journal article·2016-04

A hybrid method for detecting the onset of local necking by monitoring the bulge forming load

Bertrand Galpin, Vincent Grolleau, Arnaud Penin, Gérard Rio

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Abstract

Strain-based Forming Limit Diagrams (FLD), which are typically obtained under linear or quasi-linear loading conditions, describe the limiting strains in terms of the major and minor in-plane strains before the onset of necking or the final failure (FFD). These strains can be detected by analysing the strain field in the vicinity of necking or cracking defects. It has generally been agreed that the loading versus time signal is not suitable for detecting necking processes. A novel hybrid method of detecting the onset of necking based on the experimental and simulated bulging load is presented in this paper. This method consists mainly in comparing the experimental forming load, i.e., a load showing plastic instability, with the numerical predictions obtained by performing finite element simulation. The simulation of the bulging process does not include any damage or failure criteria. A homogeneous forming load can therefore be simulated without requiring any information about the localization. This method was applied to detecting the onset of local necking in circular and elliptic quasistatic bulge tests on sheet material, with a diameter of 200 mm. Two materials were tested, a 0.8 mm thick DP450 Dual Phase steel sheet and a 1 mm thick AA6016-T4 aluminium sheet. The onset of necking observed with our method was compared with the results obtained by performing Hogstrom’s analysis based on the measured strain field over time and similar necking strains were obtained. Beside, the Bressian Williams Hill (BWH) shear criterion was identified for each test from experimental results. A slight scattering of the shear stress values was observed.
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Research context

OpenAlex topics: Metal Forming Simulation Techniques · Metallurgy and Material Forming · Fatigue and fracture mechanics

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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