Design, Finite Element Analysis and Optimization of Helical Angular Pressing (HAP) Method as a Novel SPD Technique

Year 2023, , 959 - 968, 28.12.2023

Ömer Karabey

https://doi.org/10.17798/bitlisfen.1295905

Abstract

Severe Plastic Deformation (SPD) processes improve the mechanical properties of materials by obtaining Ultra Fine Grained (UFG) materials, orienting the grains and reforming the grains. Helical Angular Pressing (HAP) is a newly proposed Severe Plastic Deformation (SPD) method. In order to improve the efficiency of the HAP method, its die geometry should be optimized first. In this context, four parameters (helical diameter, helical pitch, helical height and channel radius) were determined for the die channel geometry, each with four levels according to the literature. Then, thanks to Taguchi L16 combinations, 16 Finite Element Analyses (FEA) were carried out using Deform 3D software instead of 256 simulations, and effective strain values and maximum pressing load values were obtained. Later on, using the SPSS 16 software, Taguchi optimization was carried out to obtain the optimum HAP die channel geometries by minimizing the press load and maximizing the effective strain values. Next, the Finite Element Analysis (FEA) was repeated with these determined optimum die channel parameters. Finally, the efficiency of this novel HAP method was compared with conventional Equal Channel Angular Pressing (ECAP) and Twist Extrusion (TE) methods. As a result, HAP method provides effective strain values equivalent to 10 number of passes after processing with ECAP. And it is approximately 4 times higher than that achieved by TE processing. As a result of the Taguchi optimization, it is concluded that the values in the combination of diameter (d)=60 mm, height (h)=50 mm, radius (r)=4 and pitch (p)=1.25 are the optimum die geometry. In conclusion, these results indicate that the proposed novel HAP method is an efficient and applicable SPD technique.

Keywords

ECAP, SPD, Helical Angular Pressing (HAP), Finite Element Analysis (FEA)

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