A Hybrid Neural Network for Prediction of Surface Roughness in Machining

Abstract:

Surface roughness is an important outcome in the machining process and it plays a major role in the manufacturing system. Prediction of surface roughness has been a challenge to researchers because it is impacted by different machining parameters and the inherent uncertainties in the machining process. Prediction of surface roughness will benefit the manufacturing process to be more productive and competitive at the same time to reduce any pre-processing of the machined workpiece in order to meet the technical specifications. In this study, a hybrid GA-LM ANN is proposed for the prediction of surface roughness during roughing process in turning operation. To verify the performance of the proposed approach, the results are compared with the results obtained by training an ANN using GA or LM. The results have shown that the hybrid ANN outperformed the other two algorithms.

• Periodical: Applied Mechanics and Materials (Volumes 633-634)
• Pages: 579-582
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.633-634.579
• Online since: September 2014
• Authors: Manik Rajora*, Alexander H. Shih, Pan Zou, Bei Zhi Li, Steven Y. Liang
• Keywords: Artificial Neural Network (ANN), Back-Propagation (BP), Genetic Algorithm (GA), Hybrid Algorithms, Levenberg-Marquardt (LM), Machining, Surface Roughness (SR)

References

[1] Choudhury, I. a., and M. a. El-Baradie. Surface Roughness Prediction in the Turning of Highstrength Steel by Factorial Design of Experiments., Journal of Materials Processing Technology 67. 1-3 (1997): 55-61.

DOI: 10.1016/s0924-0136(96)02818-x

[2] Dimla, Snr Dimla E. Application of Perceptron Neural Networks to Tool-state Classification in a Metal-turning Operation., Engineering Applications of Artificial Intelligence 12. 4 (1999): 471-77.

DOI: 10.1016/s0952-1976(99)00015-9

[3] Benardos, P. g, and G. c Vosniakos. Prediction of Surface Roughness in CNC Face Milling Using Neural Networks and Taguchi's Design of Experiments., Robotics and Computer-Integrated Manufacturing 18. 5-6 (2002): 343-354.

DOI: 10.1016/s0736-5845(02)00005-4

[4] Lin, W. s., B. y. Lee, and C. l. Wu. Modeling the Surface Roughness and Cutting Force for Turning., Journal of Materials Processing Technology 108. 3 (2001): 286-293.

DOI: 10.1016/s0924-0136(00)00835-9

[5] Goonatilake, Suran, and Sukhdev Khebbal. Intelligent Hybrid Systems. Chichester: Wiley, (1995).

[6] Mok, S. l., C. k. Kwong, and W. s. Lau. A Hybrid Neural Network and Genetic Algorithm Approach to the Determination of Initial Process Parameters for Injection Moulding., The International Journal of Advanced Manufacturing Technology 18. 6 (2001).

DOI: 10.1007/s001700170050

[7] Wang, Kesheng, Hirpa L. Gelgele, Yi Wang, Qingfeng Yuan, and Minglung Fang. A Hybrid Intelligent Method for Modelling the EDM Process., International Journal of Machine Tools and Manufacture 43. 10 (2003): 995-999.

DOI: 10.1016/s0890-6955(03)00102-0

[8] Tansel, I. n., B. Ozcelik, W. y. Bao, P. Chen, D. Rincon, S. y. Yang, and A. Yenilmez. Selection of Optimal Cutting Conditions by Using GONNS., International Journal of Machine Tools and Manufacture 46. 1 (2006): 26-35.

DOI: 10.1016/j.ijmachtools.2005.04.012

[9] Ozcelik, Babur, Hasan Oktem, and Hasan Kurtaran. Optimum Surface Roughness in End Milling Inconel 718 by Coupling Neural Network Model and Genetic Algorithm., The International Journal of Advanced Manufacturing Technology 27. 3-4 (2005): 234-241.

DOI: 10.1007/s00170-004-2175-7

[10] Liu, Wei, Xiuyan Zheng, Shuangjun Liu, and Zhenyuan Jia. A Roughness Measurement Method Based On Genetic Algorithm And Neural Network For Microheterogeneous Surface In Deep-Hole Parts., Journal of Circuits, Systems and Computers 21. 01 (2012).

DOI: 10.1142/s0218126612500053

[11] Mukherjee, Indrajit, and Srikanta Routroy. Comparing the Performance of Neural Networks Developed by Using Levenberg-Marquardt and Quasi-Newton with the Gradient Descent Algorithm for Modelling a Multiple Response Grinding Process., Expert Systems with Applications 39. 3 (2012).

DOI: 10.1016/j.eswa.2011.08.087

[12] Nalbant, Muammer, Hasan Gökkaya, Ihsan Toktaş, and Gökhan Sur. The Experimental Investigation of the Effects of Uncoated, PVD- and CVD-coated Cemented Carbide Inserts and Cutting Parameters on Surface Roughness in CNC Turning and Its Prediction Using Artificial Neural Networks., Robotics and Computer-Integrated Manufacturing 25. 1 (2009).

DOI: 10.1016/j.rcim.2007.11.004

[13] Dombaycı, Ömer Altan, and Mustafa Gölcü. Daily Means Ambient Temperature Prediction Using Artificial Neural Network Method: A Case Study of Turkey., Renewable Energy 34. 4 (2009): 11581161.

DOI: 10.1016/j.renene.2008.07.007

[14] Hao, Wangshen, Xunsheng Zhu, Xifeng Li, and Gelvis Turyagyenda. Prediction of Cutting Force for Self-propelled Rotary Tool Using Artificial Neural Networks., Journal of Materials Processing Technology 180. 1-3 (2006): 23-29.

DOI: 10.1016/j.jmatprotec.2006.04.123