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 Jan 1;5(5):11376-85.
2- Bhattacharyya D, Bowis ME, Gregory JT. 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- n, E., n, M., r, M. Milling Tool Wear Prediction by Feed Motor Current Signal using MLPs and ANFIS. AEROSPACE MECHANICS JOURNAL, 2019; 15(1): 51-62.(In Persian)
5- Lin JT, Bhattacharyya D, Kecman V. Multiple regression and neural networks analyses in composites machining. Composites Science and Technology. 2003 Feb 1;63(3-4):539-48.
6- Azmi AI, Lin RJ, Bhattacharyya D. Tool wear prediction models during end milling of glass fibre-reinforced polymer composites. The International Journal of Advanced Manufacturing Technology. 2013 Jul;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 Oct 13;20(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; 35.3(1): 123-132. doi: 10.24200/j40.2018.10749.1406.‎ (In Persian)
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 Nov 1;63:270-7.
10- Zhou G, Xu C, Ma Y, Wang XH, Feng PF, 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 Dec;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 Apr 1;66:139-49.
12- Kumar GV, Pramod R, Gouda PS, Rao CS. Artificial neural networks for the prediction of wear properties of Al6061-TiO2 composites. InIOP Conference Series: Materials Science and Engineering 2017 Aug 1 (Vol. 225, No. 1, p. 012046). IOP Publishing.
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(3): 310-327. doi: 10.52547/masm.2.3.310.(In Persian).
14- Cukier RI, Levine HB, Shuler KE. Nonlinear sensitivity analysis of multiparameter model systems. Journal of computational physics. 1978 Jan 1;26(1):1-42.
15- Saltelli A. Making best use of model evaluations to compute sensitivity indices. Computer physics communications. 2002 May 15;145(2):280-97.
16- Khorshidi K, Taheri M, Ghasemi M. Sensitivity analysis of vibrating laminated composite rec-tangular plates in interaction with inviscid fluid using efast method. Mechanics of Advanced Composite Structures. 2020 Nov 1;7(2):219-31.
17- sousanabadi farahani, A., Khalili, O., Torabi, A. Sensitivity analysis of effective parameters on bone drilling force by using E-fast method. Mechanic of Advanced and Smart Materials, 2023; 3(1): 83-94. doi: 10.52547/masm.3.1.83.‎ (In Persian)
18- Fereiduni, F., Taheri, M., Modabberifar, M. Investigation of the effect of different parameters on force in the second phase of two-dimensional nanomanipulation. Iranian Journal of Manufacturing Engineering, 2021; 8(2): 23-31. (In Persian)