Abstract: 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.

Abstract: 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.

Abstract: Various embodiments of the teachings herein include a method for recognizing causes of anomalies in a physical product during the design, fabrication, and/or service life thereof. An example method includes: creating a semantically linked digital representation of the physical product, the representation including design features and fabrication features of the physical product; transmitting information about anomalies from a quality test to the digital representation and storing said information; and identifying semantic patterns to recognize causes of anomalies using the information, the design features, and/or the fabrication features. The information about anomalies is transmitted in the form of attributes of the product. The attributes contain the location and the time point of the anomaly and machine codes and product regions. The product is assigned location coordinates in a spatial coordinate system for specific time points.

Abstract: 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.

Abstract: Various embodiments of the teachings herein include a method for subtractive machining of a workpiece using a tool. The method may include: detecting at least two process variables of a machining process; and using the process variables to infer a wear on the tool. The process variables are passed on to a neural network which assigns each process variable a respective degree of wear independently of the other. The wear is inferred by means of a logic on the basis of the respective degrees of wear. The process variables are each selected from the group consisting of: a shape of the tool, an operating current, an operating voltage, maintenance and servicing information, and interruption information of the machining. Detecting the shape of the tool includes imaging using a camera and/or a scanner.

Abstract: The invention relates to a device (100) for an additive manufacture. The device (100) comprises a laser device (110) for machining material using a laser beam (112), said laser device (110) being designed to deflect the laser beam (112) onto a machining region of a workpiece (10); at least one supply device (130) for a supply material, said supply device being designed to supply the supply material to the machining region; and an interferometer (140) which is designed to measure a distance to the workpiece (10) by means of an optical measuring beam (142).

Abstract: 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.

Abstract: Various embodiments of the teachings herein include a method for operating a machine tool comprising: during a machining process in which a first workpiece of a first batch is machined using the machine tool, detecting a measured variable using a detection device of the machine tool; determining a measured value characterizing the machining process as a function of the measured variable using an electronic computing device; and comparing the determined measured value with a reference function determined before the machining process using a reference machining process carried out before the machining process using the machine tool and/or by a further machine tool and stored in an electronic memory device, the reference function characterizing the reference machining process to machine a second workpiece of a second batch.

Abstract: 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.

Abstract: The invention relates to a device (100) for an additive manufacture. The device (100) comprises a laser device (110) for machining material using a laser beam (112), said laser device (110) being designed to deflect the laser beam (112) onto a machining region of a workpiece (10); at least one supply device (130) for a supply material, said supply device being designed to supply the supply material to the machining region; and an interferometer (140) which is designed to measure a distance to the workpiece (10) by means of an optical measuring beam (142).