Machining Tool Wear Modeling From Two-Point Signal Data
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Solution Overview
Problem
Existing methods for predicting machining tool wear are labor-intensive, require setup time, are not sustainable, and provide inaccurate predictions due to varying process parameters and unknown wear history, leading to inefficient tool replacement and potential machine damage.
Innovation Solution
A method using time-dependent modeling of wear based on two measurement points and neural network training with pre-processed process signals, specifically spindle current, to predict tool wear accurately without additional sensors or thresholds, allowing adaptation to changing cutting parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If tool wear is determined using microscopic images, then measurement precision is improved, but productivity deteriorates due to production interruption and time-consuming manual evaluation
Solution Approach 1:
The patent replaces the mechanical/microscopic examination system with an electrical/electronic signal-based monitoring system. Process signals (current, power, torque) are used to detect tool wear conditions, eliminating the need for microscopic image analysis and manual evaluation, thus maintaining measurement precision while restoring production continuity
Solution Approach 2:
The monitoring system uses the machine's own operational signals (current, power, torque) to detect tool wear conditions. The system serves itself by utilizing data already generated during normal machining operations, eliminating the need for separate inspection processes and maintaining both precision and productivity
2Reliability
If tool replacement is performed preventively at fixed intervals, then reliability is improved by avoiding tool breakage, but loss of substance deteriorates due to replacing tools before they reach optimal wear limits
Solution Approach 1:
The system continuously monitors process signals and provides feedback about actual tool wear conditions. This feedback enables dynamic adjustment of replacement timing based on real-time tool condition rather than fixed schedules, ensuring tools are replaced at optimal moments when wear reaches critical levels but before breakage occurs, thus improving reliability while minimizing tool waste
Solution Approach 2:
The patent transitions from static, fixed-interval replacement schedules to dynamic, condition-based replacement timing. The system adapts replacement decisions based on actual tool wear progression and changing process parameters, allowing tools to operate closer to their true limits without risking breakage, thereby reducing unnecessary tool replacement and material waste
3Adaptability or versatility
If monitoring of used tools with unknown wear history is attempted, then adaptability is improved, but measurement precision deteriorates due to difficulty in determining current wear state
Solution Approach 1:
The system performs preliminary characterization during initial tool use by recording process signals and establishing baseline relationships between signals and wear for each specific tool. This preliminary action creates a reference framework that enables accurate monitoring of tools with unknown wear history, maintaining both adaptability and measurement precision through signal-based wear estimation
Data Source
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AI summary
The invention relates to a method for determining the optimal time for replacing a tool. This requires determining the wear of the tool. The solution is based on a time-dependent modeling of wear between two wear measurement points, which are measured at the beginning and end of the life cycle of a machining tool. These measurement times are crucial because the machining process should not be impaired. These measurement points can be recorded using any measuring system. Based on these measured values, a neural network is then appropriately trained, which can then predict the respective degree of wear.