PREDICTION OF RESPONSES IN A CNC MILLING OPERATION USING RANDOM FOREST REGRESSOR

Shibaprasad Bhattacharya, Shankar Chakraborty

DOI Number
10.22190/FUME210728071B
First page
Last page

Abstract


In the present-day manufacturing environment, the modeling of a machining process with the help of statistical and machine learning techniques in order to understand the material removal mechanism and study the influences of the input parameters on the responses has become essential for cost optimization and effective resource utilization. In this paper, using a past CNC face milling dataset with 27 experimental observations, a random forest (RF) regressor is employed to effectively predict the response values of the said process for given sets of input parameters. The considered milling dataset consists of four input parameters, i.e. cutting speed, feed rate, depth of cut and width of cut, and three responses, i.e. material removal rate, surface roughness and active energy consumption. The RF regressor is an ensemble learning method where multiple decision trees are combined together to provide better prediction results with minimum variance and overfitting of data. Its prediction performance is validated using five statistical metrics, i.e. mean absolute percentage error, root mean squared percentage error, root mean squared logarithmic error, correlation coefficient and root relative squared error. It is observed that the RF regressor can be deployed as an effective prediction tool with minimum feature selection for any of the machining processes.

Keywords

CNC Milling, Random Forest Regressor, Prediction, Decision Tree

Full Text:

PDF

References


Gale, W.F., Totemeier, T.C. 2004, Metal Cutting and Forming. Smithells Metals Reference Book. Butterworth-Heinemann, USA.

Wang, Y.C., Kim, D.W., Katayama, H., Hsueh, W.C., 2018, Optimization of machining economics and energy consumption in face milling operations, International Journal of Advanced Manufacturing Technology, 99(9-12), pp. 2093-2100.

Han, J.H., Chi, S.Y., 2016, Consideration of manufacturing data to apply machine learning methods for predictive manufacturing, In: Proc. of 8th International Conference on Ubiquitous and Future Networks, Austria, pp. 109-113.

Lee, J., Lapira, E., Bagheri, B., Kao, H.A., 2013, Recent advances and trends in predictive manufacturing systems in big data environment, Manufacturing Letters, 1(1), pp. 38-41.

Wuest, T., Weimer, D., Irgens, C., Thoben, K.D., 2016, Machine learning in manufacturing: advantages, challenges, and applications, Production & Manufacturing Research, 4(1), pp. 23-45.

Lo, S., 2003, An adaptive-network based fuzzy inference system for prediction of workpiece surface roughness in end milling, Journal of Materials Processing Technology, 142(3), pp. 665-675.

Radhakrishnan, T., Nandan, U., 2005, Milling force prediction using regression and neural networks, Journal of Intelligent Manufacturing, 16(1), pp. 93-102.

Ozcelik, B., Bayramoglu, M., 2006, The statistical modeling of surface roughness in high-speed flat end milling, International Journal of Machine Tools and Manufacture, 46(12-13), pp. 1395-402.

Lela, B., Bajić, D., Jozić, S., 2009, Regression analysis, support vector machines, and Bayesian neural network approaches to modeling surface roughness in face milling, International Journal of Advanced Manufacturing Technology, 42(11-12), pp. 1082-1088.

Rashid, M.F.F.A., Gan, S.Y., Muhammad, N.Y., 2009, Mathematical modeling to predict surface roughness in CNC milling, World Academy of Science, Engineering and Technology, 53, pp. 393-596.

Dave, H.K., Raval, H.K., 2010, Modelling of cutting forces as a function of cutting parameters in milling process using regression analysis and artificial neural network, International Journal of Machining and Machinability of Materials, 8(1-2), pp. 198-208.

Sharkawy, A.B., 2011, Prediction of surface roughness in end milling process using intelligent systems: A comparative study, Applied Computational Intelligence and Soft Computing, 183764.

Durakbasa, M.N., Akdogan, A., Vanli, A.S., Günay, A., 2014, Surface roughness modeling with edge radius and end milling parameters on Al 7075 alloy using Taguchi and regression methods, Acta Imeko, 3(4), pp. 46-51.

Zhang, G., Li, J., Chen, Y., Huang, Y., Shao, X., Li, M., 2014, Prediction of surface roughness in end face milling based on Gaussian process regression and cause analysis considering tool vibration, International Journal of Advanced Manufacturing Technology, 75(9-12), pp. 1357-1370.

Rubeo, M.A., Schmitz, T.L., 2016, Milling force modeling: a comparison of two approaches, Procedia Manufacturing, 5, pp. 90-105.

Bandapalli, C., Sutaria, B.M., Bhatt, D.V., 2019, Estimation of surface roughness on Ti-6Al-4V in high speed micro end milling by ANFIS model, Indian Journal of Engineering and Material Sciences, 26(5-6), pp. 379-389.

Yeganefar, A., Niknam, S.A., Asadi, R., 2019, The use of support vector machine, neural network, and regression analysis to predict and optimize surface roughness and cutting forces in milling, International Journal of Advanced Manufacturing Technology, 105(1), pp. 951-965.

Lin, Y.C., Wu, K.D., Shih, W.C., Hsu, P.K., Hung, J.P., 2020, Prediction of surface roughness based on cutting parameters and machining vibration in end milling using regression method and artificial neural network, Applied Sciences, 10(11), 3941.

Khan, A.M., Jamil, M., Salonitis, K., Sarfraz, S., Zhao, W., He, N., Mia, M., Zhao, G., 2019, Multi-objective optimization of energy consumption and surface quality in nanofluid SQCL assisted face milling, Energies, 12(4), 710.

Zhou, Z.H., 2009, Ensemble learning. Encyclopaedia of Biometrics, pp. 270-273.

Oza, N.C., Russell, S.J., 2001, Online bagging and boosting, In: Proc. of International Workshop on Artificial Intelligence and Statistics, PMLR, pp. 229-236.

Dietterich, T., 2000, Ensemble methods in machine learning, In: Kittler J, Roli F (eds) Multiple Classifier Systems, Springer-Verlag Berlin Heidelberg, pp. 1-15.

Brown, G., 2011, Ensemble learning. In: Sammut C, Webb GI (eds) Encyclopedia of Machine Learning, Springer, Boston, MA.

Breiman, L., 2001, Machine Learning. Kluwer Academic Publishers, The Netherlands, pp. 5-32.

Boulesteix, A.L., Janitza, S., Kruppa, J., König, I.R., 2012, Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2(6), 493-507.

James, G., Witten, D., Hastie, T., Tibshirani, R., 2013, An Introduction to Statistical Learning: with Applications in R. Springer, New York.

Bhattacharya, S., Das, P.P., Chatterjee, P., Chakraborty, S., 2021, Prediction of responses in a sustainable dry turning operation: A comparative analysis, Mathematical Problems in Engineering, 9967970.


Refbacks

  • There are currently no refbacks.


ISSN: 0354-2025 (Print)

ISSN: 2335-0164 (Online)

COBISS.SR-ID 98732551

ZDB-ID: 2766459-4