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 tool includes: a spindle that causes a tool to rotate and move; a workpiece rotation mechanism that causes a workpiece W to rotate; a control unit that controls the spindle and the workpiece rotation mechanism in accordance with commands from a program; and a cutting load detection unit that detects a cutting load imparted on the workpiece by the tool, and the control unit controls a cutting route such that a cutting depth of the workpiece cut with the tool in a region with a small cutting load is greater than the cutting depth in a region with a large cutting load within such a range that the cutting load detected by the cutting load detection unit does not exceed a predetermined load.

Various embodiments include methods for controlling an industrial device. Some embodiments include: obtaining a state input characterizing a current state of the industrial device; processing the state input to generate an action output characterizing an expected action to be performed by the industrial device for the current state, based on a machine learning model trained based on states of the industrial device, actions each performed for each state of the industrial device and results each obtained by performing each action; and generating a control signal for the industrial device based on the action output.

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.

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 system and method for making predictions relating to products manufactured via a manufacturing process are disclosed. A processor receives input data and makes a first prediction based on the input data. The processor identifies a first machine learning model from a plurality of machine learning models based on the first prediction. The processor further makes a second prediction based on the input data and the first machine learning model, and transmits a signal to adjust the manufacturing of the products based on the second prediction.

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 machine learning device of a controller observes, as state variables that express a current state of an environment, feeding amount data indicating a feeding amount per unit cycle of a tool and vibration amount data indicating a vibration amount of a cutting part of the tool when the cutting part of the tool passes through the workpiece. In addition, the machine learning device acquires determination data indicating a propriety determination result of the vibration amount of the cutting part of the tool when the cutting part of the tool passes through the workpiece. Then, the machine learning device learns the feeding amount per unit cycle of the tool when the cutting part of the tool passes through the workpiece in association with the vibration amount data, using the state variables and the determination data.

An example system includes at least one acoustic sensor and one optical sensor to monitor a thermal spray system controlled by a plurality of control parameters and performing a process associated with a plurality of process outputs. The system includes a computing device including a machine learning module and a control module. The machine learning module is configured to determine, based on at least the plurality of control parameters, an at least one time-dependent acoustic data signal, an at least one image data signal, and the plurality of process outputs, a relationship between the plurality of control parameters and the plurality of process outputs by machine learning. The control module is configured to control the thermal spray system to adjust the plurality of process outputs toward a plurality of respective operating ranges.

A machine learning device included in a tool selecting apparatus includes a state observing unit that observes, as state variables indicative of a current environmental state, data related to machining condition, data related to cutting condition, data related to machining result, and data related to a tool, and a learning unit that, by using the state variables, learns distribution of the data related to the machining condition, the data related to the cutting condition, and the data related to the machining result, with respect to data related to the tool.