Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks

By Tugrul Özel, Yigit Karpat. 2005

739 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Electrical and Electronic Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/4a0d0312dabd9ffea062c5b7f0c1603b52b676c1

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Y. Şahin
2. V. Gaitonde
3. S. Basavarajappa
4. G. Samuel
5. R. Suresh
6. T. Kıvak
7. Fuat Kara
8. Engin Nas

In Electrical and Electronic Engineering

1. Y. Şahin
2. V. Gaitonde
3. S. Basavarajappa
4. R. Suresh
5. G. Samuel
6. T. Kıvak
7. Fuat Kara
8. Engin Nas

In Mechanical Engineering

1. Y. Şahin
2. S. Basavarajappa
3. V. Gaitonde
4. G. Samuel
5. R. Suresh
6. T. Kıvak
7. Fuat Kara
8. Engin Nas

Citations in the corpus

• A review of flank wear prediction methods for tool condition monitoring in a turning process (2012, methodology): “Özel and Karpat [118] used a profilometer and surface roughness analyzer in order to develop surface roughness and flank wear prediction models using neural networks.”
• A Comparative Study on Machine Learning Algorithms for Smart Manufacturing: Tool Wear Prediction Using Random Forests (2017, methodology): “€ Ozel and Karpat [33] presented a predictive modeling approach for surface roughness and tool wear for hard turning processes using ANNs.”
• Effect of machinability, microstructure and hardness of deep cryogenic treatment in hard turning of AISI D2 steel with ceramic cutting (2020, methodology): “Ozel and Karpat [11] investigated the machining of AISI D2 steel using multi-radii/wiper mixed alumina ceramic inserts with TiN coating and developed an ANN for predicting tool wear and surface roughness.”
• Neural Network Modeling and Particle Swarm Optimization (PSO) of Process Parameters in Pulsed Laser Micromachining of Hardened AISI H13 Steel (2009, result): “This approach decreased the size of each neural network thus enabled faster convergence and better predictions of output values as explained in [17, 18].”
• Machinability investigations on hardened AISI 4340 steel using coated carbide insert (2012, methodology): “Ozel and Karpat [19] used regression and artificial neural network models for predicting the surface roughness and tool wear in hard turning of AISI H13 steel using CBN inserts.”
• Optimization of surface roughness and flank wear using the Taguchi method in milling of Hadfield steel with PVD and CVD coated inserts (2014, background): “Good surface roughness provides important improvements in the tribologic characteristics, fatigue strength, corrosion resistance and aesthetic appearance of the product [13–15].”
• Surface roughness model in machining hardened steel with cubic boron nitride cutting tool (2008, background): “This is a good agreement with the previous researchers’ works [3,18,22,24, 30,32,34,36].”
• Analysis of Machinability During Hard Turning of Cold Work Tool Steel (Type: AISI D2) (2009, methodology): “The process modeling by response surface methodology (RSM) using statistical design of experiments is proved to be an efficient modeling tool [33, 34].”