Mechanic of Advanced and Smart Materials
Quarterly

Investigation and Sensitivity Analysis of the Effect of Different Parameters on Tool Wear During Dry Turning Process of Aluminum Matrix Composites Using E-Fast Method

Document Type : Original Article

Authors

Ahmad Homayooni 1
Hamed Faraji 2
Ali Farahani 2
Nima Rahmani 3

1 Department of Mechanical Engineering, Mechanical Engineering Faculty, Arak University of Technology, Arak, Iran

2 Arak university

3 Student

https://doi.org/10.61186/masm.4.2.308
Abstract
The use of metal-based composite materials, especially aluminum composites, has found wide applications in various industries such as automotive, aerospace, military, etc., due to their favorable mechanical properties, high strength-to-weight ratio, and high wear resistance and hardness. On the other hand, these mechanical properties and high hardness and wear resistance, which is due to the presence of reinforcing particles such as silicon carbide in these composites, makes them difficult to machine, so that only special tools and blades, such as polycrystalline diamond tools, can machine these composites optimally. The findings from this study can be valuable in optimizing the turning process of aluminum-based metal matrix composites.

In this study, the effects of four input parameters - cutting speed, feed rate, feed force in the X direction, and feed force in the Z direction - on the output parameter of tool wear rate in the dry turning process of A359 aluminum alloy metal matrix composite reinforced with 20 vol.% of silicon carbide particles using polycrystalline diamond tools were investigated.

The numerical investigation of the effect of each of the four input parameters on the output parameter was done using the E-fast statistical sensitivity analysis method, which has high speed in quantitative and qualitative data analysis. The results showed that the parameters of feed force in X direction, feed rate, feed force in Z direction, and cutting speed have 88%, 8%, 3%, and 1% effect on tool wear, respectively.

Keywords

Sensitivity analysis of aluminum matrix composite
Tool wear
turning
E-Fast
[1] Pramod R, Kumar GV, Gouda PS, Mathew AT. A study on the Al2O3 reinforced Al7075 metal matrix composites wear behavior using artificial neural networks. Materials Today: Proceedings. 2018;5:11376-85.
[2] Bhattacharyya D, Bowis M, Gregory J. The influence of alumina microsphere reinforcement on the mechanical behavior and weldability of a 6061 aluminum metal matrix composite. Machining of composite materials (A 95-15178 02-37), Materials Park, OH, ASM International, 1992. 1992:49-56.
[3] Lane C. The effect of different reinforcements on PCD tool life for aluminum composites. Machining of composite materials (A 95-15178 02-37), Materials Park, OH, ASM International, 1992. 1992:17-27.
[4] Nasernia E, Nouri-Khajavi M, Rezaee M. Milling tool wear prediction by feed motor current signal using MLPs and ANFIS. Aerospace Mechanics Journal. 2019;15:51-62.
[5] Lin J, Bhattacharyya D, Kecman V. Multiple regression and neural networks analyses in composites machining. Composites Science and Technology. 2003;63:539-48.
[6] Azmi A, Lin R, Bhattacharyya D. Tool wear prediction models during end milling of glass fibre-reinforced polymer composites. The International Journal of Advanced Manufacturing Technology. 2013;67:701-18.
[7] Wiciak-Pikuła M, Felusiak-Czyryca A, Twardowski P. Tool wear prediction based on artificial neural network during aluminum matrix composite milling. Sensors. 2020;20:5798.
[8] Fadaei A, Sori M. Prediction of cutting speed and feed effects on residual stresses in AISI 1045 steel turning shafts using artificial neural network. Sharif Journal of Mechanical Engineering. 2019;35:123-32.
[9] Özyürek D, Kalyon A, Yıldırım M, Tuncay T, Ciftci I. Experimental investigation and prediction of wear properties of Al/SiC metal matrix composites produced by thixomoulding method using Artificial Neural Networks. Materials & Design. 2014;63:270-7.
[10] Zhou G, Xu C, Ma Y, Wang X-H, Feng P-F, Zhang M. Prediction and control of surface roughness for the milling of Al/SiC metal matrix composites based on neural networks. Advances in Manufacturing. 2020;8:486-507.
[11] Karabulut Ş. Optimization of surface roughness and cutting force during AA7039/Al2O3 metal matrix composites milling using neural networks and Taguchi method. Measurement. 2015;66:139-49.
[12] Veeresh Kumar G, Pramod R, Shivakumar Gouda P, Rao C. Artificial Neural Networks for the Prediction of Wear Properties of Al6061-TiO2 Composites. Materials Science and Engineering Conference Series. 2017. 012046.
[13] Faraji H, Taheri M. Sensitivity analysis of parameters affecting cutting speed and dimensional deviation of wire electrical discharge machining. Mechanic of Advanced and Smart Materials. 2022;2:310.
[14] Tahmasbi V, Baghi MH, Aeinehbandy S, Sousanabadi Farahani A. Sensitivity analysis and optimization of material removal rate and surface quality in aluminum matrix composite. Modares Mechanical Engineering. 2023;23:395-404.
[15] Das D, Pradhan SK, Sahoo AK, Panda A, Satpathy MP, Samal C. Tool wear and cutting force investigations during turning 15 wt% SiCp-Al 7075 metal matrix composite. Materials Today: Proceedings. 2020;26:854-9.
[16] Zare Chavoshi S. Tool flank wear prediction in CNC turning of 7075 AL alloy SiC composite. Production Engineering. 2011;5:37-47.
[17] Elsadek A, Gaafer A, Lashin A. Prediction of Roughness and Tool Wear in Turning of Metal Matrix Nanocomposites. J Eng App Sci. 2017;64:387-408.
[18] Prakash U, Yogavardhanaswamy G, Prasad SA, Ravindra H, Rajan T. Tool wear prediction by regression analysis in turning A356 with 10% SiC. 2011 IEEE Recent Advances in Intelligent Computational Systems: IEEE; 2011. 682-7.
[19] Liu H, Zong W. Prediction model of tool wear volume in precision turning of ceramic particle reinforced aluminum matrix composites. The International Journal of Advanced Manufacturing Technology. 2019;100:2689-700.
[20] Taheri M, Ghane F. Investigating the effective parameters on the mechanical properties of composites reinforced by jute fiber in the compression molding process. Mechanic of Advanced and Smart Materials. 3(4):413-431.
[21] Cukier R, Levine H, Shuler K. Nonlinear sensitivity analysis of multiparameter model systems. Journal of Computational Physics. 1978;26:1-42.
[22] Saltelli A. Making best use of model evaluations to compute sensitivity indices. Computer Physics Communications. 2002;145:280-97.
Mechanic of Advanced and Smart Materials
Volume 4, Issue 2
Summer 2024
Pages 308-327

  • Receive Date 27 June 2024
  • Revise Date 13 July 2024
  • Accept Date 04 August 2024

Home

Guide for Authors

Submit Manuscript

Contact Us