A dental milling machine for producing a dental object having a sensor for detecting signals caused by a machining tool and an electronic controller for controlling the machining tool based on the detected signals.

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.

A system and method for monitoring and predicting wear of a cutting tool used for machining a workpiece is disclosed. The system includes a cutting tool having a shank and a cutting head. The system also includes a split, modular and wireless wear detection system including one or more sensors mounted to the cutting tool for providing a data signal representative of a physical condition of the system, and a data recording and data transmitting device for recording the data signal from the one or more sensors and for generating and transmitting a data signal to a processor. The processor applies a machine learning data processing technique in real time to monitor and/or predict a condition of various components and/or parameters of the system during a metal cutting operation.

A machine tool includes a workpiece spindle device which rotates a workpiece, a tool post which can move a tool in a first axis direction (X-axis direction) which is a radial direction of the workpiece and a second axis direction (Z-axis direction) which is an axial direction of the workpiece, and an articulated robot including a plurality of arms, a plurality of joints, and end effectors. The plurality of joints connect the plurality of arms in a rotatable manner around an axis parallel to a third axis (Y-axis) orthogonal to the first axis and the second axis, and the end effectors move in a plane parallel to a movement plane of the tool.

A method includes measuring raw sensor information of a tool over a period of time with a sensor tag coupled to an external surface of the tool. The method also includes determining one or more events of the tool based on the raw sensor information and calculating an aggregate time for each type of event from the one or more events determined over the period of time. The method also includes wirelessly transmitting a history of the one or more events from the sensor tag to a remote computing device. The history comprises the aggregate time for each type of event from the one or more types of events over the period of time.

To provide an automatic chuck greasing device in a crankshaft miller capable of ensuring chuck greasing without missing its timing and preventing failure to remove excess grease by wiping. In a crankshaft miller (1) which cuts, using a rotary cutter (7), a workpiece (2) held by a chuck (3), the automatic greasing device includes: a grease supply device (25) for supplying grease to sliding parts inside a chuck (3) including one between a chuck body (3a) and a chuck jaw (3b) in response to a greasing command signal outputted by a greasing command signal output unit (35) which has received a rotary cutter replacement command signal as a trigger signal from a rotary cutter replacement command signal output unit (33); and a notification means (30a, 30b) for notifying that a cutter (7) needs replacement in response to a notification command signal outputted by a notification command signal output unit (36) which has received the rotary cutter replacement command signal as the trigger signal from a rotary cutter replacement command signal output unit (33).

Tool bodies, tools and machines for operating the tool include electronic circuits for providing data, collecting data, analyzing data and for controlling machines based on such data. Tool bodies and tools may include electronic circuits having data collecting sensors, which may be embedded in a housing with the electronic circuit and/or positioned outside of such a housing. Sensors include temperature sensors, motion sensors, strain sensors, moisture sensors, electrical resistance sensors, position sensors, antennas, and other components.

A machine tool includes: a spindle that allows a tool to be detachably mounted thereon; a tool changing device that holds a plurality of the tools, and attaches and detaches any of the tools to and from the spindle; a date and time storage unit that stores date and time when operation of the spindle is stopped last; and a calculation unit that calculates unused time of the spindle on the basis of the date and time stored in the date and time storage unit, when the tool is to be detached from the spindle, wherein in a case where the unused time calculated by the calculation unit is longer than a predetermined threshold value, the tool changing device performs detaching motion of the tool from the spindle for longer time compared to a case where the calculated unused time is at or less than the threshold value.

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 thermal displacement compensation apparatus includes a temperature measuring unit, a thermal displacement estimating unit, a thermal displacement compensation unit, a displacement measuring unit, a data recording unit, a thermal displacement compensation learning unit, and a displacement measurement timing diagnostic unit. The displacement measuring unit measures a displacement of a machine tool after compensating an axis command value. The thermal displacement compensation learning unit determines a thermal displacement estimation formula based on temperature information and the displacement recorded in the data recording unit. The displacement measurement timing diagnostic unit compares the temperature information at a past displacement measurement recorded in the data recording unit with current temperature information obtained from the temperature measuring unit, and determines whether to measure the displacement by the displacement measuring unit or not at a predetermined diagnosis timing.