A numerical control device for machining a workpiece while performing vibration cutting in which a tool and the workpiece are relatively vibrated by driving a first axis that drives the tool or a second axis that drives the workpiece, includes a parameter adjustment unit that adjust a parameter related to a vibration condition for the vibration cutting, based on the amount of cutting load generated on the first axis or the second axis when the vibration cutting is performed, and a controller that controls the vibration cutting using the adjusted parameter.

A control apparatus for performing predetermined control for a predetermined system using reinforcement learning detects an event in a life cycle of the predetermined system and, in response to the detection of the event, set an exploration parameter specified in accordance with the detected event as a value for adjusting a ratio of exploration in the reinforcement learning. The control apparatus executes the predetermined control using the reinforcement learning in accordance with the set exploration parameter. When a first event is detected, the control apparatus sets the exploration parameter so that makes the ratio of the exploration set during a first period after the first event is smaller than the ratio of the exploration set during a second period before the first event is detected.

A board production management device for managing a board production line including a solution memory section for linking and storing a problem event that may occur in a board production line and that requires a countermeasure operation, a solution to serve as the countermeasure operation, and a set authority level set for a worker who may implement the solution; an solution memory section for authenticating the authority level of a worker who implements the countermeasure operation; and a solution notification section for separately reporting, when a problem event occurs, an executable solution corresponding to a set authority level equal to or less than the authority level, and an unexecutable solution corresponding to a set authority level exceeding the authority level.

To provide a numerical control device capable of directly determining whether or not a cutting fluid is applied to a cutting point. A numerical control device includes a determination unit configured to make, on a basis of image data acquired when a vision sensor photographs a cutting fluid jetted from an injection nozzle toward a cutting point, determination of whether or not the cutting fluid is applied to the cutting point, and an instruction unit configured to issue an instruction to a nozzle control device configured to control a position and an attitude of the injection nozzle on a basis of a result of the determination of the determination unit.

A compressor controller for operating a compressor within an industrial automation environment is provided. The compressor controller includes a control module, configured to control the compressor via control settings, and a machine learning module, coupled with the control module. The machine learning module is configured to receive a set of supervised data related to the compressor, and to train with the supervised data to produce a Newtonian physics model representing the inputs and outputs of the compressor within the industrial automation environment. The machine learning module is also configured to receive performance data related to the compressor, receive environment data related to the compressor, and to process the performance data and environment data to produce predicted future performance data for the compressor, and to produce control settings for the compressor.

A method and an apparatus for exclusive reinforcement learning are provided, comprising: collecting information of states of an environment through the communication interface and performing a statistical analysis on the states using the collected information; determining a first state value of a first state among the states in a training phase and a second state value of a second state among the states in an inference phase based on analysis results of the statistical analysis; performing reinforcement learning by using one reinforcement learning unit of a plurality of reinforcement learning unit which performs reinforcement learnings from different perspectives according to the first state value; and selecting one of actions determined by the plurality of reinforcement learning unit based on the second state value and applying selected action to the environment.

Provided is a machine learning device configured to perform machine learning related to optimization of a compensation value of a compensation generation unit with respect to a servo control device configured to control a servo motor configured to drive an axis of a machine tool, a robot, or an industrial machine, and that includes at least one feedback loop, a compensation generation unit configured to generate a compensation value to be applied to the feedback loop, and an abnormality detection unit configured to detect an abnormal operation of the servo motor, wherein, during a machine learning operation, when the abnormality detection unit detects an abnormality, the compensation from the compensation generation unit is stopped and the machine learning device continues optimization of the compensation value generated by the compensation generation unit.

This learning device provides a learned model to an adjuster including the learned model learned to output a predetermined compensation amount to a controller based on parameters of an object to be processed, in a system including the controller outputting a command value obtained by compensating a target value based on a compensation amount; and a control object performing a predetermined process on the object and outputting a control variable as a response to the command value. The learning device includes: a learning part generating candidate compensation amounts based on operation data including a target value, command value and control variable, learning with the generated candidate compensation amounts and the parameters of the object as teacher data, and generating or updating the learned model; and a setting part providing, to the adjuster, the generated or updated learned model.

An assistance apparatus includes a status information acquiring section that acquires a grinding condition as a status information, the grinding condition including set states associated with a plurality of movement command data, an evaluation result acquiring section that acquires evaluation results of a plurality of evaluation objects that are obtained under the grinding condition, a reward calculating section that calculates a reward for the status information based on the evaluation results, a policy storing section that stores a policy which is obtained from a value function, an action determining section that determines the movement command data to be adjusted and an adjustment amount at which said movement command data is adjusted, from among candidates of the plurality of movement command data that are adjustable, based on the status information and the policy, and an action information outputting section that is configured to output determined contents including an action information.

A machine learning apparatus able to obtaining an optimal shift amount of an assist gas. The machine learning apparatus comprises a state-observation section configured to observe machining condition data included in a machining program given to the laser machine, and measurement data of a dimension of dross generated at a cutting spot of the workpiece when the machining program is executed, as a state variable representing a current state of an environment in which the workpiece is cut; and a learning section configured to learn the shift amount in association with cutting quality of the workpiece, using the state variable.