A universal manufacturing vise jaw plate for use on a machine tool with a vise fixture, and configured for real-time operational data collection and analysis. The vise plate comprises a combination of acoustic and vibration sensors with a plate for cutting tool monitoring applications.

A tool replacement timing management system includes a data acquisition section configured to acquire time series data indicating a machining state from a machine, a data cutout section configured to cut out specimen data from the time series data according to at least one condition or a combination of conditions selected from among machining, a tool, a workpiece, and a tool speed, a machining state variable calculating section configured to calculate a machining state variable, which is a statistical index, from the specimen data, a tool deterioration state generation section configured to generate tool deterioration state data in which the machining state variable is aligned in a time series, and a tool replacement timing calculating section configured to calculate a tool replacement timing on a basis of the tool deterioration state data.

A method includes steps of: performing preparation-phase measurements on a spindle of a machine tool to generate normal-condition signals; establishing a reference model based on the normal-condition signals; generating abnormal-condition signals based on the reference model and a preset damage value; utilizing principal components analysis (PCA) to characterize the normal-condition and abnormal-condition signals to obtain normal and abnormal probabilistic models, determining normal-condition and abnormal-condition reference curves based on the normal and abnormal probabilistic models; determining an alert-triggering line based on the abnormal-condition reference curve; determining a permissible range between the alert-triggering line and the normal-condition reference curve; and generating a warning signal when it is determined that a detection value falls outside of the permissible range, wherein the detection value is obtained based on application-phase measurements performed on the spindle.

An abnormality factor identification apparatus includes a sensor signal obtaining unit that obtains sensor signals associated with the physical state of a machine, an operating state determination unit that determines operating states of the machine based on information obtained from the machine, an abnormality level calculation unit that calculates the abnormality levels of the sensor signals for each operating state of the machine determined by the operating state determination unit, and a factor identification unit that determines a factor in an abnormality in the machine from historical data being a series of the abnormality levels for each operating state.

A self-aware machine platform is implemented through analyzing operational data of machining tools to achieve machine tool damage assessment, prediction and planning in manufacturing shop floor. Machining processes are first identified by matching similar processes through an ICP algorithm. Machining processes are further clustered by Hotelling's T-squared statistics. Degradation of the machining tool is detected through a trend of the operational data within a cluster of machining processes by a monotonicity test, and the remaining useful life of the machining tool is predicted through a particle filter by extrapolating the trend under a first-order Markov process. In addition, process anomalies across machines are detected through a combination of outlier detection methods including SOMs, multivariate regression, and robust Mahalanobis distance. Warnings and recommendations are flexibly provided to manufacturing shop floor based on policy choice.

A machine tool configured to attach multiple tools to a turret is provided to improve the efficiency of a tool exchanging operation and, thus, also the production efficiency. A control device determines whether an expired tool, which is a tool needs to be exchanged, is present among the tools held by the turret. The control device sets a tool exchanging position, which is a position within the movable range of a head section and is a position where the expired tool is exchanged with a new tool. If the control device determines that an expired tool is present, the control device causes a head section driving device to move the head and rotate the turret to position the expired tool at the tool exchanging position.

A tool wear monitoring and predicting method is provided, and uses a hybrid dynamic neural network (HDNN) to build a tool wear prediction model. The tool wear prediction model adopts actual machining (cutting) conductions, sensing data detected at the current tool run of operation and the predicted tool wear value at the previous tool run of operation to predict a predicted tool wear value at the current tool run. A cyber physical agent (CPA) is adopted for simultaneously monitoring and predicting tool wear values of plural machines of the same machine type.

To efficiently estimate and detect a tool life of a machine tool while lowering an occupation ratio in a data band of a communication line between a machine tool and an upper-level controller, a management apparatus or the like. A machine tool to be managed by a production management apparatus includes a motor for moving one of a workpiece to be machined and a tool for performing a machining operation, an amplifier for driving the motor, a servo controller for controlling the motor via the amplifier based on a notification of a predetermined machining operation, and an NC device for notifying the servo controller of the predetermined machining operation. The servo controller obtains servo data representing a load on the amplifier, performs a first processing for compressing the servo data, and transmits the compressed data to the NC device.

Provided is a tool life estimating device that enables estimation of a life of a tool used in a machine tool according to changes in machining conditions. The tool life estimating device includes a state observation unit that acquires machining information indicative of a status of the machining in a state where the life of the tool remains sufficiently, wherein the machining information is acquired from log data recorded while the machine tool is operated, and creates input data based on the machining information that has been acquired; a learning unit that constructs a learning model in which clusters of the machining information are created by unsupervised learning using the input data that has been created by the state observation unit; and a learning model storage unit that stores the learning model.

A scattering direction, a scattering range, a scattering speed, or the like of chip generated by machining is acquired by image pickup unit, and a state (such as wearing and fracture) of a tool is estimated from the acquired dynamic information. A machine learning device is used to perform the estimation and the machine learning device is caused to learn a state of a tool relative to the combination of a plurality of dynamic information on the vicinity of the tool, and the state of the tool is estimated based on a result of the learning.