Matériaux & Techniques
Volume 108, Number 4, 2020
|Number of page(s)||10|
|Section||Mise en oeuvre des matériaux / Materials processing|
|Published online||21 December 2020|
Dry turning optimization of austenitic stainless steel 316L based on Taguchi and TOPSIS approaches
Structures Research Laboratory (LS), University of Blidal,
2 Mechanics and Structures Research Laboratory (LMS), May 8th 1945 University of Guelma, 24000 Guelma, Algeria
3 Applied Mechanics and Engineering Laboratory (LR-11-ES 19), University of Tunis El Manar, ENIT, BP 37, Le belvedere, 1002 Tunis, Tunisia
* e-mail: email@example.com
Accepted: 9 November 2020
Austenitic stainless steel (AISI 316L ASS) is known as a very difficult material to cut due to its high toughness, work hardening combined with built-up-edge (BUE) formation and also poor thermal conductivity. In order to improve its machinability, it seems important to carry out experimentation helping to study effects of cutting parameters on process responses. For that both Taguchi and TOPSIS approaches were applied to determine an optimal combination of cutting parameters during dry turning of AISI 316L ASS. Cutting speed (Vc), feed (f), cutting depth (ap) and cutting time (tc) were selected as four input parameters. Flank wear (VB), tangential cutting force (Fz), surface roughness (Ra) and material removal rate (MRR) were considered as the major process responses. Nine cutting tests were carried out based on Taguchi’s L9 orthogonal array. Thus, in order to distinguish the greater significant cutting parameter, Analysis of variance (ANOVA) was applied. Ultimately, in the case of Taguchi approach results show optimal combinations in terms of (Vc, f, ap and tc) for attaining minimum VB, Fz and Ra and also reaching maximization of MRR. In addition, TOPSIS approach was exploited yielding to results that indicate optimal combination of cutting parameters for achieving simultaneously minimum VB, Fz and Ra and maximum MRR.
Key words: stainless steel / process optimization / flank wear / cutting force / surface roughness / material removal rate
© SCF, 2020
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