is too close to the edge of the workpiece carrier with respect to a predefined distance 16 (see Fig. 2) or whether it is too narrow with respect to a predefined size.
  • MSER maximally stable extreme regions
  • FIGs. Figures 4a through 4c illustrate the operation of another part of the algorithm 26 (see Figure 1), namely the detection of ridges.
  • FIG. 4a shows a workpiece carrier 16 with a plurality of (support) webs 40.
  • a histogram equalization is carried out with the recording and then an edge image made up of gradients in a first direction (here the X direction) and in a second direction (here the Y direction). direction) created.
  • Fig. 4b shows the division of the recording of the workpiece carrier 16 in n sectors. For each sector the average pixel value is calculated within a rectangle which is shifted in the first direction.
  • Maxima that are more than a specified distance in the second direction to the nearest minimum are designated as a ridge segment. If a certain percentage of the ridge elements fall within a predetermined range limit, a ridge is detected.
  • FIG. 4c shows the recording of the workpiece carrier 16 with the detected webs 40. If the distance between two webs 40 is too large, a web removal 42 is detected.
  • the invention relates in summary to a method for storing a current reference image of an unloaded workpiece carrier 16.
  • a recording of the workpiece carrier 16 is first created, and in a method step B) the recording is made by an algorithm 26 assessed and in a method step C) the recording is stored as a reference recording if the algorithm 26 classifies the workpiece carrier 16 as empty.
  • the algorithm 26 can have a maximally stable extreme regions algorithm, an algorithm for detecting webs 40 of the workpiece carrier 16 and/or a neural network.
  • the invention further relates to a device 10 for carrying out such a method.

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Physics & Mathematics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Evolutionary Computation (AREA)
  • Multimedia (AREA)
  • Health & Medical Sciences (AREA)
  • Quality & Reliability (AREA)
  • Artificial Intelligence (AREA)
  • Computing Systems (AREA)
  • Databases & Information Systems (AREA)
  • General Health & Medical Sciences (AREA)
  • Medical Informatics (AREA)
  • Software Systems (AREA)
  • Image Analysis (AREA)
  • Sorting Of Articles (AREA)
  • Machine Tool Sensing Apparatuses (AREA)

Abstract

L'invention concerne un procédé de stockage d'une image de référence courante d'un support de pièce (16) non chargé. Dans le procédé, une étape A) est d'abord mise en œuvre pour générer une image du support de pièce (16) ; dans une étape B), l'image est évaluée par un algorithme (26) ; et dans une étape C), l'image est stockée sous la forme d'une image de référence si l'algorithme (26) classifie le support de pièce (16) comme étant vide. Afin d'évaluer le support de pièce (16), l'algorithme (26) peut avoir un algorithme de régions extrêmes à stabilité maximale, un algorithme pour détecter des bandes (40) du support de pièce (16) et/ou un réseau neuronal. L'invention concerne en outre un dispositif (10) permettant la mise en œuvre dudit procédé.
EP21824315.2A 2020-12-02 2021-11-30 Génération d'images de référence de supports de pièce vides Pending EP4256464A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE102020215227.6A DE102020215227B4 (de) 2020-12-02 2020-12-02 Vorrichtung und Verfahren zum Erstellen einer Referenzaufnahme des unbeladenen Zustands eines Werkstückträgers
PCT/EP2021/083619 WO2022117581A1 (fr) 2020-12-02 2021-11-30 Génération d'images de référence de supports de pièce vides

Publications (1)

Publication Number Publication Date
EP4256464A1 true EP4256464A1 (fr) 2023-10-11

Family

ID=78851158

Family Applications (1)

Application Number Title Priority Date Filing Date
EP21824315.2A Pending EP4256464A1 (fr) 2020-12-02 2021-11-30 Génération d'images de référence de supports de pièce vides

Country Status (6)

Country Link
US (1) US20230298161A1 (fr)
EP (1) EP4256464A1 (fr)
JP (1) JP7535184B2 (fr)
CN (1) CN116569222A (fr)
DE (1) DE102020215227B4 (fr)
WO (1) WO2022117581A1 (fr)

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
IT202200020220A1 (it) * 2022-09-30 2024-03-30 Salvagnini Italia Spa Macchina per lavorare e/o movimentare lamiere e relativo metodo di lavorazione e/o movimentazione
IT202200020226A1 (it) * 2022-09-30 2024-03-30 Salvagnini Italia Spa Macchina per lavorare e/o movimentare lamiere e relativo metodo di lavorazione e/o movimentazione
WO2024069513A1 (fr) * 2022-09-30 2024-04-04 Salvagnini Italia S.P.A. Machine et procédé pour travailler et/ou déplacer des plaques ou des feuilles métalliques comprenant des moyens de reconnaissance de bord

Family Cites Families (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP5893719B1 (ja) 2014-12-25 2016-03-23 ファナック株式会社 異物の有無を確認する視覚センサを備えたワーク着脱手段付加工装置
EP3214024B1 (fr) 2016-03-01 2018-06-27 EWAB Engineering AB Système de transporteur autonome
DE102016120131B4 (de) 2016-10-21 2020-08-06 Trumpf Werkzeugmaschinen Gmbh + Co. Kg Absortierunterstützungsverfahren und Flachbettwerkzeugmaschine
HUE056459T2 (hu) 2017-06-21 2022-02-28 Ewab Eng Ab Szállítószalag-rendszer mûködtetési módszere és az ilyen szállítószalag-rendszerben használható áramlástechnikai eszközök
DE102018133524A1 (de) 2018-12-21 2020-06-25 Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V. Verfahren zum Bereitstellen von Tafelplanungsgeometriedaten, Verfahren und Laserflachbettmaschine zum Ausschneiden von Werkstücken
CN111571603A (zh) * 2020-06-23 2020-08-25 苏州交驰人工智能研究院有限公司 一种工件处理系统、方法、计算机设备及存储介质

Also Published As

Publication number Publication date
CN116569222A (zh) 2023-08-08
JP7535184B2 (ja) 2024-08-15
US20230298161A1 (en) 2023-09-21
DE102020215227B4 (de) 2023-11-09
DE102020215227A1 (de) 2022-06-02
WO2022117581A1 (fr) 2022-06-09
JP2023545282A (ja) 2023-10-27

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Owner name: TRUMPF WERKZEUGMASCHINEN SE + CO. KG