Overview of support vector machine in modeling machining performances

Procedia Engineering

Volumn 24, Issue , 2011, Pages 308-312

  Mat Deris, Ashanira ^a   Mohd Zain, Azlan ^a   Sallehuddin, Roselina ^a  

^a UNIVERSITI TEKNOLOGI MALAYSIA   (Malaysia)

Author keywords

Machining; Modeling technique; Support vector machine

Indexed keywords

ARTIFICIAL BEE COLONIES; ARTIFICIAL NEURAL NETWORK; COMPUTATIONAL APPROACH; COST ESTIMATIONS; DATA CLASSIFICATION; FUNCTION ESTIMATION; GAUSSIAN KERNEL FUNCTIONS; HIGH QUALITY; KERNEL FUNCTION; MACHINING OPERATIONS; MACHINING PERFORMANCE; MATHEMATICAL TOOLS; MODELING AND OPTIMIZATION; MODELING TECHNIQUE; RADIAL BASIS FUNCTIONS; RBF KERNEL FUNCTION; SUPPORT VECTOR; TRAINING PARAMETERS;

ESTIMATION; FUNCTIONS; GENETIC ALGORITHMS; MACHINING; MACHINING CENTERS; NEURAL NETWORKS; PARTICLE SWARM OPTIMIZATION (PSO); RADIAL BASIS FUNCTION NETWORKS; VECTORS;

SUPPORT VECTOR MACHINES;

EID: 84855737380     PISSN: 18777058     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1016/j.proeng.2011.11.2647     Document Type: Conference Paper

References (21)

1. Zain, A.M., Haron,H., and Sharif,S. (2011). Integrated ANN-GA for estimating the minimum value for machining performance, International Journal fo Production Research, iFirst, 1-23.
2. Zain, A.M., Haron,H., and Sharif,S. (2011). Estimation of the minimum machining performance in the abrasive waterjet machining using integrated ANN-SA, Expert Systems with Applications 38,8316-8326.
3. Zain, A.M., Haron,H., and Sharif,S. (2010). Application of GA to optimize cutting conditions for minimizing surface roughness in end milling machining process, Expert Systems with Applications 37, 4650-4659.
4. Zain, A.M., Haron,H., and Sharif,S. (2011). Genetic Algorithm and Simulated Annealing to estimate optimal process parameters of the abrasive waterjet machining, Engineering with computers 27, 251-259.
5. Zain, A.M., Haron,H., and Sharif,S. (2010). Simulated annealing to estimate the optimal cutting conditions for minimizing surface roughness in end milling ti-6al-4v, Machining Science and Technology 14, 43-62.
6. Chandrasekaran, M., Muralidhar, M., Krishna, C. M., & Dixit, U. S. (2010). Application of soft computing techniques in machining performance prediction and optimization: a literature review. Int J Adv Manuf Technol, 46, 445-464.
7. Mukherjee, I. and Ray, P. K. (2006). A review of optimization techniques in metal cutting processes. Computers and Industrial Engineering, 50(1-2), 15-34.
8. Vapnik, V., 1995. The Nature of Statistical Learning Theory. Springer, New York.
9. Deng, C., Wu,J. and Shao,X. (2008). Reliability assessment of machining accuracy on support vector machine, ICIRA 2008, Part II, LNAI 5315, pp. 669-678.
10. Samanta,B., Al-Balushi,K.R., and Al-Araimi,S.A. (2003). Artificial neural networks and support vector machine with genetic algorithm for bearing fault detection. Engineering Application of Artificial Intelligent 16, 657-665.
11. Shin,K.S., Lee,T.S., and Kim,H.Y.(2005). An application of support vector machines in bankruptcy prediction model, Expect Systems with Application 28, 127-135.
12. Sugumaran,V., Sabareesh,G.R., and Ramachandran,K-I. (2008). Fault diagnostics of roller bearing using kernel based neighborhood score multi-class support vector machine. Expert Systems with Applications 34, 3090-3098.
13. Aggarwal, A. and Singh,H., (2005). Optimization of machining techniques - A retrospective and literature review. Sadhana Vol. 30, Part 6.pp 699-711.
14. Hsueh,Y.W., and Yang,C.Y. (2009). Tool breakage diagnosis in face milling by support vector machine, Journal of Material Processing Technology,209,145- 152.
15. Jiang,Z. (2010). Intelligent Prediction of Surface Roughness of Milling Aluminium Alloy Based on Least Square Support Vector Machine, IEEE, 2672-2876.
16. Caydas,U. and Ekici,S. (2009).#Support vector machines models for surface roughness prediction in CNC turning of AISI 304 austenitic stainless steel.#Springer Science+Business Media, LLC.
17. Wang,P., Meng,Q., Zhao,J., Li,J. and Wang,X. (2011). Prediction of Machine Tool Condition Using Support Vector Machine.Journal of Physics Conference 305.
18. Dong,H., Wu,D., and Su,H. (2006). Use of least square support vector machine in surface roughness prediction model, Third International Symposium on Precision Mechanical Measurement, Vol 64.
19. Zhang,L., Jia,Z., Wang,F., and Liu,W. (2010). A hybrid model using supporting vector machine and multi-objective genetic algorithm for processing parameters optimization in micro-EDM, International Journal Advanced Manufacturing Technology 51, 575-586.
20. Zhang,D. and Sui,W. (2011). The application of AR Model and SVM in rolling bearings condition monitoring, Sprinegr, CSIE Part 1,CCIS 152,pp. 326-331.
21. Sugumaran,V., Sabareesh,G.R., and Ramachandran,K-I. (2008). Fault diagnostics of roller bearing using kernel based neighborhood score multi-class support vector machine. Expert Systems with Applications 34, 3090-3098.