A method for predicting the remaining service life of a saw band in a bandsaw machine, in which a behavior or properties of the saw band clamped in the sawing machine are monitored by at least one sensor during operation of the sawing machine. Changes in the behavior or in properties of the saw band over time established on the basis of measurement signals of the sensor device are used for making a prediction about the time at which a band will break. The behavior or properties of the saw band are indirectly monitored using the at least one sensor device by way of detecting changes to components of the sawing machine which are operatively connected to the saw band.

A cutting tool, system, method and computer program product for increasing traceability of at least a first cutting edge of a cutting tool. The method includes detecting at least a first identification marker on the cutting tool, reading the at least first identification marker, decoding the at least first identification marker to determine at least a first cutting edge information data included in the machine readable code of the at least first identification marker, generating a first cutting tool identification data based on at least the at least first cutting edge information data, and storing at least any of the at least first cutting tool identification data and the at least first cutting edge information data in a memory.

A processing abnormality diagnostic device includes an abnormality diagnostic unit that diagnoses whether processing is abnormal using an abnormality threshold by a preset diagnostic model, a success or failure input unit that inputs success or failure of the diagnosis of the abnormality by the abnormality diagnostic unit, a measure determination unit that determines a measure when the diagnosis of the abnormality is input as failure through the success or failure input unit, an abnormality threshold change unit that updates the abnormality threshold, and a learning unit that relearns the diagnostic model using operation information of the machine tool when the diagnosis of the abnormality has failed. The measure determination unit determines which of the abnormality threshold change unit and the learning unit is to be adopted as the measure based on the operation information diagnosed by the diagnostic model when the diagnosis of the abnormality has failed.

A method includes acquiring an image of a cutter on a drill bit, determining a cutter dull condition based on the image of the cutter using a machine-learning method, wherein the machine-learning method is trained using a set of training images and a set of known cutter dull conditions, wherein each of the set of known cutter dull conditions is associated with one or more of a set of cutters depicted in the set of training images and determining a cutter degradation severity based the image of the cutter. The method also includes generating bit modification instructions based the cutter dull condition and the cutter degradation severity.

A system and a method for detecting tool status of a machine tool equipped with a controller and cutting tools are provided. The method includes the steps of: receiving a plurality of manufacturing signals; processing data from the manufacturing signals to organized information; selecting target features characterizing less noise, high effectiveness, and low multicollinearity from the organized information; fitting a classification model using tool status information with the organized information and the target features; obtaining tool status levels by using the classification model; and outputting tool treatments corresponding to the tool status levels.

A system for improving drill bit performance, comprising processors and memory storing instructions to obtain a wear report for a drill bit, wherein the wear report includes wear characteristics of the drill bit and drill operating parameters under which the drill bit was used; compare the wear characteristics of the drill bit to a threshold for acceptable drill bit wear; and adjust drill operating parameters based on the wear characteristics of the drill bit. The instructions to obtain the wear report for the drill bit include instructions to analyze images of the drill bit to identify wear characteristics; identify wear patterns based on the wear characteristics of the drill bit; identify probable drilling conditions based on the wear patterns; and generate the wear report for the drill bit based on the images of the drill bit, the wear characteristics of the drill bit, and the probable drilling conditions.

A real-time calibration-based tool condition monitoring system, device and method for repetitive machining operations to monitor tool conditions by a combination of a calibration procedure using a reference tool and similarity analysis comparing the reference tool with a working tool is disclosed.

An electronic tool monitoring system to monitor usage and wear of tools for mechanical machining, comprising, for each tool to be monitored, a tag with a unique identifier and a self-contained, stand-alone electronic monitoring device. The electronic monitoring device comprises: an electronic sensory arrangement; an electronic communication interface; an electronic operator; and an electronic control unit. The electronic communication interface and the electronic operator interface are programmed to store data indicative of a service life of the tool, identify and monitor usage and wear of the tool based on an output of the electronic sensory arrangement, and estimate residual service life of the tool based on monitored usage and wear of the tool and the stored data indicative of service life of the tool. The system will implement certain actions based on whether the service life of the tool is determined to be exhausted.

The method of the present invention includes: an integrity information collection step of collecting a current amount; a first inferiority information collection step of collecting a current amount; a setting step of setting an integrity reference value and an inferiority reference value on the basis of the information collected; an extraction step of collecting a current amount in real time, and extracting a measurement value from the collected information; a detection step of detecting an integrity index value of the machine tool by comparing the measurement value extracted with the integrity reference value and the inferiority reference value set in the setting step; and an outputting step of outputting the integrity index value detected.

The method of the present invention includes: an integrity information collection step of collecting a current amount for each of an inflow period, a constant period, and an outflow period; a first inferiority information collection step of collecting a current amount for each of the periods; a setting step of setting a warning value and a critical value in each of the periods based on the information collected; an extraction step of collecting a current amount, which is consumed when a workpiece is machined by the machine tool in real time, for each of the periods; a detection step of detecting an integrity index value of the machine tool by detecting the number of times that the current amount of the periods collected exceeds the warning value and the critical value in each of the periods; and an outputting step of outputting the integrity index value detected.