CNC Tool Audio Detection for Real-Time Defect Classification
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Solution Overview
Problem
Current tool detection methods in CNC devices are not real-time, relying on models based on vibrations and high-frequency audio signals, which are not sufficient for immediate quality control of tools.
Innovation Solution
An electronic device with a tool detecting system that uses a sound sensor to acquire cutting sounds, processes them through time-frequency feature extraction and fusion, and employs a convolutional neural network to generate a tool detection model for real-time defect identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If tool detection is performed using models based on vibrations and high-frequency audio signals, then measurement precision is improved, but response time increases (real-time detection is not achieved)
Solution Approach 1:
The patent extracts and focuses specifically on audio signal features related to tool defects, separating them from comprehensive vibration analysis. By using audio signals alone with carefully selected frequency bands (20Hz-20kHz with emphasis on 200Hz-2kHz), the system achieves real-time detection while maintaining precision, eliminating the need for complex multi-signal processing
Solution Approach 2:
The system performs preliminary action by pre-processing audio signals through bandpass filtering and feature extraction before detection. The audio signal is pre-processed to extract time-frequency features and convert to spectrograms, which are then ready for rapid classification by the neural network, enabling real-time detection without compromising precision
2Measurement precision
If comprehensive vibration and audio signal processing is used for tool detection, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent extracts and focuses specifically on audio signal features related to tool defects, separating them from comprehensive vibration analysis. By using audio signals alone with carefully selected frequency bands (20Hz-20kHz with emphasis on 200Hz-2kHz), the system achieves real-time detection while maintaining precision, eliminating the need for complex multi-signal processing
Solution Approach 2:
The patent replaces complex mechanical vibration sensing systems with acoustic sensing. Instead of using complex vibration sensors and multi-axis accelerometers, the system uses microphones or audio sensors to capture tool sounds, which are then processed through signal processing and neural network classification, simplifying the hardware while maintaining detection capability
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
Enables real-time detection and classification of tool defects, such as chipping or wear, by generating a tool detection model that recognizes time-frequency features of cutting sounds, improving the precision and speed of quality control in CNC operations.
Implementation Method 1
acquire a cutting sound of a tool during a cutting process
Data Source
AI summary
A method for detecting defects in working CNC tools in real time, implemented in an electronic device, includes acquiring sounds of operation of a tool during a cutting or other operation process and dividing the acquired cutting sounds into a plurality of recordings of audio according to a preset time interval. Time-frequency features of the plurality of recordings of audio are acquired according to multiple feature transformation methods and a fusion feature image of the cutting sound is formed according to the extracted time-frequency features. A tool detection model is generated by training the fusion feature image, and any defects of the tool and any defect types the tool has are detected according to the tool detection model.