Surface quality prediction by artificial-neural-networks;primjenaneuronskih mrežau procjenjivanju kvalitete obradivane površine

Tehnicki Vjesnik

Volumn 16, Issue 2, 2009, Pages 43-47

  Šimunović, Goran ^a   Šarić, Tomislav ^a   Lujić, Roberto ^a  

^a JOSIP JURAJ STROSSMAYER UNIVERSITY OF OSIJEK   (Croatia)

Author keywords

Artificial intelligence; Neural networks; Surface quality

Indexed keywords

BACK PROPAGATION NEURAL NETWORKS; CUTTING DEPTH; CUTTING SPEED; EXPERIMENTAL RESEARCH; FEED-RATES; NEURAL NETWORKS MODEL; PRODUCT FUNCTIONS; PRODUCTION PREPARATION; RMS ERRORS; SURFACE QUALITIES; SURFACE QUALITY;

METAL ANALYSIS; PHOTORESISTS; SURFACE PROPERTIES; SURFACE ROUGHNESS;

NEURAL NETWORKS;

EID: 70749123951     PISSN: 13303651     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (20)

1. Singh, G.; Choudhary, A. K.; Karunakaran, K. P.; Tiwari, M. K. An evolutionary approach for multi-pass turning operations. Proceedings of the Institution of Mechanical Engineers Part B-Journal of Engineering Manufacture. 220 (2), 2 (2006), str. 145-162.
2. Sardinas, R. Q.; Santana, M. R.; Brindis, E. A. Genetic algorithm-based multi-objective optimization of cutting parameters in turning processes. Engineering Applications of Artificial Intelligence. 19 (2), 3 (2006), str.127-133.
3. Jurković, Z.; Cukor, G.; Meštrović, T. Determination of optimal cutting parameters to improve surface roughness based on different optimization approaches//AMST'08 Advanced Manufacturing Systems and Technology/Kuljanić, Elso (ur.). Udine:CISM, 2008. str. 105-110.
4. Cus, F.; Balic, J. Optimization of cutting process by GA approach. Robotics And Computer-Integrated Manufacturing. 19 is (1-2), 2-4 (2003), str. 113-121.
5. Cus, F.; Milfelner, M.; Balic, J. An intelligent system for monitoring and optimization of ball-end milling process. Journal of Materials Processing Technology. 175 (1-3), 6 (2006), str. 90-97.
6. Tansel, I. N.; Ozcelik, B.; Bao, W. Y.; Chen, P.; Rincon, D.; Yang, S. Y.; Yenilmez, A. Selection of optimal cutting conditions by using GONNS. International Journal of Machine Tools & Manufacture. 46 (1), 1 (2006), str.26-35.
7. Jain, R. K.; Jain, V. K. Optimum selection of machining conditions in abrasive flow machining using neural network. Journal of Materials Processing Technology. 108 (1), 12 (2000), str. 62-67.
8. Zuperl, U.; Cus, F.; Mursec, B.; Ploj, T. A hybrid analyticalneural network approach to the determination of optimal cutting conditions. Journal of Materials Processing Technology. 157-58 (Special Issue SI), 12 (2004), str. 82-90.
9. Zuperl, U.; Cus, F. Optimization of cutting conditions during cutting by using neural networks. Robotics and Computer-Integrated Manufacturing. 19 (1-2), 2-4 (2003), str. 189-199.
10. Ozel, T.; Karpat, Y. Predictive modelling of surface roughness and tool wear in hard turning using regression and neural networks. International Journal of Machine Tools & Manufacture. 45 (4-5), 4 (2005), str. 467-479.
11. Chen Lu. Study on prediction of surface quality in machining process. Journal of materials processing technology 205 (2008), str. 439-450.
12. Wang, X. F.; Feng, C. X. Development of empirical models for surface roughness prediction in finish turning. Int J Adv Manuf Technol 20 (5) 2002, str. 348-356.
13. Naveen, Sait A.; Aravindan, S.; Noorul Haq, A. Influence of machining parameters on surface roughness of GFRP pipes, Advances in roduction Engineering & Management 4, 1-2 (2009), str. 47-58.
14. Grzesik, W. Arevised model for predicting surface roughness in turning. Wear (1996) 194, str. 143-148.
15. Abburi, N. R.; Dixit, U. S. Aknowledge-based system for the prediction of surface roughness in turning process, Robotics and Computer-Integrated Manufacturing 22 (2006), str. 363-372.
16. Bernie, P.; Huanga, Joseph C.; Chenb, Ye Lib. Artificialneural-networks- based surface roughness Pokayoke system for end-milling operations, Neurocomputing 71 (2008), str. 544-549.
17. Bajić, D.; Gjeldum, N.; Veža, I. Examinantion and Modeling of the Influence of Cutting Parameters on the Surface Roughness in Face Milling//AMST'08 Advanced Manufacturing Systems and Technology/Kuljanić, Elso (ur.). Udine:CISM, 2008. str. 123-133.
18. Durmus, K. Prediction and control of surface roughness in CNC lathe using artificial neural network. Journal of materials processing technology 209 (7) (2009), str. 3125-3137.
19. Hasan, O.; Tuncay, E.; Fehmi, E. Prediction of minimum surface roughness in end milling mold parts using neural network and genetic algorithm, Materials and Design 27 (2006), str. 735-744.
20. Bajić, D.; Lela, B.; Cukor, G. Examination and Modelling of the Influence of Cutting Parameters on the Cutting Force and the Surface Roughness in Longitudinal Turning, Journal of Mechanical Engineering 54 (2008) 5, str. 322-333.