[1] Sadeghifar M, Sedaghati R, Jomaa W, Songmene V. A comprehensive review of finite element modeling of orthogonal machining process: chip formation and surface integrity predictions. The International Journal of Advanced Manufacturing Technology: 2018 Jun; 96:3747-91.
doi: 10.1007/s00170-018-1759-6
[2] Javidi A, Rieger U, Eichlseder W. The effect of machining on the surface integrity and fatigue life. International Journal of fatigue: 2008 Oct 1;30(10-11):2050-5.
doi: 10.1016/j.ijfatigue.2008.01.005
[3] Touazine H, Chadha K, Jahazi M, Bocher P. Characterization of subsurface microstructural alterations induced by hard turning of Inconel 718. Journal of Materials Engineering and Performance: 2019 Nov; 28:7016-24.
doi: 10.1007/s11665-019-04416-1
[4] Soler D, Aristimuño PX, Garay A, Arrazola PJ, Klocke F, Veselovac D, Seimann M. Finding correlations between tool life and fundamental dry cutting tests in finishing turning of steel. Procedia engineering: 2015 Jan 1; 132:615-23.
doi: 10.1016/j.proeng.2015.12.539
[5] Karaguzel U, Bakkal M, Budak E. Mechanical and thermal modeling of orthogonal turn-milling operation. Procedia CIRP: 2017 Jan 1; 58:287-92.
doi: 10.1016/j.procir.2017.03.191
[6] Saez-de-Buruaga M, Soler D, Aristimuño PX, Esnaola JA, Arrazola PJ. Determining tool/chip temperatures from thermography measurements in metal cutting. Applied Thermal Engineering; 2018 Dec 25; 145:305-14.
doi: 10.1016/j.applthermaleng.2018.09.051
[7] Ning J, Liang SY. Predictive modeling of machining temperatures with force–temperature correlation using cutting mechanics and constitutive relation. Materials: 2019 Jan 16;12(2):284.
doi: 10.3390/ma12020284
[8] Baohai W, Di C, Xiaodong H, Dinghua Z, Kai T. Cutting tool temperature prediction method using analytical model for end milling. Chinese Journal of Aeronautics: 2016 Dec 1;29(6):1788-94.
doi: 10.1016/j.cja.2016.03.011
[9] Mirkoohi E, Bocchini P, Liang SY. Analytical temperature predictive modeling and non-linear optimization in machining. The International Journal of Advanced Manufacturing Technology: 2019 Jun 19; 102:1557-66.
doi: 10.1007/s00170-019-03296-y
[10] Richardson DJ, Keavey MA, Dailami F. Modelling of cutting induced workpiece temperatures for dry milling. International Journal of Machine Tools and Manufacture: 2006 Aug 1; 46(10):1139-45.
doi: 10.1016/j.ijmachtools.2005.08.008
[11] Karaguzel U, Budak E. Investigating effects of milling conditions on cutting temperatures through analytical and experimental methods. Journal of Materials Processing Technology: 2018 Dec 1; 262:532-40.
doi: 10.1016/j.jmatprotec.2018.07.024
[12] Karaguzel U, Bakkal M, Budak E. Modeling and measurement of cutting temperatures in milling. Procedia CIRP: 2016 Jan 1; 46:173-6.
doi: 10.1016/j.procir.2016.03.182
[13] Li L, Li B, Ehmann KF, Li X. A thermo-mechanical model of dry orthogonal cutting and its experimental validation through embedded micro-scale thin film thermocouple arrays in PCBN tooling. International Journal of Machine Tools and Manufacture: 2013 Jul 1; 70:70-87.
doi: 10.1016/j.ijmachtools.2013.03.005
[14] Sutter G, Ranc N. Temperature fields in a chip during high-speed orthogonal cutting—an experimental investigation. International Journal of Machine Tools and Manufacture: 2007 Aug 1; 47(10):1507-17.
doi: 10.1016/j.ijmachtools.2006.11.012
[15] Kryzhanivskyy V, Bushlya V, Gutnichenko O, Petrusha IA, Ståhl JE. Modelling and experimental investigation of cutting temperature when rough turning hardened tool steel with PCBN tools. Procedia Cirp: 2015 Jan 1;31:489-95.
doi: 10.1016/j.procir.2015.03.079
[16] Huang K, Yang W. Analytical model of temperature field in workpiece machined surface layer in orthogonal cutting. Journal of Materials Processing Technology: 2016 Mar 1; 229:375-89.
doi: 10.1016/j.jmatprotec.2015.07.008
[17] Ding H, Shin YC. Dislocation density-based grain refinement modeling of orthogonal cutting of titanium. Journal of Manufacturing Science and Engineering: 2014 Aug 1;136(4):041003.
doi: 10.1115/1.4027207
[18] Liu R, Salahshoor M, Melkote SN, Marusich T. A unified material model including dislocation drag and its application to simulation of orthogonal cutting of OFHC Copper. Journal of Materials Processing Technology: 2015 Feb 1; 216:328-38.
doi: 10.1016/j.jmatprotec.2014.09.021
[19] Jafarian F, Umbrello D, Jabbaripour B. Identification of new material model for machining simulation of Inconel 718 alloy and the effect of tool edge geometry on microstructure changes. Simulation Modelling Practice and Theory: 2016 Aug 1; 66:273-84.
doi: 10.1016/j.simpat.2016.05.001
[20] Caruso S, Imbrogno S, Rinaldi S, Umbrello D. Finite element modeling of microstructural changes in Waspaloy dry machining. The International Journal of Advanced Manufacturing Technology: 2017 Mar; 89:227-40.
doi: 10.1007/s00170-016-9037-y
[21] Jafarian F, Masoudi S, Umbrello D, Filice L. New strategies for improvement of numerical model accuracy in machining of nickel-based alloy. Simulation Modelling Practice and Theory: 2019 Jul 1; 94:134-48.
doi: 10.1016/j.simpat.2019.02.006
[22] akramian Zadeh S, Rashidi P. The effect of fluid velocity and microstructure on erosion corrosion of two-phase CK45 steel. Results in Materials: 2020 Jun 1; 6:100077.
doi: 10.1016/j.rinma.2020.100077
[23] Holub M, Andrs O, Kovar J, Vetiska J. Effect of position of temperature sensors on the resulting volumetric accuracy of the machine tool. Measurement: 2020 Jan 1; 150:107074.
doi: 10.1016/j.measurement.2019.107074
[24] Asif M, Ahad MA, Iqbal MF, Reyaz S. Experimental investigation of thermal properties of tool steel and mild steel with heat treatment. Materials Today: Proceedings: 2021 Jan 1; 45:5511-7.
doi: 10.1016/j.matpr.2021.02.272
[25] Razfar M, Principles of machining and instrumentation, Amirkabir University of Technology Press: 2022. [In Persian]
[26] Knight WA, Boothroyd G. Fundamentals of metal machining and machine tools. CRC Press; 2019 Aug 8.
[27] Kara F, Aslantaş K, Cicek A. Prediction of cutting temperature in orthogonal machining of AISI 316L using artificial neural network. Applied Soft Computing: 2016 Jan 1; 38:64-74.
doi: 10.1016/j.asoc.2015.09.034
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