A lathe includes a spindle, an opposite spindle, a tool post, a controller, and a contact-type breakage detector. The controller determines whether the cut-off tool is broken according to a control parameter for controlling at least one of the spindle and the opposite spindle at a first detection timing immediately after the cut-off tool cuts off the bar material while the opposite spindle holds the bar material held by the spindle. The controller determines whether the cut-off tool is broken according to a detection result by the contact-type breakage detector at a second detection timing different from the first detection timing.

A sensor (10) for inspecting a tool (28) of a machine tool (24) is provided that has a radio frequency transmitter (12) for generating a radio frequency signal, a radio frequency receiver (14) for generating a received signal from a received radio frequency signal, a coupling unit (16) to couple a radio frequency signal into the tool (28) and to decouple it from the tool (28), and a control and evaluation unit (20) to determine a time of flight of a radio frequency signal transmitted from the radio frequency transmitter (12) and received again by the radio frequency receiver (14) with reference to the received signal of the radio frequency receiver (14).

A diagnostic system includes a tool to perform machining on a workpiece, a test piece to be subjected to test machining by the tool for abnormal detection, a sensor to generate sensor data representing a physical quantity generated in the test machining, and circuitry. The test piece is different from the workpiece. The circuitry controls the tool to perform the test machining in each cycle of machining on the workpiece and calculates a degree of abnormality from the sensor data of the test machining. The test machining is performed on the test piece under a test machining condition corresponding to a machining condition of the machining on the workpiece.

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.

An on-machine monitoring system for the failure state of the rotating tool and a detection method thereof are provided. The monitoring system includes a detector, a data processing controller, a tool data server, and a Hall current sensor. A tool diameter and a tool length are determined according to image sequences of a tool. A wear state and a breakage state of the tool are determined according to the tool diameter and the tool length. A spindle current signal is acquired by the Hall current sensor. And an edge chipping state and a breaking state of the tool are determined according to the spindle current signal.

A processing system comprises a tool that processes a workpiece composed of a metal member, a motor that rotates the workpiece or the tool, a control unit that controls the motor, and a measurement unit that obtains an electrical quantity of the motor, wherein the control unit changes a rotational speed of the motor based on a difference between a first electrical quantity and a second electrical quantity, the first electrical quantity is an electrical quantity obtained by the measurement unit while the motor rotates before the workpiece is processed, and the second electrical quantity is an electrical quantity obtained by the measurement unit while the workpiece is processed.

A device for predicting a blade breakage of a bandsaw blade of a bandsaw. The device comprises an eddy-current sensor configured to generate a sensor signal that is dependent on a technical parameter of the bandsaw blade. The device further comprises an evaluation unit configured to evaluate the sensor signal, to compare the sensor signal with a predetermined tolerance range, and to generate a warning signal that indicates an imminent blade breakage of the bandsaw blade if the sensor signal lies outside of the predetermined tolerance range.

The disclosure provides a milling system and method under different lubrication conditions. The system uses a tool to mill the workpiece, a force measuring system to measure the milling force, a tool change system to replace the tools, a tool storage to store the tools. It can store the tools, provide the lubricating oil to the milling surface, select different tools according to different processing conditions, select the best angle differences of the unequal spiral angle tools according to different conditions comprising dry cutting, casting-type lubrication, minimal quantities of lubrication or minimal quantities of nanofluid lubrication, and/or choose the optimal tool according to different cutting parameters in order to obtain the minimum milling force.

A method for inspecting normality of a spindle of a machine tool is provided. Spectral analysis, time domain analysis and principal components analysis are performed on a vibration signal that results from the vibration of the spindle, so as to build a Gaussian mixture model. Then, based on a difference between the Gaussian mixture model and a predetermined reference model, whether the machine tool is operating normally can be determined in real time.

The present disclosure relates to a method for online monitoring defect of a milling tool, comprising the steps of: 1) installing a vibration sensor module on a machine tool spindle; 2) acquiring initial sample data; 3) setting a threshold value ΔS.sub.0 with a time interval of T; 4) measuring vibration signals of n blades in x, y and z directions in each period T.sub.0; 5) shaping to obtain n strong vibration cutting wave data respectively formed by n blades in x and y directions in each period T.sub.0; 6) analyzing and processing the strong vibration cutting wave data to obtain the difference ΔS.sub.0.sup./ between the cutting strong vibration wave areas formed by each blade in each period T.sub.0; 7) outputting a blade wear or defect signal to a display alarm module according to the constraint conditions by a data comparing and analyzing module, and giving an alarm by a display alarm module.