Application of an Adaptive “Neuro-Fuzzy” Inference System in Modeling Cutting Temperature during Hard Turning
Published 2019 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Application of an Adaptive “Neuro-Fuzzy” Inference System in Modeling Cutting Temperature during Hard Turning
Authors
Keywords
-
Journal
Applied Sciences-Basel
Volume 9, Issue 18, Pages 3739
Publisher
MDPI AG
Online
2019-09-09
DOI
10.3390/app9183739
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Intelligent Optimization of Hard-Turning Parameters Using Evolutionary Algorithms for Smart Manufacturing
- (2019) Mozammel Mia et al. Materials
- Surface quality and topographic inspection of variable compliance part after precise turning
- (2018) P. Nieslony et al. APPLIED SURFACE SCIENCE
- Predicting tool life in turning operations using neural networks and image processing
- (2018) T. Mikołajczyk et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- Chatter prediction using merged wavelet denoising and ANFIS
- (2018) Shailendra Kumar et al. SOFT COMPUTING
- Neural network approach for automatic image analysis of cutting edge wear
- (2017) T. Mikołajczyk et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- Optimization of surface roughness and cutting temperature in high-pressure coolant-assisted hard turning using Taguchi method
- (2016) Mozammel Mia et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- Modeling steady-state thermal defectoscopy of steel solids using two side testing
- (2016) Boban Andjelkovic et al. Thermal Science
- Modeling of cutting temperature in the biomedical stainless steel turning process
- (2016) Dusan Petkovic et al. Thermal Science
- Multi-output fuzzy inference system for modeling cutting temperature and tool life in face milling
- (2014) Pavel Kovac et al. Journal of Mechanical Science and Technology
- Cutting temperature measurement and material machinability
- (2013) Bogdan Nedic et al. Thermal Science
- Online tool wear prediction system in the turning process using an adaptive neuro-fuzzy inference system
- (2012) Muhammad Rizal et al. APPLIED SOFT COMPUTING
- Application of fuzzy logic and regression analysis for modeling surface roughness in face milliing
- (2012) P. Kovac et al. JOURNAL OF INTELLIGENT MANUFACTURING
- A new artificial neural network approach to modeling ball-end milling
- (2009) Hazim El-Mounayri et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- Artificial neural networks for machining processes surface roughness modeling
- (2009) Fabricio J. Pontes et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- Adaptive network-based fuzzy inference system for prediction of surface roughness in end milling process using hybrid Taguchi-genetic learning algorithm
- (2008) Wen-Hsien Ho et al. EXPERT SYSTEMS WITH APPLICATIONS
- Application of NN technique for predicting the in-depth residual stresses during hard machining of AISI 52100 steel
- (2008) G. Ambrogio et al. International Journal of Material Forming
- Comparison of wavelet families for texture classification by using wavelet packet entropy adaptive network based fuzzy inference system
- (2007) Engin Avci APPLIED SOFT COMPUTING
Discover Peeref hubs
Discuss science. Find collaborators. Network.
Join a conversationAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started