Modares Mechanical Engineering

Modares Mechanical Engineering

Comparison of Different Factors in Surface Roughness of Milling Process

Document Type : Original Research

Authors
Ahmad homayooni
Zahra Saadat Eghdami
Maedeh Sotoude
10.22034/mme.23.10.143
Abstract
Endmilling is a type of machining tool for chipping the surfaces of parts, which has received attention due to its wide application in industries such as molding. Therefore, today, the need of the industry to find the optimal parameters of the process is felt so that the quality of the desired surface can be achieved. In general, the selection of effective parameters in any milling process significantly affects the surface quality of a finished part. In this research, using E-fast statistical sensitivity analysis method, the simultaneous influence of input parameters including spindle speed, depth of cut, and feed rate on the output parameter of surface roughness for the samples has been investigated quantitatively. Machining experiments have been carried out under different cutting parameters as defined in steady state conditions for the milling tool. surface roughness and vibration rate of machining with non-linear quadratic forms; It has been modeled based on the cutoff parameters and its interactions through several regression analysis methods. The results of this research showed that the spindle speed time parameter is known as the most influential parameter on the surface roughness with 67% influence. It was also observed that the feed rate parameter with 30% effect of cutting depth with 3% are known as the second and third influencing parameters on surface roughness.
Keywords
Endmilling
Sensitivity Analysis
Regression Equations
Surface roughness

Subjects

[1] Imani L, Rahmani Hanzaki A, Hamzeloo SR, Davoodi B. Modeling and optimization of cutting force and surface roughness in the milling process of Inconel 738 by Neural Network and Genetic Algorithm. Iranian Journal of Manufacturing Engineering. 2019 Oct 23;6(5):25-38.
[2] Kovac P, Rodic D, Pucovsky V, Savkovic B, Gostimirovic M. Application of fuzzy logic and regression analysis for modeling surface roughness in face milliing. Journal of Intelligent manufacturing. 2013 Aug;24:755-62
[3] Feng CX, Wang XF. Surface roughness predictive modeling: neural networks versus regression. IIE Transactions. 2003 Jan 1;35(1):11-27.
[4] Thamma R. Comparison between multiple regression models to study effect of turning parameters on the surface roughness. InProceedings of the 2008 IAJC-IJME international conference 2008 (Vol. 133, No. 103, pp. 1-12).
[5] Joshi K, Patil B. Prediction of surface roughness by machine vision using principal components based regression analysis. Procedia Computer Science. 2020 Jan 1;167:382-91.
[6] Benardos PG, Vosniakos GC. Predicting surface roughness in machining: a review. International journal of machine tools and manufacture. 2003 Jun 1;43(8):833-44
[7] Felhő C, Karpuschewski B, Kundrák J. Surface roughness modelling in face milling. Procedia CIRP. 2015 Jan 1;31:136-41.
[8] Lee KY, Kang MC, Jeong YH, Lee DW, Kim JS. Simulation of surface roughness and profile in high-speed end milling. Journal of Materials Processing Technology. 2001 Jun 15;113(1-3):410-5.
[9] T.-H. Hou, C.-H. Su, and W.-L. J. P. t. Liu, "Parameters optimization of a nano-particle wet milling process using the Taguchi method, response surface method and genetic algorithm," vol. 173, no. 3, pp. 153-162, 2007.
[10] A. Nekahi, K. J. M. Dehghani, and Design, "Modeling the thermomechanical effects on baking behavior of low carbon steels using response surface methodology," vol. 31, no. 8, pp. 3845-3851, 2010.
[11] Cukier RI, Levine HB, Shuler KE. Nonlinear sensitivity analysis of multiparameter model systems. Journal of computational physics. 1978 Jan 1;26(1):1-42.
[12] T. Homma, A. J. R. E. Saltelli, and S. Safety, "Importance measures in global sensitivity analysis of nonlinear models," vol. 52, no. 1, pp. 1-17, 1996.
Modares Mechanical Engineering
Volume 23, Issue 10
October 2023
Pages 143-147