Abstract:
A machine tool has a cover that prevents chips generated by machining and cutting fluid from scattering around and a plurality of movable nozzles with liquid discharge directions thereof being movable so as to wash out chips that have adhered to or accumulated on an inner surface of the cover. The machine tool is capable of changing a discharge direction of each of the movable nozzles individually. The machine tool compares the state of the inside of the cover before start of machining with the state of the inside of the cover after chips are generated to determine an adhesion or accumulation state of chips. The machine tool thus calculates the liquid discharge direction to wash out chips from the movable nozzles based on a result of the determination.
Abstract:
A blade of an impeller has a blade surface made up of line elements. The line elements forming the blade surface are not parallel to each other. A relationship between a twist angle of the line elements and a distance between the origin and an intersection of a central line of the impeller and an extension line of a Z-axis projection line obtained by projecting each of the line elements onto a Z-axis projection plane is represented by a curve.
Abstract:
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.
Abstract:
A control device includes: a machine learning unit that machine-learns a control parameter that determines operating characteristics of a driving unit of a machine serving as a driving target of a motor and sets the control parameter to a motor control device; a health check operation instruction unit that outputs an instruction for a health check operation of driving the motor control device; an operation evaluation unit that acquires information indicating the operating characteristics of the driving unit, calculates an evaluation value on the basis of an evaluation function, and stores the evaluation value in a storage unit; and a deterioration estimation operation unit that estimates deterioration in the operating characteristics of the driving unit of the machine on the basis of the evaluation value stored in the storage unit and the evaluation value calculated by the operation evaluation unit when the health check operation was performed.
Abstract:
An abnormality detection apparatus includes a machine learning apparatus for learning waveform data concerning a physical quantity detected when a machine tool is normally operating. The machine learning apparatus observes the waveform data concerning the physical quantity detected when the machine tool is normally operating, as a state variable indicating a current environmental state, and learns a feature of the waveform data concerning the physical quantity detected when the machine tool is normally operating, using the observed state variable.
Abstract:
A machine tool acquires information related to manual operation from log data recorded when machining and the manual operation are performed in the machine tool, creates input data on the basis of the acquired information, acquires information related to occurrence or non-occurrence of a collision of a spindle or a tool at the time of the manual operation from the log data, and creates teacher data on the basis of the acquired information. Supervised learning is performed using the created input data and the created teacher data, and a learning model is constructed.
Abstract:
A machine tool of the present invention includes: a visual sensor that takes an image of unworked workpiece; an unworked workpiece shape information storing unit that stores unworked workpiece shape information obtained by the visual sensor; a worked workpiece shape information storing unit in which worked workpiece shape information is stored; a burr information calculating unit that recognizes a burr by comparing the unworked workpiece shape information with the worked workpiece shape information; a burr determining unit that determines the burr based on conditions including at least one of the location and the direction of the burr in the workpiece; a working method judging unit that decides whether or not to perform burring with a tool of the machine tool based on the determination result concerning the burr; and a working path generating unit that generates a working path for removing the burr judged to be a burr on which burring is to be performed with the tool.
Abstract:
Provided is a machine tool which performs a cutting work on a workpiece, in which holes are provided at three or more points in a circumferential direction of a jig in the vicinity of a point at which the jig comes into contact with the workpiece when the workpiece is placed on the jig, and the workpiece is moved toward a rotation center of the jig by blowing air from an air supply unit toward the workpiece through the holes.