Milling Tool Wear State Recognition by Vibration Signal Using a Stacked Generalization Ensemble Model
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Summary
Vibration signals collected during the milling process are analyzed through the time domain, frequency domain, and time-frequency domain to extract signal features and the proposed SG ensemble model based on vibration signals has better recognition accuracy and stability than other models.
- Type
- article
- Published
- 2019-11-03
- Cited by
- 38
- References
- 44
- Access
- Open access
- OpenAlex
- https://openalex.org/W2988022918
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:209924841
Keywords
Support vector machine, Tool wear, Vibration, Time domain, Pattern recognition (psychology)
References
- Soft Margins for AdaBoost
- Online tool wear prediction in drilling operations using selective artificial neural network ensemble model
- Prediction of drill flank wear using ensemble of co-evolutionary particle swarm optimization based-selective neural network ensembles
- Using neural network and decision tree for machine reliability prediction
- Classifier ensembles: Select real-world applications
- Tool wear monitoring using naïve Bayes classifiers
- Estimation of tool wear during CNC milling using neural network-based sensor fusion
- Weak fault signature extraction of rotating machinery using flexible analytic wavelet transform
- Sparse representation and its applications in micro-milling condition monitoring: noise separation and tool condition monitoring
- Effect of load and velocity on wear behaviour of Cu based self-lubricating composite
- Quality improvement of ball-end milled sculptured surfaces by ball burnishing
- Force Sensor Based Tool Condition Monitoring Using a Heterogeneous Ensemble Learning Model
- An automated flank wear measurement of microdrills using machine vision
- On-line metal cutting tool condition monitoring.: I: force and vibration analyses
- Meta-learning for time series forecasting and forecast combination
- An introduction to decision tree modeling
- Tool wear evaluation by vibration analysis during end milling of AISI D3 cold work tool steel with 35 HRC hardness
- A 'non-parametric' version of the naive Bayes classifier
- Top 10 algorithms in data mining
- Machine learning ensembles for wind power prediction
Cited by
- Tool Life Prediction of Ti [C,N] Mixed Alumina Ceramic Cutting Tool Using Gradient Descent Algorithm on Machining Martensitic Stainless Steel
- Deep learning-based tool wear prediction and its application for machining process using multi-scale feature fusion and channel attention mechanism
- Vibration-based tool condition monitoring in milling of Ti-6Al-4V using an optimization model of GM(1,N) and SVM
- Fault Diagnosis of Rolling Bearing Based on KPCA and Stacking Algorithm
- Tool wear classification based on machined surface images using convolution neural networks
- Robust tool wear monitoring system development by sensors and feature fusion
- Machine learning model for discrete, nonlinear datasets from machining operation: an industrial need to have high quality
- Artificial intelligence systems for tool condition monitoring in machining: analysis and critical review
- Recognition of bovine milk somatic cells based on multi-feature extraction and a GBDT-AdaBoost fusion model.
- Single Seed Identification in Three Medicago Species via Multispectral Imaging Combined with Stacking Ensemble Learning
- Generalizability analysis of tool condition monitoring ensemble machine learning models
- Chatter detection in milling processes—a review on signal processing and condition classification
- Tool wear condition monitoring method based on relevance vector machine
- Tool life and wear prediction of HSS and PVD material using ANFIS system
- Tool Wear State Identification Based on SVM Optimized by the Improved Northern Goshawk Optimization
- Experimental Investigation of Tool Lifespan Evolution During Turning Operation Based on the New Spectral Indicator OLmod
- Monitoring of drill bit wear using sound and vibration signals analysis recorded during rock drilling operations
- A Hybrid Nondominant-Based Genetic Algorithm (NSGA-II) for Multiobjective Optimization to Minimize Vibration Amplitude in the End Milling Process
- Tool wear state recognition and prediction method based on laplacian eigenmap with ensemble learning model
- An online monitoring method of milling cutter wear condition driven by digital twin
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