Analytical temperature predictive modeling and non-linear optimization in machining
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Different process parameters can alter the temperature during machining. Consequently, selecting process parameters that lead to a desirable cutting temperature would help to increase the tool life, decrease the tensile residual stress, and controls the microstructure evolution of the workpiece. An inverse computational methodology is proposed to design the process parameters for a specific cutting temperature. A physics-based analytical model is used to predict the temperature induced by cutting forces. The shear deformation and chip formation model is implemented to calculate machining forces as functions of process parameters, material properties, and etc. To calculate the temperature induced by the deformation in the shear zone, a moving point heat source approach is used. The proposed model uses an iterative non-linear regression to predict the cutting process parameters based on the desirable temperature which is assigned by the user. In order to achieve the cutting process parameters, an iterative gradient search is used to adaptively approach the specific temperature by the optimization of process parameters such that an inverse reasoning can be achieved. Experimental data are used to illustrate the implementation and validate the viability of the computational methodology.
KeywordsOptimization Temperature prediction Process parameters Cutting speed Depth of cut
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E.M. conceived and developed the proposed analytical model, extracted and analyzed the data, and wrote the paper. P. B provided general guidance. S.Y.L. provided general guidance and proofread the manuscript writing.
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Conflict of interest
The authors declare that they have no conflict of interest.
- 6.Al Hazza MHF et al. Cutting temperature and surface roughness optimization in CNC end milling using multi objective genetic algorithm. in Advanced Computer Science Applications and Technologies (ACSAT), 2012 International Conference on. 2012. IEEEGoogle Scholar
- 11.Mirkoohi E, Malhotra R (2017) Effect of particle shape on neck growth and shrinkage of nanoparticles. in ASME 2017 12th International Manufacturing Science and Engineering Conference collocated with the JSME/ASME 2017 6th International Conference on Materials and Processing. American Society of Mechanical Engineers.Google Scholar
- 14.Mirkoohi E, Ning J, Bocchini P, Fergani O, Chiang KN, Liang S (2018) Thermal modeling of temperature distribution in metal additive manufacturing considering effects of build layers, latent heat, and temperature-sensitivity of material properties. J Manuf Mater Process 2(3):63Google Scholar
- 15.AOKI H et al (1997) Use of alternative protein sources as substitutes for fish meal in red sea bream diets. Aquacult Sci 45(1):131–139Google Scholar
- 21.Chen X et al (2017) Determining Al6063 constitutive model for cutting simulation by inverse identification method. Int J Adv Manuf Technol:1–8Google Scholar
- 23.Mirkoohi E, Bocchini P, Liang SY (2018) An analytical modeling for process parameter planning in the machining of Ti-6Al-4V for force specifications using an inverse analysis. Int J Adv Manuf Technol:1–9Google Scholar
- 29.Trigger K (1951) An analytical evaluation of metal-cutting temperatures. Trans ASME 73:57Google Scholar
- 30.Oxley PLB (1989) The mechanics of machining: an analytical approach to assesing machinability. Ellis HorwoodGoogle Scholar
- 31.Johnson GR (1983) A constitutive model and data for materials subjected to large strains, high strain rates, and high temperatures. Proc. 7th Inf. Sympo. Ballistics, p 541–547Google Scholar
- 35.Duan C et al (2009) Finite element simulation and experiment of chip formation process during high speed machining of AISI 1045 hardened steel. Int J Recent Trends Eng 1(5):46Google Scholar