The disclosure provides a method to estimate tool life on a cutting machine. A sensor is installed on the cutting machine to measure the cutting thrust of a cutting tool during operation. Specifically, the information gathered from the sensor enables the cutting tool to cut a workpiece at different settings of cutting rate, with a maximum cutting thrust of the cutting tool measured during each cut, and defined as a characteristic signal. Through the characteristic signals obtained from the multiple cuts, factorization processing and inverse processing are initiated to obtain plural values in a complex remaining life index for the cutting tool. Using the cutting rate as the horizontal axis and the remaining life as the vertical axis, the complex remaining life and corresponding cutting rates are plotted on the vertical axis and the horizontal axis to form a line graph illustrating the complex remaining life index.

An information processing apparatus acquires detection information of a physical quantity that changes according to an operation state of a target device, transmits an acquisition request to the target device at predetermined time, to acquire context information relating to an operation status of the target device in response to the acquisition request, specifies a processing period in which the target device is in the middle of processing of the target object based on the predetermined time and the context information, extracts processing period detection information of the specified processing period from the detection information, and determines an occurrence of a defect relating to processing by the target device during the processing period based on the processing period detection information and the context information used for specifying the processing period.

An actuator of a machine is controlled by a control device with a controller structure in a normal operating mode and a special operating mode. The control device determines from a position setpoint value and a position actual value a control signal for the actuator. In the normal operating mode, the setpoint values are determined using a utility program, and in the special operating mode by a system program that is different from the normal operating program. In the special operating mode, a frequency characteristic for the actuator is determined on the basis of the sequence of setpoint values and associated actual values, and an evaluation for the actuator and/or of the controller structure is performed using the frequency characteristic and parameters of the controller structure. Depending on the evaluation, a message is transmitted to an operator of the machine or to a computing device over a network.

A machine learning apparatus that can determine the state of a tool from a force applied from the tool to a robot while the robot performs a work using the tool. A machine learning apparatus for learning a state of a tool used for a work by a robot includes a learning data acquisition section that acquires, as a learning data set, data of a force applied from the tool to the robot while the robot causes the tool to perform a predetermined operation, and data indicating the state of the tool during the predetermined operation, and a learning section that generates a learning model representing a correlation between the force and the state of the tool, using the learning data set.

A machine tool operation monitoring system which detects an abnormal operation of a machine tool 1 and in which when the operation of each machine tool 1 exceeds a normal operating range of each machine tool 1 and/or when a moving state of a constituent portion of the machine tool 1 and a material exceeds a normal range, wherein the machine tool 1 breaking out an operation abnormality is identified by any one of the following operations:

a. monitoring an image obtained by projecting reflected light from a reflecting display plate 22 provided for each machine tool 1 onto a camera 31; and
b. monitoring a difference in projecting direction of emitted light from a lamp 21 provided for each machine tool 1 onto an optical sensor 32.

A machine tool operation monitoring system which detects an abnormal operation of a machine tool 1 and in which when the operation of each machine tool 1 exceeds a normal operating range of each machine tool 1 and/or when a moving state of a constituent portion of the machine tool 1 and a material exceeds a normal range, wherein the machine tool 1 breaking out an operation abnormality is identified by any one of the following operations: a. monitoring an image obtained by projecting reflected light from a reflecting display plate 22 provided for each machine tool 1 onto a camera 31; and b. monitoring a difference in projecting direction of emitted light from a lamp 21 provided for each machine tool 1 onto an optical sensor 32.

A balance and runout amount adjustment system for a rotary tool includes a rotary tool constituted of a tool holder mounted on a spindle, a balance determining device configured to obtain outer circumference position data of the rotary tool and to determine a mass balance of the rotary tool based on the outer circumference position data obtained, in the course of rotation of the rotary tool and a runout determining device configured to obtain shape data of the rotary tool and to determine a runout amount of the rotary tool based on the shape data obtained, in the course of rotation of the rotary tool. The rotary tool is configured to be capable of adjustment of the mass balance based on the result of the determination made by the balance determining device and capable also of adjustment of the runout amount based on the result of the determination made by the runout determining device.

A machine learning apparatus that can determine the state of a tool from a force applied from the tool to a robot while the robot performs a work using the tool. A machine learning apparatus for learning a state of a tool used for a work by a robot includes a learning data acquisition section that acquires, as a learning data set, data of a force applied from the tool to the robot. while the robot causes the tool to perform a predetermined operation, and data indicating the state of the tool during the predetermined operation, and a learning section that generates a learning model representing a correlation between the force and the state of the tool, using the learning data set.

An information processing apparatus acquires detection information of a physical quantity that changes according to an operation state of a target device, transmits an acquisition request to the target device at predetermined time, to acquire context information relating to an operation status of the target device in response to the acquisition request, specifies a processing period in which the target device is in the middle of processing of the target object based on the predetermined time and the context information, extracts processing period detection information of the specified processing period from the detection information, and determines an occurrence of a defect relating to processing by the target device during the processing period based on the processing period detection information and the context information used for specifying the processing period.

An actuator of a machine is controlled by a control device with a controller structure in a normal operating mode and a special operating mode. The control device determines from a position setpoint value and a position actual value a control signal for the actuator. In the normal operating mode, the setpoint values are determined using a utility program, and in the special operating mode by a system program that is different from the normal operating program. In the special operating mode, a frequency characteristic for the actuator is determined on the basis of the sequence of setpoint values and associated actual values, and an evaluation for the actuator and/or of the controller structure is performed using the frequency characteristic and parameters of the controller structure. Depending on the evaluation, a message is transmitted to an operator of the machine or to a computing device over a network.