Estimation of Surface Roughness in Turning by Considering the Cutting Tool Vibration, Cutting Force and Tool Wear
الموضوعات :A. Salimi 1 , A. Ebrahimpour 2 , M. Shalvandi 3 , E. Seidi 4
1 - Department of Mechanical Engineering, Payame Noor University, Iran
2 - Miyaneh Technical College, University of Tabriz, Tabriz, Iran
3 - Department of Mechanical Engineering, University of Tabriz, Tabriz, Iran
4 - Department of Agricultural Engineering, Payame Noor University, I.R. of Iran
الکلمات المفتاحية: Cutting Forces, Artificial Neural Networks, Surface Roughness, Vibration,
ملخص المقالة :
Surfacequality along with the low production cost, play significant role in today’s manufacturing market. Quality of a product can be described by various parameters. One of the most important parameters affecting the product quality is surface roughness of the machined parts. Good surface finish not only assures quality, but also reduces the product cost. Before starting any machining process, surface finish is predictable using cutting parameters and estimation methods. Establishing a surface prediction system on a machine tool, avoids the need for secondary operation and leads to overall cost reduction. On the other hand, creating a surface estimation system in a machining plant, plays an important role in computer integrated manufacturing systems (CIMS). In this study, the effect of cutting parameters, cutting tool vibration, tool wear and cutting forces on surface roughness are analyzed by conducting experiments using different machining parameters, vibration and dynamometers sensors to register the amount of tool vibration amplitude and cutting force during the machining process. For this, a number of 63 tests are conducted using of different cutting parameters. To predict the surface quality for different parameters and sensor variables, an ANN model is designed and verified using the test results. The results confirm the model accuracy in which the R2 value of the tests was obtained as 0.99 comparing with each other.
[1] Elangovan, M., Sakthivel, N. R., Saravanamurugan, S., Nair, B. B., and Sugumaran, V., “Machine Learning Approach to the Prediction of Surface Roughness Using Statistical Features of Vibration Signal Acquired in Turningˮ, Procedia Computer Science, Vol. 50, 2015, pp. 282-288.
[2] Xiao, L., Rosen, B. G., Amini, N., and Nilsson, P. H., “A Study on the Effect of Surface Topography on Rough Friction in Roller Contactˮ, Wear, Vol. 254, No. 11, 2003, pp. 1162-1169.
[3] Mahrenholtz, O., Bontcheva, N., and Iankov, R., “Influence of Surface Roughness on Friction During Metal Forming Processesˮ, Journal of Materials Processing Technology, Vol. 159, No. 1, 2005, pp. 9-16.
[4] Rao, C. J., Rao, D. N., and Srihari, P., “Influence of Cutting Parameters on Cutting Force and Surface Finish in Turning Operationˮ, Procedia Engineering, Vol. 64, 2013, pp. 1405-1415.
[5] de Agustina, B., Rubio, E. M., and Sebastián, M. Á., “Surface Roughness Model Based on Force Sensors for the Prediction of the Tool Wearˮ, Sensors (Basel, Switzerland), Vol. 14, No. 4, 2014, pp. 6393-6408.
[6] Prasad, B. S., Babu, M. P., “Correlation Between Vibration Amplitude and Tool Wear in Turning: Numerical and Experimental Analysisˮ, Engineering Science and Technology, an International Journal.
[7] Zhang, S. J., To, S., Zhang, G. Q., and Zhu, Z. W., “A Review of Machine-Tool Vibration and its Influence Upon Surface Generation in Ultra-Precision Machiningˮ, International Journal of Machine Tools and Manufacture, Vol. 91, 2015, pp. 34-42.
[8] Thomas, M., Beauchamp, Y., Youssef, A. Y., and Masounave, J., “Effect of Tool Vibrations on Surface Roughness During Lathe Dry Turning Processˮ, Computers & Industrial Engineering, Vol. 31, No. 3, 1996, pp. 637-644.
[9] Bartarya, G., Choudhury, S. K., “Effect of Cutting Parameters on Cutting Force and Surface Roughness During Finish Hard Turning AISI52100 Grade Steelˮ, Procedia CIRP, Vol. 1, 2012, pp. 651-656.
[10] Olufayo, O. A., Abou-El-Hossein, K., “Predictive Modelling of Cutting Force and its Influence on Surface Accuracy in Ultra-high Precision Machining of Contact Lensesˮ, Procedia CIRP, Vol. 31, 2015, pp. 563-567.
[11] Asiltürk, İ., Akkuş, H., “Determining the Effect of Cutting Parameters on Surface Roughness in Hard Turning Using the Taguchi Methodˮ, Measurement, Vol. 44, No. 9, 2011, pp. 1697-1704.
[12] Saini, S., Ahuja, I. S., and Sharma, V. S., “Influence of Cutting Parameters on Tool Wear and Surface Roughness in Hard Turning of AISI H11 Tool Steel Using Ceramic Toolsˮ, International Journal of Precision Engineering and Manufacturing, Vol. 13, No. 8, 2012, pp. 1295-1302.
[13] Pavel, R., Marinescu, I., Deis, M., and Pillar, J., “Effect of Tool Wear on Surface Finish for a Case of Continuous and Interrupted Hard Turningˮ, Journal of Materials Processing Technology, Vol. 170, No. 1–2, 2005, pp. 341-349.
[14] Benardos, P. G., Vosniakos, G. C., “Predicting Surface Roughness in Machining: A Reviewˮ, International Journal of Machine Tools and Manufacture, Vol. 43, No. 8, 2003, pp. 833-844.
[15] Tseng, T. L., Konada, U., and Kwon, Y., “A Novel Approach to Predict Surface Roughness in Machining Operations Using Fuzzy Set Theoryˮ, Journal of Computational Design and Engineering, Vol. 3, No. 1, 2016, pp. 1-13.
[16] Venkata Rao, K., Murthy, B. S. N., and Mohan Rao, N., “Prediction of Cutting Tool Wear, Surface Roughness and Vibration of Work Piece in Boring of AISI 316 Steel with Artificial Neural Networkˮ, Measurement, Vol. 51, 2014, pp. 63-70.
[17] Sharma, V. S., Dhiman, S., Sehgal, R., and Sharma, S. K., “Estimation of Cutting Forces and Surface Roughness for Hard Turning Using Neural Networksˮ, Journal of Intelligent Manufacturing, Vol. 19, No. 4, 2008, pp. 473-483.
[18] Lin, W. S., Lee, B. Y., and Wu, C. L., “Modeling the Surface Roughness and Cutting Force for Turningˮ, Journal of Materials Processing Technology, Vol. 108, No. 3, 2001, pp. 286-293.
[19] Pal, S. K., Chakraborty, D., “Surface Roughness Prediction in Turning Using Artificial Neural Networkˮ, Neural Computing & Applications, Vol. 14, No. 4, 2005, pp. 319-324.
[20] Salgado, D. R., Alonso, F. J., Cambero, I., and Marcelo, A., “In-process Surface Roughness Prediction System Using Cutting Vibrations in Turningˮ, The International Journal of Advanced Manufacturing Technology, Vol. 43, No. 1, 2009, pp. 40-51.
[21] Zhang, J. Z., Chen, J. C., “The Development of an In-Process Surface Roughness Adaptive Control System in End Milling Operationsˮ, The International Journal of Advanced Manufacturing Technology, Vol. 31, No. 9, 2007, pp. 877-887.
[22] Balamurugamohanraj, G., Vijaiyendiran, K., Mohanaraman, P., and Sugumaran, V., “Prediction of Surface Roughness Based on Machining Condition and Tool Condition in Boring Stainless Steel-304ˮ, 2016.
[23] Haykin., S., Neural Networks: A Comprehensive Foundation, 1999, pp. 156-254.
[24] RH, N., “Kolmogrov’s Mapping Neural Network Existence Theoremˮ, in Second IEEE International Conference on Neural Networks, San Diego, June, Vol. 21-24, 1987, pp. 11-14.
[25] Achanta, A. S., K. I., Rhodes, C. T., “Artificial Neural Networks: Implications for Pharmaceutical Sciencesˮ, 1995, pp. 119-155.
[26] Baughman DR, L. Y., Neural Networks in Bioprocessing and Chemical Engineering, New York, 1995, pp. 50-160.
