A machine learning device includes a virtual temperature model calculating unit having an equation including a first coefficient for determining a heat generation amount and a second coefficient for determining a heat dissipation amount. The virtual temperature model calculating unit is configured to calculate virtual temperature data by estimating a temperature of a specific portion of a machine by the equation using heat generation factor data. A thermal displacement model calculating unit is configured to calculate, using the calculated virtual temperature data and actual temperature data acquired from at least one temperature sensor mounted to a portion other than the specific portion, an error between thermal displacement estimated by the equation and actually measured thermal displacement, in which the virtual temperature model calculating unit performs machine learning to search for the first coefficient and the second efficient so that the error is minimized.

An information processing apparatus includes an information processing portion. The information processing portion is configured to accept registration of first teach data and second teach data such that the first teach data and the second teach data are associated with each other. The first teach data is related to a robot arm. The second teach data is related to a peripheral apparatus disposed around the robot arm.

A system to aid in design for manufacturing an object includes a processor and a memory configured to store instructions. The processor is configured to receive first data representing a design of the object to be manufactured and second data representing a machine-learning model. The processor is configured to execute the instructions to generate third data using the first data and the second data. The third data indicates at least one of a modification to the design of the object or process conditions for production of the object. The processor is configured to send the design of the object, the process conditions, or both, to a manufacturing tool to enable production of the object. The machine-learning model is representative of production data and based at least partially on one or more of: object features, process parameters, environmental factors, and quality data.

Vibration of a machine end and an error of a moving trajectory are suppressed. A machine learning device performs machine learning of optimizing first coefficients of a filter provided in a motor controller that controls a motor and second coefficients of a velocity feedforward unit of a servo control unit provided in the motor controller on the basis of an evaluation function which is a function of measurement information after acceleration and deceleration by an external measuring instrument provided outside the motor controller, a position command input to the motor controller, and a position error which is a difference between the position command value and feedback position detection value from a detector of the servo control unit.

A motor controller includes a drive control unit that drives a motor on the basis of a control command, operates a control target made up of the motor and a mechanical load, and performs an initialization operation of setting the control target in an initial state and an evaluation operation starting from the initial state. Further, there is a learning unit that determines the control command to be used in the evaluation operation, on the basis of the result of learning the control command used in the evaluation operation, and a state sensor signal in association with each other. Further, there is an adjustment management unit that determines, on the basis of the timing at which to perform a first process.

A numerical control device for controlling a plurality of drive shafts to drive a tool and cause the tool to cut a workpiece while vibrating the tool in a fixed vibrating direction regardless of a cutting direction, a comparison unit that compares a command value of a cutting depth with an actual value of the cutting depth based on a vibration amplitude of the drive shaft when the vibrating direction and the cutting direction are not the same as each other, the cutting depth being a difference between a position of a face to be machined of the workpiece before machining and a position of the machined face after machining; and an adjustment unit that adjusts a movement of the tool so that the actual value becomes smaller when the actual value is larger than the command value.

Provided is a thermal displacement correction method using a machine learning method but making it possible to, on a user side, calculate a thermal displacement amount appropriate to a machine tool of the user and correct the thermal displacement. In a machine tool on a target user side, a thermal displacement amount between workpiece and tool corresponding to a temperature at a preset measurement point is calculated based on a parameter defining a relation between the temperature and the thermal displacement amount, and a positioning position for workpiece and tool is corrected in accordance with the calculated thermal displacement amount. On a manufacturer side, operational status information of the machine tool on the target user side is obtained, an operational status identical to the obtained operational status on the target user side is reproduced with a machine tool of a same type as the machine tool on the target user side based on the obtained operational status information, a temperature at a measurement point identical to the measurement point on the machine tool on the target user side and a thermal displacement amount between workpiece and tool are measured during reproduction, and the parameter is calculated by machine learning based on the measured temperature and thermal displacement amount. The parameter in the machine tool on the target user side is updated with the calculated parameter.

A three-finger mechanical gripper system is provided, which includes a torque sensor, a three-finger mechanical gripper, an image capturing module and a controller. The three-finger mechanical gripper is connected to the torque sensor. The controller is connected to the torque sensor, the three-finger mechanical gripper and the image capturing module. The image capturing module captures the image of a training object. The controller controls the three-finger mechanical gripper to grip the training object by a plurality of gripper postures respectively and calculates the torque information of each gripper posture according to the measured values of the torque sensor. Then, the controller performs a training process according to the image of the training object and the torque information of the gripper postures in order to obtain a training result of the training object.

A system and method for optimized industrial production using machine learning. The method includes creating a model defining dependencies among a plurality of parameters for an industrial production process, the plurality of parameters including a plurality of controlled parameters and a plurality of monitored parameters; training an agent via reinforcement learning based on iterative application of the model, wherein the agent is trained to determine new values for the plurality of controlled parameters based on current values of the plurality of monitored parameters in order to optimize the industrial production process with respect to at least one predetermined objective; and iteratively modifying, by the trained agent, current values of the plurality of controlled parameters in real-time during operation of the industrial production process.

The example systems, methods, and/or computer-readable media described herein help with design of highly accurate models of un-erupted or partially erupted teeth and help fabricate of aligners for un-erupted or partially erupted teeth. Automated agents that use machine learning models to parametrically represent three-dimensional (3d) virtual representations of teeth as 3D descriptors in a 3D descriptor space are provided herein. In some implementations, the automated agents described herein provide instructions to fabricate aligners for at least partially un-erupted teeth using representative 3D descriptor(s) of a tooth type.