In-Process Cutting Tool Wear Estimation from Machining Signals
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Solution Overview
Problem
Current methods for estimating the state of wear of cutting tools during machining are either imprecise or require significant downtime, leading to waste and inefficiency in the machining process.
Innovation Solution
A system that acquires operating signals from machining machines, such as power, torque, or vibration, to calculate wear indicators using a predetermined wear model, allowing for real-time and accurate estimation of tool wear without stopping the machining process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If direct measurement tools (binocular loupes, profilometers, three-dimensional scanners, lasers, cameras) are used to estimate cutting tool wear, then measurement precision is improved, but manufacturing downtime increases due to measurements taking place outside the machining process
Solution Approach 1:
The patent replaces direct mechanical measurement tools with indirect measurement based on acoustic emission signals and vibration analysis. The system uses sensors to capture acoustic and vibration data during machining, then processes these signals through algorithms to estimate wear, eliminating the need for physical contact measurements that cause downtime.
Solution Approach 2:
The patent introduces acoustic emission signals and vibration patterns as intermediary indicators of tool wear. Instead of directly measuring wear geometry, the system uses these intermediate physical phenomena that correlate with wear states, allowing continuous monitoring without interrupting the machining process.
2Loss of time
If indirect measurement by counting machining time from predetermined tool life is used, then manufacturing downtime is reduced, but measurement precision deteriorates due to variable wear evolution and conservative lifetime criteria
Solution Approach 1:
The patent implements a feedback system where acoustic emission and vibration signals are continuously monitored during machining. The system compares real-time signal characteristics against reference data and wear models to dynamically adjust wear estimates, providing precise feedback on actual tool condition rather than relying on predetermined conservative timelines.
Solution Approach 2:
The patent transitions from using time-based parameters (machining hours, predetermined tool life) to physics-based parameters (acoustic emission amplitude, vibration frequency, signal energy). This parameter transformation enables precise wear estimation that reflects actual tool degradation physics rather than conservative time-based assumptions.
3Reliability
If conservative lifetime criteria are used to specify tool lifetime upstream of machining, then reliability is improved, but loss of substance increases due to waste of cutting tools
Solution Approach 1:
The patent enables the cutting tool to essentially monitor its own condition through acoustic emission and vibration signals it generates during operation. The system processes these self-generated signals to detect wear onset and predict remaining life, allowing the tool to serve itself for condition monitoring and eliminating the need for conservative predetermined replacement schedules.
Solution Approach 2:
The patent performs preliminary detection of wear indicators through continuous acoustic and vibration monitoring, identifying signs of wear before they lead to catastrophic failure or poor surface quality. This early detection allows optimization of tool replacement timing, extending tool life beyond conservative estimates while maintaining reliability.
Data Source
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AI summary
The invention relates to a method and a system for estimating the wear state of a cutting tool mounted on a machining center, said system (1) comprising: - an acquisition module (11) configured to acquire, during a determined machining time, values of an operating signal specific to the cutting tool (3) mounted on the machining center (7), and - a microprocessor (13) configured to: - calculate current values of a set of wear indicators from said values of the operating signal, and - determine the wear state of the cutting tool (3) as a function of said current values of the set of indicators using a predetermined wear model (21) modeling the wear state of the cutting tool as a function of the learning values of said set of wear indicators.