An electromechanical joining system that uses an output force or output torque for performing a joining method and includes an electrical drive connected for driving a screw drive and is configured for generating actual values of force or torque that are provided as input variables to a monitoring device. The system includes a sensor configured for measuring the course of the forces or torques over time during the joining method and for detecting additional measurement values that are supplied to the monitoring device as input variables. Wherein the monitoring device links the supplied actual values with the supplied additional measurement values to detect upcoming wear of a wear-prone component of the electromechanical joining system. A method for analyzing the status of the electromechanical joining system is also disclosed.

Tool wear monitoring is critical for quality and precision of manufacturing of parts in the machining industry. Existing tool wear monitoring and prediction methods are sensor based, costly and pose challenge in ease of implementation. Embodiments herein provide method and system for monitoring tool wear to estimate Remaining Useful Life (RUL) of a tool in machining is disclosed. The method provides a tool wear model, which combines tool wear physics with data fitting, capture practical considerations of a machining system, which makes the tool wear prediction and estimated RUL more stable, reliable and robust. Further, provides cost effective and practical solution. The disclosed physics based tool wear model for RUL estimation captures privilege of physics of tool wear and easily accessible data from CNC machine to monitor and predict tool wear and RUL of the tool in real-time.

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.

An apparatus includes an input unit, a processing unit, and an output unit. The input unit is configured to provide the processing unit with sensor data for an item of equipment. The processing unit is configured to implement at least one machine learning algorithm, which has been trained on the basis of a plurality of calibration sensor data for the item of equipment. Training of the at least one machine learning algorithm includes processing the plurality of calibration sensor data to determine at least two clusters representative of different equipment states. The processing unit is configured to implement the at least one machine learning algorithm to process the sensor data to assign the sensor data to a cluster of the at least two clusters to determine an equipment state for the item of equipment. The output unit is configured to output the equipment state for the item of equipment.

A device that estimates a life of a clamping mechanism clamping rotation of a rotary table includes a machine learning device. The machine learning device observes operating state data of the rotary table and operation history data of the rotary table as a state variable indicative of a current state of an environment, and acquires life data indicative of the life of the clamping mechanism as label data. In addition, the device uses the state variable that has been observed and the label data that has been acquired and learns the operating state data and the operation history data and the life data in association with each other.

A tool notification system for determining tool usage information that would be otherwise unavailable or not easily discoverable by comparing information output by a tool and information obtained from a source. The tool notification system includes a tool operatively coupled to a sensor which may communicate information about the use, location, or other status of the tool to a processing system of the tool notification system. The processing system also receives the information from the source, which may include information about the parts supplied to the tool, the designated location of the tool, or other threshold parameters associated with supplying or using the tool. The processing system is configured to compare the information output from the tool and the information from the source and determine whether a condition is met for thereby sending a notification about tool usage.

A machine tool machining dimensions prediction device (100) includes: a data collector (10) to acquire driving state information of a machine tool; a feature amount extractor (211) to extract a feature amount from the driving state information; a data analyzer (311) to analyze the extracted feature amount; and a machining quality prediction model generator (312) to generate, from the analyzed information, a prediction model of a machining dimension of a workpiece. The machine tool machining dimensions prediction device (100) applies the feature amount and the driving state information to the prediction model during machining of the workpiece to predict a machining quality and refers to a machining dimension quality regulation to determine whether the machining quality satisfies a standard.

In an information processing device according to the present invention, a statistics estimation unit estimates a value of a state quantity by using a statistical model constructed based on values of past state quantities of a target device. A physical estimation unit estimates a value of a state quantity by using a physical model constructed based on design data of the target device. A specification unit specifies a value to be used to manage the target device from the value estimated by the statistics estimation unit and the value estimated by the physical estimation unit based on deterioration of the target device with time.

The present invention relates to a control device 200 for use on a numerically controlled machine tool 100, comprising a machine control unit 230 for controlling actuators of the machine tool for a machining process for a workpiece to be performed on the machine tool 100, in particular on the basis of control data, and a monitoring unit 250 for monitoring an operating state of the machine tool 100. In accordance with the invention, the monitoring unit 250 has a computer-implemented neural network 253 (NN), which in particular is designed to read input data from the machine control unit 230 and to output output data specifying an operating state of the machine tool 100.