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.

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.

The present disclosure relates to a multi-tiered computing environment for balancing compute resources in support of robot operations. In an example, a first server can receive from a robot located on a premises, a request and a first data, The first server can access a local configuration table to determine a program code associated with the first operation. The first server can generate second data based on the first data and the local configuration table. The first server can transmit the second data to the robot. The first server can receive, from the robot, third data indicating performance of the operation. The first server can transmit to a second server associated with the airplane model, fourth data, the fourth data generated based on the third data.

Aspects of the disclosure are directed towards updated an object representation. An example method can include causing, based on a representation of a part of an object, a robot to position an image capturing device relative to the part, the representation indicating a first characteristic of the object. The method can further include receiving a first image generated by the image capturing device while the image capturing device is positioned relative to the part. The method can further include generating a first input to a machine learning model based on the first image. The method can further include determining a first output of the machine learning model based on the first input, the first output indicating a second characteristic different than the first characteristic. The method can further include generating an updated representation that indicates the second characteristic in place of the first characteristic.

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.

Method for machining articles by means of a numerical-control machine tool (1) with at least one tool (2) mounted on a rotating spindle (4) and movement means (6), comprising a step i) of carrying out test machining operations on test articles using predetermined operating conditions, a step ii) of detecting at least first values relating to operating parameters of the tool (2) and/or spindle (4) and/or movement means (6) and at least second values relating to the amplitude and the frequency of the vibrations and/or the loads acting on the machine (1), a step iii) of analysing and processing the operating conditions of the first and second values to obtain optimized reference values of the operating parameters, and a step iv) of carrying out one or more machining operations on an article based on the optimized reference values. The step iii) of analysing and processing the operating conditions and the first and second values is performed by means of a process for training a software based on at least one artificial intelligence algorithm. The present invention also relates to a machine tool (1) for machining the articles.

A robotic surgical system. a surgeon console operatively coupled to a patient console and one or more surgical instruments. A surgeon computer is coupled to or integrated with the surgeon console, the surgeon computer further operatively connected to the one or more surgical instruments; A surgical robot is coupled to a robotic surgery control system and a feedback loop. The robotic surgery control system includes or is coupled to an artificial intelligence (AI) system. A feedback loop is further configured to receive performance-related data from the one or more sensors, the data analyzed by the robotic surgery control system or the AI system to dynamically adjust the robotic system's operation as needed. A data extraction module retrieves, from the robotic surgery control system or the AI system. one or more programmed steps executed by the surgeon for positioning at least one of the surgical instruments during the surgical procedure.

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.

A digital twin of a facility defines relationships between different components of the facility and a system of record for the facility. Information from different monitoring systems for the facility are related to events by the digital twin of the facility. Historical operation information for the facility is used to train a machine learning model. The trained machine learning model facilitates operations at the facility by providing descriptive information, predictive information, and/or prescriptive information on the operations at the facility.

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.