A workpiece data processing method and apparatus are for accurately determining a relationship between production equipment processing parameters/ambient condition data and workpiece quality inspection results. A workpiece data method includes acquiring processing condition data, a quality attribute value and quality inspection result data of each of multiple workpieces processed by a piece of production equipment, the processing condition data of one workpiece including a processing parameter used by the production equipment when processing the workpiece and ambient condition data of the production equipment when processing the workpiece; determining a first relationship between the quality attribute value of the workpiece processed by the production equipment and the ambient condition data of the production equipment when processing the workpiece and the processing parameter of the production equipment; and determining a second relationship between the quality inspection result data and quality attribute value of the workpiece processed by the production equipment.

A machine learning device performs machine learning with respect to a servo motor controller that converts a three-phase current to a two-phase current of the d- and q-phase. The machine learning device includes: a state information acquisition unit configured to acquire, from the servo motor controller, state information including velocity or a velocity command, reactive current, and an effective current command and effective current or a voltage command; an action information output unit configured to output action information including a reactive current command to the servo motor controller; a reward output unit configured to output a value of a reward of reinforcement learning based on the voltage command or the effective current command and the effective current; and a value function updating unit configured to update a value function on the basis of the output value of the reward, the state information, and the action information.

A method, according to the present invention, of adjusting control parameters used in a control apparatus of an electric motor includes the steps of: computing a first frequency characteristic (Step 1); computing a present speed-proportional gain range (Step 2); computing a present mechanical-system characteristic constant (Step 3); computing a present proportional gain range (Step 4); computing a secular characteristic (Step 5); computing a secular speed-proportional gain range (Step 6); computing a secular proportional gain range (Step 7); and selecting proportional gain values (Step 8).

Systems and methods for operating a motor according to parameters provided by an autotuning component if available are described. A controller can be coupled to a drive which operates a motor for executing a task that can be related to a drilling operation for oil and gas. The controller stores initial parameters and checks for new parameters provided by the autotuning component which are stored on the drive after the autotuning component autotunes the motor. If there are new parameters, they are given priority over the initial parameters.

A control circuit includes: a controller; a controlled system; and a filter for smoothing a return signal. The controller acts on the controlled system vis--vis a control signal and the return signal acts on the controller. The controller and the filter are simultaneously adjustable by an adjustment. In an embodiment, the adjustment is made on the basis of a method that includes: measuring or estimating an output signal as a measurement or estimate; transferring, using the measurement or estimate, the output signal into the return signal; determining a power density spectrum of the return signal; and limiting a control signal of the controller such that a power of the control signal does not exceed a predefined limiting value.

A method, according to the present invention, of adjusting control parameters used in a control apparatus of an electric motor includes the steps of: computing a first frequency characteristic (Step 1); computing a present speed-proportional gain range (Step 2); computing a present mechanical-system characteristic constant (Step 3); computing a present proportional gain range (Step 4); computing a secular characteristic (Step 5); computing a secular speed-proportional gain range (Step 6); computing a secular proportional gain range (Step 7); and selecting proportional gain values (Step 8).

A control device of a wire electric discharge machine and a machine learning device are provided that can appropriately and readily determine a correction parameter. The control device, which optimizes the correction parameter for wire electrical discharge machining process, includes a machine learning device configured to learn the correction parameter for the wire electrical discharge machining process. The machine learning device includes a state observation unit configured to observe, as a state variable, condition data indicative of a condition for the wire electrical discharge machining process, a determination data acquisition unit configured to acquire determination data indicative of the correction parameter of the case where machining precision is favorable in the wire electrical discharge machining process, and a learning unit configured to learn the correction parameter in association with the condition for the wire electrical discharge machining process using the state variable and the determination data.

A method, according to the present invention, of adjusting control parameters used in a control apparatus of an electric motor includes the steps of: computing a first frequency characteristic (Step 1); computing a present speed-proportional gain range (Step 2); computing a present mechanical-system characteristic constant (Step 3); computing a present proportional gain range (Step 4); computing a secular characteristic (Step 5); computing a secular speed-proportional gain range (Step 6); computing a secular proportional gain range (Step 7); and selecting proportional gain values (Step 8).

A machine learning device performs machine learning with respect to a servo control device including a velocity feedforward calculation unit. The machine learning device comprises: a state information acquisition unit configured to acquire from the servo control device, state information including at least position error, and combination of coefficients of a transfer function of the velocity feedforward calculation unit; an action information output unit configured to output action information including adjustment information of the combination of coefficients included in the state information, to the servo control device; a reward output unit configured to output a reward value in reinforcement learning based on the position error included in the state information; and a value function updating unit configured to update an action value function on the basis of the reward value output by the reward output unit, the state information, and the action information.

A learning model construction device used in a machine tool which performs cutting processing constructs a learning model for learning temperature-related information after processing of a spindle motor during cutting processing. A learning model construction device includes an input unit that inputs cutting processing conditions and a present temperature of a spindle motor. The learning model construction device also includes a learning unit that receives the cutting processing conditions and the present temperature of the spindle motor and a label which is a temperature of the spindle motor after the cutting processing is performed as a set of teaching data and performs machine learning on the basis of the teaching data to thereby construct a learning model for learning temperature-related information. after processing of the spindle motor during the cutting processing.