A simulation device includes: a simulation system including a predetermined feedback system having at least a predetermined control block structure corresponding to a predetermined device-side configuration; a holding unit holding impulse response information for calculation that is information on an impulse response relating to the predetermined device-side configuration; a first response calculation unit calculating a time response of the predetermined device-side configuration to a predetermined input value by convolution processing using the impulse response information for calculation and the predetermined input value; and a second response calculation unit calculating a response of the simulation system to a command value input to the simulation system by using the time response of the predetermined device-side configuration calculated by the first response calculation unit. According to this configuration, simulation accuracy of a control system is improved.

A pick and place nozzle performance analytics system streams production data from pick and place machines used in electronic assembly to a cloud platform as torrential data streams, and performs analytics on the production data to track, visualize, and predict performance of individual nozzles in terms of rejects or miss-picks. The analytics system generates a performance vector for each nozzle based on the collected production data, the performance vector tracking both the accumulated rejects and the percentage of rejects as respective dimensions of an x-y plane. The system monitors and analyzes the trajectory of this vector in the x-y plane to predict when performance degradation of the nozzle will reach a critical threshold. In response to predicting that nozzle performance degradation will exceed a threshold at a future time, the system can generate and deliver notifications to appropriate client devices.

An industrial data presentation system leverages structured data types defined on industrial devices to generate and deliver meaningful presentations of industrial data. Industrial devices are configured to support structured data types referred to as basic information data types (BIDTs) comprising a finite set of structured information data types, including a rate data type, a state data type, an odometer data type, and an event data type. The BIDTs can be referenced by both automation models of an industrial asset and non-automation models of the asset, allowing data points of both types of models to be easily linked using a common data source nomenclature.

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 control system for an actuator includes a non-transitory computer readable medium storing a database in which first information, second information and third information are stored and correlated with each other, and processing circuitry that performs machine learning based on the first information, the second information and the third information stored in the database. The first information is associated with a rotational speed of a reducer in the actuator, the second information is associated with a torque acting on the reducer, the third information indicates a concentration of iron powder in grease in the reducer, and the machine learning builds a concentration estimation model indicating a relationship between the first information, the second information and the third information.

A process plan optimization method for manufacturing a workpiece by adding a material in a plurality of layers is provided. The method includes: building a predicting model, the predicting model configured to predict a temperature variation of at least a portion of the workpiece; predicting an expected temperature variation of the portion of the workpiece to be manufactured during a given time period based on the predicting model and the process plan; and adjusting the process plan in response to the expected temperature variation of the portion failing to meet a preset condition, to make the expected temperature variation of the portion meet the preset condition.

Algorithmic reasoning about a cutting tool assembly's space of feasible configurations can be effectively harnessed to construct a sequence of motions that guarantees a collision-free path for the tool assembly to remove each support structure in the sequence. A greedy algorithm models the motion of the cutting tool assembly through the free-spaces around the intermediate shapes of the part as the free-spaces iteratively reduce in size to the near-net shape to determine feasible points of contact for the cutting tool assembly. Each support beam is evaluated for a contact feature along the boundary of the near-net shape that constitutes a feasible point of contact. If a support beam has at least one feasible configuration at each point, the support beam is deemed ‘accessible’ and a collection of tool assembly configurations that are guaranteed to be non-colliding but which can access all points of contact of each accessible support beam can be generated.

Robust machine learning predictions. Temporal dependencies of process targets for different machine learning models can be captured and evaluated for the impact on process performance for target. The most robust of these different models is selected for deployment based on minimizing variance for the desired performance characteristic.

Embodiments provide methods, apparatuses, and systems for cutting wood workpieces, such as logs and cants, into desired products. In various embodiments, after a log is chipped into a cant, the cant may be scanned and re-optimized based on the new scan data and information about the source log, such as simulated orientation parameters, a 3D model, and/or potential cut solutions. In other embodiments, data from multiple sensor types may be used in combination to detect splits in logs, cants, or both. Optionally, re-optimization and split detection techniques may be used in combination to improve wood volume recovery, value, and/or throughput speed.