A processing method for automatically generating machining features is provided. A workpiece CAD file is obtained to perform a CAD numerical analysis on a blank body. With the workpiece CAD file being used as a target, a workpiece CAD appearance is compared with the blank body to obtain a feature identification result of a first to-be-processed blank body, which includes identifying data of a to-be-removed blank body and a feature of a first processing surface. A geometric analysis is performed on the first processing surface feature and a tool selection range is determined. A virtual cutting simulation is performed on the first processing surface to generate a processed area data and an unprocessed area data. A spatial coordinate mapping comparison between the unprocessed area data and a surface data of the workpiece CAD file is performed to obtain a feature identification result of a second to-be-processed blank body.

A robot fleet management platform includes a job configuration system that determines tasks to be performed by robots of a robot fleet based on a job request and a first fleet objective. A proxy service applies fleet configuration services to the tasks to produce a data structure. An intelligence layer activates intelligence services to produce a robot task and associated contextual information that facilitates robot selection and task ordering. A job workflow system generates a workflow defining a performance order of the tasks. A workflow simulation system simulates performance of the job request based on the workflow to recursively redefine the tasks, the data structure, or the workflow until the simulation result satisfies a second fleet objective. In response to the simulation result satisfying the set of fleet objectives, a plan generator generates a job execution plan based on the set of robot tasks, the data structure, and the workflow.

Provided is a control apparatus comprising a control unit configured to control a control target by a control model machine-learned so as to output an operation amount of the control target according to a state of equipment provided with the control target; a simulation unit configured to simulate, by using a simulation model, the state of the equipment in a case where the operation amount, which is output by the control model, is given to the control target; and a stop unit configured to stop control of the control target by the control model, based on a simulation result.

A dynamic vision system includes a variable focus liquid lens optical assembly. The dynamic vision system includes a control system configured to adjust one or more optical parameters and data collected from the variable focus liquid lens optical assembly in real time. The dynamic vision system includes a processing system that dynamically learns on a training set of outcomes, parameters, and data collected from the variable focus liquid lens optical assembly to train one or more machine learning models to recognize an object.

A dynamic vision system includes a variable focus liquid lens optical assembly. The dynamic vision system includes a variable lighting assembly. The dynamic vision system includes a control system configured to adjust one or more optical parameters and data collected from the variable focus liquid lens optical assembly in real time. The dynamic vision system includes a control system configured to adjust the variable lighting assembly. The dynamic vision system includes a processing system that dynamically learns on a training set of outcomes, parameters, and data collected from the variable focus liquid lens optical assembly to train a set of machine learning models to control the variable focus liquid lens optical assembly to optimize collection of data for processing by the set of machine learning models.

An autonomous additive manufacturing platform includes sensors positioned in, on, and/or near a part and configured to collect sensor data related to the part. An adaptive intelligence system is configured to receive the sensor data from the sensors. The adaptive intelligence system includes a machine learning system configured to input the sensor data as training data into one or more machine learning models. The machine learning models are configured to transform the sensor data into simulation data. A digital twin system is configured to create a part twin based on the simulation data. The part twin provides for representation of the part and simulation of a possible future state of the part via the simulation data. An artificial intelligence system is configured to execute simulations on the digital twin system. The machine learning models are utilized to make classifications, predictions, and other decisions relating to the part.

An information technology system for a distributed manufacturing network includes an additive manufacturing management platform with an artificial intelligence system configured to learn on a training set of outcomes, parameters, and data collected from a set of distributed manufacturing network entities and execute simulations on digital twins of the set of distributed manufacturing network entities to make classifications, predictions, and optimization-related decisions for the set of distributed manufacturing network entities. The information technology system includes a distributed ledger system integrated with a digital thread and configured to provide unified views of workflow and transaction information to the set of distributed manufacturing network entities.

A method includes the steps of receiving user input indicative of prioritized aspects of manufacturing of an object, the prioritized aspects including tool life or surface quality or object manufacturing speed; obtaining a model of an object to be manufactured via subtractive manufacturing; identifying, based on the model, a geometric feature to be machined as part of manufacturing the object; obtaining a plurality of strategies for machining the geometric feature, by accessing a database, the plurality of strategies defining alternative ways of machining the geometric feature; selecting at least one strategy from the plurality of strategies by ranking the plurality of strategies using the prioritized aspects of manufacturing and selecting at least one strategy having the highest ranking, providing, based on the at least one selected strategy, instructions for causing the one or more machine tools to manufacture the object via subtractive manufacturing.

A method and a corresponding system and computer program are provided. A model of an object to be manufactured via subtractive manufacturing is obtained. Geometric features to be machined as part of manufacturing the object are identified based on the model. The identified geometric features include a composite geometric feature including a plurality of geometric subfeatures. A database including strategies for machining different geometric features is accessed. The database includes a composite strategy for machining the composite geometric feature and separate strategies for machining the respective geometric subfeatures. Strategies for machining the respective geometric features are selected from the strategies included in the database. Instructions for causing one or more machine tools to manufacture the object in accordance with the selected strategies are provided. Selecting strategies for machining the respective geometric features via subtractive manufacturing includes selecting the composite strategy for machining the composite geometric feature.

A method and apparatus for machining parts with variable stiffness includes determining, by a controller, a chatter-lobe plot of a cutter assembly. A preliminary tool path is developed by the controller. Virtual machining of a blank part using the preliminary tool path is performed by the controller. A chatter-lobe plot of the virtually machined part is determined by the controller. A dynamic chatter-lobe plot using the chatter-lobe plot of the cutting tool assembly and the chatter-lobe plot of the virtually machined part is determined by the controller. A chatter-free rotational speed of the cutting tool from the dynamic chatter-lobe plot is determined by the controller. A machining apparatus, controlled by the controller, uses the determined chatter-free rotational speed of the cutting tool to machine a blank part.