Optimal Selection of Machining Parameters for Minimization of Elementary Energy Consumption during Machining of 304 Austenitic Stainless Steel

Abstract:

The dry turning was done on 304 austenitic stainless steel using a carbide insert (Tungaloy, SNMG 12 04 04). Speed, feed, and depth of cut (DOC) were the machining parameters. The chip reduction coefficient (CRC) and elementary energy consumption were the output responses. Twenty-seven experiments were performed by following the L27 Taguchi design plan-decoupled TOPSIS method. The minimal elementary energy consumption was attained at moderate speed (44.28 m/min.), high feed (0.20 mm/rev.), and low DOC (0.66 mm). This result has been validated by the fuzzy inference system (FIS) simulation. The details of minimising elementary energy consumption have been explained by implementing Taguchi design plans, TOPSIS, chip macrographs, and FIS simulation studies. The very thin continuous chip (very low CRC) was found at optimal parameters, indicating minimal energy consumption during machining. The aim of the work is to find out the machining condition at which the elementary energy consumption during machining was minimum. The validation of the optimal solution by qualitative assessment and by the FIS simulation emphasizes the novelty of the work. The nature of the study is newer in kind with validation of results. The simulation result shows the increasing trend of closeness coefficient (CC) with increased feed at moderate speed and low depth of cut (DOC).

• Periodical: Materials Science Forum (Volume 1133)
• Pages: 3-9
• DOI: https://doi.org/10.4028/p-Ywfq8W
• Online since: December 2024
• Authors: Neeraj Baishya, Kaushik Das, Karan Roy, Helemoon Ahmed, Kalyan Chakraborty*
• Keywords: 304 Austenitic Stainless Steel, Carbide Insert, Fuzzy Inference System, Speed

References

[1] T. Kong, D. Nhg DR, V.T. Shi, Method for optimizing energy consumption in machining manufacturing process, Mathematical problems in engineering, (2022), Article ID 8300666.

DOI: 10.1155/2022/8300666

[2] J. Ma, X. Ge, S. I. Chang, S. Lei S, Assesment of cutting energy consumption and energy efficiency in machining of 4140 steel, Int. J. Adv. Manuf. Technol. 74 (2014).

DOI: 10.1007/s00170-014-6101-3

[3] G.Y. Zhao, Z.Y. Liu, Y. He, H.J. Cao, Y.B. Guo, Energy consumption in machining: Classification, prediction, and reduction strategy. Energy 133(15) (2017) 142-157.

DOI: 10.1016/j.energy.2017.05.110

[4] Q. Wang, F. Liu, X. Wang. Multi-objective optimization of machining parameters considering energy consumption. Int. J. Adv. Manuf. Technol. 71 (2014) 1133-1142.

DOI: 10.1007/s00170-013-5547-z

[5] J. Lv, R. Tang, W. Tang, Y. Liu, Y. Zhang, S. Jia. An investigation into reducing the spindle acceleration energy consumption of machine tools. Journal of cleaner production. 143 (2017) 794-803.

DOI: 10.1016/j.jclepro.2016.12.045

[6] SAA. Bagaber, A.R. Yusoff, Sustainable optimization of dry turning od stainless steel based on energy consumption and machining cost. 8th CIRP conference on high performance cutting (HPC), Procedia CIRP 77 (2018) 397-400.

DOI: 10.1016/j.procir.2018.08.300

[7] S. Pawanr, G.K. Garg, S. Routroy, Modelling of variable energy consumption for CNC machine tools, 28th CIRP conference on life cycle engineering, Procedia CIRP 98 (2021) 247-251.

DOI: 10.1016/j.procir.2021.01.038

[8] S. Pawanr, G.K. Garg, Selection of optimum cutting parameters for minimization of specific energy consumption during machining of Al 6061, journal of physics. Conference series, IOP publishing ltd, 1240 012064.

DOI: 10.1088/1742-6596/1240/1/012064

[9] J. Lv, R. Tang, W. Tang, S. Jia, Y. Liu, Y. Coo. An investigation into methods for predicting material removal energy consumption in turning, Journal of cleaner production. 193 (2018) 128-139.

DOI: 10.1016/j.jclepro.2018.05.035

[10] Y. Guo , J. Loenders, J. Duflou, B. Lauwers, Optimization of energy consumption and surface quality in finish turning. 5th CIRP Conference on high performance cutting , Procedia CIRP 1 (2012) 512-517 http://dx.doi.org/.

DOI: 10.1016/j.procir.2012.04.091

[11] V.P. Astakhov, The assessment of plastic deformation in metal cutting, Journal of materials processing technology. 146 (2004) 193-202.

DOI: 10.1016/j.jmatprotec.2003.10.015

[12] Y. Lee, S. Yang, S. Chang, Assessment of chip breaking characteristics using new chip-breaking index. Journal of materials processing technology. 173 (2006) 166-171.

DOI: 10.1016/j.jmatprotec.2005.05.057

[13] A.K. Sahu, N. K. Sahu, A.K. Sahu, M. S. Rajput, H. K. Narang, T-SAW methodology for parametric evaluation of surface integrity aspects in AlMg3 (AA5754) alloy Comparison with T-TOPSIS methodology, Measurement. 132 (2019) 309–323.

DOI: 10.1016/j.measurement.2018.09.037

[14] S.R. Balasubramanian, T. Selvaraj, Application of integrated Taguchi and TOPSIS method for optimization of process parameters for dimensional accuracy in turning of EN25 steel, Journal of the Chinese Institute of Engineers 40 (4) (2017) 267–274 .

DOI: 10.1080/02533839.2017.1308233

[15] B. Sen, S.A.L. Hussain, S. M. Mia, U. K. Mandal, S.P. Mondal, Selection of an ideal MQL-assisted milling condition: an NSGA-II-coupled TOPSIS approach for improving machinability of Inconel 690, The International Journal of Advanced Manufacturing Technology 103 (2019) 1811–1829.

DOI: 10.1007/s00170-019-03620-6