Classification algorithms
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Machine Learning - Special issue on learning with probabilistic representations
In-process surface recognition of a CNC milling machine using the fuzzy nets method
Proceedings of the 21st international conference on Computers and industrial engineering
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Expert Systems and Probabiistic Network Models
Expert Systems and Probabiistic Network Models
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A Soft Computing System to Perform Face Milling Operations
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part II: Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living
Data mining for quality control: Burr detection in the drilling process
Computers and Industrial Engineering
Journal of Intelligent Manufacturing
A Bayesian network for burr detection in the drilling process
Journal of Intelligent Manufacturing
Using artificial intelligence to predict surface roughness in deep drilling of steel components
Journal of Intelligent Manufacturing
The evolutionary development of roughness prediction models
Applied Soft Computing
Improvement of surface roughness models for face milling operations through dimensionality reduction
Integrated Computer-Aided Engineering
Hi-index | 0.00 |
The literature reports many scientific works on the use of artificial intelligence techniques such as neural networks or fuzzy logic to predict surface roughness. This article aims at introducing Bayesian network-based classifiers to predict surface roughness (Ra) in high-speed machining. These models are appropriate as prediction techniques because the non-linearity of the machining process demands robust and reliable algorithms to deal with all the invisible trends present when a work piece is machining. The experimental test obtained from a high-speed milling contouring process analysed the indicator of goodness using the Naive Bayes and the Tree-Augmented Network algorithms. Up to 81.2% accuracy was achieved in the Ra classification results. Therefore, we envisage that Bayesian network-based classifiers may become a powerful and flexible tool in high-speed machining.