Modeling the effect of variable work piece hardness on surface roughness in an end milling using multiple regression and adaptive Neuro fuzzy inference system

Desale, P.; Jahagirdar, R.

Growing Science » International Journal of Industrial Engineering Computations » Modeling the effect of variable work piece hardness on surface roughness in an end milling using multiple regression and adaptive Neuro fuzzy inference system

Journals

IJIEC Volumes

Volume 1 (17)
- Issue 1 (9)
- Issue 2 (8)

Volume 2 (68)
- Issue 1 (12)
- Issue 2 (20)
- Issue 3 (20)
- Issue 4 (16)

Volume 3 (76)
- Issue 1 (9)
- Issue 2 (15)
- Issue 3 (20)
- Issue 4 (12)
- Issue 5 (20)

Volume 4 (50)
- Issue 1 (14)
- Issue 2 (10)
- Issue 3 (12)
- Issue 4 (14)

Volume 5 (47)
- Issue 1 (13)
- Issue 2 (12)
- Issue 3 (12)
- Issue 4 (10)

Volume 6 (39)
- Issue 1 (7)
- Issue 2 (12)
- Issue 3 (10)
- Issue 4 (10)

Volume 7 (47)
- Issue 1 (10)
- Issue 2 (14)
- Issue 3 (10)
- Issue 4 (13)

Volume 8 (30)
- Issue 1 (9)
- Issue 2 (7)
- Issue 3 (8)
- Issue 4 (6)

Volume 9 (32)
- Issue 1 (9)
- Issue 2 (6)
- Issue 3 (7)
- Issue 4 (10)

Volume 10 (34)
- Issue 1 (8)
- Issue 2 (10)
- Issue 3 (8)
- Issue 4 (8)

Volume 11 (36)
- Issue 1 (9)
- Issue 2 (8)
- Issue 3 (9)
- Issue 4 (10)

Volume 12 (29)
- Issue 1 (9)
- Issue 2 (6)
- Issue 3 (8)
- Issue 4 (6)

Volume 13 (41)
- Issue 1 (10)
- Issue 2 (8)
- Issue 3 (10)
- Issue 4 (13)

Volume 14 (50)
- Issue 1 (11)
- Issue 2 (15)
- Issue 3 (9)
- Issue 4 (15)

Volume 15 (55)
- Issue 1 (19)
- Issue 2 (15)
- Issue 3 (12)
- Issue 4 (9)

Volume 16 (12)
- Issue 1 (12)

Keywords

Authors

Countries

International Journal of Industrial Engineering Computations

ISSN 1923-2934 (Online) - ISSN 1923-2926 (Print) Quarterly Publication Volume 5 Issue 2 pp. 265-272 , 2014

Modeling the effect of variable work piece hardness on surface roughness in an end milling using multiple regression and adaptive Neuro fuzzy inference system Pages 265-272 Download PDF

Authors: Purushottam S. Desale, Ramchandra S. Jahagirdar

DOI: 10.5267/j.ijiec.2013.11.005

Keywords: End Milling, Fuzzy inference system, Regression, Surface roughness, Tool steel

Abstract: The aim of this study is to correlate work piece material hardness with surface roughness in prediction studies. The proposed model is for prediction of surface roughness of tool steel materials of hardness 55 HRC to 62 HRC (±2 HRC). The machining experiments are performed under various cutting conditions using work piece of different hardness. The surface roughness of these specimens is measured. The result showed that the influence of work piece material hardness on surface finish is significant for cutting speed and feed in CNC end milling operation. It is also observed that the surface roughness prediction accuracy of Adaptive neuro fuzzy inference system using triangular membership function is better than Gaussian, bell shape membership function and regression analysis. Surface roughness prediction accuracy with material hardness as input parameter is 97.61%.

How to cite this paper

 Desale, P & Jahagirdar, R. (2014). Modeling the effect of variable work piece hardness on surface roughness in an end milling using multiple regression and adaptive Neuro fuzzy inference system.International Journal of Industrial Engineering Computations , 5(2), 265-272.

Refrences

Altan, T., Lilly, B., & Yen, Y. C. (2001). Manufacturing of dies and molds. CIRP Annals-Manufacturing Technology, 50(2), 404-422.

Benardos, P. G., & Vosniakos, G.-C. (2003). Predicting surface roughness in machining: a review, International Journal of Machine Tools Manufacturing, 43, 833 – 844.

Dong, M., & Wang N. (2011). Adaptive network based fuzzy inference system with leave-one-out cross-validation approach for prediction of surface roughness. Applied Mathematical Modelling, 35 (3), 1024–1035.

Dweiri M., Al-Jarrah F., & Al-Wedyan H. (2003). Fuzzy surface modelling of CNC down milling. Journal of Material Processing Technology, 133, 266 – 275.

Ho W. H., Tsai J. T., Lin B. T., & Chou J. H. (2009). Adaptive network-based fuzzy inference system for prediction of surface roughness in end milling process using hybrid Taguchi-genetic learning algorithm. Expert Systems with Applications, 36, 3216–3222.

Jang, J. S. (1993). ANFIS: adaptive-network-based fuzzy inference system.Systems, Man and Cybernetics, IEEE Transactions on, 23(3), 665-685.

Jang, J. S. R., Sun, C. T., & Mizutani, E. (2004). Neuro-fuzzy and soft computing, Taipei: Pearson Education.

Kovac P., Rodic D., Pucovsky V., Savkovic B., & Gostimirovic M. (2013). Application of fuzzy logic and regression analysis for modeling surface roughness in face milling. Journal of Intelligent manufacturing, 24(4), 755-762.

Lo, S. P. (2003). An adaptive-network based fuzzy inference system for prediction of workpiece surface roughness in end milling. Journal of Materials Processing Technology, 142(3), 665-675.

Lou, M. S., Chen, J. C., & Li, C. M. (1998). Surface roughness technique for CNC End milling. Journal of Industrial Technology, 15 (1), 1-6.

Rajasekaran, T., Palanikumar, K., & Vinayagam, B. K. (2012). Application of fuzzy logic for modeling surface roughness in turning CFRP composites using CBN tool. Production Engineering, 5(2), 191 – 199.

Topal E.S. (2009). Role of stepover ratio in prediction of surface roughness in flat milling. International Journal of Mechanical Sciences, 51, 782 – 789.

Tsai, J. T., Liu, T. K., & Chou J. H. (2004). Hybrid Taguchi-genetic algorithm for global numerical optimization. IEEE Transactions on Evolutionary Computation, 8, 365–377.