A machining apparatus is provided with a machine learning device that performs machine learning. The machine learning device performs the machine learning by receiving the input of machining accuracy between a machining shape of a workpiece measured on-machine and design data on the workpiece and machining time of the workpiece measured by a measurement device. Based on a result of the machine learning, the machining apparatus changes machining conditions such that the machining accuracy increases and the machining time becomes as short as possible.

A system includes a processing device, operatively coupled to the memory device, to perform operations comprising obtaining a plurality of sensor values associated with a deposition process performed, according to a recipe, in a process chamber to deposit film on a surface of a substrate; generating a manufacturing data graph based on the plurality of sensor values; receiving, via a user interface, a selection of a data point on the manufacturing graph; receiving failure data associated with the data point; and storing, in a data structure, the failure data to be accessible via the user interface presenting the manufacturing data graph.

A system includes a processing device, operatively coupled to the memory device, to perform operations comprising obtaining a plurality of sensor values associated with a deposition process performed, according to a recipe, in a process chamber to deposit film on a surface of a substrate. A machine-learning model is applied to the plurality of sensor values. The machine-learning model is trained based on historical sensor data of a sub-system of the process chamber and task data associated with the recipe for depositing the film. An output of the machine-learning model is generated that is indicative of a suspected failure of the sub-system and a corrective action is generated based on the suspected failure of the sub-system.

To provide a numerical controller and a machine learning device that predict an abnormality, based on machine learning with perception of temporal change in data. The numerical controller includes the machine learning device provided with a learning unit that conducts machine learning of trends in operation of a machine on occasions of occurrence of abnormalities in the machine, based on time-series data acquired by a data logger device and relating to the operation of the machine and abnormality information relating to the abnormalities which have occurred in the machine and a prediction unit that predicts an abnormality which will occur in the machine, based on results of the machine learning in the learning unit and time-series data acquired by the data logger device and relating to current operation of the machine.

A machine learning apparatus that learns a condition associated with a gain of a magnetic flux controller and a time constant of a magnetic flux estimator in a motor control apparatus includes: a state observation unit that observes a state variable defined by at least one of data relating to an acceleration of a motor, data relating to a jerk of the motor, and data relating to an acceleration time of the motor; and a learning unit that learns the condition associated with the gain of the magnetic flux controller and the time constant of the magnetic flux estimator in accordance with a training data set defined by the state variable.

The invention relates to a method and a computer-implemented system for automatically monitoring and determining the status of entire process sections in a process unit in a computer-implemented manner.

A machining apparatus is provided with a machine learning device that performs machine learning. The machine learning device performs the machine learning by receiving the input of machining accuracy between a machining shape of a workpiece measured on-machine and design data on the workpiece and machining time of the workpiece measured by a measurement device. Based on a result of the machine learning, the machining apparatus changes machining conditions such that the machining accuracy increases and the machining time becomes as short as possible.

A machine learning apparatus that learns a condition associated with a gain of a magnetic flux controller and a time constant of a magnetic flux estimator in a motor control apparatus includes: a state observation unit that observes a state variable defined by at least one of data relating to an acceleration of a motor, data relating to a jerk of the motor, and data relating to an acceleration time of the motor; and a learning unit that learns the condition associated with the gain of the magnetic flux controller and the time constant of the magnetic flux estimator in accordance with a training data set defined by the state variable.

A system includes a processing device, operatively coupled to the memory device, to perform operations comprising obtaining a plurality of sensor values associated with a deposition process performed, according to a recipe, in a process chamber to deposit film on a surface of a substrate. A machine-learning model is applied to the plurality of sensor values. The machine-learning model is trained based on historical sensor data of a sub-system of the process chamber and task data associated with the recipe for depositing the film. An output of the machine-learning model is generated that is indicative of a suspected failure of the sub-system and a corrective action is generated based on the suspected failure of the sub-system.