Investigation of the effect of process parameters in sheet hydroforming process

Modanloo, Vahid; Akhoundi, Behnam; Khatir, Farzad Ahmadi; Talebi-Ghadikolaee, Hossein; Mashayekhi, Ahmad

doi:10.1007/s12008-023-01373-x

Cited by 3 publications

(1 citation statement)

References 32 publications

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“…In the HDDRP experiments, a cylindrical cup (ϕ80 mm), formed from a 2.5 mm sheet made of two materials, pure copper and DIN 1.0338 steel, respectively, was considered. In addition, to further investigate the pressure medium influence on key process variables, namely final thickness, sheet thinning, and maximum punch force, FEM simulations have been implemented in ABAQUS/Explicit [ 31 ] and validated through experiments on both materials. Laboratory experiments were carried out considering both media and showed that the application of the SMG resulted in a higher accuracy between FEM and experimental results, especially in terms of thickness distribution at the radius closest to the blank holder.…”

Section: Introductionmentioning

confidence: 99%

On Improving Sheet Metal Formability in Hydrodynamic Deep Drawing Process through a Solid‐Oil‐Type Mineral Grease Pressure Medium

Modanloo,

Akhoundi,

Lee

et al. 2023

steel research int.

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In this research, the improvement in sheet metal formability obtained through the utilization of a mineral grease in solid oil type (SMG) pressure medium in the hydrodynamic deep drawing process assisted by radial pressure (HDDRP) was investigated by experiments and finite element method (FEM) simulations on flat‐bottom cups realized with pure copper and DIN 1.0338 steel. To determine the process parameters ranges, a FEM model was developed in ABAQUS/Explicit and validated against experimental results. Afterward, experiments were carried out using two pressure mediums with different viscosities, id est water, and SMG. Considering the results relevant for both sheet materials, the SMG medium allowed an average reduction of the sheet metal thinning of 15% and 10% on the corner edges while also granting a negligible thickness variation on the cup walls. On the other hand, the higher viscosity of the SMG, in comparison to the water medium, also resulted in an average 16% increase in the forming force when compared to the water pressure medium.This article is protected by copyright. All rights reserved.

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Section: Introductionmentioning

confidence: 99%

On Improving Sheet Metal Formability in Hydrodynamic Deep Drawing Process through a Solid‐Oil‐Type Mineral Grease Pressure Medium

Modanloo,

Akhoundi,

Lee

et al. 2023

steel research int.

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Design of a direct rapid tool for hydroforming of metal bellows using additively manufactured polymer die

Sathishkumar,

Arumaikkannu,

Hariharan

2024

Int J Interact Des Manuf

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Predictive modeling of spring-back in pre-punched sheet roll forming using machine learning

Zeinolabedin-Beygi,

Naeini,

Talebi-Ghadikolaee

et al. 2024

The Journal of Strain Analysis for Engineering Design

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This study outlines an experimental and computational endeavor aimed at developing a machine learning model to estimate spring-back values utilizing the decision tree methodology. A design of experiment approach was employed to collect a dataset, and based on the experimental results, a precise model was constructed to predict spring-back values. The model considered parameters such as thickness, diameter of circle hole, distance between the center hole and flange edge, and hole spacing. Various hyper parameters, including max depth and minimum samples for split, were explored, with configurations such as (30,5), (20,8), and (10,2) being evaluated to identify the optimal model for spring-back prediction. Analysis of the results demonstrated that the decision tree models accurately estimated spring-back values in cold roll forming of pre-punched sheets based on the input parameters. The coefficient of determination in the test section for decision tree models with parameters (30,5), (20,8), and (10,2) was found to be 0.90, 0.98, and 0.96, respectively. Additionally, the percentage of absolute error in the test section for the same decision tree models was calculated as 8.84%, 6.18%, and 7.6%, respectively.

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Investigation of the effect of process parameters in sheet hydroforming process

Cited by 3 publications

References 32 publications

On Improving Sheet Metal Formability in Hydrodynamic Deep Drawing Process through a Solid‐Oil‐Type Mineral Grease Pressure Medium

On Improving Sheet Metal Formability in Hydrodynamic Deep Drawing Process through a Solid‐Oil‐Type Mineral Grease Pressure Medium

Design of a direct rapid tool for hydroforming of metal bellows using additively manufactured polymer die

Predictive modeling of spring-back in pre-punched sheet roll forming using machine learning

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