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 first diamond layer made of polycrystalline diamonds and doped with foreign atoms, is arranged on a metal surface of a machining tool, and is used to detect the degree of wear of an undoped polycrystalline second diamond layer, which is arranged on the doped diamond layer and forms a functional region of the machining tool, wherein at least one physical parameter is detected continuously or periodically during operation of the tool, and wherein a change in the parameter indicates the degree of wear of the undoped second diamond layer. The doped diamond layer forms an “intelligent stop layer” for the tool because as a result of change in the transition from the undoped to the doped layer, the conductivity of the system changes, for example, and this change can be used to form a stop signal for the machine drive before the tool and the machined workpiece are damaged.

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.

A numerical control system detects a state amount indicating a state of machining operation of a machine tool, creates a characteristic amount that characterizes the state of machining operation from the detected state amount, infers an evaluation value of the state of machining operation from the characteristic amount, and detects an abnormality in the state of machining operation on the basis of the inferred evaluation value. The numerical control system generates and updates a learning model by machine learning that uses the characteristic amount, and stores the learning model in correlation with a combination of conditions of the machining operation of the machine tool.

A cutting apparatus includes a management unit having a measuring unit for measuring an amount of light emitted from a light emitter and received by a light receiver while a cutting blade is positioned between the light emitter and the light receiver, a measured waveform forming section for forming a measured waveform representing the configuration of an outer circumferential region of the cutting blade, and an ideal waveform recognizing section for recognizing one of the comparative waveforms that has the greatest number of waveform regions similar to the measured waveform as an ideal waveform, a difference calculating section for calculating the area of a region where there is a difference between the measured waveform and the ideal waveform.

Apparatuses, methods and storage medium associated with monitoring or assisting in monitoring of an equipment process are disclosed herein. In embodiments, an apparatus may comprise an analyzer to: receive a plurality of simulation results of a plurality of control limit and alert zone combinations for potential use with a control chart to monitor the equipment process, and calculate a plurality of performance metrics for each of the plurality of control limit and alert zone combinations, using the plurality of simulation results. The apparatus may further select an optimal combination of control limits and alert zones, based at least in part on the plurality of performance metrics, and configure an equipment process monitor with the selected optimal combination of control limits and alert zones for use with a control chart to monitor the equipment process. Other embodiments may be described or claimed.

A method for automated condition monitoring whereby techniques of automated vibration analysis and signal processing are combined with deep learning/machine learning techniques for an enhanced system of automated anomaly detection, problem classification, and problem regression. The method may be implemented in software, firmware or hardware to run autonomously. Machines monitored and analyzed according to the disclosed method are typically found in industrial plants or commercial applications, but the disclosed invention may be applied to any rotating equipment such as motors, fans, pumps, compressors, and etc., in any environment where they are functioning.

A monitoring device for managing at least one applicator fixture mounted on an applicator which has a magnetic body and connects a terminal to wiring via up and down movement. The monitoring device having a magnetic sensor which is mounted on the applicator and senses the movement of the magnetic body. At least one slave module receives identification information of the applicator fixture and mobility information of the magnetic body sensed by the magnetic sensor. A master module receives the identification information of the applicator fixture and the mobility information of the magnetic body in real time from the at least one slave module, while an interface module generates a monitoring interface indicating the degree of use of at least one applicator fixture by using the identification information of the applicator fixture and the mobility information of the magnetic body.

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.

An embodiment of the present invention provides a system in which it is possible to control a movement speed of a machining unit such that a machining load value of the machining unit is maintained to be equal to or smaller than a reference machining load value, and thus it is possible to lower a defective rate of a machining target and to improve stability of a robot. According to an embodiment of the present invention, there is provided a robot system for adjusting a machining load depending on tool wear, the robot system including: a robot that is coupled to the machining unit, moves the machining unit to change a position of a tool with respect to a machining target, and has a plurality of joints; a support that supports the machining target and moves the machining target to change a position of the machining target with respect to the tool; a sensor unit that is provided on the machining unit and measures an amount of current supplied to a machining motor which operates the tool or an operation force of the tool; and a controller that receives a measurement signal from the sensor unit and transmits a control signal to the robot and the support.