The present invention is in the field of computer-implemented methods for monitoring or controlling a chemical plant. It relates to a computer-implemented method for monitoring and/or controlling a chemical plant comprising (a1) providing unstructured data containing instructions for monitoring and/or controlling a chemical plant, (a2) providing information about the chemical plant at least including information of the geographical location of the plant or the compound handled in the plant through an interface, (b1) providing the unstructured data and the information about the chemical plant to a model suitable for extracting the instructions from the structured data, (b2) obtaining from the model instruction together with metadata including the applicability of the instruction related to at least one of time period, a geographical scope, or the compounds to be handled in the plant, and (c) outputting the instructions received from the model.

The method for self-learning manufacturing scheduling for a flexible manufacturing system (FMS) with processing entities that are interconnected through handling entities is disclosed. The manufacturing scheduling is learned by a reinforcement learning system on a model of the flexible manufacturing system. The model represents at least the behavior and the decision making of the flexible manufacturing system, and the model is transformed in a state matrix to simulate the state of the flexible manufacturing system. A self-learning system for online scheduling and resource allocation is also provided. The system is trained in a simulation and learns the best decision from a defined set of actions for many every situation within an FMS. A decision may be made in near real-time during a production process and the system finds the optimal way through the FMS for every product using different optimization goals.

A tangible, non-transitory, computer-readable medium includes instructions that, when executed by processing circuitry, are configured to cause the processing circuitry to receive user input indicative of a selection of a user experience of a plurality of user experiences. The plurality of user experiences include a first user experience associated with a first event that occurred in an industrial automation system at a first time prior to receiving the user input and a second user experience associated with a second event expected to occur in the industrial automation system at a second time after receiving the user input. When executed, the instructions also cause the processing circuitry to determine, based on the user input, output representative data associated with the industrial automation system and instruct an extended reality device to present the output representative data.

According to some embodiments, system and methods are provided comprising receiving, via a communication interface of a part parameter dictionary module comprising a processor, geometry data for a plurality of geometric structures forming a plurality of parts, wherein the parts are manufactured with an additive manufacturing machine; determining, using the processor of the part parameter dictionary module, a feature set for each geometric structure; generating, using the processor of the part parameter dictionary module, one of a coupon and a coupon set for the feature set; generating an optimized parameter set for each coupon, using the processor of the part parameter dictionary module, via execution of an iterative learning control process for each coupon; mapping, using the processor of the part parameter dictionary module, one or more parameters of the optimized parameter set to one or more features of the feature set; and generating a dictionary of optimized scan parameter sets to fabricate geometric structures with a material used in additive manufacturing. Numerous other aspects are provided.

This learning device provides a learned model to an adjuster containing a learned model learned to output a predetermined compensation amount to a controller, in a control system including the controller outputting a command value obtained by compensating a target value based on a compensation amount and a control object controlled to process an object to be processed. The learning device includes: an evaluation part obtaining operation data including the target value, command value and control variable and evaluates the quality of the control variable; a learning part generating candidate compensation amounts based on the operation data, and learning, as teacher data, the generated candidate compensation amount and the specific parameter of the object, and generating a learned model; and a setting part providing the learned model to the adjuster if the evaluated quality is within an allowable range.

A robot apparatus includes a storage that stores first instructional information which serves as a guide to first work operation, an acquirer that acquires second instructional information which serves as a guide to second work operation similar to the first work operation or second work operation related to the first work operation from a different apparatus having the second instructional information, and a work controller that performs the first work operation based on the first instructional information stored in the storage and the second work operation based on the second instructional information acquired by the acquirer.

A machine learning device includes an input data acquisition unit which acquires input data containing a machining condition for any wire-cut electrical discharge machining applied to any workpiece by any wire-cut electrical discharge machining machine and consumables information including the degree of degradation of at least one of an electrode wire, ion exchange resin, a power supply die, and an electrode wire guide roller before wire-cut electrical discharge machining. The device also includes a label acquisition unit which acquires label data indicating the degree of degradation of at least one of the electrode wire, the ion exchange resin, the power supply die, and the electrode wire guide roller after the wire-cut electrical discharge machining under the machining condition contained in the input data, and a learning unit which uses the input data and the label data to execute supervised learning, thereby generating a learned model.

This application discloses a computing system implementing a mask synthesis system to generate synthetic image clips of design shapes and corresponding mask data for the synthetic image clips. The mask data can describe lithographic masks capable of being used to fabricate the design shapes on an integrated circuit. The mask synthesis system can utilize the synthetic image clips of the design shapes and the corresponding mask data to train a machine-learning system to determine pixelated output masks from portions of the layout design. The mask synthesis system can identify one or more pixelated output masks for portions of a layout design describing an electronic system using the trained machine-learning. The mask synthesis system can synthesize a mask layout design for the electronic system based, at least in part, on the layout design describing the electronic system and the one or more pixelated output masks for the layout design.

A machine learning device includes: a measured data acquisition unit that acquires a measured data group; a thermal displacement acquisition unit that acquires a thermal displacement actual measured value about a machine element; a storage unit that uses the measured data group acquired by the measured data acquisition unit as input data, uses the thermal displacement actual measured value about the machine element acquired by the thermal displacement acquisition unit as a label, and stores the input data and the label in association with each other as teaching data; and a calculation formula learning unit that performs machine learning based on the measured data group and the thermal displacement actual measured value about the machine element, thereby setting a thermal displacement estimation calculation formula used for calculating the thermal displacement of the machine element based on the measured data group.

A method includes obtaining at least one model associating areas of competency with job roles and job responsibilities of personnel, the at least one model also associating the areas of competency with curricula of training exercises and content. The method also includes obtaining a library of intervention assets associated with the areas of competency, the intervention assets comprising content for training personnel in at least one of the areas of competency. The method further includes evaluating a trainee to determine a competency gap analysis of the trainee, the competency gap analysis comprising a competency gap associated with job responsibilities of the trainee, the competency gap identifying at least one of the areas of competency in which the trainee requires training. In addition, the method includes providing web-based training to the trainee based on the competency gap, the training comprising at least one intervention asset and at least one intervention activity.