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.

Disclosed is a reconfigurable device for dispensing/distributing tablets in the blister pockets of a packaging strip subjected to longitudinal movement in a substantially horizontal plane of a packaging machine having a blister-packer, the reconfigurable device having a frame designed to be mounted on the blister-packer, the frame having a front attachment wall preferably arranged vertically, at least one accessory for dispensing/distributing the tablets in the blister pockets of the strip, the accessory being designed to be mounted removably on the frame. The device includes an intermediate interface intended to be attached removably to the frame, against the front attachment wall thereof, in order to support the at least one accessory. The interface includes at least one drive member of a mobile element of the accessory/accessories supported thereby.

A tendency of an action of a robot may vary based on learning data used for training. The learning data may be generated by an agent performing an identical or similar task to a task of the robot. An apparatus and method for updating a policy for controlling an action of a robot may update the policy of the robot using a plurality of learning data sets generated by a plurality of heterogeneous agents, such that the robot may appropriately act even in an unpredicted environment.

A state determination device that determines an operation state of an injection molding machine stores respective specification data of a reference injection molding machine and an injection molding machine that is different from the reference injection molding machine, and acquires data related to the injection molding machine. Then, the state determination device converts the acquired data into yardstick data by a conversion formula set for every type of data, by using the stored specification data of the reference injection molding machine and the stored specification data of the injection molding machine and performs machine learning using the yardstick data obtained through the conversion so as to generate a learning model.

A device for assisting molding condition determination is used with a molding method that molds an article by feeding molten material into a mold. The device includes a learning model generating unit, an input unit, and an output unit. The learning model generating unit creates a learning model through machine learning in which a plurality of molding condition element items used to mold the article and a plurality of quality element items of the molded article are used as learning data. The learning model relates to a degree of influence of each molding condition element item on each quality element item. The input unit receives input of a subject quality element item to be checked, selected from the quality element items. The output unit outputs, using the learning model, the multiple molding condition element item that has the degree of influence on the subject quality element item.

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.

A servo controller includes a speed command creation unit, a torque command creation unit, a speed detection unit, a speed control loop, a speed control gain, a filter, a parameter storage unit, a sinusoidal disturbance input unit, a frequency characteristics calculation unit, and a parameter adjustment unit. The parameter storage unit stores a history of past frequency characteristics obtained by the frequency characteristics calculation unit in correlation with past parameter history.

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.

An automatic parameter adjustment device capable of quantitatively determine the degree of importance of a processing time and a processing accuracy, and effectively adjusting a parameter in accordance with a processing condition. The adjustment device includes: a parameter changing part which changes a control parameter; a test program executing part which transmits a test program to a numerical controller for executing the test program; an execution result obtaining part which obtains an execution result of the test program; a storing part which stores the execution result and the parameter corresponding thereto; a weighting part which determines weighting coefficients of the processing time and processing accuracy as evaluation criteria based on input or setting by an operator; and a parameter extracting part which evaluates the execution result based on the weighted evaluation criteria, and extracts an optimum parameter from the storing part based on the evaluated execution result.