Abstract
In this study, a milling system based on the in-line surface roughness measurement during machining process is developed using Artificial Neural Network (ANN) technique. In the proposed system, optimum feed rate and cutting speed are determined by ANN so as to provide the desired surface roughness, which is an important criterion for high quality surface. For this purpose, firstly an algorithm determining the operating principle of the system is developed. According to this algorithm, the optimum cutting parameters are predicted for end milling (finishing) operation by measuring semi-finish machining surface roughness via an optical sensor and then end milling operation is performed with the cutting parameters determined by the system. In the experimental part of this study, surface quality is observed for the milling process before and after the intervention of the system and the results is compared. The experimental results show that the system can be integrated with the modern machining systems in order to obtain the desired surface quality levels.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
Abbreviations
- Vc:
-
cutting speed
- f:
-
feed rate
- d:
-
depth of cut
- Ra :
-
surface roughness
References
Black, J. T., “Flow Stress Model in Metal Cutting,” Journal of Engineering for Industry, Vol. 101, No. 4, pp. 403–415, 1979.
Çolak, O., Kurbanoğlu, C., and Kayacan, M. C., “Milling Surface Roughness Prediction using Evolutionary Programming Methods,” Materials & Design, Vol. 28, No. 2, pp. 657–666, 2007.
Hayajneh, M. T., Tahat, M. S., and Bluhm, J., “A Study of the Effects of Machining Parameters on the Surface Roughness in the End-Milling Process,” Jordan Journal of Mechanical and Industrial Engineering, Vol. 1, No. 1, pp. 1–5, 2007.
Zhang, J. Z., Chen, J. C., and Kirby, E. D., “Surface Roughness Optimization in an End-Milling Operation using the Taguchi Design Method,” Journal of Materials Processing Technology, Vol. 184, No. 1, pp. 233–239, 2007.
Agarwal, N., “Surface Roughness Modeling with Machining Parameters (Speed, Feed and Depth of Cut) in CNC Milling,” MIT International Journal of Mechanical Engineering, Vol. 2, No. 1, pp. 55–61, 2012.
Rosales, A., Vizán, A., Diez, E., and Alanís, A., “Prediction of Surface Roughness by Registering Cutting Forces in the Face Milling Process,” European Journal of Scientific Research, Vol. 41, No. 2, pp. 228–237, 2011.
Nouri, M., Fussell, B. K., Ziniti, B. L., and Linder, E., “Real-Time Tool Wear Monitoring in Milling using a Cutting Condition Independent Method,” International Journal of Machine Tools and Manufacture, Vol. 89, pp. 1–13, 2015.
Ali, S. M. and Dhar, N. R., “Modeling of Tool Wear and Surface Roughness under MQL Condition-A Neural Approach,” Canadian Journal on Artificial Intelligence, Machine Learning & Pattern Recognition, Vol. 1, No. 2, pp. 7–25, 2010.
Chi, J. and Chen, L. Q., “The Real-Time Prediction of Surface Roughness based on Genetic Wavelet Network,” Advanced Materials Research, Vols. 102–104, pp. 610–614, 2010.
Tsai, Y.-H., Chen, J. C., and Lou, S.-J., “An in-Process Surface Recognition System based on Neural Networks in End Milling Cutting Operations,” International Journal of Machine Tools and Manufacture, Vol. 39, No. 4, pp. 583–605, 1999.
Kuttolamadom, M., Hamzehlouia, S., and Mears, L., “Effect of Machining Feed on Surface Roughness in Cutting 6061 Aluminum,” SAE International, Vol. 3, No. 1, pp. 108–119, 2010.
Bajiæ, D., Lela, B., and Živkoviæ, D., “Modeling of Machined Surface Roughness and Optimization of Cutting Parameters in Face Milling,” Metalurgija, Vol. 47, No. 4, pp. 331–334, 2008.
Baek, D. K., Ko, T. J., and Kim, H. S., “Optimization of Feedrate in a Face Milling Operation using a Surface Roughness Model,” International Journal of Machine Tools and Manufacture, Vol. 41, No. 3, pp. 451–462, 2001.
Ehmann, K. F. and Hong, M. S., “A Generalized Model of the Surface Generation Process in Metal Cutting,” CIRP Annals-Manufacturing Technology, Vol. 43, No. 1, pp. 483–486, 1994.
Moshat, S., Datta, S., Bandyopadhyay, A., and Pal, P., “Optimization of CNC End Milling Process Parameters using PCA-based Taguchi Method,” International Journal of Engineering, Science and Technology, Vol. 2, No. 1, pp. 95–102, 2010.
Chen, J. C. and Savage, M., “A Fuzzy-Net-based Multilevel in-Process Surface Roughness Recognition System in Milling Operations,” The International Journal of Advanced Manufacturing Technology, Vol. 17, No. 9, pp. 670–676, 2001.
Lee, K. Y., Kang, M. C., Jeong, Y. H., Lee, D. W., and Kim, J. S., “Simulation of Surface Roughness and Profile in High-Speed End Milling,” Journal of Materials Processing Technology, Vol. 113, No. 1, pp. 410–415, 2001.
Michalik, P., Zajac, J., Hatala, M., Mital, D., and Fecova, V., “Monitoring Surface Roughness of Thin-Walled Components from Steel C45 Machining Down and Up Milling,” Measurement, Vol. 58, pp. 416–428, 2014.
Routara, B. C., Bandyopadhyay, A., and Sahoo, P., “Roughness Modeling and Optimization in CNC End Milling using Response Surface Method: Effect of Workpiece Material Variation,” The International Journal of Advanced Manufacturing Technology, Vol. 40, No. 11–12, pp. 1166–1180, 2009.
Zawada-Tomkiewicz, A., “Estimation of Surface Roughness Parameter based on Machined Surface Image,” Metrology and Measurement Systems, Vol. 17, No. 3, pp. 493–503, 2010.
Bradley, C., “Automated Surface Roughness Measurement,” The International Journal of Advanced Manufacturing Technology, Vol. 16, No. 9, pp. 668–674, 2000.
Erzurumlu, T. and Oktem, H., “Comparison of Response Surface Model with Neural Network in Determining the Surface Quality of Moulded Parts,” Materials & Design, Vol. 28, No. 2, pp. 459–465, 2007.
Oktem, H., Erzurumlu, T., and Erzincanli, F., “Prediction of Minimum Surface Roughness in End Milling Mold Parts using Neural Network and Genetic Algorithm,” Materials & Design, Vol. 27, No. 9, pp. 735–744, 2006.
Che, Z.-G., Chiang, T.-A., and Che, Z.-H., “Feed-Forward Neural Networks Training: A Comparison Between Genetic Algorithm and Back-Propagation Learning Algorithm,” International Journal of Innovative Computing, Information and Control, Vol. 7, No. 10, pp. 5839–5850, 2011.
Karimi, B., Menhaj, M. B., and Saboori, I., “Multilayer Feed Forward Neural Networks for Controlling Decentralized Large-Scale Non-Affine Nonlinear Systems with Guaranteed Stability,” International Journal of Innovative Computing, Information and Control, Vol. 6, No. 11, pp. 4825–4841, 2010.
ZareNezhad, B. and Aminian, A., “A Multi-Layer Feed Forward Neural Network Model for Accurate Prediction of Flue Gas Sulfuric Acid Dew Points in Process Industries,” Applied Thermal Engineering, Vol. 30, No. 6, pp. 692–696, 2010.
Questex Media Group, Inc., “Measuring Surface Roughness with an Optical Sensor,” http://archives.sensorsmag.com/articles/0499/0499_58/ (Accessed 18 MAR 2016).
Surya, M. S. and Atla, S., “Effect of Approach Angle in Face Milling Using Tungsten Carbide Tool,” International Journal of Recent advances in Mechanical Engineering, Vol. 4, No. 2, pp. 15–27, 2015.
Palanikumar, K., “Modeling and Analysis for Surface Roughness in Machining Glass Fibre Reinforced Plastics using Response Surface Methodology,” Materials & Design, Vol. 28, No. 10, pp. 2611–2618, 2007.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Taga, Ö., Kiral, Z. & Yaman, K. Determination of cutting parameters in end milling operation based on the optical surface roughness measurement. Int. J. Precis. Eng. Manuf. 17, 579–589 (2016). https://doi.org/10.1007/s12541-016-0070-4
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12541-016-0070-4