ON-LINE AND INDIRECT TOOL WEAR MONITORING IN TURNING WITH ARTIFICIAL NEURAL NETWORKS: A REVIEW OF MORE THAN A DECADE OF RESEARCH
By Bernhard Sick. 2002
500 citations recorded by the source corpus.
Subfields: Civil and Structural Engineering, Electrical and Electronic Engineering, Mechanical Engineering
View Paper: https://www.semanticscholar.org/paper/5007671563b58a974663b277982fd3461a49989a
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Civil and Structural Engineering
- Yigit Karpat
- Mustafa Kuntoğlu
- D. Pimenov
- Robert X. Gao
- Soundar Kumara *
- Andrés Bustillo
- Arjan Siddhpura
- Connor Jennings
In Electrical and Electronic Engineering
- R. Paurobally
- Arjan Siddhpura
- Munish Kumar Gupta
- Tugrul Özel
- David A. Dornfeld
- Garret E. O'Donnell
- Roberto Teti
- Mustafa Kuntoğlu
In Mechanical Engineering
- R. Paurobally
- Arjan Siddhpura
- Yigit Karpat
- D. Pimenov
- Munish Kumar Gupta
- Tugrul Özel
- Garret E. O'Donnell
- David A. Dornfeld
Citations in the corpus
- A Comparative Study on Machine Learning Algorithms for Smart Manufacturing: Tool Wear Prediction Using Random Forests (2017, methodology): “While ANNs and SVR have been applied in the area of data-driven prognostics, little research has been conducted to evaluate the performance of other machine learning algorithms [21].”
- Artificial intelligence systems for tool condition monitoring in machining: analysis and critical review (2022, methodology): “For example, Sick (2002) and Kunto˘glu et al., (2020) considered on-line and indirect tool wear monitoring for turning using an artificial neural network.”
- A review of flank wear prediction methods for tool condition monitoring in a turning process (2012, methodology): “During digital preprocessing, digitized signals are filtered using digital filters such as low or band pass filters, linearized and conditioned in order to reduce the noise and increase the level of information content [48].”
- Advanced monitoring of machining operations (2010, background): “Sick [69] presented an interesting classification of feature selection procedures for tool wear estimation in turning.”
- Application of soft computing techniques in machining performance prediction and optimization: a literature review (2010, background): “Sick [ 38 ]h as reviewed a number of research papers dealing with online and indirect tool wear monitoring in turning using artificial neural networks.”
- Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks (2005, background): “An exclusive review of the current literature is presented by Sick [27].”