Siemens Aktiengesellschaft

Precitec Gmbh & Co. Kg

Daniel Regulin

Benjamin Lutz

Dirk Hartmann

Birgit Obst

Markus Kogel-Hollacher

Sven Tauchmann

Henning Hanebuth

Thibault Bautze

Florian Ulli Wolfgang Schnös

Michael Jaentsch

Tobias Kamps

Christian Staudenmaier

Heinz-Ingo Schneider

Florian Ulli Wolfgang SCHNÖS

Various embodiments include a computer-implemented method for determining a level of wear of a tool. The method includes: obtaining an image data set mapping a wear-relevant region of the tool; allocating, using a computing unit and an artificial neural network, one class of a predetermined quantity of classes to each image point of a plurality of image points of the image data set; and determining a characteristic value based on a result of the allocation for the level of wear. The quantity of classes includes at least one wear class.

Determining the level of wear of a tool

A method for operating a numerical controlled machine comprising receiving a sequence of control commands which, when executed by a numerical controlled machine, cause the numerical controlled machine to machine a workpiece to obtain a predetermined workpiece geometry, wherein the sequence of control commands includes while machining the workpiece based on the received sequence of control commands measuring a value of a first interaction parameter for a first position of the tool, comparing a measured value of the first interaction parameter for the first position of the tool with the simulated value of the first interaction parameter for the first position of the tool, and determining an adapted value of the second interaction parameter for a following position of the tool based on a result of the comparison.

Online multi-force-adaption during machining

In a method for training a neural network to recognize a tool condition based on image data, the neural network is trained to recognize the tool condition of a first tool type, and image data of a second tool type is applied. The image data is subjected to image processing. Via this, the image data of the second tool type is converted into image data of the first tool type. The neural network is trained based on the converted image data. In a method for machining and/or production via the first tool type, the tool condition of the first tool type is recognized via a neural network that is trained in accordance with such a method.

Method to train a neural network to detect a tool status from images, method of machining and/or manufacturing, and installation

The invention relates to a device () for an additive manufacture. The device () comprises a laser device () for machining material using a laser beam (), said laser device () being designed to deflect the laser beam () onto a machining region of a workpiece (); at least one supply device () for a supply material, said supply device being designed to supply the supply material to the machining region; and an interferometer () which is designed to measure a distance to the workpiece () by means of an optical measuring beam ().

Apparatus and method for additive manufacturing

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Munich, Germany

Daniel Regulin