CNC Milling Tool Life Prediction with Wavelet Signal Features
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Solution Overview
Problem
Current methods for predicting the remaining life of numerical control machine tools, particularly through indirect measurement, face challenges in accurately extracting relevant features from signal data, leading to low precision in predicting tool wear and life.
Innovation Solution
A method using data de-noising, feature extraction, and a multi-kernel weighted least squares support vector machine (W-LSSVM) algorithm is employed to establish a prediction model for the remaining life of milling tools in CNC machines, incorporating PLC controller signals and external sensor data, including vibration and current signals, to enhance prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If indirect measurement methods are used to predict tool remaining life, then the measurement can be performed during machine operations, but the prediction precision is insufficient due to difficulty in extracting relevant features from signal data
Solution Approach 1:
The patent segments the tool wear prediction process into distinct phases: signal acquisition from multiple sensors (vibration, acoustic emission, current), wavelet transform-based denoising and decomposition, feature extraction from different frequency bands, and prediction model construction. This segmentation allows each phase to be optimized independently, improving overall prediction precision while maintaining operational measurement capability
Solution Approach 2:
The patent transforms the prediction problem from time-domain analysis to frequency-domain analysis using wavelet transform. By decomposing signals into different frequency bands and extracting features from multiple dimensional representations (time, frequency, energy), the system achieves higher prediction precision while maintaining the ability to measure during operation
2Device complexity
If traditional signal processing methods are used, then the processing is simpler, but the extraction of relevant features from signal data is insufficient leading to low prediction accuracy
Solution Approach 1:
The patent applies wavelet transform denoising and signal decomposition as preliminary processing steps before feature extraction. By pre-processing the raw signals to remove noise and decompose into meaningful frequency components, the subsequent feature extraction becomes more accurate. This preliminary action enables better feature extraction without requiring overly complex processing in later stages
Solution Approach 2:
The patent replaces traditional mechanical signal processing methods with wavelet transform-based signal processing. Instead of using simple filtering or Fourier transform, the wavelet transform provides time-frequency localization that better captures the transient characteristics of tool wear signals, significantly improving feature extraction accuracy while maintaining computational feasibility
Data Source
AI summary
A method for predicting a remaining life of a tool of a computer numerical control machine is provided. In the method, indirect measurement indicators of the tool are selected based on monitoring and analyzing a current state of the tool, a prediction model for the remaining life of the tool is established based on data de-noising, feature extraction and a multi-kernel W-LSSVM algorithm. Thereby, a method for predicting a remaining life of a tool of a computer numerical control machine is provided.