Abstract
Modern manufacturing’s objective is to guarantee quality while lowering production costs and increasing productivity. In this context, the selection of cutting materials is important in increasing productivity due to their wide range of applications. In this paper, when machining AISI 4140 alloy steel, an experimental study is presented to find the cutting conditions that result in minimum Ra, Vb, and maximum MRR values. Different optimization methods are used, namely, Taguchi, grey relational analysis (GRA), technique by order of preference by similarity to ideal solution (TOPSIS), and multi-objective optimization ratio analysis (MOORA). Taguchi’s L16 design was used to arrange 16 experiments. Cutting speed (Vc), feed rate (f), and depth of cut (ap) were the input parameters, with four levels for each cutting parameter. The feed rate and cutting speed had the greatest effect on Ra and Vb, according to an ANOVA analysis of the experimental results. MRR was significantly affected by the depth of cut and feed rate. To achieve the minimum surface roughness, flank wear, and maximum material removal rate, the cutting speed, feed rate, and depth of cut were required to be 250 m/min, 0.11 mm/rev, and 1.4 mm, respectively, according to the optimization results.
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Laouissi A, Nouioua M, Yallese MA, Abderazek H, Maouche H, Bouhalais ML (2021) Machinability study and ANN-MOALO-based multi-response optimization during eco-friendly machining of EN-GJL-250 cast iron. Int J Adv Manuf Technol 117:1179–1192
Ben-Arieh D, Qian L (2003) Activity-based cost management for design and development stage. Int J Prod Econ 83:169–183
Mia M, Gupta MK, Lozano JA, Carou D, Pimenov DY, Królczyk G, Khan AM, Dhar NR (2019) Multi-objective optimization and life cycle assessment of eco-friendly cryogenic N2 assisted turning of Ti-6Al-4V. J Clean Prod 210:121–133
Singh V, Haque S, Niwas R, Srivastava A, Pasupuleti M, Tripathi C (2017) Strategies for fermentation medium optimization: an in-depth review. Front, Microbiol 7:2087
Vats P, Singh T, Dubey V, Sharma AK (2022) Optimization of machining parameters in turning of AISI 1040 steel using hybrid MCDM technique. Mater Today: Proc 50:1758–1765
Chodha V, Dubey R, Kumar R, Singh S, Kaur S (2022) Selection of industrial arc welding robot with TOPSIS and Entropy MCDM techniques. Mater Today: Proc 50:709–715
Petković D, Madić M, Radovanović M, Gečevska V (2017) Application of the performance selection index method for solving machining MCDM problems. Facta Universitatis Series: Mech Eng 15(1):97–106
Venkata Vishnu A, Babu SS, Kumar PJ (2022) Multi-response optimization of machining characteristics using MQL through GRA and TOPSIS approach. In: Srinivasa Pai P, Krishnaraj V (eds) Sustainable machining strategies for better performance. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-16-2278-6_3
Hussain SAI, Sen B, Das Gupta A, Mandal UK (2020) Novel multi-objective decision-making and trade-off approach for selecting optimal machining parameters of inconel-800 superalloy. Arab J Sci Eng 45(7):5833–5847
Nguyen PH, Tsai JF, Kumar VA, Hu G, Y.C. (2020) Stock investment of agriculture companies in the Vietnam stock exchange market: an AHP integrated with GRA-TOPSIS-MOORA approaches. J Asian Financ Econ Bus 7(7):113–121
Bellubbi S, Mallick B (2022) Multi response optimization of ECDM process parameters for machining of microchannel in silica glass using Taguchi–GRA technique. SILICON 14(8):4249–4263
Rajeev D, Singh SCE, Rejilin DRA, Devadhas GG, Ajitha S (2020) Optimization tool wear on hard turning of AISI4140 steel with coated carbide tool cutting conditions. Int J Innov Technol Explor Eng 9(5):2051–2054
Touggui Y, Belhadi S, Mabrouki T, Temmar M, Yallese MA (2020) Dry turning optimization of austenitic stainless steel 316L based on Taguchi and TOPSIS approaches. Matériaux Tech 108(4):401
Upadhyay VV (2020) Machining parameters optimization by grey relational analysis of alloy steel AISI 4140. PalArch’s J Archaeol Egypt/Egyptol 17(7):4107–4121
Aslan E, Camuşcu N, Birgören B (2007) Design optimization of cutting parameters when turning hardened AISI 4140 steel (63 HRC) with Al2O3+ TiCN mixed ceramic tool. Mater, Des 28(5):1618–1622
Karaaslan F, Şahinoğlu A (2020) Determination of ideal cutting conditions for maximum surface quality and minimum power consumption during hard turning of AISI 4140 steel using TOPSIS method based on fuzzy distance. Arab J Sci Eng 45(11):9145–9157
Abhilash PM, Chakradhar D (2022) Multi-response optimization of wire EDM of Inconel 718 using a hybrid entropy weighted GRA-TOPSIS method. Process Integr Optim Sustain 6(1):61–72
Adam Khan M, Gupta K (2021) Optimization of machining parameters for material removal rate and machining time while cutting inconel 600 with tungsten carbide textured Tools. In: Pathak S (eds) Intelligent manufacturing. Materials Forming, Machining, and Tribology. Springer, Cham. https://doi.org/10.1007/978-3-030-50312-3_2
Anitha J, Das R (2021) Optimization of process parameters in EDM using standard deviation and MOORA method. In: Rajmohan T, Palanikumar K, Davim JP (eds) Advances in materials and manufacturing engineering. Springer Proceedings in Materials, vol 7. Springer, Singapore. https://doi.org/10.1007/978-981-15-6267-9_18
Majumder H, Mishra SK, Sahu AR, Bavche AL, Valekar M, Padaseti BK (2020) Application of MOORA to optimize WEDM process parameters: a multi-criteria decision making approach. In: Gunjan V, Singh S, Duc-Tan T, Rincon Aponte G, Kumar A (eds) ICRRM 2019 – System Reliability, Quality Control, Safety, Maintenance, and Management. ICRRM 2019. Springer, Singapore. https://doi.org/10.1007/978-981-13-8507-0_12
Umamaheswarrao P, Raju DR, Suman KNS, Sankar BR (2019) TOPSIS based optimization of process parameters while hard turning of AISI 52100 Steel. Acta Mech Malaysia 2(2):28–31
Singaravel B, Selvaraj T, Vinodh S (2016) Multi-objective optimization of turning parameters using the combined MOORA and entropy method. Trans Can Soc Mech Eng 40(1):101–111
Sultana MN, Zaman PB, Dhar NR (2020) GRA-PCA coupled with Taguchi for optimization of inputs in turning under cryogenic cooling for AISI 4140 steel. J Prod Syst Manuf Sci 1(2):40–62
Sayuti M, Sarhan AAD, Salem F (2014) Novel uses of SiO2 nano-lubrication system in hard turning process of hardened steel AISI4140 for less tool wear, surface roughness and oil consumption. J Clean Prod 67:265–276
Elbah M, Yallese MA, Aouici H, Mabrouki T, Rigal J-F (2013) Comparative assessment of wiper and conventional ceramic tools on surface roughness in hard turning AISI 4140 steel. Measurement 46(9):3041–3056
Ben Fathallah B, Saidi R, Mabrouki T, Belhadi S, Yallese MA (2020) Multi-optimization of stellite 6 turning parameters for better surface quality and higher productivity through RSM and grey relational analysis. In: et al. Design and modeling of mechanical systems - IV. CMSM 2019. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-27146-6_41
Yallese MA, Boulanouar L, Chaoui K (2004) Usinage de l’acier 100Cr6 trempé par un outil en nitrure de bore cubique. Mech Ind 5(4):355–368
Modi M, Agarwal G, Patil V, Khare A, Shukla S, Shankhla A (2019) Modeling and analysis of turning process on lathe machine by Taguchi and ANOVA approach. Int J Sci Technol Res 8(10):1466–1470
Petrović G, Mihajlović J, Ćojbašić Ž, Madić M, Marinković D (2019) Comparison of three fuzzy MCDM methods for solving the supplier selection problem. Facta Universitatis Series: Mech Eng 17(3):455–469
Prabhu SR, Ilangkumaran M (2019) Selection of 3D printer based on FAHP integrated with GRA-TOPSIS. Int J Mater Prod Technol 58(2–3):155–177
Belhadi S, Kaddeche M, Chaoui K, Yallese MA (2016) Machining optimization of HDPE pipe using the Taguchi method and grey relational analysis. Int Polym Process 31(4):491–502
Jozić S, Dumanić I, Bajić D (2020) Experimental analysis and optimization of the controllable parameters in turning of EN AW-2011 alloy; dry machining and alternative cooling techniques. Facta Universitatis Series: Mech Eng 18(1):013–029
Takale AM, Chougule NK (2018) Multi-objective optimization of WEDM process parameters of Ti49. 4-Ni50. 6 shape memory alloy for orthopedic implant application. Arch Mater Sci Eng 93(1):12–31. https://doi.org/10.5604/01.3001.0012.6943
Capraz O, Meran C, Wörner W, Gungor A (2015) Using AHP and TOPSIS to evaluate welding processes for manufacturing plain carbon stainless steel storage tank. Arch Mater Sci 158:158
Zhao D, Bezgans Y, Vdonin N, Du W (2021) The use of TOPSIS-based-desirability function approach to optimize the balances among mechanical performances, energy consumption, and production efficiency of the arc welding process. Int J Adv Manuf Technol 112(11):3545–3559
Brauers WKM (2013) Multi-objective seaport planning by MOORA decision making. Ann Oper Res 206(1):39–58
Brauers WK, Zavadskas EK (2009) Robustness of the multi-objective MOORA method with a test for the facilities sector. Technol Econ Dev Econ 15(2):352–375
Chate GR, Patel GCM, Harsha HM, Shubham UU, Sanadi SA, Jadhav AP, Hiremath S, Deshpande AS (2021) Materials today: Proceedings sustainable machining: modelling and optimization using Taguchi, MOORA and DEAR methods. Mater Today Proc 46:8941–8947
Gupta K, Roy S, Poonia RC, Kumar R, Nayak SR, Altameem A, Saudagar AKJ (2022) Multi-criteria usability evaluation of mHealth applications on type 2 diabetes mellitus using two hybrid MCDM models: CODAS-FAHP and MOORA-FAHP. Appl Sci 12(9):4156
Gadakh VS, Shinde VB, Khemnar NS, Kumar A (2018) Application of MOORA method for friction stir welding tool material selection. In: Pawar P, Ronge B, Balasubramaniam R, Seshabhattar S (eds) Techno-societal 2016. ICATSA 2016. Springer, Cham. https://doi.org/10.1007/978-3-319-53556-2_86
Panda SN, Bagal DK, Patnaik D, Barua A, Jeet S, Parida B, Naik B (2020) Comparative evaluation for studying the parametric influences on quality of electrode using Taguchi method coupled with MOORA, DFA, and TOPSIS method for electrochemical machining. In: Parwani A, Ramkumar P (eds) Recent advances in mechanical infrastructure. Lecture Notes in Intelligent Transportation and Infrastructure. Springer, Singapore. https://doi.org/10.1007/978-981-32-9971-9_13
Acknowledgements
The authors would like to thank all members of the LMS laboratory for their support.
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The work is funded by (LMS) Laboratory of the 8 May 1945 Guelma University, Algeria, and received funding from the General Directorate of Scientific Research and Technological Development (DGRSDT) under the PRFU research project A11N01UN240120220002.
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Hadjela, S., Belhadi, S., Ouelaa, N. et al. Straight turning optimization of low alloy steel using MCDM methods coupled with Taguchi approach. Int J Adv Manuf Technol 124, 1607–1621 (2023). https://doi.org/10.1007/s00170-022-10584-7
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DOI: https://doi.org/10.1007/s00170-022-10584-7