Abstract
In recent years, the measurement of surface roughness of a workpiece plays a vital role since the roughness of a surface has a considerable influence on the product quality and the functional aspects. In this work, a differential evolution algorithm (DEA)-based artificial neural network (ANN) has been used for the prediction of surface roughness in turning operations. Cutting speed, feed rate, depth of cut, and average gray level of the surface image of workpiece, acquired by computer vision, were taken as the input parameters and surface roughness as the output parameter. The results obtained from the DEA-based ANN model were compared with the backpropagation (BP)-based ANN. It is found that the error percentage is very close, and it is also observed that the convergence speed for the DEA-based ANN is higher than the BP-based ANN.
Similar content being viewed by others
References
Lee B-Y, Tarng Y-S (2001) Surface roughness inspection by computer vision in turning operations. Int J Adv Manuf Technol 41:1251–1263
Ho SY et al (2002) Accurate modeling and prediction of surface roughness by computer vision in turning operations using an adaptive neuro-fuzzy inference system. Int J Machine Tools Manuf 42:1441–1446
Kiran M-B et al (1998) Evaluation of surface roughness by vision system. Int J Machine Tools Manuf 38(5–6):685–690
Dhanasekaran B et al (2008) Evaluation of surface roughness based on monochromatic speckle correlation using image processing. Precision Eng 32:196–206
Yamaguchi I et al (2004) Measurement of surface roughness by speckle correlation. Soc Photo-Optical Instrum Eng 43(11):2753–61
Persson U (1993) Measurement of surface roughness on rough machined surface using speckle correlation and image analysis. Wear 160:221–225
Persson U (1992) Real time measurement of surface roughness on ground surfaces using speckle contrast technique. Opt Laser Eng 17:61–67
Shahabi HH, Ratnam MM (2009) Non-contact roughness measurement of turned parts using machine vision. Int J Adv Manuf Technol. doi:10.1007/s00170-009-2101-0
Shahabi HH, Ratnam MM (2008) Assessment of flank wear and nose radius wear from workpiece roughness profile in turning operation using machine vision. Int J Adv Manuf Technol. doi:10.1007/s00170-008-1688-x
Shahabi HH, Ratnam MM (2008) In-cycle monitoring of tool nose wear and surface roughness of turned parts using machine vision. Int J Adv Manuf Technol 40:1148–1157. doi:10.1007/s00170-008-1430-8
Zhongxiang H et al (2008) Evaluation of three dimensional surface roughness parameters based on digital image processing. Int J Adv Manuf Technol 40:342–348. doi:10.1007/s00170-007-1357-5
Eberhart R-C, Shi Y (1998) Comparison between genetic algorithms and particle swarm optimisation. The 7th Annual Conference on Evolutionary Programming, San Diego, USA
Rajasekaran S, Vijayalakshmi Pai GA (1996) Genetic algorithm based weight determination for back-propagation networks. Proceedings of Fourth International Conference on Advanced Computing, pp 73–79
Kennedy J, Eberhart RC (1995) Particle swarm optimization. Proceedings of IEEE International conference on neural networks IV, pp 1942–1948
Du J-X et al (2007) Shape reconstruction based on neural networks trained by differential evolution algorithm. Neurocomputing 70:896–903
Magoulas GD, Plagianakos VP, Vrahatis MN (2004) Neural network-based colonoscopic diagnosis using on-line learning and differential evolution. Applied Soft Computing 4(1):369–379
dos Santos Coelho L et al (2010) Model-free adaptive control design using evolutionary-neural compensator. Expert Syst Appl 37(1):499–508
Nikunj Chauhan, Ravi V, Karthik Chandra D (2009) Differential evolution trained wavelet neural networks: application to bankruptcy prediction in banks. Expert Syst Appl 36(4):7659–7665
dos Santos Coelho L, Guerra FA (2008) B-spline neural network design using improved differential evolution for identification of an experimental nonlinear process. Applied Soft Computing 4(8):1513–1522
Basturk A, Gunay E (2009) Efficient edge detection in digital images using a cellular neural network optimized by differential evolution algorithm. Expert Syst Appl 36(2):2645–2650
Ilonen J et al (2003) Differential evolution training algorithm for feed-forward neural networks. Neural Process Lett 17:93–105
Zelinka I, Lampinen J (1999) An evolutionary learning algorithms for neural networks. Fifth International Conference on Soft Computing: MENDEL’99:410–414
Storn R, Price K (2005) Differential evolution—a practical approach to global optimization. Springer, Berlin
Storn R, Price K (1997) Differential evolution—a simple evolution strategy for fast optimization. Dr Dobb’s J 22(4):18–24
Storn R, Price K (1995) Differential evolution—a simple and efficient adaptive scheme for global optimization over continuous spaces. Technical Report TR-95-012, International Computer Science Institute, Berkeley, CA, USA
Mayer D-G et al (2005) Differential evolution—an easy and efficient evolutionary algorithm for model optimization. Agric Syst 83:315–328
Coello CA et al (2007) Evolutionary algorithms for solving multi-objective problems, 2nd edn. Springer, Heidelberg
Abbass H-A, Sarker R (2002) The Pareto differential evolution algorithm. Int J Artif Intell Tools 11(No.4):531–552
Babu B-V, Rakesh Angira (2002) A differential evolution approach for global optimization of MNLP problems. Proceedings of 4th Asia-Pacific Conference on Simulated Evolution and Learning (SEAL’02), Singapore, paper no. 1033, vol. 2, pp 880–884, November 18–22
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Yang, S.H., Natarajan, U., Sekar, M. et al. Prediction of surface roughness in turning operations by computer vision using neural network trained by differential evolution algorithm. Int J Adv Manuf Technol 51, 965–971 (2010). https://doi.org/10.1007/s00170-010-2668-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00170-010-2668-5