| People | Locations | Statistics |
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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Revuelta, Alejandro
VTT Technical Research Centre of Finland
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (17/17 displayed)
- 2024Effects of surface finishes, heat treatments and printing orientations on stress corrosion cracking behavior of laser powder bed fusion 316L stainless steel in high-temperature watercitations
- 2024Process monitoring by deep neural networks in directed energy deposition : CNN-based detection, segmentation, and statistical analysis of melt poolscitations
- 2024Effect of laser focal point position on porosity and melt pool geometry in laser powder bed fusion additive manufacturingcitations
- 2024Process monitoring by deep neural networks in directed energy depositioncitations
- 2024Process monitoring by deep neural networks in directed energy deposition:CNN-based detection, segmentation, and statistical analysis of melt poolscitations
- 2023SCC behaviour of laser powder bed fused 316L stainless steel in high-temperature water at 288 °Ccitations
- 2022AM NPP - High temperature solution annealing of AM 316L
- 2021Additive manufacturing in nuclear power plants (AM-NPP)
- 2021Method for embedding components during additive manufacturing of metal parts
- 2020On the effect of shielding gas flow on porosity and melt pool geometry in laser powder bed fusion additive manufacturingcitations
- 2018Design and Verification of a Wireless Readout System for Integrated Motor Axle Condition Monitoringcitations
- 2017Soft magnetic alloys for selective laser melting
- 2017Feasibility of selective laser melting process in manufacturing of digital spare parts
- 2016Manufacturing of topology optimized soft magnetic core through 3D printing
- 2016Optimization and simulation of SLM process for high density H13 tool steel partscitations
- 2007High velocity forming of magnesium and titanium sheetscitations
- 2007Comparison of two commercial FE-codes for sheet metal forming
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document
Comparison of two commercial FE-codes for sheet metal forming
Abstract
There is urgent need to develop new advanced fast and cost-effectivemass-production methods for small sheet metal components. Traditionallyprogressive dies have been designed by using various CAD techniques. Recentresults in mass production of small sheet metal parts using progressive diesand a transfer press showed that the tool design time may be cut in up to ahalf by using 3D finite element simulation of forming. In numerical simulationof sheet metal forming better constitutive models are required to obtain moreaccurate results, reduce the time for tool design and cut the productioncosts further. Accurate models are needed to describe the initial yielding,subsequent work hardening and to predict the formability. In this work twocommercially available finite element simulation codes, PAM-STAMP and LS-DYNA,were compared in forming of small austenitic stainless steel sheet part forelectronic industry. Several constitutive models were used in both codes andthe results were compared. Comparisons were made between the same models ineach of the codes and also between different models in the same code. Materialmodels ranged from very simple to advanced ones, which took into accountanisotropy and both isotropic and kinematic hardening behavior. In order tomake a valid comparison we employed similar finite element meshes. The effectsof the material models parameters were studied and the results were comparedwith experiments. The effects of the computational time were also studied.