JP2023508308A - 切断エッジに対する切断パラメータの影響を明らかにするための方法、コンピュータプログラム製品及びデバイス - Google Patents
切断エッジに対する切断パラメータの影響を明らかにするための方法、コンピュータプログラム製品及びデバイス Download PDFInfo
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- 238000005520 cutting process Methods 0.000 title claims abstract description 87
- 238000000034 method Methods 0.000 title claims abstract description 71
- 238000004590 computer program Methods 0.000 title claims abstract description 12
- 230000000694 effects Effects 0.000 title description 5
- 238000013528 artificial neural network Methods 0.000 claims abstract description 38
- 238000004458 analytical method Methods 0.000 claims abstract description 3
- 238000013527 convolutional neural network Methods 0.000 claims description 11
- 238000003698 laser cutting Methods 0.000 claims description 7
- 238000000354 decomposition reaction Methods 0.000 claims description 5
- 239000000463 material Substances 0.000 claims description 5
- 238000002844 melting Methods 0.000 claims description 2
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- 238000012360 testing method Methods 0.000 description 1
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- 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
- B23K26/36—Removing material
- B23K26/38—Removing material by boring or cutting
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- 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
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- 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 only one of the preceding main groups
- B23K31/006—Processes relevant to this subclass, specially adapted for particular articles or purposes, but not covered by only one of the preceding main groups relating to using of neural networks
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/18—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form
- G05B19/4155—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form characterised by programme execution, i.e. part programme or machine function execution, e.g. selection of a programme
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- G06N3/08—Learning methods
- G06N3/084—Backpropagation, e.g. using gradient descent
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/36—Nc in input of data, input key till input tape
- G05B2219/36199—Laser cutting
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Abstract
Description
記録32a:ガス圧20 15bar
フィード22 21m/分
ノズル-ワークピース距離24 1.5mm
ノズル-焦点距離26 -2mm
これと比較して、記録32bは、ノズル-焦点距離26を増加して作成された。記録32cは、記録32aと比較してフィード22を低減して作成された。図3から、切断パラメータ18(図1を参照されたい)の影響は、人間のユーザにとって記録32a~cから直接推測可能でないことが明らかである。
12 切断ヘッド
14 ワークピース
16 切断エッジ
18 切断パラメータ
20 ガス圧
22 フィード
24 ノズル-ワークピース距離
26 ノズル-焦点距離
28 焦点位置
30 カメラ
32、32a~c 記録
34 アルゴリズム
36 ニューラルネットワーク
38 切断パラメータ18の判定
40 逆伝播
42a、b 記録ピクセル
44a~e ニューラルネットワーク36のブロック
46a~l ニューラルネットワーク36の層
48a~e ニューラルネットワーク36のフィルタ
50 出力
Claims (12)
- 工作機械(10)によって作成された切断エッジ(16)を分析するための方法であって、
C)前記切断エッジ(16)の少なくとも1つの記録(32、32a~c)を読み込む方法ステップであって、前記記録(32、32a~c)は、複数の記録ピクセル(42a、b)を有する、方法ステップ、
D)少なくとも1つの切断パラメータ(18)を判定するために、訓練されたニューラルネットワーク(36)によって前記記録(32、32a~c)を分析する方法ステップ、
E)前記判定された切断パラメータ(18)を確認するために分析された前記記録ピクセル(42a、b)の関連性を判定するために、前記ニューラルネットワーク(36)の逆伝播(40)を行う方法ステップ、
F)方法ステップE)で確認された前記特に関連性のある記録ピクセル及び/又は特に関連性のない記録ピクセル(42a、b)の識別を用いて、前記記録(32、32a~c)を出力する方法ステップ
を含む方法。 - 方法ステップD)における前記分析は、複数の層(46a~l)を有し、特に層(46a~l)ごとに複数のフィルタ(48a~e)を有する畳み込みニューラルネットワークによって行われる、請求項1に記載の方法。
- 方法ステップE)における前記逆伝播(40)は、層ごとの関連性の伝播によって行われる、請求項1又は2に記載の方法。
- 方法ステップE)における前記関連性の割り当ては、ディープテイラー分解に基づく、請求項1~3のいずれか一項に記載の方法。
- 方法ステップF)における前記出力は、ヒートマップの形態で行われる、請求項1~4のいずれか一項に記載の方法。
- 方法ステップC)における前記記録(32、32a~c)は、RGB写真又は3D点群の形態で存在する、請求項1~5のいずれか一項に記載の方法。
- B)カメラ(30)、特に前記工作機械(10)のカメラ(30)により、前記記録(32、32a~c)を作成する方法ステップ
を含む、請求項1~6のいずれか一項に記載の方法。 - A)前記工作機械(10)により、前記切断エッジ(16)を作成する方法ステップ
を含む、請求項1~7のいずれか一項に記載の方法。 - 前記工作機械(10)は、レーザ切断機の形態で構成され、特に、以下の切断パラメータ(18):
・ビームパラメータ、特に焦点直径及び/若しくはレーザ出力、
・搬送パラメータ、特に焦点位置(28)、ノズル-焦点距離(26)及び/若しくはフィード(22)、
・ガスダイナミクスパラメータ、特にガス圧(20)及び/若しくはノズル-ワークピース距離(24)、並びに/又は
・材料パラメータ、特にガス純度の程度及び/若しくはワークピース(14)の融点
は、方法ステップD)において判定される、請求項8に記載の方法。 - 請求項1~9のいずれか一項に記載の方法ステップC)~F)を実行するためのコンピュータプログラム製品であって、前記ニューラルネットワーク(36)を含むコンピュータプログラム製品。
- 請求項1~9のいずれか一項に記載の方法を実行するための、特にレーザ切断機の形態の工作機械(10)、コンピュータ及び請求項10に記載のコンピュータプログラム製品を含むデバイス。
- 前記カメラ(30)を含む、請求項7と組み合わせた請求項11に記載のデバイス。
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DE102020212510.4 | 2020-10-02 | ||
DE102020212510.4A DE102020212510A1 (de) | 2020-10-02 | 2020-10-02 | Verfahren und Vorrichtung zum Aufzeigen des Einflusses von Schneidparametern auf eine Schnittkante |
PCT/EP2021/077086 WO2022069702A1 (de) | 2020-10-02 | 2021-10-01 | Verfahren, computerprogrammprodukt, und vorrichtung mit einem solchen produkt zum aufzeigen des einflusses von schneidparametern auf eine schnittkante |
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DE102018129425A1 (de) * | 2018-11-22 | 2020-05-28 | Precitec Gmbh & Co. Kg | System zur Erkennung eines Bearbeitungsfehlers für ein Laserbearbeitungssystem zur Bearbeitung eines Werkstücks, Laserbearbeitungssystem zur Bearbeitung eines Werkstücks mittels eines Laserstrahls umfassend dasselbe und Verfahren zur Erkennung eines Bearbeitungsfehlers eines Laserbearbeitungssystems zur Bearbeitung eines Werkstücks |
JP2020121338A (ja) * | 2019-01-31 | 2020-08-13 | 三菱電機株式会社 | 加工条件解析装置、レーザ加工装置、レーザ加工システムおよび加工条件解析方法 |
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JP6625914B2 (ja) | 2016-03-17 | 2019-12-25 | 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 |
DE102018123363B4 (de) | 2018-09-24 | 2021-01-07 | Bystronic Laser Ag | Verfahren zur Kollisionsvermeidung und Laserbearbeitungsmaschine |
EP3654248A1 (en) | 2018-11-19 | 2020-05-20 | Siemens Aktiengesellschaft | Verification of classification decisions in convolutional neural networks |
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DE102018129425A1 (de) * | 2018-11-22 | 2020-05-28 | Precitec Gmbh & Co. Kg | System zur Erkennung eines Bearbeitungsfehlers für ein Laserbearbeitungssystem zur Bearbeitung eines Werkstücks, Laserbearbeitungssystem zur Bearbeitung eines Werkstücks mittels eines Laserstrahls umfassend dasselbe und Verfahren zur Erkennung eines Bearbeitungsfehlers eines Laserbearbeitungssystems zur Bearbeitung eines Werkstücks |
JP2020121338A (ja) * | 2019-01-31 | 2020-08-13 | 三菱電機株式会社 | 加工条件解析装置、レーザ加工装置、レーザ加工システムおよび加工条件解析方法 |
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J STAHL; ET AL: "QUICK ROUGHNESS EVALUATION OF CUT EDGES USING A CONVOLUTIONAL NEURAL NETWORK", PROCEEDINGS OF SPIE, vol. 11172, JPN5023003839, 16 July 2019 (2019-07-16), pages 111720 - 1, ISSN: 0005093379 * |
SEBASTIAN BACH; ET AL: "ON PIXEL-WISE EXPLANATIONS FOR NON-LINEAR CLASSIFIER DECISIONS BY LAYER-WISE RELEVANCE PROPAGATION", PLOS ONE, vol. 10, no. 7, JPN5023003838, 2015, US, pages 0130140 - 1, ISSN: 0005093378 * |
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US20220339739A1 (en) | 2022-10-27 |
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EP4221930A1 (de) | 2023-08-09 |
WO2022069702A1 (de) | 2022-04-07 |
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