A machine learning device includes: a state information acquisition unit configured to cause the control device to execute a tapping program to acquire from the control device, state information including a torque command value with respect to the spindle motor, a drive state including deceleration, a ratio of a movement distance in acceleration and a movement distance in deceleration; an action information output unit configured to output action information including adjustment information of the ratio of the movement distance in acceleration and the movement distance in deceleration, to the control device; a reward output unit configured to output a reward value in reinforcement learning based on a torque command value in deceleration, and a target torque command value in deceleration; and a value function update unit configured to update an action value function based on the reward value, the state information, and the action information.

A learned model is generated which accurately outputs power consumption by running a newly created machining program without performing simulation, and the learned model is utilized to accurately predict the power consumption. A machine learning device includes an input data acquisition unit that, in machining a workpiece with an arbitrary machine tool by running an arbitrary machining program, acquires, as input data, information relating to the machine tool, an auxiliary operation device, and the workpiece, and machining information including the machining program. A label acquisition unit acquires label data indicating power consumption information relating to the machine tool and the auxiliary operation device in the running of the machining program. A learning unit performs supervised learning using the input and label data, and generates a learned model that inputs machining information of machining to be performed and outputs the power consumption information in the machining to be performed.

A machine learning apparatus determines a control parameter of an active vibration isolation apparatus on which an industrial machine is mounted. The industrial machine includes a movable part, a drive source that drives the movable part, and a drive source control section that controls the drive source to position the movable part at a command position. The machine learning apparatus includes: an acquiring section that acquires, as teacher data, a positional deviation, which is a difference between the command position and an actual position of the movable part; a storage section that stores a learning model that outputs the control parameter corresponding to a state quantity concerning the industrial machine; and a learning section that updates the learning model using the teacher data.

A machine learning device performs machine learning with respect to a numerical control device that operates a machine tool on the basis of a machining program. The machine learning device includes a state information acquisition unit configured to acquire state information including conditions of a spindle speed, a feed rate, a number of cuts, and a cutting amount per one time or a tool compensation amount, and a cycle time of cutting a workpiece, and machining accuracy of the workpiece; an action information output unit configured to output action information including modification information of the condition; a reward output unit configured to output a reward value in reinforcement learning on the basis of the cycle time and the machining accuracy; and a value function updating unit configured to update an action value function on the basis of a reward value, the state information, and the action information.

Provided is a machining equipment system including machining equipment that performs machining of a workpiece; a control device that controls the machining equipment on the basis of a machining condition; a state obtaining device that obtains a state of the machining equipment during the machining; an inspection device that inspects the workpiece after the machining; and a machine learning device that performs machine learning on the basis of a result of inspection by the inspection device and the state of the machining equipment, obtained by the state obtaining device, wherein the machine learning device modifies the machining condition on the basis of a result of the machine learning so as to improve the machining accuracy of the workpiece or so as to minimize the defect rate of the workpiece.

A modular robotic apparatus includes one or more sensors configured to generate sensor signals representing a manufacturing environment in which the modular robotic apparatus is located. A machine learning module is communicably coupled to the one or more sensors and includes a computer processor. The computer processor generates, by a machine learning model trained based on one or more manufacturing parameters, a computer numerical control (CNC) configuration. The one or more manufacturing parameters define a manufacturing task to be performed by the modular robotic apparatus. The machine learning model adjusts the CNC configuration based on the sensor signals. A robotic machine tool is communicably coupled to the machine learning module and includes an end effector. The robotic machine tool is configured to operate the end effector in accordance with the adjusted CNC configuration.

A robot manipulator including limbs moveable via bearings controlled by actuators; sensors to capture a bearing position and a bearing torque/bearing force; a first sensor to capture a force screw W; a housing downstream of the first sensor; a second sensor to capture a user force applied to the housing and/or a user torque; a computing unit to determine, using a dynamics model of the robot manipulator and based on particular bearing torque/bearing force, the force screw W, and the user force and/or the user torque, a first force and/or a first torque to shift the limbs and a second force and/or a second torque to apply to an external object via an effector, wherein the dynamics model includes at least gravitational forces and inertial forces based on the bearing position; and a storage unit to store the first and/or the second force, and/or the first and/or the second torque.

An object is to provide a servo controller which constantly optimizes parameters according to the state of a machine. A servo controller for controlling an electric motor which drives the axis of an industrial machine includes: a state value derivation unit which derives, from an operation program and/or operation plan information of the industrial machine, the chronological or event-sequential data of the state value of the electric motor or a driven member that is operated with the electric motor; and a parameter change unit which changes at least one parameter of a velocity gain, a position gain, a feedforward gain, a filter frequency and an acceleration/deceleration time constant after interpolation based on the chronological or event-sequential data derived in the state value derivation unit either chronologically or event-sequentially.

A miller, a non-transitory computer readable medium, and a method for milling a multi-layered object. The method may include (i) receiving or determining milling parameters related to a milling process, the milling parameters may include at least two out of (a) a defocus strength, (b) a duration of the milling process, (c) a bias voltage supplied to an objective lens during the milling process, (d) an ion beam energy, and (e) an ion beam current density, and (ii) forming a crater by applying the milling process while maintaining the milling parameters, wherein the applying of the milling process includes directing a defocused ion beam on the multi-layered object.

During machining of a workpiece, a gripping force adjustment device takes into account the state of the machining and the state of the workpiece in order to set a more appropriate gripping force. The gripping force adjustment device acquires data indicating a machining state implemented by a machine tool and data relating to a gripping state realized on the workpiece by a jig, and creates data to be used in machine learning on the basis of the acquired data. The gripping force adjustment device then executes machine learning processing relating to the gripping force exerted on the workpiece by the jig in the environment in which the machine tool machines the workpiece on the basis of the created data.