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.

A polishing tool wear amount prediction device, machine learning device, and system capable of predicting a wear amount of a polishing tool unit of a polishing tool during polishing are provided. The polishing tool wear amount prediction device includes a machine learning device which observes polishing condition data indicating a processing condition of polishing as a state variable indicating a current environment state and performs, based on the state variable, learning or prediction by using a learning model which stores a correlation of the wear amount of the polishing tool with respect to the processing condition of polishing.

An abnormality detection apparatus for working tools configured to be used in a machining process performed by a machine tool, the abnormality detection apparatus includes a storage portion which previously stores correlations between features of a plurality of operating portions relation to the machining process performed by the machine tool, and a tool condition of each of a plurality of working tool types, and a tool condition determining portion which determines the tool condition of the working tools based on the correlations.

Automatic system (1) for inspecting one cutting edge (2, 2′) of a ring shaped blade (3), wherein the ring shaped blade (3) is configured to be used in a plant for cutting one sheet of metallic material and extends around a central symmetry axis (y-y), the system (1) comprising: one supporting and moving group (4) rotatably mounted around one rotation axis (x-x) and configured to support the ring shaped blade (3) between at least one parking position and at least one reading position and for putting it in rotation around the rotation axis (x-x), wherein when the ring shaped blade (3) is supported in the at least one reading position, the rotation axis (x-x) is coincident with the central symmetry axis (y-y) of the ring shaped blade (3); one first emitting group (7), configured to emit at least one first inspection light beam (71) toward the supporting and moving group (4) and toward the cutting edge (2, 2′) of the ring shaped blade (3), when the blade is supported by the supporting and moving group (4); one second emitting group (8), configured to emit at least one second inspection light beam (81) toward the cutting edge (2, 2′) of the ring shaped blade (3); one first detecting group (9), configured to detect a first light beam reflected from the ring shaped blade (3), and one second detecting group (9′), configured to detect a second light beam reflected from the ring shaped blade (3), the first detecting group (9) and the second detecting group (9′) being both positioned at the supporting and moving group (4) and configured to detect the first light beam and second light beam reflected from the ring shaped blade (3), respectively, and output at least one respective detection signal (911, 912); one control and processing unit, configured to receive in input and process the at least one detection signal (911, 912), and output at least one quality index (I) of the cutting edge (2, 2′) of the ring shaped blade (3).

This machine tool system uses a plurality of mobile robots to convey workpieces to a plurality of machine tools, the machine tool system being provided with: machine tool control unit that issues work requests to the machine tools; a mobile robot control unit that determines workable times for the mobile robots on the basis of the work requests; and a determining unit that compares the workable times which are for the mobile robots and respectively planned by the mobile robots, and causes the mobile robot with the fastest workable time to execute the requested work.

A method, system and computer-usable medium are disclosed for monitoring and controlling a machining process of parts. Data as to dimensions of produced parts are gathered during a production process. The parts are produced based on part control plan. The data of the dimensions are plotted as to statistical information related to a distribution curve. Determination is made if a trend in the dimensional data approaches an upper control limit and a lower control limit. Corrective action is taken if the trend approaches either the upper control limit or the lower control limit.

An abnormality determination apparatus of the present invention acquires state data from work equipment provided with an attaching part to which a plural kinds of work parts are attached in a replaceable manner, identifies the kind of a work part attached to the attaching part, sets, corresponding to the identified kind of the work part, abnormality determination data for determining an abnormality of the work equipment, acquires, from among state data acquired from the work equipment, state data of a time when the identified kind of the work part was being attached, and compares the acquired state data with the set abnormality determination data to determine an abnormality of the work equipment.

A method for determining part wear, such as using a wear model, includes receiving, from a sensor, sensor data representing a surface of a wear part. The method further includes determining an estimated time until the part should be replaced. The method further includes batching together multiple wear parts that need replacing to enable a user to replace multiple parts in one maintenance period. The method may also include providing information to the user during replacement of a worn part to indicate the part location.

In a method for determining the state of wear of a tool, at least one optical image of a surface of the tool is recorded. Image data of the at least one optical image are processed in order to detect a wear zone. A surface extent and/or spatial extent of the wear zone is determined. The state of wear of the tool is classified on the basis of the extent determined. An apparatus for determining the state of wear of a tool correspondingly comprises a camera for recording at least one optical image of a surface of the tool, an image processing module which is configured in such a way that it processes image data of the at least one optical image in order to detect a wear zone, a computation module which is configured in such a way that it determines a surface extent and/or spatial extent of the wear zone, and a classifier module which is configured in such a way that it classifies the state of wear of the tool on the basis of the extent determined.