A tool management system of a machine tool includes: a detection unit to detect at least one of vibration, acoustic waves produced during operation of the machine tool, and current value of a server motor of a driving machine tool; a tool replacement determination unit that determines the necessity to replace the tool on the basis of information related to a detection value of at least one of the vibration, the acoustic waves, and the current value detected during operation of the machine tool; and a detection start/end command setting unit that adds commands for a detection start point and a detection end point of at least one of the vibration, the acoustic waves, and the current value of the servo motor to a machining program.

A wire disconnection prediction device includes: a data acquisition part configured to acquire data relating to machining of a workpiece during machining of the workpiece by a wire electric discharge machine; a preprocessing part configured to create, based on the data acquired by the data acquisition part, machining condition data, machining member data and machining state data, as state data indicating a state of the machining; and a machine learning device configured to execute, based on the state data created by the preprocessing part, processing relating to machine learning using a learning model indicating correlation between a machining state in the wire electric discharge machine and presence/absence of a possibility of disconnection occurrence of a wire electrode in the wire electric discharge machine and a disconnection cause by a plurality of class sets.

A dental milling machine (100) for manufacturing a dental object (101), having a microphone (103) for detecting sound signals during a milling operation or an accelerometer for detecting vibration signals; and a position determining device (105) for determining a position of a milling tool (107) and/or a workpiece (111) based on the sound signals or the vibration signals.

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.

A testbed device includes high performance actuators, a video microscopy system and a plurality of high resolution, throughput sensors adapted or configured for collecting data that may be used in predictive modelling of machine processes.

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 turning tool includes a power receiving coil, a sensor, and a wireless unit. The power receiving coil receives power transmitted from a power transmitting coil in a non-contact manner. The sensor is electrically connected to the power receiving coil. The wireless unit transmits data detected by the sensor to an outside.

The invention relates to a method for determining the degree of wear of at least one component of a machining device (10), wherein at least one actual condition (19) of the machining device (10) is established and the at least one actual condition (19) is compared to at least one comparative condition (18) of the machining device (10), and a conclusion is drawn as to the degree of wear of the at least one component as a function of a deviation determined between the at least one actual condition (19) and the at least one comparative condition (18), wherein, to establish the at least one actual condition (19) and/or the at least one comparative condition (18), sound emissions (16) of the machining device (10) are captured. The invention also relates to a device (13) for determining a degree of wear of at least one component of a machining device (10), a machining device (10) for machining workpieces (11), and a computer program for determining a degree of wear of at least one component of a machining device (10).

The present application provides an on-machine point cloud detection and compensation method for processing complex surfaces, which comprises: step S1, installing a detecting and scanning actuator on an ultrasonic rolling machine tool; step S2, installing a processed workpiece on the chuck which is scanned by the detecting and scanning actuator to obtain the point cloud data of the workpiece in a coordinate system of detecting and scanning actuator, which is converted into the point cloud data of the workpiece in a coordinate system of machine tool; step S3, processing the point cloud data of the workpiece in the coordinate system of machine tool; step S4, obtaining and compensating the shape error feature of the workpiece according to theoretical design data of the processed workpiece and processed point cloud data of the workpiece in the coordinate system of machine tool. The accuracy and efficiency of complex surface strengthening is improved in the present application.

A CNC machine shaping tool is efficiently used by monitoring human audible sound during shaping. A sound information set is created for a tool shaping a workpiece. Shaping sounds are recorded and sliced into short term units. A human operator assigns tool condition labels to each slice. Short term units are combined into mid term units. Noise is reduced by profiling. Mid term sound related features of time and frequency domains are extracted. Dimensionality is reduced by robust principal component analysis. The principal component set is balanced, e.g. by SMOTE. A classifier and principal components are selected. An information set of patterns of values of selected principal components for the tool is created. In an industrial setting, shaping sounds are recorded, noise reduced and select principal component vector values are compared to the tool condition labeled patterns of values in the information set to identify tool condition before deterioration.