EDP Sciences logo
Submit your paper
Search
Menu
Home All issues Volume 9 (2022) Manufacturing Rev., 9 (2022) 9 References
Open Access
Issue
Manufacturing Rev.
Volume 9, 2022
Article Number 9
Number of page(s) 12
DOI https://doi.org/10.1051/mfreview/2022007
Published online 22 April 2022
  1. S. Nguyen Hong, U. Vo Thi Nhu, Multi-objective optimization in turning operation of AISI 1055 steel using DEAR method, Tribol. Ind. 43 (2021) 57–65 [CrossRef] [Google Scholar]
  2. T.J. Ko, H.S. Kim, Surface integrity and machineability in intermittent hard turning, Int. J. Adv. Manufactur. Technol. 18 (2001) 168–175 [CrossRef] [Google Scholar]
  3. T.V. Dich, N.T. Binh, N.T. Dat, N.V. Tiep, T.X. Viet, Manufacturing process, Science and Technics Publishing House (2003) [Google Scholar]
  4. O. Onur, S. Hamit, The effect of vibration and cutting zone temperature on surface roughness and tool wear in eco-friendly MQL turning of AISI D2, J. Mater. Res. Technol. 9 (2020) 2762–2772 [CrossRef] [MathSciNet] [Google Scholar]
  5. C.M. Rao, K. Venkatasubbaiah, Application of MCDM approach-TOPSIS for the multi-objective optimization problem, Int. J. Grid Distrib. Comput. 9 (2016) 17–32 [Google Scholar]
  6. B. Singaravel, T. Selvaraj, Optimization of machining parameters in turning operation using combined TOPSIS and AHP method, Tehnički vjesnik 22 (2015) 1475–1480 [Google Scholar]
  7. P. Umamahesarrao, D.R. Rauju, K.N.S. Suman, B. Ravi Sankar, Optimizing cutting parameters in hard turning of AISI 52100 steel using topsis approach, J. Mech. Energy Eng. 3 (2019) 227–232 [CrossRef] [Google Scholar]
  8. C.M. Rao, K. Jagadeeswara Rao, K.L. Rao, Multi-objective optimization of MRR, Ra and Rz using Topsis, Int. J. Eng. Sci. Res. Technol. 5 (2016) 376–384 [Google Scholar]
  9. S.S. Mane, A.M. Mulla, Relevant optimization method selection in turning of AISI D2 steel using Crygenic cooling, Int. J. Creative Res. Thoughts 8 (2020) 803–812 [Google Scholar]
  10. K. Maity, A. Khan, Application of MCDM-based TOPSIS method for the selection of optimal process parameter in turning of pure titanium, Benchmarking 24 (2017) 1–19 [Google Scholar]
  11. N.V. Thien, D.H. Tien, N.T. Nguyen, D.D. Trung, Multi-objective optimization of turning process using VIKOR method, J. Appl. Eng. Sci. 4 (2021) 868–873 [Google Scholar]
  12. A. Khan, K. Maity, A novel MCDM approach for simultaneous optimization of some correlated machining parameters in turning of CP-titanium grade 2, Int. J. Eng. Res. Africa 22 (2016) 94–111 [Google Scholar]
  13. K.A. Vikram, T.V.K. Kanth, Shabana, K. Suresh, Experimental evaluation for multi-response optimality on AISI 316L materials with coated carbide inserts using GRA and Vikor methods, Int. J. Mech. Prod. Eng. Res. Dev. 8 (2018) 1197–1206 [Google Scholar]
  14. I. Nayak, J. Rana, Selection of a suitable multiresponse optimization technique for turning operation, Decis. Sci. Lett. 5 (2016) 129–142 [Google Scholar]
  15. G.K. Kumar, C.M. Rao, V.V.S. Kesava Rao, Investigation of effects of speed and depth of cut on multiple responses using Vikor analysis, Int. J. Mod. Trends Eng. Res. 5 (2018) 164–168 [CrossRef] [Google Scholar]
  16. D.D. Trung, N.T. Nguyen, D.V. Duc, Study on multi-objective optimization of the turning process of EN 10503 steel by combination of Taguchi method and Moora technique, Eureka 2021 (2021) 52–65 [CrossRef] [MathSciNet] [Google Scholar]
  17. B. Singaravel, T. Selvaraj, S. Vinodh, Multi − objective optimization of turning parameters using the combined Moora and Entropy method, Trans. Canadian Soc. Mech. Eng. 40 (2016) 101–111 [CrossRef] [Google Scholar]
  18. M. Abas, B. Salah, Q.S. Khalid, I. Hussain, A. Rehman Babar, R. Nawaz, R. Khan, W. Saleem, Experimental investigation and statistical evaluation of optimized cutting process parameters and cutting conditions to minimize cutting forces and shape deviations in Al6026-T9, Materials 13 (2020) 1–21 [Google Scholar]
  19. A. Khan, K. Maity, D. Jhodkar, An integrated fuzzy-MOORA method for the selection of optimal parametric combination in turing of commercially pure titanium, Springer Ser. Adv. Manufactur. 2020 (2020) 163–184 [CrossRef] [Google Scholar]
  20. A. Saha, H. Majumder, Multi criteria selection of optimal machining parameter in turning operation using comprehensive grey complex proportional assessment method for ASTM A36, Int. J. Eng. Res. Africa 23 (2016) 24–32 [Google Scholar]
  21. D.D. Trung, N.H. Quang, T.Q. Hoang, C.T. Anh, N.H. Linh, H.T. Kien, D.T. Tam, N.A. Tuan, Optimization study on turning process by using taguchicopras method, E3S Web Conf. 309 (2021) 1–6 [Google Scholar]
  22. A. Krishnaveni, D. Jebakani, K. Jeyakumar, P. Pitchipoo, Turning parameters optimization using Copras − Taguchi technique, Int. J. Adv. Eng. Technol. 7 (2016) 463–468 [Google Scholar]
  23. V.R. Pathapalli, V.R. Basam, S.K. Gudimetta, M.R. Koppula, Optimization of machining parameters using WASPAS and MOORA, World J. Eng. 17 (2020) 237–246 [Google Scholar]
  24. H. Majumder, A. Saha, Application of MCDM based hybrid optimization tool during turning of ASTM A588, Decis. Sci. Lett. 7 (2018) 143–156 [Google Scholar]
  25. R. Singh, J.S. Dureja, M. Dogra, J.S. Randhawa, Optimization of machining parameters under MQL turning of Ti-6Al-4V alloy with textured tool using multi-attribute decision-making methods, World J. Eng. 16 (2019) 648–659 [Google Scholar]
  26. D.D. Trung, Application of TOPSIS an PIV methods for multi-criteria decision making in hard turning process, J. Mach. Eng. 21 (2021) 57–71 [CrossRef] [Google Scholar]
  27. D.D. Trung, A combination method for multi-criteria decision making problem in turning process, Manufactur. Rev. 8 (2021) 1–17 [CrossRef] [EDP Sciences] [Google Scholar]
  28. E. Roszkowska, Rank ordering criteria weighting methods − a comparative overview, J. Dedic. Needs Sci. Practice 5 (2013) 1–168 [Google Scholar]
  29. S.R. Besharati, V. Dabbagh, H. Amini, A.A.D. Sarhan, J. Akbari, M. Hamdi, Z.C. Ong, Multi-objective selection and structural optimization of the gantry in a gantry machine tool for improving static, dynamic, and weight and cost performance, Concurr. Eng.: Res. Appl. 24 (2016) 83–93 [CrossRef] [Google Scholar]
  30. K. Maniya, M.G. Bhatt, A selection of material using a novel type decision-making method: preference selection index method, Mater. Des. 31 (2010) 1785–1789 [Google Scholar]
  31. R.K. James, J.A. David, A new method for group decision making and its application in medical trainee selection, Med. Educ. 50 (2016) 1045–1053 [CrossRef] [Google Scholar]
  32. A.A. Tabriz, A. Ahmadi, M.H. Maleki, M.A. Afshari, J.S. Moradi, Applying Pareto multi-criteria decision making in concurrent engineering: a case study of polyethylene industry, Manag. Sci. Lett. 1 (2011) 289–294 [CrossRef] [Google Scholar]
  33. M. Selmi, T. Kormi, N.B.H. Ali, Comparison of multi-criteria decision methods through a ranking stability index, Int. J. Oper. Res. 27 (2016) 1–20 [Google Scholar]
  34. K. Mela, T. Tiainen, M. Heinisuo, Comparative study of multiple criteria decision making methods for building design, Adv. Eng. Inf. 26 (2012) 716–726 [CrossRef] [Google Scholar]
  35. S.R. Besharati, V. Dabbagh, H. Amini, A.A.D. Sarhan, J. Akbari, M. Hamdi, Z.C. Ong, Multi-objective selection and structural optimization of the gantry in a gantry machine tool for improving static, dynamic, and weight and cost performance, Concurr. Eng.: Res. Appl. 24 (2016) 83–93 [CrossRef] [Google Scholar]
  36. H. Anysz, A. Nicał, Z. Stevic, M. Grzegorzewski, K. Sikora, Pareto optimal decisions in multi-criteria decision making explained with construction cost cases, Symmetry 13 (2020) 1–25 [Google Scholar]
  37. R. Attri, S. Grover, Application of preference selection index method for decision making over the design stage of production system life cycle, J. King Saud Univ. − Eng. Sci. 27 (2015) 207–216 [Google Scholar]
  38. B. Vahdani, S.M. Mousavi, S. Ebrahimnejad, Soft computing-based preference selection index method for human resource management, J. Intell. Fuzzy Syst. 26 (2014) 393–403 [CrossRef] [Google Scholar]
  39. S.H. Sahir, J. Afriani, G. Ginting, B. Fachri, D. Siregar, R. Simbolon, L. Lindawati, M. Syarizal, S. Aisyah, M. Mesran, F. Fadlina, J. Simarmata, The preference selection index method in determining the location of used laptop marketing, Int. J. Eng. Technol. 7 (2018) 260–263 [Google Scholar]
  40. D.D. Trung, Influence of cutting parameters on surface roughness during milling AISI 1045 steel, Tribol. Ind. 42 (2020) 658–665 [CrossRef] [Google Scholar]
  41. D.D. Trung, Influence of cutting parameters on surface roughness in grinding of 65G steel, Tribol. Ind. 43 (2021) 167–176 [Google Scholar]
  42. V.V.K. Lakshmi, K.V. Subbaiah, A.V. Kothapalli, K. Suresh, Parametric optimization while turning Ti-6Al-4V alloy in Mist-MQCL (Green environment) using the DEAR method, Manufactur. Rev. 7 (2020) 1–13 [CrossRef] [EDP Sciences] [Google Scholar]
  43. F. Klocke, T. Krieg, Coated tools for metal cutting − features and applications, CIRP Ann. 48 (1999) 515–525 [CrossRef] [Google Scholar]
  44. H.G. Prengel, W.R. Pfouts, A.T. Santhanam, State of the art in hard coatings for carbide cutting tools, Surf. Coat. Technol. 102 (1998) 183–190 [CrossRef] [Google Scholar]
  45. V.T.N. Uyen, N.H. Son, Improving accuracy of surface roughness model while turning 9XC steel using a Titanium Nitride-coated cutting tool with Johnson and Box-Cox transformation, AIMS Mater. Sci. 8 (2020) 1–17 [Google Scholar]
  46. N.T. Nguyen, D.D. Trung, Development of surface roughness model in turning process of 3 × 13 steel using TiAlN coated carbide insert, EUREKA 2021 (2021) 113–124 [CrossRef] [MathSciNet] [Google Scholar]
  47. A. Dean, D. Voss, D. Draguljić, Design and Analysis of Experiments − Second Edition, Springer, 2007 [Google Scholar]
  48. Y. Huang, L. Wang, S.Y. Liang, Handbook of Manufacturing, World Scientific Publishing, 2019 [CrossRef] [Google Scholar]
  49. S. Coutu, L. Rossi, D.A. Barry, N. Chèvre, Methodology to account for uncertainties and tradeoffs in pharmaceutical environmental hazard assessment, J. Environ. Manag. 98 (2012) 183–190 [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.