Izvorni znanstveni članak

https://doi.org/10.21278/TOF.482054923

Multi-Objective Optimization of Single Point Incremental Forming of 316L Stainless Steel Using Grey Relational and Principal Component Analyses

Visagan A. orcid.org/0000-0002-2198-3208 ; Department of Production Technology, Anna University, Chennai, India *
Ganesh P. ; Department of Production Technology, Anna University, Chennai, India
Ethiraj N. orcid.org/0000-0002-7174-5443 ; Department of Mechanical Engineering, Dr.M.G.R Educational and Research Institute, Maduravoyal, Chennai, India
Kalaichelvan K. ; Department of Ceramic Technology, Anna University, Chennai, India

* Dopisni autor.

Sažetak

The single point incremental forming process has a wide range of applications, and the process can be carried out without any specialized tooling and punches. However, if the process parameters are not carefully chosen, the components being manufactured are limited. This paper presents the single point incremental forming of 316L stainless steel sheets of 0.8 mm in thickness by varying input process parameters, such as tool diameter, wall angle, step depth, spindle speed, and feed rate, to study their effect on output parameters, strain and surface roughness. The Taguchi method was coupled with principal component analysis to identify the optimum process parameter combination to improve strain and reduce surface roughness. The wall angle has been determined to be the most influential process parameter, and the analysis of variance showed that the wall angle had the highest contribution (52.44 %), followed by step depth (18.55 %), tool diameter (9.72 %), spindle speed (1.64 %), and feed rate (0.83 %). The suggested design of experiments determined the best process parameter combination and executed the confirmation run. The grey relational analysis combined with principal component analysis improved the single point incremental forming of 316L stainless steel sheets.

Ključne riječi

Single Point Incremental Forming; 316L Stainless Steel; Optimization; Grey Relation Analysis; Principal Component Analysis; ANOVA

Hrčak ID:

315352

URI

https://hrcak.srce.hr/315352

Datum izdavanja:

25.2.2024.

Posjeta: 1.130 *