Tool Wear Modeling in Drilling Process of AISI1020 and AISI8620 Using Genetic Programming
الموضوعات :
Vahid Zakeri Mehrabad
1
,
Vahid Pourmostaghimi
2
1 - Department of Mechanical Engineering,
Tabriz Branch, Islamic Azad University, Tabriz, Iran
*Corresponding author
2 - Department of Mechanical Engineering,
University of Tabriz, Iran
الکلمات المفتاحية: Genetic Programming, Flank wear, Drilling operation, Tool wear,
ملخص المقالة :
In manufacturing industry, it has been acknowledged that tool wear prediction has an important role in higher quality of products and acceptable efficiency. Being an emerging area of research in recent years, drilling tool wear is an important factor which directly affects quality parameters of machined hole such as hole centring, roundness, burr formation and finished surface. In this paper, the genetic equation for prediction of drilling tool flank wear was developed using the experimentally measured wear values and genetic programming for two different materials, AISI1020 and AISI8620 steels. These equations could be used to compare the behaviour of wear in both mentioned materials and analyse the effect of materials characteristics on wear rate and wear pattern. The suggested equations have been shown to correspond well with experimental data obtained for flank wear when machining in various cutting conditions.The results of experiments and equations showed that properties of work material can affect drill bit flank wear drastically. It was concluded that greater toughness and strength of AISI8620, compared to AISI1020, lead to higher cutting stresses and temperatures, resulting more flank wear.
[1] Abu-Mahfouz, I., “Drilling wear detection and classification using vibration signals and artificial neural network”, International Journal of Machine Tools and Manufacture, Vol. 43, No. 7, 2003, pp. 707-720.
[2] Yu, J., “Online tool wear prediction in drilling operations using selective artificial neural network ensemble model”, Neural Computing and Applications, Vol. 20, No. 4, 2001, pp. 473-485.
[3] Bakkal, M., Shih, A. J., McSpadden, S. B., and Scattergood, R. O., “ Thrust force, torque, and tool wear in drilling the bulk metallic glass”, International Journal of Machine Tools and Manufacture, Vol. 45, No. 7, 2005, pp. 863-872.
[4] Gómez, M. P., Hey, A. M., Ruzzante, J. E., and D’Attellis, C. E., “Tool wear evaluation in drilling by acoustic emission”, Physics Procedia, Vol. 3, No. 1, 2010, pp. 819-825.
[5] Taskesen, A., and Kutukde, K., “Analysis and optimization of drilling parameters for tool wear and hole dimensional accuracy in B4”, The Chinese Journal of Nonferrous Metals, Vol. 23, No. 9, 2015, pp. 2526-2536.
[6] Chethan, Y. D., Ravindra, H. V., Gowda, Y. K., and Kumar, G. M, “Parametric Optimization in Drilling EN-8 Tool Steel and Drill Wear Monitoring Using Machine Vision Applied with Taguchi Method”, Procedia Materials Science, Vol. 5, 2014, pp. 1442-1449
[7] Astakhov, V. P., “The assessment of cutting tool wear ”, International Journal of Machine Tools and Manufacture, Vol. 44, No. 6, 2004, pp. 637-647.
[8] Lim, C. Y. H., Lau, P. P. T., and Lim, S. C., “The effects of work material on tool wear”, Wear, Vol. 250, No. 1, 2001, pp. 344-348.
[9] Lin, S. C., Ting, C. J., “Drill wear monitoring using neural networks”, International Journal of Machine Tools and Manufacture, Vol. 36, No. 4, 1996, pp. 465-475.
[10] Garg, S., Pal, S. K., and Chakraborty, D., “Evaluation of the performance of backpropagation and radial basis function neural networks in predicting the drill flank wear”, Neural Computing and Applications, Vol. 16, No. 4-5, 2007, pp. 407-417.
[11] Panda, S. S., Chakraborty, D., and Pal, S. K., “Flank wear prediction in drilling using back propagation neural network and radial basis function network”, Applied Soft Computing, Vol. 8, No. 2, 2008, pp. 858-871.
[12] Abu-Mahfouz, I., “Drill flank wear estimation using supervised vector quantization neural networks”, Neural Computing and Applications, Vol. 14, No. 3, 2005, pp. 167-175.
[13] Wang, X., Wang, W., Huang, Y., Nguyen, N., and Krishnakumar, K., “Design of neural network-based estimator for tool wear modeling in hard turning”, Journal of intelligent manufacturing, Vol. 19, No. 4, 2008, pp. 383-396.
[14] [14] Desai, C. K., and Shaikh, A. A., “Drill wear monitoring using artificial neural network with differential evolution learning,” International Conference on Industrial Technology ICIT, IEEE, 2006, pp. 2019-2022.
[15] Sette, S., Boullart, L., “Genetic programming: principles and applications”, Engineering applications of artificial intelligence, Vol. 14, No. 6, 2001, pp. 727-736.
[16] Koza, J. R., “Genetic programming: on the programming of computers by means of natural selection”, MIT press, massachusetts, US, 1992.
[17] Zadshakoyan, M., Pourmostaghimi, V., “Genetic equation for the prediction of tool–chip contact length in orthogonal cutting”, Engineering Applications of Artificial Intelligence, Vol. 26, No. 7, 2013, pp. 1725-1730.
[18] Brezocnik, M., Kovacic, M., and Ficko, M., “Prediction of surface roughness with genetic programming”, Journal of materials processing technology, Vol. 157, 2004, pp. 28-36.
[19] Metenidis, M. F., Witczak, M., and Korbicz, J., “A novel genetic programming approach to nonlinear system modelling: application to the DAMADICS benchmark problema”, Engineering Applications of Artificial Intelligence, Vol. 17, No. 4, 2004, pp. 363-370.
[20] Milfelner, M., Kopac, J., Cus, F., and Zuperl, U., “Genetic equation for the cutting force in ball-end milling”, Journal of Materials Processing Technology, Vol. 164, 2005, pp. 1554-1560.
[21] Dimopoulos, C., Zalzala, A. M., “Investigating the use of genetic programming for a classic one-machine scheduling problem”, Advances in Engineering Software, Vol. 32, No. 6, 2001, pp. 489- 498.
[22] Zadshakoyan, M., Pourmostaghimi, V., “Cutting Tool Crater Wear Measurement in Turning Using Chip Geometry and Genetic Programming”, Int. J. Applied Metaheuristic Computing (IJAMC), Vol. 6, No. 1, 2015, pp. 47-60.
