Browse > Article
http://dx.doi.org/10.14775/ksmpe.2015.14.3.065

Prediction of Surface Roughness and Electric Current Consumption in Turning Operation using Neural Network with Back Propagation and Particle Swarm Optimization  

Punuhsingon, Charles S.C (Department of Systems Management & Engineering, Graduate School, Pukyong National University)
Oh, Soo-Cheol (Department of Systems Management & Engineering, Pukyong National University)
Publication Information
Journal of the Korean Society of Manufacturing Process Engineers / v.14, no.3, 2015 , pp. 65-73 More about this Journal
Abstract
This paper presents a method of predicting the machining parameters on the turning process of low carbon steel using a neural network with back propagation (BP) and particle swarm optimization (PSO). Cutting speed, feed rate, and depth of cut are used as input variables, while surface roughness and electric current consumption are used as output variables. The data from experiments are used to train the neural network that uses BP and PSO to update the weights in the neural network. After training, the neural network model is run using test data, and the results using BP and PSO are compared with each other.
Keywords
Neural Network; Particle Swarm Optimization; Surface Roughness; Electric Current Consumption;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Ahilan at al., "Modeling and prediction of machining quality in CNC turning process using intelligent hybrid decision making tools", Applied Soft Computing, Vol. 13, pp. 1543-1551, 2013.   DOI   ScienceOn
2 Asiltürk, I., "Predicting surface roughness of hardened AISI 1040 based on cutting parameters using neural networks and multiple regression", Int. J. Adv. Manuf. Techno, 2012.
3 Benardos, P.G, Vosniakos, G.C, "Predicting surface roughness in machining: a review", International Journal of Machine Tools & Manufacture, Vol. 43, pp. 833-844, 2003.   DOI
4 Bhattacharya at al., "Estimating the effect of cutting parameters on surface finish and power consumption during high speed machining of AISI 1045 steel using Taguchi design and ANOVA", Prod. Eng. Res. Devel, Vol. 3, pp. 1-40, 2009.   DOI
5 Che, Z.H at al., "PSO-based back-propagation artificial neural network for product and mold cost estimation of plastic injection molding", Computers & Industrial Engineering Vol. 58, pp. 625-637, 2010.   DOI
6 Davim at al., "Investigations into the effect of cutting conditions on surface roughness in turning of free machining steel by ANN models", Journal of materials processing technology, Vol. 205, pp. 16-23, 2008.   DOI   ScienceOn
7 Ezilarasan at al., "An experimental analysis and measurement of process performances in machining of nimonic C-263 super alloy", Measurement, Vol. 46, pp. 185-199, 2013.   DOI   ScienceOn
8 Gilat, A., "MATLAB An introduction with applications", John Wiley& Sons, Inc, 2008.
9 Hines, J. W., "Fuzzy and neural approaches in engineering", John Wiley& Sons, Inc, 1997.
10 Jiang H. M at al., "Modeling customer satisfaction for new product development using a PSO-based ANFIS approach", Applied Soft Computing, Vol. 12, pp. 726-734, 2012.   DOI
11 Jafarian at al., "Improving surface integrity in finish machining of Inconel 718 alloy using intelligent systems", Int. J. Adv. Manuf. Techno, Vol. 71, pp. 817-827, 2014   DOI   ScienceOn
12 Myung-Il Bae, Yi-Seon Rhie, "Predict of Surface Roughness Using Multi-regression Analysis in Turning of Plastic Mold Steel", Journal of the Korean Society of Manufacturing Process Engineers, Vol. 12(4), pp.87-92, 2013.
13 Myung-Il Bae, Yi-Seon Rhie, "Surface Roughness Prediction of Interrupted Cutting in SM45C Using Coated Tool", Journal of the Korean Society of Manufacturing Process Engineers, Vol. 13(3), pp.77-82, 2014.   DOI
14 Saisahanmuga, R.V, Rajagopalan, S.P, "Comparative analysis of optimization techiques for artificial neural network in bio medical applications", Journal of computer science, Vol. 10(1), pp. 106-114, 2014.   DOI
15 Zhang at al., "A hybrid particle swarm optimization-back-propagation algorithm for feedforward neural network training", Applied Mathematics and Computation, Vol. 185, pp. 1026-1037, 2007.   DOI