Detecting machining errors of a laser machining system using deep convolutional neural networks
AssigneePrecitec GmbH & Co. KG
InventorJoachim Schwarz
Life of this patent
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Abstract
A system for detecting machining errors for a laser machining system for machining a workpiece includes: a detection unit for detecting image data and height data of a machined workpiece surface; and a computing unit. The computing unit is designed to generate an input tensor based on the detected image data and height data and to determine an output tensor on the basis of the input tensor using a transfer function. The output tensor contains information on a machining error.
Claims11
A system for recognizing a machining error for a laser machining system for machining a workpiece, comprising:
- an imaging device for detecting raw image data of a machined workpiece surface;
- a height sensor for detecting raw height data of a machined workpiece surface;
- and
- computer;
- wherein said computer is configured to create an input tensor based on the detected raw image data and raw height data, and to determine an output tensor based on the input tensor using a transfer function, the output tensor containing information about a machining error;
- and
- wherein said input tensor comprises a two-channel image of the raw height data and raw image data,
- wherein the transfer function between the input tensor and the output tensor is formed by a taught deep convolutional neural network,
- wherein said taught deep convolutional neural network is adaptable to a changed situation using transfer learning in advance of a commissioning of the system.
A method for recognizing a machining error in a laser machining system for machining a workpiece, said method comprising the steps of:
- detecting raw image data and raw height data of a machined workpiece surface wherein raw image data is detected by an imaging device and raw height data is detected by a height sensor;
- creating an input tensor based on the detected raw image data and raw height data, wherein said input tensor comprises a two-channel image of raw data of the height data and image data;
- and
- determining an output tensor containing information about a machining error by means of a transfer function,
- wherein the transfer function between the input tensor and the output tensor is formed by a taught deep convolutional neural network,
- wherein said taught deep convolutional neural network is adapted to a changed situation using transfer learning in advance of a commissioning of the system performing the method.
Dependencies are inferred from the claim text.