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Progress in Additive Manufacturing
Published in: Progress in Additive Manufacturing 1/2021

04-01-2021 | Full Research Article

Multi-objective optimization of machining factors on surface roughness, material removal rate and cutting force on end-milling using MWCNTs nano-lubricant

Authors: I. P. Okokpujie, O. S. Ohunakin, C. A. Bolu

Published in: Progress in Additive Manufacturing | Issue 1/2021

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Abstract

The art of nano-additive manufacturing in developing advanced mechanical components via machining cannot be over-emphasized when developing mechanical parts for aerospace, automobile, and structural application through the end-milling of aluminum alloys. However, the end-milling process generates heat and friction due to the machining parameter that initiated the contact between the cutting tool and the workpiece. This excess heat leads to high surface roughness (SR), low material removal rate (MRR), and high cutting force (CF). This study aimed to resolve the machining parameters and the material adhesion by carrying out an experimental evaluation with multi-objective optimization of the machining factors on end-milling of AL8112 alloy using copra oil-based multi-walled carbon nanotube (MWCNTs) nano-lubricant. The nano-lubricant preparation was done using the two-step method, and nano-lubricants were implemented via the minimum quantity lubrication (MQL) method with the five machining factors. Additionally, the multi-objective optimization and prediction study was achieved using the ramp and desirability bar plot for the three responses, i.e., SR, MRR, and CF under the quadratic rotatable central composite design (QRCCD). The multi-objective optimization result shows that the minimum SR of 1.16 µm, maximum MRR of 52.1 mm3/min, and minimum CF of 33.75 N was obtained at the optimized machining factors. Furthermore, the models predicted the experimental results accurately. In conclusion, the multi-objective optimization with copra oil-based MWCNT’s-nano-lubricant enhanced machine parts’ production for sustainable additive manufacturing.
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Literature
1.
Rapeti P, Pasam VK, Gurram KMR, Revuru RS (2018) Performance evaluation of vegetable oil-based nano cutting fluids in machining using a grey relational analysis-a step towards sustainable manufacturing. J Clean Prod 172:2862–2875CrossRef
2.
Okokpujie I, Okonkwo U, Okwudibe C (2015) Cutting parameters effects on surface roughness during end milling of aluminum 6061 alloy under dry machining operation. Internat J Sci Res 4(7):2030–2036
3.
Sharma S, Kumar R, Gaur A (2015) A model for magnetic nanoparticle transport in a channel for targeted drug delivery. Proc Mater Sci 10:44–49CrossRef
4.
Okokpujie IP, Ohunakin OS, Adelekan DS, Bolu CA, Gill J, Atiba OE, Aghedo OA (2019) Experimental investigation of nano-lubricants effects on temperature distribution of mild steel machining. Proc Manufact 35:1061–1066CrossRef
5.
Gugulothu S, Pasam VK (2020) Testing and performance evaluation of vegetable-oil–based hybrid nano cutting fluids. J Test Eval 48:5CrossRef
6.
Liu MS, Lin MCC, Tsai CY, Wang CC (2006) Enhancement of thermal conductivity with Cu for nanofluids using chemical reduction method. Int J Heat Mass Transf 49(17–18):3028–3033CrossRef
7.
Lee CG, Hwang YJ, Choi YM, Lee JK, Choi C, Oh JM (2009) A study on the tribological characteristics of graphite nano lubricants. Internat J Precis Eng Manufact 10(1):85–90CrossRef
8.
Najiha MS, Rahman MM, Kadirgama K (2016) Performance of water-based TiO2 nanofluid during the minimum quantity lubrication machining of aluminum alloy, AA6061-T6. J Clean Product 135:1623–1636CrossRef
9.
Okonkwo UC, Okokpujie IP, Sinebe JE, Ezugwu CA (2015) Comparative analysis of aluminum surface roughness in end-milling under dry and minimum quantity lubrication (MQL) conditions. Manufact Rev 2(30):1–15
10.
Pereira O, Martín-Alfonso JE, Rodríguez A, Calleja A, Fernández-Valdivielso A, De Lacalle LL (2017) Sustainability analysis of lubricant oils for minimum quantity lubrication based on their tribo-rheological performance. J Clean Prod 164:1419–1429CrossRef
11.
Tazehkandi AH, Shabgard M, Pilehvarian F (2015) On the feasibility of a reduction in cutting fluid consumption via a spray of biodegradable vegetable oil with compressed air in machining Inconel 706. J Clean Prod 104:422–435CrossRef
12.
Junior ASA, Sales WF, da Silva RB, Costa ES, Machado ÁR (2017) Lubri-cooling and tribological behavior of vegetable oils during milling of AISI 1045 steel focusing on sustainable manufacturing. J Clean Prod 156:635–647CrossRef
13.
Nandakumar A, Rajmohan T, Vijayabhaskar S (2019) Experimental evaluation of the lubrication performance in MQL grinding of Nano SiC reinforced Al matrix composites. Silicon 1:1–13
14.
Das A, Patel SK, Biswal BB, Sahoo N, Pradhan A (2020) Performance evaluation of various cutting fluids using MQL technique in hard turning of AISI 4340 alloy steel. Measurement 150:107079CrossRef
15.
Agrawal SM, Patil NG (2018) Experimental study of non-edible vegetable oil as a cutting fluid in the machining of M2 Steel using MQL. Proc Manufact 20:207–212CrossRef
16.
Abubakar S, Azwadi CN, Ahmad A (2016) The use of Fe3O4-H2O4 nanofluid for heat transfer enhancement in rectangular microchannel heatsink. J Adv Res Mater Sci 23:15–24
17.
Anil KC, Vika MG, Teja BS, Rao KS (2017). Effect of cutting parameters on surface finish and machinability of graphite-reinforced Al-8011 matrix composite. In IOP Conference Series: Materials Science and Engineering (p. 012025). IOP Publishing.
18.
Ali H, Babar H, Shah T, Sajid M, Qasim M, Javed S (2018) Preparation techniques of TiO2 nanofluids and challenges: a review. Appl Sci 8(4):587CrossRef
19.
Chen M, Liu G, Dang J, Li C, Ming W (2018) Effects of tool helix angles on machined surface morphology in tilt side milling of cantilever thin-walled plates. Procedia CIRP 71:93–98CrossRef
20.
Gutnichenko O, Bushlya V, Bihagen S, Ståhl JE (2018) Influence of GnP additive to vegetable oil on machining performance when MQL-assisted is turning Alloy 718. Proc Manuf 25:330–337
21.
Li M, Yu T, Yang L, Li H, Zhang R, Wang W (2019) Parameter optimization during minimum quantity lubrication milling of TC4 alloy with graphene-dispersed vegetable-oil-based cutting fluid. J Clean Product 209:1508–1522CrossRef
22.
Khalil ANM, Azmi AI, Murad MN, Ali MAM (2018) The effect of cutting parameters on cutting force and tool wear in machining Nickel-Titanium shape memory alloy ASTM F2063 under minimum quantity nano lubricant. Proc CIRP 77:227–230CrossRef
23.
Bai W, Roy A, Sun R, Silberschmidt VV (2019) Enhanced machinability of SiC-reinforced metal-matrix composite with hybrid turning. J Mater Process Technol 268:149–161CrossRef
24.
Basar G, Kirli Akin H, Kahraman F, Fedai Y (2018) Modeling and optimization of face milling process parameters for AISI 4140 steel. Tehnički Glasnik 12(1):5–10CrossRef
25.
Duc TM, Long TT, Dong PQ (2019) Effect of the alumina nanofluid concentration on minimum quantity lubrication hard machining for sustainable production. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 0954406219861992.
26.
Huang WT, Liu WS (2016) Investigations into lubrication in grinding processes using MWCNTs nanofluids with ultrasonic-assisted dispersion. J Clean Product 137:1553–1559CrossRef
27.
Lawal SA, Choudhury IA, Nukman Y (2014) Evaluation of vegetable and mineral oil-in-water emulsion cutting fluids in turning AISI 4340 steel with coated carbide tools. J Clean Product 66:610–618CrossRef
28.
Shukla A, Kotwani A, Unune DR (2020) Performance comparison of dry, flood, and vegetable oil-based minimum quantity lubrication environments during CNC milling of aluminium 6061. Mater Today Proc 21:1483–1488CrossRef
29.
Prajina NV (2013) Multi response optimization of CNC end milling using response surface methodology and desirability function. Internat J Eng Res Technol 6(6):739–746
30.
Okokpujie IP, Okonkwo UC (2015) Effects of cutting parameters on surface roughness during end milling of aluminum under minimum quantity lubrication (MQL). Internat J Sci Res 4(5):2937–2942
31.
Reddy BS, Kumar JS, Reddy KVK (2011) Optimization of surface roughness in CNC ends milling using response surface methodology and genetic algorithm. Internat J Eng Sci Technol 3(8):102–109
32.
Gupta MK, Jamil M, Wang X, Song Q, Liu Z, Mia M, Imran GM (2019) Performance evaluation of vegetable oil-based nano-cutting fluids in environmentally friendly machining of inconel-800 alloy. Materials 12(17):2792CrossRef
33.
34.
Sharma AK, Tiwari AK, Dixit AR, Singh RK, Singh M (2018) Novel uses of alumina/graphene hybrid nanoparticle additives for improved tribological properties of lubricant in turning operation. Tribol Int 119:99–111CrossRef
35.
Azwadi CN, Adamu I, Jamil M (2016) Preparation methods and thermal performance of hybrid nanofluids. J Adv Rev Sci Res 24(1):13–23
36.
Arnault JC (Ed) (2017) Nanodiamonds: advanced material analysis, properties, and applications. William Andrew.
37.
Okokpujie IP, Ajayi OO, Afolalu SA, Abioye AA, Salawu EY, Udo M, Ikumapayi OM (2018) Modeling and optimization of surface roughness in end milling of aluminum using least square approximation method and response surface methodology. Internat J Mech Eng Technol (IJMET) 9(1):587–600
38.
Okokpujie IP, Bolu CA, Ohunakin OS, Akinlabi ET, Adelekan DS (2019) A Review of recent application of machining techniques, based on the phenomena of CNC machining operations. Proc Manuf 35:1054–1060
39.
Agu CK, Lawal SA, Abolarin MS, Agboola JB, Abutu J, Awode EI (2019) Multi-response optimization of machining parameters in turning AISI 304L using different oil-based cutting fluids. Niger J Technol 38(2):364–375CrossRef
40.
Okokpujie IP, Bolu CA, Ohunakin OS (2020) Comparative performance evaluation of TiO2, and MWCNTs nano-lubricant effects on surface roughness of AA8112 alloy during end-milling machining for sustainable manufacturing process. Internat J Adv Manuf Technol 4:1–25
41.
Darshan C, Jain S, Dogra M, Gupta MK, Mia M, Haque R (2019) Influence of dry and solid lubricant-assisted MQL cooling conditions on the machinability of Inconel 718 alloy with the textured tool. Internat J Adv Manuf Technol 4:1–15
42.
Okokpujie IP, Ikumapayi OM, Okonkwo UC, Salawu EY, Afolalu SA, Dirisu JO, Ajayi OO (2017) Experimental and mathematical modeling for prediction of tool wear on the machining of aluminum 6061 alloy by high speed steel tools. Open Eng 7(1):461–469CrossRef
43.
Dong PQ, Duc TM (2019) Performance evaluation of MQCL hard milling of SKD 11 tool steel using MoS2 nanofluid. Metals 9(6):658CrossRef
44.
Okokpujie IP, Ohunakin OS, Bolu CA, Okokpujie KO (2018) Experimental data-set for prediction of tool wear during turning of Al-1061 alloy by high speed steel cutting tools. Data Brief 18:1196–1203CrossRef
45.
Davoudinejad A, Li D, Zhang Y, Tosello G (2019) Optimization of corner micro end milling by finite element modeling for thin machining features. CIRP 82:362–367CrossRef
46.
Kumar S, Gupta D (2016) To determine the effect of machining parameters on material removal rate of aluminium 6063 using turning on lathe machine. Int J of Multidiscip Curr Res 4:688–691
47.
Badrinathan KS, Karunamoorthy L (2013) Study the effect of progressive feed rate on the cutting force in CNC ends milling AISI 1045 steel. Internat J Eng Technol 5(6):4741–4751
48.
Kalidass S, Ravikumar TM (2015) Cutting force prediction in the end milling process of AISI 304 steel using solid carbide tools. Int J Eng Trans A 28(7):1074–1081
49.
Hegab H, Darras B, Kishawy HA (2018) Sustainability assessment of machining with nano-cutting fluids. Proc Manuf 26:245–254
50.
Hegab H, Kishawy H (2018) Towards sustainable machining of Inconel 718 using nanofluid minimum quantity lubrication. J Manuf Mater Proc 2(3):50
51.
Ibrahim MR, Latif AA, Hassan MF, Arifin AMT, Amran AZ, Peter CPE (2017) Effect of feed rate and depth of cut on cutting forces and surface roughness when end milling of mild steel using the NOVIANO cutting tool
52.
Ogundimu O, Lawal SA, Okokpujie IP (2018) Experimental study and analysis of variance of material removal rate in high speed turning of AISI 304L alloy steel. Mater Sci Eng 413:1–9
53.
Ojolo SJ, Adjaottor AA, Olatunji RS (2016) Experimental prediction and optimization of material removal rate during hard turning of austenitic 304l stainless steel. J Sci Technol (Ghana) 36(2):34–49CrossRef
54.
Asiltürk I, Çunkaş M (2011) Modeling and prediction of surface roughness in turning operations using artificial neural networks and multiple regression methods. Expert Syst Appl 38(5):5826–5832CrossRef
55.
Kini MV, Chincholkar AM (2010) Effect of machining parameters on surface roughness and material removal rate in finish turning of±30 glass fiber reinforced polymer pipes. Mater Design 31(7):3590–3598CrossRef
56.
Paturi UMR, Devarasetti H, Narala SKR (2018) Application of regression and artificial neural network analysis in modelling of surface roughness in hard turning of AISI 52100 steel. Mater Today 5(2):4766–4777
57.
Rahman SS, Ashraf MZI, Amin AN, Bashar MS, Ashik MFK, Kamruzzaman M (2019) Tuning nanofluids for improved lubrication performance in turning biomedical grade titanium alloy. J Clean Product 206:180–196CrossRef
Metadata
Title
Multi-objective optimization of machining factors on surface roughness, material removal rate and cutting force on end-milling using MWCNTs nano-lubricant
Authors
I. P. Okokpujie
O. S. Ohunakin
C. A. Bolu
Publication date
04-01-2021
Publisher
Springer International Publishing
Published in
Progress in Additive Manufacturing / Issue 1/2021
Print ISSN: 2363-9512
Electronic ISSN: 2363-9520
DOI
https://doi.org/10.1007/s40964-020-00161-3

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