A diagnostic apparatus of the invention acquires normal data related to an operating state during normal operation of an industrial machine, stores the normal data, generates a learning model by learning based on the stored normal data, and performs an estimation process for normality or abnormality of an operation of the industrial machine using the learning model. The diagnostic apparatus of the invention further generates verification data including at least one piece of abnormal data based on the stored normal data to verify validity of the learning model on receiving a result of the estimation process using the learning model based on the verification data.

A numerical control system detects a state amount indicating a state of machining operation of a machine tool, creates a characteristic amount that characterizes the state of machining operation from the detected state amount, infers an evaluation value of the state of machining operation from the characteristic amount, and detects an abnormality in the state of machining operation on the basis of the inferred evaluation value. The numerical control system generates and updates a learning model by machine learning that uses the characteristic amount, and stores the learning model in correlation with a combination of conditions of the machining operation of the machine tool.

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.

The state of a cutting fluid after machining is predicted. A machine learning device includes: an input data acquisition unit that acquires input data including arbitrary machining conditions for an arbitrary work in machining by an arbitrary machine tool and state information indicating a state of a cutting fluid before machining is performed under the machining conditions; a label acquisition unit that acquires label data indicating state information of the cutting fluid after the machining is performed under the machining conditions included in the input data; and a learning unit that executes supervised learning using the input data acquired by the input data acquisition unit and the label data acquired by the label acquisition unit to generate a learned model.

A teaching device capable of teaching not only movement work but also more detailed working content. The teaching device is provided with input section for inputting work information such as work of pinching workpieces which is carried out by a robot arm at a working position. When carrying out motion capture by moving jig (an object which mimics the robot arm) which is provided with marker section, a user manipulate input section at an appropriate timing to input the working content to be performed by the robot arm as work information, and thus it is possible to set fine working content of the robot arm in teaching device. Accordingly, teaching device is capable of linking positional information of jig and the like and work information generating control information for controlling the robot arm.

A machine learning device of a controller observes, as state variables expressing a current state of an environment, teaching position compensation amount data indicating a compensation amount of a teaching position in control of a robot according to the teaching position and data indicating a disturbance value of each of the motors of the robot in the control of the robot, and acquires determination data indicating an appropriateness determination result of the disturbance value of each of the motors of the robot in the control of the robot. Then, the machine learning device learns the compensation amount of the teaching position of the robot in association with the motor disturbance value data by using the observed state variables and the determination data.

A control and learning module for controlling a robotic arm includes at least one learning module including at least one neural network. The at least one neural network is configured to receive and be trained by both state measurements based on measurements of current state and observation measurements based on observation data during an initial learning phase. The at least one learning module is further configured to be re-tuned by updated observation data for improved performance during an operations and secondary learning phase when the robotic arm is in normal operation and after the initial learning phase.

A machine learning device for learning a threshold value of detecting an abnormal load in a machine tool, includes a state observation unit, and a learning unit. The state observation unit observes a state variable obtained based on at least one of information about a tool, main spindle revolution rate, and amount of coolant of the machine tool, material of a workpiece, and moving direction, cutting speed, and cut depth of the tool, and the learning unit learns the threshold value of detecting an abnormal load based on training data created from an output of the state observation unit and data related to detection of an abnormal load in the machine tool and on teacher data.

A non-transitory computer readable information recording medium stores an evaluation program for operating first and second motor control units, for evaluating operation characteristics related to a control device including a first motor control unit configured to control a first motor driving a first axis, and a second motor control unit configured to control a second motor driving a second axis. The evaluation program operates the first and second motor control units so that a shape of a movement path of a control target moved by the first and second axes includes at least a cornered shape in which both rotation directions of the first and second motors do not invert, and an arc shape in which one of the first and second motors rotates in one direction, and a rotation direction of the other of the first and second motors inverts.

An abnormality detector includes a signal output unit for detecting a sign of an abnormality based on a physical quantity acquired from a manufacturing machine and outputting a signal; and a machine learning device including state observation unit for observing, as a state variable representing a present state of the environment, physical quantity data indicating the physical quantity related to an operation of the manufacturing machine from the manufacturing machine; a label data acquisition unit for acquiring, as label data, operation state data indicating an operation state of the manufacturing machine; a learning unit for learning the operation state of the manufacturing machine with respect to the physical quantity, using the state variable and the label data; and an estimation result output unit for estimating the operation state of the manufacturing machine using a learning result by the learning unit and outputting an estimation result.