• Publications
  • Influence
Application of Clustering Methods for Online Tool Condition Monitoring and Fault Diagnosis in High-Speed Milling Processes
TLDR
This paper illustrates the performance of clustering techniques on high-speed end milling experimental data. Expand
  • 32
  • 1
Intelligent selective packet discarding using general fuzzy automata
TLDR
General Fuzzy Automata (GFA) is a discrete-event-based formalism and is thus naturally similar to communication systems. Expand
  • 5
  • 1
Sequential fuzzy clustering based dynamic fuzzy neural network for fault diagnosis and prognosis
TLDR
We introduce a new architecture that incorporates the advantages of fuzzy clustering into well known dynamic fuzzy neural networks in DFNN to form an online condition monitoring system which is tolerant to slight drifts in process dynamics and adaptable to variations in parameters and device. Expand
  • 50
A survey on artificial intelligence technologies in modeling of High Speed end-milling processes
High Speed Machining centers (HSM) are considered as complicated industrial instruments. Finishing is a critical process in production procedure which is carried on by these machines. Among manyExpand
  • 18
A Survey on Artificial Intelligence-Based Modeling Techniques for High Speed Milling Processes
TLDR
In this paper, an extensive literature survey of the state-of-the-art modeling techniques of milling processes will be carried out, more specifically of recent advances and applications of AI-based modeling techniques. Expand
  • 15
  • PDF
Genetic Dynamic Fuzzy Neural Network (GDFNN) for Nonlinear System Identification
TLDR
This paper discusses an optimization of Dynamic Fuzzy Neural Network (DFNN) for nonlinear system identification. Expand
  • 7
Adaptive Network Fuzzy Inference System and support vector machine learning for tool wear estimation in high speed milling processes
  • Xiang Li, M. Er, +5 authors A. Torabi
  • Computer Science, Engineering
  • IECON - 38th Annual Conference on IEEE…
  • 24 December 2012
TLDR
We examine two popular methods of machine learning, namely the Adaptive Network Fuzzy Inference System (ANFIS) and Support Vector Machine (SVM) are used to estimate the tool wear and correlation models for tool wear estimation using ANFIS and SVM are estimated. Expand
  • 6
Application of classical clustering methods for online tool condition monitoring in high speed milling processes
Tool Condition Monitoring (TCM) is a necessary action during end-milling process as worn milling-tool might irreversibly damage the work-piece. So, there is an urgent need for a TCM system to provideExpand
  • 8
Flute based analysis of ball-nose milling signals using continuous wavelet analysis features
  • A. Torabi, O. Massol, +7 authors L. San
  • Engineering, Computer Science
  • 11th International Conference on Control…
  • 1 December 2010
TLDR
This paper investigates the existing correlation between the resulted wavelet coefficients and ball-nose tool-wear using Cascaded Feed Forward Neural Networks (CFFNN)· Considering the changes in the shape of the signals during the cutting process and the similarity of the resulting signals to some mother wavelets and the lack of literature on wavelet analysis for ball- nose cutters' signals, this specific analysis is chosen. Expand
  • 3
...
1
2
...