Li et al., 2023 - Google Patents
A steps-ahead tool wear prediction method based on support vector regression and particle filteringLi et al., 2023
- Document ID
- 33831489123817451
- Author
- Li Y
- Huang X
- Tang J
- Li S
- Ding P
- Publication year
- Publication venue
- Measurement
External Links
Snippet
This paper develops a steps-ahead tool wear prediction method based on particle filtering and support vector regression. A degradation phase classification method is presented based on clustering algorithm and support vector machine. The support vector regression …
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
- G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
- G06K9/6247—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/50—Computer-aided design
- G06F17/5009—Computer-aided design using simulation
- G06F17/5018—Computer-aided design using simulation using finite difference methods or finite element methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F17/30705—Clustering or classification
- G06F17/3071—Clustering or classification including class or cluster creation or modification
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F17/30634—Querying
- G06F17/30657—Query processing
- G06F17/30675—Query execution
- G06F17/3069—Query execution using vector based model
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Li et al. | A steps-ahead tool wear prediction method based on support vector regression and particle filtering | |
Li et al. | Physics-informed meta learning for machining tool wear prediction | |
Cheng et al. | Prediction of surface residual stress in end milling with Gaussian process regression | |
Li et al. | A data-driven approach for tool wear recognition and quantitative prediction based on radar map feature fusion | |
Jin et al. | Diagnostic feature extraction from stamping tonnage signals based on design of experiments | |
Danish et al. | Machine learning models for prediction and classification of tool wear in sustainable milling of additively manufactured 316 stainless steel | |
Yu | Machine tool condition monitoring based on an adaptive Gaussian mixture model | |
Li et al. | Roughness prediction model of milling noise-vibration-surface texture multi-dimensional feature fusion for N6 nickel metal | |
Kothuru et al. | Audio-based tool condition monitoring in milling of the workpiece material with the hardness variation using support vector machines and convolutional neural networks | |
Giannella et al. | Neural networks for fatigue crack propagation predictions in real-time under uncertainty | |
Liu et al. | An accurate prediction method of multiple deterioration forms of tool based on multitask learning with low rank tensor constraint | |
Zhang et al. | Model-data hybrid driven approach for remaining useful life prediction of cutting tool based on improved inverse Gaussian process | |
Zhao et al. | A subsequent-machining-deformation prediction method based on the latent field estimation using deformation force | |
Losi et al. | Gas turbine health state prognostics by means of Bayesian hierarchical models | |
Thakker et al. | Pushing the limits of rnn compression | |
Wang et al. | Tool wear prediction based on SVR optimized by hybrid differential evolution and grey wolf optimization algorithms | |
Xie et al. | Knowledge-embedded meta-learning model for lift coefficient prediction of airfoils | |
Chauhan et al. | Classification of surface roughness for CNC face milling of Inconel 625 superalloy utilizing cutting force signal features with SVM and ANN | |
He et al. | Digital twin-driven design and manufacturing | |
Jia et al. | Tool wear condition monitoring method based on relevance vector machine | |
Long et al. | Theoretical study of GDM-SA-SVR algorithm on RAFM steel | |
Suman et al. | Predictive modeling of real contact area on rough surfaces using deep artificial neural network | |
Guan et al. | Improved depth residual network based tool wear prediction for cavity milling process | |
Liu et al. | X-ray scatterometry using deep learning | |
Li et al. | Statistical parameterized physics-based machine learning digital twin models for laser powder bed fusion process |