The present disclosure provides an intelligent robot based on artificial intelligence. The intelligent robot (10) includes a plurality of components (11). Each component (11) has an independent function. Different components (11) can be detachable from each other and combinable.

A CNC machine shaping tool is efficiently used by monitoring human audible sound during shaping. A sound information set is created for a tool shaping a workpiece. Shaping sounds are recorded and sliced into short term units. A human operator assigns tool condition labels to each slice. Short term units are combined into mid term units. Noise is reduced by profiling. Mid term sound related features of time and frequency domains are extracted. Dimensionality is reduced by robust principal component analysis. The principal component set is balanced, e.g. by SMOTE. A classifier and principal components are selected. An information set of patterns of values of selected principal components for the tool is created. In an industrial setting, shaping sounds are recorded, noise reduced and select principal component vector values are compared to the tool condition labeled patterns of values in the information set to identify tool condition before deterioration.

Aspects of the disclosure are directed towards path generation. A method includes a user interface (UI) displaying a first page on a first pane, wherein the first page provides a first control input for registering a working frame of a target object with a reference frame of a robot. The method further includes receiving, via the UI, a first user selection of the first control input for registering the working frame with the reference frame, based on detection of the first user selection. The UI can display a second page on the first pane, wherein the second page provides a second control input for generating a path for the robot to traverse over a surface of the target object. The method further includes receiving, via the UI, a second user selection of the second control input for generating the path, based on detection of the second user selection.

Aspects of the disclosure are directed towards artificial intelligence-based modeling of target objects, such as aircraft parts. In an example, a system initially trains a machine learning (ML) model based on synthetic images generated based on multi-dimensional representation of target objects. The same system or a different system subsequently further trains the ML model based on actual images generated by cameras positioned by robots relative to target objects. The ML model can be used to process an image generated by a camera positioned by a robot relative to a target object based on a multi-dimensional representation of the target object. The output of the ML model can indicate, for a detected target, position data, a target type, and/or a visual inspection property. This output can then be used to update the multi-dimensional representation, which is then used to perform robotics operations on the target object.

Systems for and methods of providing a feed rate for three-dimensional printing a part are presented. The disclosed techniques include: obtaining computer readable toolpath instructions for the part, where the toolpath instructions specify a nominal feed rate for a toolpath segment and spatial toolpath data of the toolpath segment; providing an input including the spatial toolpath data to a trained machine learning system, where the trained machine learning system has been trained using training data including: training spatial toolpath data, training closed loop gain data, and training feed rate data; obtaining a revised feed rate for the toolpath segment different from the nominal feed rate for the toolpath segment, where the revised feed rate is output from the trained machine learning system; and providing revised computer readable toolpath instructions, where the revised machine learning toolpath instructions include the revised feed rate.

Aspects of the disclosure are directed towards path generation. A method includes a computing system registering a first coordinate system of a target object with a second coordinate system of a robot. The computing system can generate a trajectory over the surface of the target object based on the registration. The computing system can generate a robot job file based at least in part on the generated trajectory. The computing system can transmit the robot job file to a robot controller.

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.

The Relevance Based Digital Building [RBDB] invention describes a method and apparatus consisting of a hardware and software environment that creates a relevance based digital building intelligence system. RBDB defines a Network Lighting System [NLS] that delivers the network fabric for an array of intelligent luminaries configured with Internet of Things [IoT] devices. Layered on the NLS network fabric is a Digital Building Intelligence information architecture that enables the building to become Self-Aware with Digital Personas. The building can manage many of its own needs through operational optimization, Machine to Machine [M2M] and machine to stakeholder interactions. RBDB allows the stakeholders associated with the building to take on a multiplicity of digital personas enabling high relevance interactions with the building and each other through these digital intelligence representations. The digital building adapts to the occupants (stakeholders) preferences and requirements and empowers he interaction between individuals (stakeholders) and their environment (Digital Building Intelligence).

A method and an apparatus for operating an automation system is provided. The method for operating an automation system includes the method steps of: providing a learning-based prediction model for the automation system trained by process data including context of an automation process, receiving information about current context of the automation process, verifying context change by comparing the current context to the context of said process data, in the case of any context change verifying a concept drift by comparing pre-drift process data and post-drift process data, in the case of any concept drift re-training said model with post-drift process data, in the case of no context change testing for random concept drift not detected by verifying context change, in the case of any random concept drift extend the current context by using data comprising previous context changes, otherwise no further method steps are required.

A computer-implemented method for configuring a fieldbus that connects at least two participants of an associated fieldbus system, an associated fieldbus system, a computer program, a computer-readable storage medium, a training data set and a method for training a configuration AI model. The method comprises the following steps: an information collection step comprising collecting one or more fieldbus system information that characterize the associated fieldbus system, a configuration parameter value determination step comprising determining one or more parameter values of one or more configuration parameters for configuring the fieldbus of the fieldbus system. The determination of the one or more parameter values is dependent on at least part of the collected fieldbus system information. A configuration step includes configuring the fieldbus with the one or more parameter values of the one or more configuration parameters determined in the configuration parameter value determination step.