A model for machining with nano-additives based minimum quantity lubrication
- 24 Downloads
The high temperature generated when machining aerospace alloys namely, titanium and nickel alloys, accelerate the tool wear rate and affects the physical properties of the machined surface. Flood coolant is usually the effective traditional solution to dissipate the heat and reduce its negative impact on tool performance and surface integrity. The disposal of the coolant causes environmental concerns, and the generated fumes during machining also present health concerns. Minimum quantity lubricant is presented as an alternative coolant strategy to reduce the amount of used coolant and environmental concerns associated with flood coolant. Experimental investigations showed that MQL does not offer the same results obtained when using flood coolant during machining titanium and Inconel. However, the addition of nano-additives significantly improved the performance of MQL. In this work, an integrated model (i.e., finite element and finite volume) is developed to analyze various unique aspects of machining with nano-fluids under minimum quantity lubrication during cutting Inconel 718 and Ti-6Al-4V alloys. These aspects include the heat transfer characteristics of the resultant nano-cutting fluid, the interactions between the cutting tool and workpiece, the generated cutting temperature at different zones, and resulting residual stresses. The investigation was carried out through two main phases. A 2-D axisymmetric computational fluid dynamics (CFD) model is developed to simulate the thermal effect of resultant nano-mist and obtain the thermal characteristics of the nano-fluid. The obtained results are then used in the finite element model to simulate the machining process with nano-fluid. The average heat convection coefficients results provided from the proposed CFD model at standard room temperature demonstrated a good agreement with the theoretical values calculated throughout this work. Also, the simulated and experimental cutting forces showed better agreement in the case of cutting test performed without nano-additives (accuracy % ≈ 90%) than the cutting test performed with nano-additives (accuracy % ≈ 82.3%). This work presents a first attempt in the open literature to simulate the machining processes using MQL-nano-fluid.
KeywordsFinite element analysis Nano-fluids Minimum quantity lubrication Machining
Unable to display preview. Download preview PDF.
The authors acknowledge the support of the Natural Sciences and Engineering Research Council of Canada (NSERC) and the International Scientific Partnership Program ISPP at King Saud University for funding this research work through ISPP# 0059.
- 2.Davim JP (2011) Machining of hard materials. Springer Science & Business MediaGoogle Scholar
- 3.Gupta MK, Sood PK, Singh G, Sharma VS (2017) Experimental investigation and optimization on MQL-assisted turning of Inconel-718 super alloy. Adv Manuf Technol (pp. 237–248). Springer, ChamGoogle Scholar
- 13.Eltaggaz A, Zawada P, Hegab HA, Deiab I, Kishawy HA (2017) Coolant strategy influence on tool life and surface roughness when machining ADI. Int J Adv Manuf Technol, 1–13Google Scholar
- 14.Kishawy H (2002) An experimental evaluation of cutting temperatures during high speed machining of hardened D2 tool steelGoogle Scholar
- 22.Hegab H, Umer U, Deiab I, Kishawy H (2018) Performance evaluation of Ti-6Al-4V machining using nano-cutting fluids under minimum quantity lubrication. Int J Adv Manuf Technol, 1–13Google Scholar
- 24.Lee KM, Huang Y, Ji J, Lin CY (2018) An online tool temperature monitoring method based on physics-guided infrared image features and artificial neural network for dry cutting. IEEE TranGoogle Scholar
- 30.Chien SEM, Reddy MM, Lee VCC, Sujan D (2017) The study of coated carbide ball end milling tools on Inconel 718 using numerical simulation analysis to attain cutting force and temperature predictive models at the cutting zone. In Materials Science Forum (Vol. 882, pp. 28–35). Trans Tech PublicationsGoogle Scholar
- 38.Rohit JN, Kumar S, Sura Reddy N, Kuppan P, Balan ASS (2016) Computational fluid dynamics analysis of MQL spray parameters and its influence on MQL milling of SS304Google Scholar
- 39.Abd Rahim EH, Dorairaju D, Asmuin N, Mantari AR, Hanafi M (2014) Determination of mist flow characteristic for MQL technique using particle image velocimetry (PIV) and computer fluid dynamics (CFD). International Integrated Engineering Summit (IIES 2014), 1-4 December 2014, University Tun Hussein Onn Malaysia, JohorGoogle Scholar
- 43.Saarinen S (2014) Heat transfer in nanoscale colloidsGoogle Scholar
- 44.Lienhard J (2006) Heat transfer by impingement of circular free-surface liquid jets. In Proceedings of 18th National and 7th ISHMT-ASME Heat and Mass Transfer Conference, Guwahati, IndiaGoogle Scholar
- 45.Shen Y (1962) Theoretical analysis of jet-ground plane interaction: Institute of the Aerospace SciencesGoogle Scholar
- 46.Duan CZ, Dou T, Cai YJ, Li YY (2009) Finite element simulation and experiment of chip formation process during high speed machining of AISI 1045 hardened steel. International Journal of Recent Trends in Engineering 1(5):46–50Google Scholar
- 47.Oh JW (2013) Experimental investigation and analysis of chip rebonding phenomenon in turning superalloys, The University of MichiganGoogle Scholar
- 48.Kay G (2003) Failure modeling of titanium 6A1-4V and aluminum 2024-T3 with the Johnson-Cook material model final report: Office of Aviation Research Washington, D.C. 20591Google Scholar
- 52.Hegab H, Kishawy HA, Gadallah MH, Umer U, Deiab I (2018) On machining of Ti-6Al-4V using multi-walled carbon nanotubes-based nano-fluid under minimum quantity lubrication. Int J Adv Manuf Technol:1–11Google Scholar
- 53.Eltaggaz A, Hegab H, Deiab I, Kishawy HA (2018) Hybrid nano-fluid-minimum quantity lubrication strategy for machining austempered ductile iron (ADI). Int J Interact Des Manuf 1–9Google Scholar
- 54.Hegab H, Darras B, Kishawy HA (2018) Sustainability assessment of machining with nano-cutting fluids. Procedia Manufacturing 26:245–254Google Scholar