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 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 comprises a state information acquisition unit configured to acquire state information including conditions including conditions of a spindle speed, a feed rate, the 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.

A machining apparatus is provided with a machine learning device that performs machine learning. The machine learning device performs the machine learning by receiving the input of machining accuracy between a machining shape of a workpiece measured on-machine and design data on the workpiece and machining time of the workpiece measured by a measurement device. Based on a result of the machine learning, the machining apparatus changes machining conditions such that the machining accuracy increases and the machining time becomes as short as possible.

An evaluation program creation device according to the present disclosure comprises a parameter acquisition unit that acquires a parameter relating to machining by an industrial machine, a shape selection unit that selects a machining shape necessary for evaluating the parameter on the basis of the acquired parameter, a dimension calculation unit that calculates the dimension of each part of the machining shape necessary for evaluating the parameter on the basis of the acquired parameter and the machining shape necessary for evaluation, a program creation unit that creates an evaluation program on the basis of the machining shape selected by the shape selection unit and the dimension of each part calculated by the dimension calculation unit, and a program output unit that outputs the evaluation program created by the program creation unit.

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.

The present invention includes (1) inputting, into a model expression that defines relation between various operating values of a facility operating on a material to be rolled and energy consumption of the facility, various actual operating values as the various operating values, to calculate an actual calculation value of the energy consumption; (2) dividing the actual value of the energy consumption by the actual calculation value to calculate a reference learning value of the energy consumption; (3) inputting the set operating value defined by the setting calculation unit only in one operating value, among various operating values of the model expression, while inputting the actual operating values collected by the actual value unit in other operating values to calculate a pseudo-actual calculation value of the energy consumption; (4) dividing the actual calculation value by the pseudo-actual calculation value to calculate a correction learning value; and (5) inputting the various set operating values as the various operating values of the model expression to calculate a prediction value of the energy consumption for the material to be rolled, which is scheduled to be conveyed to the rolling line next time or later, and multiplies the prediction value by the reference learning value and the correction learning value to calculate a corrected prediction value of the energy consumption.

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 numerical controller calculates a machining path based on a lathe turning cycle instruction and the settings of a machining path and machining conditions of the lathe turning cycle instruction. An evaluation value used to evaluate cycle time required for machining a workpiece performed according to the calculated machining path and the machining quality of the machined workpiece is calculated to perform machine learning of adjustment of the machining path and the machining conditions. By the machine learning, a machining path based on a complex lathe turning cycle instruction is optimized.

A machining apparatus is provided with a machine learning device that performs machine learning. The machine learning device performs the machine learning by receiving the input of machining accuracy between a machining shape of a workpiece measured on-machine and design data on the workpiece and machining time of the workpiece measured by a measurement device. Based on a result of the machine learning, the machining apparatus changes machining conditions such that the machining accuracy increases and the machining time becomes as short as possible.

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.