A multi-performance prediction model based on ANFIS and new modified-GA for machining processes
Published 2013 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
A multi-performance prediction model based on ANFIS and new modified-GA for machining processes
Authors
Keywords
Prediction model, Adaptive network-based fuzzy inference systems (ANFIS), Modified genetic algorithm (MGA), Population, Machining process
Journal
JOURNAL OF INTELLIGENT MANUFACTURING
Volume 26, Issue 4, Pages 703-716
Publisher
Springer Nature
Online
2013-09-04
DOI
10.1007/s10845-013-0828-9
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Linguistic fuzzy model identification based on PSO with different length of particles
- (2012) Debao Chen et al. APPLIED SOFT COMPUTING
- Evolutionary techniques in optimizing machining parameters: Review and recent applications (2007–2011)
- (2012) Norfadzlan Yusup et al. EXPERT SYSTEMS WITH APPLICATIONS
- Multi-objective optimization of material removal rate and surface roughness in wire electrical discharge turning
- (2012) S. Aravind Krishnan et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- Application of fuzzy logic and regression analysis for modeling surface roughness in face milliing
- (2012) P. Kovac et al. JOURNAL OF INTELLIGENT MANUFACTURING
- Modeling customer satisfaction for new product development using a PSO-based ANFIS approach
- (2011) H.M. Jiang et al. APPLIED SOFT COMPUTING
- Modified genetic algorithms for manufacturing process planning in multiple parts manufacturing lines
- (2011) F. Musharavati et al. EXPERT SYSTEMS WITH APPLICATIONS
- Adaptive network-based fuzzy inference system with leave-one-out cross-validation approach for prediction of surface roughness
- (2010) Minggang Dong et al. APPLIED MATHEMATICAL MODELLING
- Forward and reverse mappings of electrical discharge machining process using adaptive network-based fuzzy inference system
- (2010) Kuntal Maji et al. EXPERT SYSTEMS WITH APPLICATIONS
- Optimization of wire electrical discharge machining for pure tungsten using a neural network integrated simulated annealing approach
- (2010) Hsien-Ching Chen et al. EXPERT SYSTEMS WITH APPLICATIONS
- Prediction of surface roughness in the end milling machining using Artificial Neural Network
- (2009) Azlan Mohd Zain et al. EXPERT SYSTEMS WITH APPLICATIONS
- Application of GA to optimize cutting conditions for minimizing surface roughness in end milling machining process
- (2009) Azlan Mohd Zain et al. EXPERT SYSTEMS WITH APPLICATIONS
- A study on the performance of some multi-response optimisation methods for WEDM processes
- (2009) Susanta Kumar Gauri et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- Zigzag machining surface roughness modelling using evolutionary approach
- (2009) Cevdet Göloğlu et al. JOURNAL OF INTELLIGENT MANUFACTURING
- An adaptive neuro-fuzzy inference system (ANFIS) model for wire-EDM
- (2008) Ulaş Çaydaş et al. EXPERT SYSTEMS WITH APPLICATIONS
- 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
- Thermal modeling of the material removal rate and surface roughness for die-sinking EDM
- (2008) K. Salonitis et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- Artificial neural network models for the prediction of surface roughness in electrical discharge machining
- (2008) Angelos P. Markopoulos et al. JOURNAL OF INTELLIGENT MANUFACTURING
- Development of hybrid model and optimization of surface roughness in electric discharge machining using artificial neural networks and genetic algorithm
- (2008) Krishna Mohana Rao G. et al. JOURNAL OF MATERIALS PROCESSING TECHNOLOGY
Find the ideal target journal for your manuscript
Explore over 38,000 international journals covering a vast array of academic fields.
SearchBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started