A method for controlling a virtual assistant for an industrial plant includes receiving by an input interface an information request, wherein the information request comprises at least one request for receiving information about at least part of the industrial plant; determining by a control unit a model specification using the received information request; determining by a model manager a machine learning model using the model specification; and providing by the control unit a response to the information request using the determined machine learning model.

A body and at least one control unit that stores and/or controls data for drilling processes is disclosed. At least one machining tool is located on the body extends outward from the body, and provides part shaping, at least one image capturing device that is controlled by the control unit and connected with the control unit for capturing images is also present.

A dental machining system for manufacturing a dental restoration/appliance, including: a dental tool machine which includes: a dental blank holder for movably holding at least one dental blank relative to one or more dental tools; one or more driving units each for movably holding one or more dental tools, a control unit for controlling the dental blank holder and the driving units based on construction data of the dental restoration/appliance and a plurality of machining processes specific for the manufacturing of the dental restoration/appliance from the dental blank. The control unite executes a trained artificial intelligence algorithm that predicts the machining time for manufacturing the dental restoration/appliance based on input data including: process parameters defining the machining processes respectively; and mappings which include information on the target geometry of the dental restoration/appliance.

In a device and method for determining a contact between a tool and a workpiece, which are displaceable relative to each other, the tool or workpiece being rotationally fixedly connected to a shaft, the device includes a measuring arrangement including a measuring scale rotationally fixedly disposed on the shaft and at least one position encoder disposed in a stationary manner relative to the shaft, and a processing device. The position encoder is adapted to scan the measuring scale and to generate position values indicating a position of the shaft. The position values are fed to the processing device, which determines contact between the tool and the workpiece by evaluating a progression of the position values and signals the result of the evaluation by the status of a displacement signal.

A geometric model-based evaluation method, system, and apparatus for a key performance indicator in a production procedure are disclosed. The method includes extracting a geometric characteristic and a characteristic attribute thereof from a product model; converting the geometric characteristic into a process characteristic so as to obtain the steps of a process related to a whole production procedure of producing a product, and devices corresponding to the steps of the process; and based on the steps of the process related to the production procedure and the devices corresponding to the steps of the process, simulating the whole industrial manufacturing procedure of the product, and outputting the KPI of the industrial manufacturing. At least one embodiment can evaluate the KPI of the whole industrial manufacturing procedure of the product according to the product model.

A method for processing a part from a workpiece using an industrial cutting system. The method includes receiving first data corresponding to the part to be processed from the workpiece using the industrial cutting system. The method further includes receiving second data corresponding to expertise data generated over a time period. The method also includes identifying features of the part based on the first data and the second data. The method further includes generating a part program design including geometry data and processing parameters for at least one of the features of the part. The method also includes processing the part from the workpiece using the industrial cutting system based on the part program design.

A method for determining cutting parameters for a laser cutting machine includes the following steps: Receiving at least one machine parameter, at least one process parameter and/or at least one material parameter; outputting properties that can be influenced by the cutting parameters, of a laser-cut edge to be cut by the laser cutting machine; receiving a weighting of the properties; and determining the cutting parameters using the at least one machine parameter, the at least one process parameter, and/or the at least one material parameter and also using the weighted properties. There is also described an apparatus for carrying out the method, in particular an apparatus for machining a workpiece and/or an apparatus which is designed to simulate a production process.

A system for tuning of axis control of a multi-axis machine and a method of operating the same are provided. The system includes a knowledge base for acquiring and maintaining factual knowledge associated with the tuning of the axis control. The factual knowledge has a uniform ontology a uniform data representation, and includes known input facts associated with known output facts. The system further includes an inference unit for automatically inferring new output facts associated with given new input facts in accordance with the factual knowledge.

The present disclosure provides a system and a method for controlling a semiconductor manufacturing apparatus. The system includes an inspection unit capturing at least one image of a wafer, a sensor interface generating at least one input signal for a database server, and a control unit. The control unit includes a front-end subsystem, a calculation subsystem, and a message and tuning subsystem. The front-end subsystem receives the at least one input signal from the database server and performs a front-end process to generate a data signal. The calculation subsystem performs an artificial intelligence analytical process to determine, according to the data signal, whether damage marks have been caused by the semiconductor manufacturing apparatus and to generate an output signal. The message and tuning subsystem generates an alert signal and a feedback signal according to the output signal and transmits the alert signal to a user.

A data intersection is assessed of data to be used between at least two parties. The data is to be used in an artificial intelligence (AI) application. Evaluation is performed of set of instructions required for the AI application, where the evaluation creates a modified set of instructions where operands are symbolically associated with corresponding privacy levels. Using the assessed data intersection and the modified set of instructions, a mapping is created from the data to operands with associated privacy metrics. The mapping treats overlapping data from the assessed data intersection differently from data that is not overlapping to improve privacy relative to without the mapping. The AI application is executed using the data to produce at least one parameter of the AI application. The at least one parameter is output for use for a trained version of the AI application. Apparatus, methods, and computer program products are described.