Machine Tool Chatter Detection Using Motor and Acoustic Signals
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing machine tool chatter detection methods require sensors and data acquisition equipment, structural dynamic models, and are computationally intensive, often missing low-amplitude chatter and providing false positives due to noise in sensorless signals.
Innovation Solution
A method combining sensorless and sensor-based measurement techniques, using spindle torque and servo motor data with microphone sound data, filtered in the frequency domain to detect chatter by evaluating common frequency indicators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If sensorless chatter detection is used, then no additional sensors are required, but false positives occur due to noise in the signals
Solution Approach 1:
The patent combines sensorless chatter detection (using spindle motor and servo motor signals) with sensor-based detection (using microphone) into an integrated system. The sensorless branch processes motor signals while the sensor-based branch processes acoustic signals, and both branches work together to confirm chatter conditions, reducing false positives while maintaining ease of implementation
Solution Approach 2:
The patent introduces frequency domain filtering as an intermediary processing step between signal acquisition and chatter detection. The filtering operation removes noise and harmonics from both sensorless and sensor-based signals, allowing accurate chatter detection without requiring additional hardware while improving reliability
2Reliability
If existing sensor-based methods are used, then chatter can be detected, but additional sensors and data acquisition equipment are required
Solution Approach 1:
The patent makes the machine tool controller perform multiple functions: it serves as both the motion controller and the chatter detection system. By utilizing existing motor control signals and adding microphone input processing, the controller becomes a multi-functional device that eliminates the need for separate dedicated chatter detection hardware
Solution Approach 2:
The patent enables the machine tool system to detect its own chatter conditions using its existing components (spindle motor, servo motors, and microphone) without requiring external specialized equipment. The controller analyzes signals already present in the system or easily obtainable (acoustic signals), making the system self-diagnostic
3Reliability
If structural dynamic models are used for chatter prediction, then chatter can be predicted, but the analysis becomes computationally intensive
Solution Approach 1:
The patent replaces complex, computationally intensive structural dynamic models with simpler signal processing approaches. Instead of performing heavy computational analysis, the system uses frequency domain filtering and pattern recognition on motor signals and acoustic signals, achieving comparable or superior detection accuracy with minimal computational resources
4Measurement precision
If existing detection methods are used, then severe chatter can be detected, but light chatter conditions are missed
Solution Approach 1:
The patent merges sensorless detection (sensitive to mechanical vibrations transmitted through the motor system) with sensor-based acoustic detection (sensitive to airborne sound waves). This combination allows the system to detect light chatter conditions that may be imperceptible to either method alone, improving detection sensitivity while maintaining precision through cross-validation
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Accurately detects chatter at any frequency and amplitude without additional sensors, reducing false positives by filtering out noise and harmonics, and enabling real-time remediation.
Implementation Method 1
a time-series sample is recorded, the data is converted to the frequency domain and filtered
Data Source
AI summary
A method for machine tool chatter detection which combines sensorless and sensor-based measurement methods and makes a determination based on analysis of both sensorless and sensor-based signals. The sensorless branch uses data known to the machine tool controller, such as spindle torque data or servo motor position/velocity data, where a time-series sample is recorded, the data is converted to the frequency domain and filtered, and criteria are evaluated to detect chatter. The sensor-based branch uses sound data recorded by a microphone, where data is again recorded, converted to the frequency domain, filtered, and evaluated. When both sensorless and sensor-based evaluation branches detect chatter at a common frequency, it is determined that chatter is occurring in the machine tool and appropriate steps are taken. The integrated chatter detection technique avoids false indicators of chatter which may arise due to noise present at different frequencies in the sensorless and/or sensor-based signals.