The present invention relates to a method and a system for controlling a process of a process plant. Hie system is configured to control a process in the process plant by recommending control operations and perform the control operations. The system obtains the control operations and detects availability of control room operators using historical data related to actions performed by the control room operators, sensory parameters of the control room operators and a status associated with the process. The system provides queries related to the control operations, the plant parameters, status of the process to the control room operators upon detecting the availability. Hie queries are validated by the control room operators. The system thereafter autonomously controls tire control room based on the validated data.

An industrial device supports device-level data modeling that pre-models data stored in the device with known relationships, correlations, key variable identifiers, and other such metadata to assist higher-level analytic systems to more quickly and accurately converge to actionable insights relative to a defined business or analytic objective. Data at the device level can be modeled according to modeling templates stored on the device that define relationships between items of device data for respective analytic goals (e.g., improvement of product quality, maximizing product throughput, optimizing energy consumption, etc.). This device-level modeling data can be exposed to higher level systems for creation of analytic models that can be used to analyze data from the industrial device relative to desired business objectives.

A drive apparatus which controls a speed or a torque of a servo motor of a processing machine according to a command obtained by converting an external command input from outside includes a machine learning apparatus which learns appropriate conversion from the external command to the command. The machine learning apparatus includes: a state observing unit which observes the external command, the command, and a state of the processing machine or the servo motor as state variables that represent a present state of an environment; a determination data acquiring unit which acquires determination data indicating an evaluation result of processing by the processing machine; and a learning unit which learns the external command and the state of the processing machine or the servo motor, and the command, in association with each other using the state variables and the determination 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.

A state determination device acquires data related to an injection molding machine, stores a learning model obtained by learning an operation state of the injection molding machine with respect to the data, and performs estimation using the learning model based on the data. Further, the state determination device acquires a correction coefficient, which is associated with a type of the injection molding machine and equipment attached to the injection molding machine and numerically converts and corrects the estimation result with a predetermined correction function to which the acquired correction coefficient is applied.

A system including a deep learning processor obtains response data of at least two data types from a set of process stations performing operations as part of a manufacturing process. The system analyzes factory operation and control data to generate expected behavioral pattern data. Further, the system uses the response data to generate actual behavior pattern data for the process stations. Based on an analysis of the actual behavior pattern data in relation to the expected behavioral pattern data, the system determines whether anomalous activity has occurred as a result of the manufacturing process. If it is determined that anomalous activity has occurred, the system provides an indication of this anomalous activity.

A manufacturing system is disclosed herein. The manufacturing system includes one or more stations, a monitoring platform, and a control module. Each station of the one or more stations is configured to perform at least one step in a multi-step manufacturing process for a component. The monitoring platform is configured to monitor progression of the component throughout the multi-step manufacturing process. The control module is configured to dynamically adjust processing parameters of each step of the multi-step manufacturing process to achieve a desired final quality metric for the component.

A system for rapidly adapting production of a product based on classification of production data using a classifier trained on prior production data is provided. A production control system includes a learning system and an adaptive system. The learning system trains a production classifier to label or classify previously collected production data. The adaptive system receives production data in real time and classifies the production data in real time using the production classifier. If the classification indicates a problem with the manufacturing of the product, the adaptive system controls the manufacturing to rectify the problem by taking some corrective action. The production classifier is trained using bootstrap data and corresponding example data extracted from prior production data. Once the bootstrap data is labeled, the corresponding example data is automatically labeled for use as training data.

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.

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.