EVERGREEN

Joint Journal of Novel Carbon Resource Sciences and Green Asia Strategy

ISSN:2189-0420 (Print until Mar 2020)
ISSN:2432-5953 (Online)

SCImago Journal & Country Rank

Open Access
Scopus
Google Scholar
Crossref
SCImago Journal & Country Rank
4.3
2024CiteScore
 
69th percentile
Powered by Scopus
Metrics by SCOPUS 2024
CiteScore
4.3
SJR
0.391
SNIP
1.192


Optimization of Surface Roughness and Diameter Error in Thin-Walled AA6063 during Internal Turning under Minimum Quantity Lubrication

Albertus Rianto Suryaningrat1,2, Arif Wahjudi1,*, Suhardjono1, Muizuddin Azka2
1Department of Mechanical Engineering, Faculty of Industrial Technology and Systems Engineering, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia
2Research Center for Manufacturing Technology of Production Machinery, National Research and Innovation Agency, Jakarta, Indonesia
*Author to whom correspondence should be addressed:
E-mail: arif_w@me.its.ac.id (AW)
Received: May 28, 2025 | Revised: November 10, 2025 | Accepted: December 07, 2025 | Published: March 2026
Abstract
Thin-walled aluminum alloy 6063 (AA6063) components are commonly used in landing gear manufacturing through internal turning. However, tool and workpiece deflection cause diameter errors, altering the depth of cut, inducing chatter, and degrading surface finish. While cutting fluids reduce friction and vibration, conventional mineral oils harm the environment. This study employed the Taguchi L9 (3³) design and the Multi-Response Performance Index (MRPI) to optimize internal turning under minimum quantity lubrication (MQL) using virgin coconut oil (VCO). MRPI combines surface roughness and diameter error into a single index for parameter selection. The optimal settings—depth of cut 0.8 mm, feed rate 0.075 mm/rev, and cutting speed 200 m/min—yielded 4.72 µm surface roughness and 0.09 mm diameter error, demonstrating that MQL with VCO enhances machining quality and supports sustainable manufacturing.
Keywords
green manufacture; internal turning; MQL; surface roughness; thin-walled
Available Repositories
Share Article
Article Metrics
--
Views
--
Downloads
--
Citations
Full Text
Download PDF
References
  1. 1) MethodsTaguchi Method
  2. 2) Multi-Response Optimization
  3. 3) Results and Discussion
  4. 4) Surface Roughness Testing
  5. 5) Error Diameter Testing
  6. 6) Multi-Response Optimization
  7. 7) Minimum Quantity Lubrication
  8. 8) I. Asiltürk, and H. Akkuş, "Determining the effect of cutting parameters on surface roughness in hard turning using the taguchi method," Measurement (Lond), 44 (9) 1697-1704 (2011) doi:10.1016/j.measurement.2011.07.003
  9. 9) J. Eapen, S. Murugappan, and S. Arul, "A Study on Chip Morphology of Aluminum Alloy 6063 during Turning under Pre Cooled Cryogenic and Dry Environments-review under responsibility ofthe Committee Members of International Conference on Advancements in Aeromechanical Materials for Manufacturing (ICAAMM-2016)," 2017. www.sciencedirect.comwww.materialstoday.com/proceedings
  10. 10) J. Du, K. Liu, Z. Feng, C. Xu, and P. Hao, "Investigation on crashworthiness of lightweight thin-walled protective structure of mav inspired by beetle exoskeleton," Mechanics of Advanced Materials and Structures, 31 (26) 8169-8179 (2024) doi:10.1080/15376494.2023.2255171
  11. 11) F.A.R. Rozhbiany, and S.R. Jalal, "Reinforcement and processing on the machinability and mechanical properties of aluminum matrix composites," Journal of Materials Research and Technology, 8 (5) 4766-4777 (2019) doi:10.1016/j.jmrt.2019.08.023
  12. 12) A. Krisbudiman, K. Rezqi, R.L. Gumilang, M. Dahsyat, H. Zenal, A.M. Kadir, and E. Yulianto, "Structural design analysis torque links of nose landing gear on light aircraft," International Journal on Advanced Science, Engineering and Information Technology, Vol. 14, No. 2, Apr. 2024, 14 (2) (2024) doi:10.18517/ijaseit.14.2.19317
  13. 13) H. Manikandan, and T. Chandra Bera, "Modelling of dimensional and geometric error prediction in turning of thin-walled components," Precis Eng, 72 382-396 (2021) doi:10.1016/j.precisioneng.2021.05.013
  14. 14) Y. Altintas, D. Lappin, D. van Zyl, and D. Östling, "Automatically tuned boring bar system," CIRP Annals, 70 (1) 313-316 (2021) doi:10.1016/j.cirp.2021.04.058
  15. 15) A. Susanto, M. Azka, K. Yamada, K. Sekiya, P. Novia, R. Tanaka, and M.D. Prasetio, "Analysis of Transient Signal using Hilbert-Huang Transform for Chatter Monitoring in Turning Process," 2019
  16. 16) D. van Zyl, Y. Altintas, and D. Ostling, "Parametric design of boring bars with adaptive tuned mass dampers," CIRP J Manuf Sci Technol, 38 491-499 (2022) doi:10.1016/j.cirpj.2022.06.003
  17. 17) G. Quintana, and J. Ciurana, "Chatter in machining processes: a review," Int J Mach Tools Manuf, 51 (5) 363-376 (2011) doi:10.1016/j.ijmachtools.2011.01.001
  18. 18) M. Eynian, and Y. Altintas, "Chatter stability of general turning operations with process damping," J Manuf Sci Eng, 131 (4) 0410051-04100510 (2009) doi:10.1115/1.3159047
  19. 19) M.N. Derani, & Mani, and M. Ratnam, "The use of tool flank wear and average roughness in assessing effectiveness of vegetable oils as cutting fluids during turning-a critical review," The International Journal of Advanced Manufacturing Technology, 112 1841-1871 (2022) doi:10.1007/s00170-020-06490-5/Published
  20. 20) J. Haider, and M.S.J. Hashmi, "Health and Environmental Impacts in Metal Machining Processes," in: Comprehensive Materials Processing: Thirteen Volume Set, Elsevier, 2014: pp. V8-7-V8-33 doi:10.1016/B978-0-08-096532-1.00804-9
  21. 21) S. Akhai, "Navigating the potential applications and challenges of intelligent and sustainable manufacturing for a greener future," Evergreen, 10 (4) 2237-2243 (2023) doi:10.5109/7160899
  22. 22) S. Ravi, P. Gurusamy, and V. Mohanavel, "A review and assessment of effect of cutting fluids," in: Mater Today Proc, Elsevier Ltd, 2020: pp. 220-222 doi:10.1016/j.matpr.2020.05.054
  23. 23) B.S. Ajay Vardhaman, M. Amarnath, D. Jhodkar, J. Ramkumar, H. Chelladurai, and M.K. Roy, "Influence of coconut oil on tribological behavior of carbide cutting tool insert during turning operation," Journal of the Brazilian Society of Mechanical Sciences and Engineering, 40 (9) (2018) doi:10.1007/s40430-018-1379-y
  24. 24) A. Wahjudi, A.R. Yusoff, S. Sabil, B.O. P Soepangkat, S. Suhardjono, and P. Al-Sultan Abdullah, "A study of end milling process parameter’s effect on thin wall aluminum-7075 surface roughness under minimum quantity lubrication," 2024
  25. 25) X. Luo, S. Wu, D. Wang, Y. Yun, Q. An, and C. Li, "Sustainable development of cutting fluids: the comprehensive review of vegetable oil," J Clean Prod, 473 143544 (2024) doi:10.1016/J.JCLEPRO.2024.143544
  26. 26) A. Sharma, and R. Kumar, "Potential use of minimum quantity lubrication (MQL) in machining of biocompatible materials using environment friendly cutting fluids: An overview," in: Mater Today Proc, Elsevier Ltd, 2021: pp. 5315-5319 doi:10.1016/j.matpr.2021.01.904
  27. 27) C.L. He, W.J. Zong, and J.J. Zhang, "Influencing factors and theoretical modeling methods of surface roughness in turning process: state-of-the-art," Int J Mach Tools Manuf, 129 15-26 (2018) doi:10.1016/j.ijmachtools.2018.02.001
  28. 28) D. Chen, B. Lin, Z. Han, and Y. Zhang, "Study on the optimization of cutting parameters in turning thin-walled circular cylindrical shell based upon cutting stability," International Journal of Advanced Manufacturing Technology, 69 (1-4) 891-899 (2013) doi:10.1007/s00170-013-5073-z
  29. 29) Z. Li, Z. Zeng, Y. Yang, Z. Ouyang, P. Ding, J. Sun, and S. Zhu, "Research progress in machining technology of aerospace thin-walled components," J Manuf Process, 119 463-482 (2024) doi:10.1016/j.jmapro.2024.03.111
  30. 30) A. Gerasimenko, M. Guskov, A. Gouskov, P. Lorong, and A. Shokhin, "Analytical modeling of a thin-walled cylindrical workpiece during the turning process. stability analysis of a cutting process," Journal of Vibroengineering, 19 (8) 5825-5841 (2017) doi:10.21595/jve.2017.18061
  31. 31) B. Toubhans, P. Lorong, F. Viprey, G. Fromentin, and H. Karaouni, "A versatile approach, considering tool wear, to simulate undercut error when turning thin-walled workpieces," (n.d.) doi:10.1007/s00170-021-07243-8/Published
  32. 32) M. Wan, H.N. Wang, and Y. Yang, "Dynamics of the truncated conical thin-wall turning process," J Manuf Process, 94 49-62 (2023) doi:10.1016/j.jmapro.2023.03.059
  33. 33) T. Aijun, and L. Zhanqiang, "Deformations of thin-walled plate due to static end milling force," J Mater Process Technol, 206 (1-3) 345-351 (2008) doi:10.1016/j.jmatprotec.2007.12.089
  34. 34) J. Falta, M. Sulitka, M. Janota, and V. Frkal, "Model of force interaction for stability prediction in turning of thin-walled cylindrical workpiece," International Journal of Advanced Manufacturing Technology, 125 (1-2) 195-212 (2023) doi:10.1007/s00170-022-10343-8
  35. 35) P. Jayaraman, and L. Mahesh kumar, "Multi-response optimization of machining parameters of turning AA6063 T6 aluminium alloy using grey relational analysis in Taguchi method," in: Procedia Eng, Elsevier Ltd, 2014: pp. 197-204 doi:10.1016/j.proeng.2014.12.242
  36. 36) G. Singh, A. Kumar, V. Aggarwal, and S. Singh, "Experimental investigations and optimization of machining performance during turning of EN-31 steel using TOPSIS approach," in: Mater Today Proc, Elsevier Ltd, 2021: pp. 1089-1094 doi:10.1016/j.matpr.2021.07.381
  37. 37) F. Ziyad, H. Alemayehu, D. Wogaso, F. Dadi, and M. Badri, "Multi-objective optimization of machining parameters of mild steel aisi 1018 under compressed air-assisted cooling by using genetic algorithm," International Journal on Interactive Design and Manufacturing, 19 (7) 5291-5311 (2025) doi:10.1007/s12008-024-02134-0
  38. 38) B. Sen, A. Bhowmik, N. Rachchh, N. Patil, A. Khatibi, and R. Kumar, "Exploring cryo-mql medium for hard machining of hastelloy c276: a multi-objective optimization approach," International Journal on Interactive Design and Manufacturing, (2024) doi:10.1007/s12008-024-02069-6
  39. 39) M. Imran, S. Shuangfu, B. Yuzhu, W. Yuming, and N. Raheel, "Optimising subsurface integrity and surface quality in mild steel turning: a multi-objective approach to tool wear and machining parameters," Journal of Materials Research and Technology, 35 3440-3462 (2025) doi:10.1016/j.jmrt.2025.01.246
  40. 40) T. Gopi, P.S. Goud, K. Abhishek, N. Sateesh, R. Karthikeyan, A. Kumar, and B.C.H. Nookaraju, "A hybrid multi-optimization of cutting rate and surface roughness using pca-based improved-gwo in precise cnc turning of aa2014," International Journal on Interactive Design and Manufacturing, 19 (6) 4113-4121 (2025) doi:10.1007/s12008-024-02031-6
  41. 41) P. Mastan Rao, C. Deva Raj, S.H. Dhoria, M. Vijaya, and J.R.R. Chowdary, "Multi-objective optimization of turning for nickel-based alloys using taguchi-gra and topsis approaches," Journal of The Institution of Engineers (India): Series D, (2023) doi:10.1007/s40033-023-00554-y
  42. 42) J.T. Vieira, R.B.D. Pereira, C.H. Lauro, L.C. Brandão, and J.R. Ferreira, "Multi-objective evolutionary optimization of extreme gradient boosting regression models of the internal turning of peek tubes," Expert Syst Appl, 238 (2024) doi:10.1016/j.eswa.2023.122372
  43. 43) M. Fan, G. Sun, J. Ding, and J. Song, "Investigation on multi-objective optimization for in-situ laser-assisted machining of glass-ceramic," Appl Phys A Mater Sci Process, 130 (10) (2024) doi:10.1007/s00339-024-07911-y
  44. 44) Y. Şahin, and D. Akbar, "Multi-response optimization in cutting mild steels," Applied Chemical Engineering, 7 (1) (2023) doi:10.24294/ace.v7i1.2599
  45. 45) A.D. Tura, E.O. Isaya, U.L. Adizue, B.Z. Farkas, and M. Takács, "Optimization of ultra-precision cbn turning of aisi d2 using hybrid ga-rsm and taguchi-gra statistic tools," Heliyon, 10 (11) e31849 (2024). 2024.E31849 doi:10.1016/J.HELIYON
  46. 46) Janet. Evanovich, and Lorelei. King, "Optimization of surface roughness, tool wear and material removal rate in turning of inconel 718 with ceramic composite tools using mcdm methods based on taguchi methodology," Sadhana - Academy Proceedings in Engineering Sciences, 48 (2022)
  47. 47) K. Safi, M.A. Yallese, S. Belhadi, T. Mabrouki, and A. laouissi, "Tool wear, 3d surface topography, and comparative analysis of gra, moora, dear, and waspas optimization techniques in turning of cold work tool steel," International Journal of Advanced Manufacturing Technology, 121 (1-2) 701-721 (2022) doi:10.1007/s00170-022-09326-6
  48. 48) G.R. Chate, M.P. Manjunath, H. H.m., S.U. Urankar, S.A. Sanadi, A.P. Jadhav, S. Hiremath, and A.S. Deshpande, "Sustainable machining: Modelling and optimization using Taguchi, MOORA and DEAR methods," in: Mater Today Proc, Elsevier Ltd, 2021: pp. 8941-8947 doi:10.1016/j.matpr.2021.05.365
  49. 49) S. Kalyanakumar, S.T. Chandy, K.T. Adil Muhammed, and P.S. Rohith, "Multi-response optimization of machining parameters of turning operation with green environment in EN24T using grey relational analysis in Taguchi method," in: Mater Today Proc, Elsevier Ltd, 2020: pp. 6193-6197 doi:10.1016/j.matpr.2020.10.508
  50. 50) D. Sarma, J. Borah, and M. Chandrasekaran, "Multi optimization of nano fluid based machining of titanium alloy: A green manufacturing approach," in: Mater Today Proc, Elsevier Ltd, 2021: pp. 8921-8926 doi:10.1016/j.matpr.2021.05.362
  51. 51) C. Moganapriya, & R. Rajasekar, & T. Mohanraj, V.K. Gobinath, & P. Sathish Kumar, and C. Poongodi, "Dry machining performance studies on tialsin coated inserts in turning of aisi 420 martensitic stainless steel and multi-criteria decision making using taguchi - dear approach," Silicon, 14 (2021) doi:10.1007/s12633-021-01202-4/Published
  52. 52) V.V.K. Lakshmi, K.V. Subbaiah, A.V. Kothapalli, and K. Suresh, "Parametric optimization while turning ti-6al-4v alloy in mist-mqcl (green environment) using the dear method," Manuf Rev (Les Ulis), 7 (2020) doi:10.1051/mfreview/2020034
  53. 53) A.S. Wadhwa, M. Abbass, S. Akhai, D. Kumar, and P. Kumar, "Integrating Taguchi optimization for multi-criteria decision making in engineering applications," in: Recent Theories and Applications for Multi-Criteria Decision-Making, IGI Global, 2024: pp. 125-150 doi:10.4018/979-8-3693-6502-1.ch005
  54. 54) S.H. ALI, Y. YAO, B. WU, B. ZHAO, W. DING, M. JAMIL, A. KHAN, A. BAIG, Q. LIU, and D. XU, "Recent developments in mql machining of aeronautical materials: a comparative review," Chinese Journal of Aeronautics, 38 (1) (2025) doi:10.1016/j.cja.2024.01.018
  55. 55) E.S. Alaba, R.A. Kazeem, A.S. Adebayo, M.O. Petinrin, O.M. Ikumapayi, T.C. Jen, and E.T. Akinlabi, "Evaluation of palm kernel oil as cutting lubricant in turning aisi 1039 steel using taguchi-grey relational analysis optimization technique," Advances in Industrial and Manufacturing Engineering, 6 (2023) doi:10.1016/j.aime.2023.100115
  56. 56) K. Maneesh, M. Shan, S. Xavier, M.B. Vinayak, and M. Shafeek, "Quality characteristic optimization in CNC turning of aluminum bronze by using Taguchi’s approach and ANOVA," in: Mater Today Proc, Elsevier Ltd, 2023: pp. 620-628 doi:10.1016/j.matpr.2022.11.059
  57. 57) A.S. Sobh, E.M. Sayed, A.F. Barakat, and R.N. Elshaerr, "Turning parameters optimization for tc21 ti-alloy using taguchi technique," Beni Suef Univ J Basic Appl Sci, 12 (1) (2023) doi:10.1186/s43088-023-00356-x
  58. 58) M.V. Ramana, G.K. Mohana Rao, B. Sagar, R.K. Panthangi, and B.V.R. Ravi Kumar, "Optimization of surface roughness and tool wear in sustainable dry turning of iron based nickel a286 alloy using taguchi’s method," Clean Eng Technol, 2 (2021) doi:10.1016/j.clet.2020.100034
  59. 59) S. Dutta, and S. Kumar Reddy Narala, "Optimizing turning parameters in the machining of am alloy using taguchi methodology," Measurement (Lond), 169 (2021) doi:10.1016/j.measurement.2020.108340
  60. 60) P. Kittali, V. Kalwa, D. Athith, K.P. Prashanth, and B.K. Venkatesh, "Optimization of machining parameters in turning operation to minimize the surface roughness using taguchi technique for en1a alloy steel," Mater Today Proc, 54 463-467 (2022) doi:10.1016/j.matpr.2021.10.323
  61. 61) M. Kurt, E. Bagci, and Y. Kaynak, "Application of taguchi methods in the optimization of cutting parameters for surface finish and hole diameter accuracy in dry drilling processes," International Journal of Advanced Manufacturing Technology, 40 (5-6) 458-469 (2009) doi:10.1007/s00170-007-1368-2
  62. 62) Y.F. Lin, P.Y. Lai, G.Y. Chen, and Z.P. Zhang, "Optimization of surface roughness and cylindricity using the taguchi method in boring of s45c steel with tungsten steel and phosphor bronze damping materials," International Journal of Advanced Manufacturing Technology, (2024) doi:10.1007/s00170-024-14796-x
  63. 63) V. Tiwari, A. Bansal, A. Kaushik, and U. Punia, "Optimization of jig boring process parameter by taguchi approach," Mater Today Proc, (2023) doi:10.1016/j.matpr.2023.02.051
  64. 64) E. Kuram, B. Ozcelik, and E. Demirbas, "Environmentally Friendly Machining: Vegetable Based Cutting Fluids," in: 2013: pp. 23-47 doi:10.1007/978-3-642-33792-5_2
  65. 65) N. Khanna, P. Shah, M. Sarikaya, and F. Pusavec, "Energy consumption and ecological analysis of sustainable and conventional cutting fluid strategies in machining 15-5 phss," Sustainable Materials and Technologies, 32 (2022). 2022.e00416 doi:10.1016/j.susmat
Other Papers in This Issue