Optimization on the Turning Process Parameters of SS 304 Using Taguchi and TOPSIS
Tóm tắt
Turning is a basic machining technique where parameters may be optimised to improve machining performance. The Taguchi and TOPSIS methods were used to find the parameters of optimum process in turning SS 304 using coated carbide tools. Cutting speed, feed rate, and depth of cut are all considered in the operation. This improves tool life while lowering production time and surface roughness. TOPSI and an orthogonal array are used to investigate the effects of input parameters on output parameters. In this work, S/N ratios are utilized to create a decision matrix, which is then utilized to convert a problem with multiple criteria for solving into a single-criteria issue using the TOPSIS approach. The results demonstrated that the strategy proposed is suitable for resolving multi-criteria process parameter enhancements. The best combination of process specifics was found to be 350 m/min cutting speed, 0.12 mm/rev feed rate, and 0.40 mm cut depth.
Tài liệu tham khảo
Olson DL, Shi Y (2007) Introduction to business data mining. McGraw-Hill/Irwin, New York
Pawnar S, Garg GK, Routroy S (2019) Multi-Objective optimization of Machining parameters to minimize surface roughness and power consumption using TOPSIS. Procedia CIRP 86:116–120
Karaaslan F, Sahinoglu 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
K Maity, A Khan (2017) Application of MCDM-based TOPSIS method for the selection of optimal process parameter in turning of pure titanium. Emerald Insight: BIJ-01-2016-0004
Balasubramaniyan S, Selvaraj T (2017) Application of integrated Taguchi and TOPSIS method for optimization of process parameters for dimensional accuracy in turning of EN25 steel. J Chin Inst Eng 40:267–274
Rao CM, Rao SS, Herbert MA (2019) Studies on the effect of process parameters in turning of Ti-6Al-4V alloy using topsis. IOP Conf Ser Mater Sci Eng 577:012069
Ramakrishnan H, Balasundaram R, Selvaganapathy P, Santhakumari M, Sivasankaran P, Vignesh P (2019) Experimental investigation of turning Al 7075 using Al2O3 nano-cutting fuid: ANOVA and TOPSIS approach. SN Appl Sci 1:1639
Arunkarthikeyan K, Balamurugan K (2020) Studies on the effects of deep cryogenic treated WC–Co insert on turning of Al6063 using multi-objective optimization. SN Appl Sci 2:2103
Palanisamy A, Selvaraj T, Sivasankaran S (2017) Optimization of turning parameters of machining incoloy 800H superalloy using cryogenically treated multilayer CVD-coated tool. Arab J Sci Eng 43(9):4977–4990
Warsi SS, Agha MH, Ahmad R, Jaffery SHI, Khan M (2018) Sustainable turning using multi-objective optimization: a study of Al 6061 T6 at high cutting speeds. Int J Adv Manuf Technol 100:843–855
Khan MA, Jaffery SHI, Khan M, Younas M, Butt SI, Ahmad R, Warsi SS (2019) Multi-objective optimization of turning titanium-based alloy Ti-6Al-4V under dry, wet, and cryogenic conditions using gray relational analysis (GRA). Int J Adv Manuf Technol 106:3897–3911
Dutta Sunil, Narala SKR (2021) Optimizing turning parameters in the machining of AM alloy using Taguchi methodology. Measurement 169:108340
Soorya Prakash K, Gopal PM, Karthik S (2020) Multi-objective optimization using Taguchi based grey relational analysis in turning of rock dust reinforced aluminium MMC. Measurement 157:107664
Singh TS, Rajak U, Samuel OD, Chaurasiya PK, Natarajan K, Verma TN, Nashine P (2021) Optimization of performance and emission parameters of direct injection diesel engine fuelled with microalgae Spirulina (L.)–Response surface methodology and full factorial method approach. Fuel 285:119103
Salam S, Verma TN (2021) An empirical investigation on the influence of operating conditions on internal combustion engine behavior and their graded significance. Energy Sour Part A Recovery Util Environ Eff 27:1–9
Singh DV, Verma TN, Kumar R, Sharifpur M, Ghazvini M, Assad ME (2021) Exergy approach in decision-based design of absorption refrigeration system using artificial intelligence and simulink. J Therm Anal Calorim 23:1–27
Pacheco AG, Krohling RA (2018) Ranking of classification algorithms in terms of mean–standard deviation using A-TOPSIS. Ann Data Sci 1:93–110
Bera AK, Jana DK, Banerjee D, Nandy T (2021) A two-phase multi-criteria fuzzy group decision making approach for supplier evaluation and order allocation considering multi-objective, multi-product and multi-period. Ann Data Sci 8:577–601
Majeed A (2019) Improving time complexity and accuracy of the machine learning algorithms through selection of highly weighted top k features from complex datasets. Ann Data Sci 6:599–621
Shi Y, Tian YJ, Kou G, Peng Y, Li JP (2011) Optimization based data mining: theory and applications. Springer, Berlin
Tien JM (2017) Internet of things, real-time decision making, and artificial intelligence. Ann Data Sci 4(2):149–178