Methods, systems, and computer programs are presented for predicting the performance of semiconductor manufacturing equipment operations. One method includes an operation for obtaining machine-learning (ML) models, each model related to predicting a performance metric for an operation of a semiconductor manufacturing tool. Further, each ML model utilizes features defining inputs for the ML model. The method further includes an operation for receiving a process definition for manufacturing a product with the semiconductor manufacturing tool. One or more ML models are utilized to estimate a performance of the process definition used in the semiconductor manufacturing tool. Additionally, the method includes presenting, on a display, results showing the estimate of the performance of the manufacturing of the product. In some aspects, the use of hybrid models improves the predictive accuracy of the system by augmenting the capabilities of data-driven models with the reinforcement provided by the physics-based models.

A method of monitoring an electrical machine, wherein the method includes: a) obtaining temperature measurement values of the temperature at a plurality of locations of the electrical machine, b) obtaining estimated temperatures at the plurality of locations given by a thermal model of the electrical machine, the thermal model including initial weight parameter values, c) minimizing a difference between the temperature measurement values and the estimated temperatures by finding optimal weight parameter values, d) storing the initial weight parameter values to thereby obtain a storage of used weight parameter values, and updating the optimal weight parameter values as new initial weight parameter values, and repeating steps a)-d) over and over during operation of the electrical machine.

Regarding each of a plurality of operation patterns virtually generated in regard to the operation performed by a worker with respect to an actual plant, an information processing device uses plant data related to the operation of the actual plant and uses a virtual plant which follows the actual plant, and predicts the state transition of the actual plant in the case of implementing each operation pattern. Then, the information processing device outputs each operation pattern in a corresponding manner to the state transition of the actual plant as obtained by the virtual plant.

An information processing device uses models generated under different conditions, and predicts alarms occurring in the case in which each of a plurality of operation patterns, which is virtually generated in relation to the operation of an actual plant by a worker, is implemented with respect to the actual plant. Then, the information processing device sets the degree of reliability of the prediction result regarding each of the plurality of operation patterns. Subsequently, based on the degree of reliability of the prediction result, the information processing device performs display control with respect to the alarms.

Various embodiments of the present technology generally relate to solutions for integrating machine learning models into industrial automation environments. More specifically, embodiments include systems and methods for implementing machine learning models within industrial control code to improve performance, increase productivity, and add capability to existing programs. In an embodiment, a system comprises: a control component configured to run a closed-loop industrial process comprises a first machine learning model; a measurement component configured to measure a gap between outcome data predicted by the first machine learning model and actual outcome data; a determination component configured to determine, based on the gap, that the first machine learning model has degraded; and a management component configured to replace the first machine learning model with a second machine learning model, wherein the second machine learning model is trained based at least in part on the actual outcome data.

Provided is an apparatus including: a first acquisition unit acquiring an operation plan of a piece of equipment, and at least identification information of a parameter among target setting data used for learning of an operation model operating the piece of equipment, the target setting data including identification information of a parameter for which a target range is to be set among parameters relating to the piece of equipment and a target range set for the parameter; and a first learning processing unit performing, by using learning data including the identification information of the parameter and the operation plan acquired by the first acquisition unit, learning processing of a target setting model outputting at least one of the identification information or the target range of the parameter among the target setting data that should be used for learning of the operation model, in response to the operation plan being input.

A cell controller capable of optimizing the operation of a production system having a plurality of industrial machines operated by an operation program. The cell controller includes: a system operational information analyzer configured to analyze time-series operational information received from the industrial machines via a network, so as to find a part in the system which generates an adverse effect on a cycle time of the entire production system; a state quantity analyzer configured to analyze a state quantity of the industrial machines so as to calculate a degree of margin of motion of each industrial machine; a program modifier configured to automatically modify a velocity or acceleration in the operation program based on the degree of margin; and a simulator configured to execute an operational simulation of the production system in order to confirm a modification result of the operation program.

A method, apparatus, system, and a computer program product for managing a manufacturing of an object. Work orders for the object that have work areas with less than a minimum safety distance from each other are identified by a computer system. A set of actions is performed by the computer system for the work orders to manage the manufacturing of the object.

Methods and data processing systems simulate and handle anti-collision management for an area of a production plant controlled by a data processing system. The method includes determining possible spatial trajectories of objects, such as humans, production components, stationary and mobile robots, AGV's and the like, in a predefined area in an industrial scenario, such as a production process, an assembling process, material handling, item sorting and the like. A static 4D structure of the area where the possible locations of the objects are determined in terms of their location and the time that the object will be in that location is determined in order to identify potential collision events and remedy those potential collision events.

A method for operating an industrial automation system may involve receiving, via a first module of a plurality of modules in a control system, an indication that an error between a measurement associated with a target variable that corresponds with at least a portion of the industrial automation system and a modeled value for the target variable. The method may then involve determining, via the first module, whether the error is within a first range of values and retraining a model used to generate the modeled value for the target variable based on a portion of a plurality of sets of data points acquired via a plurality of sensors disposed in the industrial automation system in response to the error being within the first range of values.