US20060243410A1 - Method for classifying casting defects within the framework of an X-ray analysis - Google Patents

Method for classifying casting defects within the framework of an X-ray analysis Download PDF

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Publication number
US20060243410A1
US20060243410A1 US11/414,589 US41458906A US2006243410A1 US 20060243410 A1 US20060243410 A1 US 20060243410A1 US 41458906 A US41458906 A US 41458906A US 2006243410 A1 US2006243410 A1 US 2006243410A1
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United States
Prior art keywords
casting
defect
automatically
recognized
casting defect
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Abandoned
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US11/414,589
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English (en)
Inventor
Frank Herold
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Yxlon International X Ray GmbH
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Yxlon International X Ray GmbH
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Application filed by Yxlon International X Ray GmbH filed Critical Yxlon International X Ray GmbH
Assigned to YXLON INTERNATIONAL X-RAY GMBH reassignment YXLON INTERNATIONAL X-RAY GMBH ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: HEROLD, FRANK
Publication of US20060243410A1 publication Critical patent/US20060243410A1/en
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    • 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
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10116—X-ray image
    • 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

Definitions

  • the invention relates to a method for classifying casting defects in a casting within the framework of an X-ray analysis.
  • the founder carries out the classification of a casting defect by viewing the X-ray image of the casting.
  • a great deal of experience is necessary for this, and the casting defect is usually also detected only afterwards, i.e. if the examined casting has an unacceptable casting defect above the specification limit.
  • This is extremely fatiguing for the founder who, with this method, must continuously monitor the live image at the casting machine, which under certain circumstances can result in false assessments as to the casting defect involved.
  • the result is that although the parameters of the casting process are modified, a wrongly accepted casting defect cannot be remedied.
  • the result is an avoidable, additional rejection of castings with casting defects compared with the situation had the casting defect actually present been correctly recorded.
  • the object of the invention is therefore to provide a method with which a casting defect actually present in the casting can be established automatically—i.e. without the fatiguing monitoring process for the founder.
  • An advantageous development of the invention provides that the known casting defect types be grouped into classes that have comparable features in the X-ray image. It is advantageous in particular if the classes with comparable features in the X-ray image are grouped into superclasses. It is equally possible, instead of the two given levels, to carry out even further groupings at levels above these.
  • a structured implementation of the method is thereby possible in which the whole database of training images does not have to be constantly run through in order to arrive at a match with all known casting defect types. Time can be saved by the rough classification by type, and thus a real-time classification of the casting defect is possible. By saving additional time, an even earlier reaction with regard to the modification of the parameters of the casting process is possible, with the result that the quality of the castings can be still further improved.
  • the method according to the invention is particularly efficient if the classification is based on a decision tree which is used to proceed from the abstract superclasses or classes with simple features to the specific casting defect with complex features.
  • a very reliable yet very rapid classification of the specific casting defect is thereby possible, as the few first-stage superclasses can be examined very rapidly and then only the branch of the decision tree under the simple features of which the detected casting defect falls need be pursued further. This then applies analogously to each further level of the decision tree until the specific casting defect with the complex features has been detected.
  • a further advantageous development of the invention provides that, once the specific casting defect has been automatically recognized, the casting process is automatically controlled and the parameters of the casting process are automatically modified according to the recognized casting defect.
  • the pressure on the founder is even further eased and he must merely make a correction if the system makes a rough false classification.
  • the progressive development of a casting defect is already counteracted at the earliest possible point, with the result that the trend towards this casting defect is automatically reversed by the adaptation of the parameters of the casting process. This results in a marked reduction in rejects, as the specification limit of the casting defects is not exceeded. Accordingly, defect-free castings are produced for the most part so that the required number can be produced in less time.
  • FIG. 1 Further advantages and details of the invention are shown with the help of the embodiment of a decision tree.
  • the single FIGURE shows an embodiment of a decision tree according to the invention with which a method according to the invention method can be carried out.
  • ADR systems can also detect defects below the specification limits. If these defects are reported to the founder in good time, he can introduce appropriate countermeasures, depending on the casting defect type, before the critical limit of this casting defect type—the specification limit allocated to it—is exceeded. In order to subsequently improve the casting process, it is also necessary to provide detailed statistics on the casting defects that have occurred. Also of particular interest in this connection is how many parts display which casting defect type in the presence of which parameters of the casting process. A prompt, detailed classification of the casting defects that occur is therefore required in order to predict a trend or compile statistics on the defect types.
  • the represented embodiment shows an adaptive decision tree which facilitates a detailed classification according to the requirements of the respective user.
  • each user can himself decide which specific casting defects he would like to detect at the lowest level and how he would like to group these casting defect types at a level lying above it or would like to further group them at levels lying above these so that the topmost level contains only single features about which a decision can very easily be reached.
  • the training images of found casting defect types or also theoretically predetermined casting defect types, the individual levels can be trained from the roots to the leaves. For this, in each level specific features must be allocated with the help of which a decision is made for a specific type—at the lowest level the specific casting defect type. It therefore suggests itself to divide the features at the topmost level, where merely simple features are to be decided, down into the more complex features for the specific casting defect types.
  • the density is higher, it is assumed in the present embodiment that an inclusion—at the second level, i.e. the level of the classes of casting defect types—is involved.
  • the “higher density” superclass is not split any further, but this is by no means mandatory.
  • the “inclusion” class is further divided, which likewise is by no means mandatory.
  • the “lower density” superclass covers a total of three classes at the second level in the embodiment: “shrink hole”, “blowhole” and “surface defect”.
  • different, fewer or also more classes can always be defined—depending on the user.
  • the “shrink hole” class is subdivided into the specific casting defect types “single shrink hole”, “cluster of shrink holes” and “sponge”. At this lowest level, the allocation is carried out to the highest level of detail.
  • the “blowhole” class is subdivided into the specific casting defect types “single blowhole” and “porosity”.
  • the “surface defect” class is divided into the specific casting defects “die mark” and “blacking defect”.
  • the whole decision tree can be redesigned, depending on the application and intention of the user, using suitable training images according to the respective requirement.
  • An individually tailored, detailed classification with respect to different casting defect types is thereby made possible.
  • a different division—as already indicated above—at the lowest level of the specific casting defect types is also possible.
  • the only important point is that the appropriate training images are made available to the system in each case so that all the casting defect types relevant for the respective casting process can be recognized and the specification limits are best never exceeded, so that efficiency is increased during the casting process.

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  • Engineering & Computer Science (AREA)
  • Quality & Reliability (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Analysing Materials By The Use Of Radiation (AREA)
US11/414,589 2005-04-28 2006-04-28 Method for classifying casting defects within the framework of an X-ray analysis Abandoned US20060243410A1 (en)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE102005019800A DE102005019800B4 (de) 2005-04-28 2005-04-28 Verfahren zur Klassifikation von Giessfehlern im Rahmen einer Röntgenanalyse
DEDE102005019800.7 2005-04-28

Publications (1)

Publication Number Publication Date
US20060243410A1 true US20060243410A1 (en) 2006-11-02

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US11/414,589 Abandoned US20060243410A1 (en) 2005-04-28 2006-04-28 Method for classifying casting defects within the framework of an X-ray analysis

Country Status (5)

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US (1) US20060243410A1 (de)
EP (1) EP1717754A3 (de)
JP (1) JP2006305635A (de)
CN (1) CN1854724A (de)
DE (1) DE102005019800B4 (de)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090238432A1 (en) * 2008-03-21 2009-09-24 General Electric Company Method and system for identifying defects in radiographic image data corresponding to a scanned object
US20090279772A1 (en) * 2008-05-12 2009-11-12 General Electric Company Method and System for Identifying Defects in NDT Image Data
US20110222754A1 (en) * 2010-03-09 2011-09-15 General Electric Company Sequential approach for automatic defect recognition

Families Citing this family (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1995995B (zh) * 2006-12-07 2010-05-12 华南理工大学 大型铸件缺陷检测的控制方法
ES2526554T3 (es) 2008-03-17 2015-01-13 Southwire Company, Llc Detección de porosidad
EP2578335B1 (de) * 2011-10-07 2018-02-14 Nemak, S.A.B. de C.V. Verfahren zur Steuerung einer Giessprozessvorrichtung
CN104502383B (zh) * 2014-12-17 2017-04-19 重庆理工大学 一种铸件缺陷射线检测系统
KR102370144B1 (ko) * 2020-07-24 2022-03-03 한국해양대학교 산학협력단 기계학습기반 다이캐스팅 주조품 결함검출 및 원인분석을 이용한 자동 공정 변수 제어 방법 및 장치
CN115825118B (zh) * 2022-11-18 2023-09-12 华中科技大学 一种铸件x射线探伤装备的自动评片集成系统及方法

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20030099330A1 (en) * 2000-02-05 2003-05-29 Domingo Mery Method for automatically detecting casting defects in a test piece
US6738450B1 (en) * 2002-12-10 2004-05-18 Agilent Technologies, Inc. System and method for cost-effective classification of an object under inspection

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1140797C (zh) * 2001-09-03 2004-03-03 华南理工大学 铸件内部缺陷自动分析识别装置及其分析识别方法

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20030099330A1 (en) * 2000-02-05 2003-05-29 Domingo Mery Method for automatically detecting casting defects in a test piece
US6738450B1 (en) * 2002-12-10 2004-05-18 Agilent Technologies, Inc. System and method for cost-effective classification of an object under inspection

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090238432A1 (en) * 2008-03-21 2009-09-24 General Electric Company Method and system for identifying defects in radiographic image data corresponding to a scanned object
US8238635B2 (en) 2008-03-21 2012-08-07 General Electric Company Method and system for identifying defects in radiographic image data corresponding to a scanned object
US20090279772A1 (en) * 2008-05-12 2009-11-12 General Electric Company Method and System for Identifying Defects in NDT Image Data
US8131107B2 (en) 2008-05-12 2012-03-06 General Electric Company Method and system for identifying defects in NDT image data
US20110222754A1 (en) * 2010-03-09 2011-09-15 General Electric Company Sequential approach for automatic defect recognition
US8345949B2 (en) 2010-03-09 2013-01-01 General Electric Company Sequential approach for automatic defect recognition

Also Published As

Publication number Publication date
EP1717754A2 (de) 2006-11-02
JP2006305635A (ja) 2006-11-09
CN1854724A (zh) 2006-11-01
EP1717754A3 (de) 2008-01-16
DE102005019800A1 (de) 2006-11-16
DE102005019800B4 (de) 2007-10-04

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Owner name: YXLON INTERNATIONAL X-RAY GMBH, GERMANY

Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:HEROLD, FRANK;REEL/FRAME:017916/0296

Effective date: 20060412

STCB Information on status: application discontinuation

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