A machine learning device includes: a data extraction unit that extracts a first parameter and a second parameter from each of a plurality of machining programs for numerically controlling a machine tool, the first parameter being a parameter to be adjusted, the second parameter being a parameter to be used for adjusting the parameter to be adjusted; and a machine learning unit that learns a value of the first parameter according to a data set including the first parameter and the second parameter extracted by the data extraction unit.

A robot fleet management platform includes a job parsing system that applies filters to identify portions of a job request suitable for robot automation. Based on the identified portions and a first fleet objective of the job request, a task system establishes tasks that define a robot type and task objective. A proxy service associates a robot of a robot fleet to each task and adaptation instructions to define how to adapt the robot fleet to perform the tasks. A workflow system generates a workflow defining a performance order of the tasks. A simulation system applies the workflow in an environment that includes digital models of the robot fleet and the tasks. The simulation is used to iteratively redefine the tasks and workflow until a second fleet objective is satisfied. A generation system generates a job execution plan in response to the simulation satisfying the first and second fleet objectives.

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.

Methods, systems, and apparatus, including medium-encoded computer program products, for computer aided design and manufacture of physical structures using roughing toolpath sequences generated for subtractive manufacturing include, in one aspect, a method including: obtaining 3D models of a part and a workpiece and information regarding different cutting tools and cutting data therefor; determining a set of candidate combinations of the different cutting tools to effect the roughing operations by estimating a target machining result for each of multiple, tool-size-ordered lists of the different cutting tools; generating an expanded set of combinations of the different cutting tools to effect the roughing operations by adding variations of the candidate combinations; populating a multidimensional roughing operations representation vector using the expanded set of combinations; optimizing values of the multidimensional roughing operations representation vector using simulation of the roughing operations; and providing specified tool selections and operational parameters for use in roughing the part.

A robotic assembly operation is provided for assembling a second part to a first part. During setup of the assembly operation, control parameters and a control scheme are set and changed by simulating the operation and testing whether performance requirements are met. A dry run may be performed thereafter, and test data may be collected after running the simulation to determine if the performance requirements are satisfied during the dry run. During production, production data may also be collected and control parameters may be tuned when changes occur during production in order to maintain stable assembly.

A distributed manufacturing network information technology system includes a cloud-based additive manufacturing management platform with a user interface, connectivity facilities, data storage facilities, and monitoring facilities. The distributed manufacturing network information technology system includes a set of applications for enabling the additive manufacturing management platform to manage a set of distributed manufacturing network entities. The distributed manufacturing network information technology system includes an artificial intelligence system configured to learn on a training set of outcomes, parameters, and data collected from the distributed manufacturing network entities to optimize manufacturing and value chain workflows.

A distributed manufacturing network includes a distributed ledger system and an artificial intelligence system. The distributed ledger system is integrated with digital threads of a set of distributed manufacturing network entities for storing information on event, activities and transactions related to the distributed manufacturing network entities. The artificial intelligence system is configured to learn on a training set of outcomes, parameters, and data collected from the distributed manufacturing network entities to optimize manufacturing and value chain workflows.

A robot fleet platform for preparing a job request includes one or more processors configured to execute instructions. The instructions include a job request ingestion system configured to receive job content relating to at least one of picking, packing, moving, storing, warehousing, transporting or delivering of items in a supply chain. The job content includes an electronic job request and related data. The instructions include a job content parsing system configured to apply filters to the received job content to identify candidate portions thereof for robot automation. The instructions include a fleet intelligence layer that activates a set of intelligence services to process terms in the candidate portions of the job content and receive therefrom at least one recommended robot task and associated contextual information. The instructions include a demand intelligence layer that provides real time information relating to a parameter of demand for the items in the supply chain.

A robot fleet management platform includes one or more processors configured to execute instructions. The instructions include receiving a job request comprising information descriptive of job deliverable and request-specific constraints for delivering the job deliverable. The instructions include applying content and structural filters to content received in association with a job request to identify portions thereof suitable for robot automation. The instructions include establishing a set of robot tasks, each defining at least a type of robot and a task objective, based on the portions of the job request that are suitable for robot automation and meet a first fleet objective. The instructions include applying fleet configuration services to the job content and the set of robot tasks to produce a fleet resource configuration data structure for the job request that associates at least one robot operating unit with each task in the set of tasks and robot adaptation instructions.

A numerical controller is provided with a control unit configured to control a machine tool and acquire feedback data in relative positions of a tool and a workpiece, a machining simulation unit configured to perform simulation processing for machining based on a machining program and create the shape of the machined workpiece, and a display unit configured to display the machined workpiece shape created by the machining simulation unit. The machining simulation unit performs machining simulation processing using the feedback data acquired by the control unit, in place of relative movement paths for the tool and the workpiece based on a command by the machining program.