Provided is an abnormality detection device with which an abnormality of a machining state of a machine tool can be detected based on information on a section where machining is actually performed in machine tool-based machining. The abnormality detection device detects an abnormality of a machining state of a machine tool machining a workpiece with a tool. The machine tool includes a determination unit determining the machining state by using information related to an actual cutting section in the tool-based machining of the workpiece in the machine tool. The determination unit performs the machining state determination by using a deviation in position and length of a section recognized as the actual cutting section and a physical quantity in the actual cutting section acquired from the machine tool.

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 method is provided for assessing wear of a drill bit throughout its use for carrying out the drilling of elements to be drilled constituted by at least one layer and at least one material. The wear of the drill bit expresses its capacity to perform a drilling that meets at least one criterion of quality of a drilling. The method includes at least: measuring or detecting at least one parameter having an effect on the wear of the drill bit, the parameter being chosen from: the depth of drilling performed by the drill bit, and the entry of the drill bit into the element to be drilled; and determining at least one state of wear of the drill bit, each state of wear being determined as a function of one of the parameters and being characteristic of one of the criteria of quality of a drilling.

Information is collected from a management target device having a drive unit such as a machine tool, and this collected information is more practically used. An information collection device includes a collection unit that collects, from management target devices having a drive unit, operating state information which is information indicating an operating state of the management target device while operating accompanying movement of the drive unit; and a comparison unit that extracts a plurality of sets of information matching in a predetermined condition from the operating state information thus collected, and outputs a comparison result of the plurality of sets of information thus extracted.

This control system takes into account the thermomechanical aspects of materials to quickly and easily determine optimal cutting conditions and to automatically control machining to preserve the integrity of the workpiece. This system includes an acquisition module configured to acquire values of a set of input parameters relating to cutting conditions and material properties of the piece, and a microprocessor configured for determining at least one operating cutting parameter representative of a cutting signal from the machining apparatus using a set of output parameters of an integrity model previously constructed during a learning phase. The integrity model connects the set of input parameters to the set of output parameters comprising specific cutting coefficients representative of the material integrity of the piece, and establishes at least one fatigue threshold of the at least one cutting operating parameter. The fatigue threshold allows control of the progress of cutting operations.

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.

The disclosure relates to a tool part for a cutting tool having an identification marker arranged at the tool part, wherein the identification marker is a unique machine readable code associated with individual dimension information data. The individual dimension information data includes at least one individually measured dimension of the tool part as measured when manufacturing the tool part that will change while wearing the tool part. The disclosure further relates to a system, a method and computer program product for utilizing an identification marker on a tool part for a cutting tool, for determining a tool wear of the tool part.

The numerical controller creates a machining path on which a wire electrode is moved by analyzing blocks of a machining program, and creates interpolation data indicating an amount of movement for each interpolation period on the machining path. Further, the numerical controller calculates a length of the wire electrode used for machining (machined surface length) for each interpolation period, calculates a consumption amount of the wire electrode for each interpolation period, calculates a compensation amount for compensating the amount of movement based on the interpolation data, on the basis of the calculated consumption amount and compensates the amount of movement indicated by the interpolation data, based on the calculated compensation amount.

An accumulated wear amount calculation section calculates a wear amount of a blade edge of a punch and a wear amount of a blade edge of a die when a tool set of the punch and the die has processed a sheet metal once. The accumulated wear amount calculation section calculates an accumulated wear amount of the blade edge of the punch and an accumulated wear amount of the blade edge of the die by accumulating wear amounts of respective times when the tool set has processed the sheet metal multiple times. A tool management information managing section manages a state of the blade edge of each punch and each die by causing a tool management information storage section to store tool management information associating a tool identification code given to each punch and each die with the accumulated wear amounts calculated by the accumulated wear amount calculation section.

The wear status of a micro-endmill tool may be inferred by monitoring the chip production rate of the tool in operation. Chips may be extracted from a work area, captured on an adhesive surface, imaged, and counted to determine the chip production rate. When the rate of chip production falls, the feed rate of the micro-endmill may be modified to a level suitable for the current state of tool wear. In this manner, costly and inconvenient work stoppages to evaluate the wear status of a tool are eliminated.