In a controller, a machine learning device, and a system that are capable of addressing change in a clamping force without use of expensive equipment, the controller includes the machine learning device that observes machining condition data indicating machining conditions for cutting, spindle torque data indicating spindle torque during the cutting, and cutting force component direction data indicating cutting force component direction information on cutting resistance against a cutting force, as state variables representing a current state of an environment, and that carries out learning or decision making with use of a learning model modelling the machining conditions for the cutting on which the cutting force that allows holding by a clamping force from a machining jig is exerted on a workpiece based on the state variables.

An operation adjustment system includes estimation circuitry and generation circuitry. The estimation circuitry is configured to generate a calculation model based on a plurality of pairs of a parameter set and an evaluation index. The calculation model indicates a relationship between the parameter set and the evaluation index. The parameter set affects an operation of a motor control device. The evaluation index relates to a machine operated according to the parameter set by the motor control device. The generation circuitry is configured to generate a new parameter set based on the calculation model in order to update the calculation model with the new parameter set.

A machine tool includes an operation evaluation section that evaluates an operation thereof and a machine learning device that performs the machine learning of a movement amount of an axis thereof. The machine learning device calculates a reward based on state data including the output of the operation evaluation section, performs the machine learning of the determination of the movement amount of the axis, and determines the movement amount of the axis based on a machine learning result and outputs the determined movement amount. The machine learning device performs the machine learning of the determination of the movement amount of the axis based on the determined movement amount of the axis, the acquired state data, and the calculated reward.

A machining defect factor estimation device includes a machine learning device that learns an occurrence factor of a machined-surface defect based on an inspection result on a machined surface of a workpiece. The machine learning device observes the inspection result on the machined surface of the workpiece from an inspection device, as a state variable, acquires label data indicating the occurrence factor of the machined-surface defect, and learns the state variable and the label data in a manner such that they are correlated each other.

A controller for a machine tool includes a machine learning apparatus configured to learn an Nth-order time-derivative component of a speed of each axis of the machine tool. The machine learning apparatus includes: a state observation section configured to observe first state data representing the Nth-order time-derivative component of the speed of each axis as a state variable representing a current state of an environment; a determination data acquisition section configured to acquire determination data representing a properness determination result of at least any one of machining accuracy, surface quality, and machining time of the machined workpiece; and a learning section configured to learn the Nth-order time-derivative component of the speed of each axis in relation to at least any one of the machining accuracy, the surface quality, and the machining time of the machined workpiece using the state variable and the determination data.

A numerical controller calculates a machining path based on a lathe turning cycle instruction and the settings of a machining path and machining conditions of the lathe turning cycle instruction. An evaluation value used to evaluate cycle time required for machining a workpiece performed according to the calculated machining path and the machining quality of the machined workpiece is calculated to perform machine learning of adjustment of the machining path and the machining conditions. By the machine learning, a machining path based on a complex lathe turning cycle instruction is optimized.

A numerical controller includes a machine learning device for performing machine learning of machining condition adjustment of a machine tool. The machine learning device calculates a reward based on acquired machining-state data on a workpiece, and determines an adjustment amount of machining condition based on a result of machine learning and machining-state data, and adjusts machining conditions based on the adjustment amount. Further, the machine learning of machining condition adjustment is performed based on the determined adjustment amount of machining condition, the machining-state data, and the reward.

A machining processing apparatus or the like is provided, which is suitable for reducing a burden on a user in adjusting machining conditions using machining results. The machining processing apparatus 1 machines a workpiece according to machining conditions. The machining conditions include multiple items and setting values for each item. The machining processing apparatus 1 includes an individual input processing unit 13, a support input processing unit 15, and an input/output unit 3. The individual input processing unit 13 allows the user to input setting values for each machining conditions item. The support input processing unit 15 calculates a change amount for one or multiple machining conditions items according to a modification of the machining shape of the workpiece input by the user, and instructs the input/output unit 3 to display the one or multiple items to be modified as change details together with the change amount for each item.

A machine tool includes an operation evaluation section that evaluates an operation thereof and a machine learning device that performs the machine learning of a movement amount of an axis thereof. The machine learning device calculates a reward based on state data including the output of the operation evaluation section, performs the machine learning of the determination of the movement amount of the axis, and determines the movement amount of the axis based on a machine learning result and outputs the determined movement amount. The machine learning device performs the machine learning of the determination of the movement amount of the axis based on the determined movement amount of the axis, the acquired state data, and the calculated reward.

A machine tool includes an operation evaluation section that evaluates an operation thereof and a machine learning device that performs the machine learning of a movement amount of an axis thereof. The machine learning device calculates a reward based on state data of the machine tool including output data from the operation evaluation section, performs the machine learning of the determination of the movement amount of the axis, and determines the movement amount of the axis based on a machine learning result and outputs the determined movement amount. The machine learning device performs the machine learning of the determination of the movement amount of the axis based on the determined movement amount of the axis, the acquired state data, and the calculated reward.