OKUMA CORPORATION

A machining accuracy diagnosing device includes a temperature regulation operating pattern setting unit, a cutting condition setting unit, a temperature information acquiring unit, and a machining accuracy influence amount predicting unit. The temperature regulation operating pattern setting unit sets an operating pattern of a temperature regulating unit. The cutting condition setting unit sets a scheduled machining start time and a scheduled machining end time. The temperature information acquiring unit acquires an influencing temperature of the temperature regulating unit on the machine body temperature and/or an air temperature outside the plant. The machining accuracy influence amount predicting unit predicts an influence amount of the temperature regulating unit on the machining accuracy based on the operating pattern, the scheduled machining start time and the scheduled machining end time, and at least one of the influencing temperature and/or the air temperature outside the plant and a set temperature of the temperature regulating unit.

A machining accuracy diagnosing device includes a diagnosing information acquiring, an opening time setting unit, a cutting time setting unit, and a machining accuracy influence amount predicting unit. The diagnosing information acquiring unit acquires at least one of a room temperature inside the plant, a set temperature of a temperature regulating device, an air temperature outside the plant, a wind speed outside the plant, an opening degree of the door or the shutter, and a machine body temperature. The opening time setting unit sets an opening time of the door or the shutter. The machining accuracy influence amount predicting unit predicts an influence amount of opening of the door or the shutter on the machining accuracy based on the acquired information for diagnosis, the set opening time of the door or the shutter, and a set scheduled machining start time and a set scheduled machining end time.

A method for labeling a data set of a supervised learning machine includes: calculating a deviation degree from a reference state A for each corresponding data in cases where a plurality of state estimation targets are in the reference state A and a reference state B; calculating a normalized deviation degree by taking a map of the deviation degree from A for each of the corresponding data with a map set for each of the state estimation targets. The map is allowed to map the deviation degree from A in A on a specified value to be output by the supervised learning machine in A; allowed to map the deviation degree from A in B on a specified value to be output by the supervised learning machine in B.

A method for estimating a cause of a bearing failure includes: calculating a magnitude of a vibration with respect to a frequency calculating a vibration mean value by removing an influence of the rotation speed and calculating a mean value of the magnitudes of the vibrations having a same ratio of the frequency with respect to a rotational frequency from a result of the frequency analysis at the plurality of rotation speeds obtained in the calculating of the magnitude; extracting a bearing-caused vibration value of a characteristic ratio frequency from the vibration mean values; determining whether or not the bearing is abnormal based on the bearing-caused vibration value; and estimating a failure occurrence cause based on a relation of the bearing-caused vibration values in the integer multiplied characteristic ratio frequencies at which the vibrations are caused by the bearing when the bearing is determined to be abnormal in the determining.

A main spindle monitoring device determines an abnormality of a main spindle in a machine tool. The main spindle monitoring device includes a main spindle operation monitoring unit and a feed axis operation monitoring unit. The main spindle operation monitoring unit monitors a change of an operation condition of the main spindle based on a main spindle load. The feed axis operation monitoring unit monitors a change of an operation condition of a feed axis based on a feed axis load. The abnormality of the main spindle is determined when the main spindle operation monitoring unit detects the change of the operation condition of the main spindle and the feed axis operation monitoring unit does not detect the change of the operation condition of the feed axis in a state where a constant rotation speed is commanded to the main spindle while the feed axis is driving.

An environmental temperature change prediction device includes an environmental temperature acquisition unit, an outside temperature acquisition unit, a plant environment pattern setting unit, a prediction model generating unit, and an environmental temperature change prediction unit. The environmental temperature acquisition unit measures a machine body temperature. The plant environment pattern setting unit defines in advance a classification rule for classifying change trends of the environmental temperature into a plurality of patterns based on data of the environmental temperature and the plant outside temperature and environmental temperature prediction models. The prediction model generating unit selects the applicable plant environment pattern and determines a parameter of the environmental temperature prediction model corresponding to the selected plant environment pattern. The environmental temperature change prediction unit predicts a change in the environmental temperature in a future by the environmental temperature prediction model generated in the prediction model generating unit.

An accuracy diagnostic device includes a change amount detecting unit, a change amount recording unit, an accuracy change predictor, and an accuracy adjustment timing presenting unit. The change amount detecting unit detects a magnitude of a change in at least one of a state of a machine tool and a surrounding environment of the machine tool as a change amount. The change amount recording unit records the change amount. The accuracy change predictor predicts a future accuracy change of the machine tool using the change amount recorded in the change amount recording unit. The accuracy adjustment timing presenting unit presents a timing at which an accuracy adjustment is to be required for the machine tool based on the accuracy change predicted by the accuracy change predictor.

An abnormality diagnostic device for a feed axis mechanism diagnoses an abnormality occurrence and an abnormal portion of the feed axis mechanism. The feed axis mechanism transmits a rotation of a motor to a ball screw coupled by a coupling to rotate the ball screw. The abnormality diagnostic device includes a resonance frequency measuring unit and a diagnosis unit. The resonance frequency measuring unit measures a resonance frequency at a plurality of stroke positions within a stroke range of the feed axis mechanism. The diagnosis unit identifies the abnormal portion based on a relationship between the stroke positions and change amounts relative to a standard value of the measured respective resonance frequencies.

A relearning necessity determination method is provided for determining a necessity of relearning of a learned diagnostic model in a machine tool including a machining abnormality diagnosing unit. The machining abnormality diagnosing unit determines normal or abnormality of machining using the diagnostic model generated through machine learning. The method includes storing a cumulative cutting time or a cumulative cutting distance of a tool mounted to the machine tool as a tool usage, storing the tool usage when the machining abnormality diagnosing unit diagnoses the machining as machining abnormality, and determining the necessity of the relearning of the diagnostic model based on a frequency distribution of the tool usage stored in the storing of the tool usage.

A machining accuracy diagnosing device includes a diagnosing information acquiring, an opening time setting unit, a cutting time setting unit, and a machining accuracy influence amount predicting unit. The diagnosing information acquiring unit acquires at least one of a room temperature inside the plant, a set temperature of a temperature regulating device, an air temperature outside the plant, a wind speed outside the plant, an opening degree of the door or the shutter, and a machine body temperature. The opening time setting unit sets an opening time of the door or the shutter. The machining accuracy influence amount predicting unit predicts an influence amount of opening of the door or the shutter on the machining accuracy based on the acquired information for diagnosis, the set opening time of the door or the shutter, and a set scheduled machining start time and a set scheduled machining end time.