Tool wear monitoring and prognostics challenges: a comparison of connectionist methods toward an adaptive ensemble model
Explore this paper's citation graph
Summary
An ensemble of Summation Wavelet-Extreme Learning Machine models is proposed with incremental learning scheme for tool condition monitoring application and is validated on cutting force measurements data from Computer Numerical Control machine.
- Type
- article
- Published
- 2018-12-01
- Cited by
- 114
- References
- 54
- OpenAlex
- https://openalex.org/W2344695726
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:58008102
Keywords
Prognostics, Downtime, Tool wear, Process (computing), Machining
References
- A robust and reliable data-driven prognostics approach based on Extreme Learning Machine and Fuzzy Clustering
- Surface roughness prediction by extreme learning machine constructed with abrasive water jet
- Cloud-enabled prognosis for manufacturing
- Machine prognostics based on sparse representation model
- Prognostic modelling options for remaining useful life estimation by industry
- An overview of advances in reliability estimation of individual predictions in machine learning
- Application of Extreme Learning Machine Method for Time Series Analysis
- Current status of machine prognostics in condition-based maintenance: a review
- On line tool wear monitoring based on auto associative neural network
- Enhanced random search based incremental extreme learning machine
- Extreme learning machines: a survey
- Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights
- Enabling Health Monitoring Approach Based on Vibration Data for Accurate Prognostics
- Effect of Different Tool Edge Conditions on Wear Detection by Vibration Spectrum Analysis in Turning Operation
- On-line wear estimation using neural networks
- Online tool wear prediction system in the turning process using an adaptive neuro-fuzzy inference system
- Prediction of Melting Points of Organic Compounds Using Extreme Learning Machines
- Prognosis of the probability of failure in tool condition monitoring application-a time series based approach
- Practical options for selecting data-driven or physics-based prognostics algorithms with reviews
- The evolutionary development of roughness prediction models
Cited by
- Tool-Wear Analysis Using Image Processing of the Tool Flank
- INPUT SIGNIFICANCE ANALYSIS: FEATURE RANKING THROUGH SYNAPTIC WEIGHTS MANIPULATION FOR ANNS-BASED CLASSIFIERS
- Review of tool condition monitoring methods in milling processes
- A Multisensor Fusion Method for Tool Condition Monitoring in Milling
- Research on adaptive CNC machining arithmetic and process for near-net-shaped jet engine blade
- Tool wear predicting based on multi-domain feature fusion by deep convolutional neural network in milling operations
- Milling Tool Wear State Recognition by Vibration Signal Using a Stacked Generalization Ensemble Model
- Tool Wear Predicting Based on Multisensory Raw Signals Fusion by Reshaped Time Series Convolutional Neural Network in Manufacturing
- Influence of machining parameters on the polymer concrete milling process
- The unordered time series fuzzy clustering algorithm based on the adaptive incremental learning
- Multi-sensor measurement and data fusion technology for manufacturing process monitoring: a literature review
- A review of prognostics and health management of machine tools
- Indirect online tool wear monitoring and model-based identification of process-related signal
- Data mining for fast and accurate makespan estimation in machining workshops
- Tool Wear Condition Monitoring in Milling Process Based on Current Sensors
- Intelligent monitoring and diagnostics using a novel integrated model based on deep learning and multi-sensor feature fusion
- A novel ResNet-based model structure and its applications in machine health monitoring
- A Product Quality Monitor Model With the Digital Twin Model and the Stacked Auto Encoder
- Machine monitoring system: a decade in review
- Intelligent recognition of milling cutter wear state with cutting parameter independence based on deep learning of spindle current clutter signal
Related papers
- Quantifying expected gains from implementing a prognostics algorithm on systems with long logistics delay times
- A Review of Machinery Diagnostics and Prognostics Implemented on a Centrifugal Pump
- Cutting Tool Wear Monitoring
- Investigation into the effectiveness of cutting parameters on wear regions of the flank wear curve and associated cutting tool life improvement
- A novel method for accurately monitoring and predicting tool wear under varying cutting conditions based on meta-learning
- Study on Detection of Tool Wear in End Mill Cutting by Acoustic Emission Method
- An experimental approach to characterize the performance of PCD and PCBN tools in milling nano Al-8081-Zr/Mg/TiO2 metal matrix composites using multi-sensor data fusion