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Residual Stress Analysis for Additive Manufactured Large Automobile Parts by Using Neutron and Simulation
Technical Paper
2020-01-1071
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Metal additive manufacturing has high potential to produce automobile parts, due to its shape flexibility and unique material properties. On the other hand, residual stress which is generated by rapid solidification causes deformation, cracks and failure under building process. To avoid these problems, understanding of internal residual stress distribution is necessary. However, from the view point of measureable area, conventional residual stress measurement methods such as strain gages and X-ray diffractometers, is limited to only the surface layer of the parts. Therefore, neutron which has a high penetration capability was chosen as a probe to measure internal residual stress in this research. By using time of flight neutron diffraction facility VULCAN at Oak Ridge National Laboratory, residual stress for mono-cylinder head, which were made of aluminum alloy, was measured non-distractively. From the result of precise measurement, interior stress distribution was visualized. According to the result, bottom area where was just above a base plate showed smaller stress gradient than top where was the farthest side from a base plate. Comparing actual stress and simulation results, building direction shows higher linearity than the others. This result shows the implication for improvement of simulation software.
Authors
- Tomohiro Ikeda - Honda R&D Co., Ltd.
- Satoshi Hirose - Honda R&D Co., Ltd.
- Hisao Uozumi - Honda R&D Co., Ltd.
- Ke An - Oak Ridge National Laboratory
- Yan Chen - Oak Ridge National Laboratory
- Alan Seid - Honda R & D Americas Inc
- Tatsuya Okayama - Honda R&D Co., Ltd.
- TAKASHI KATSURAI - Honda R&D Co., Ltd.
Topic
Citation
Ikeda, T., Hirose, S., Uozumi, H., An, K. et al., "Residual Stress Analysis for Additive Manufactured Large Automobile Parts by Using Neutron and Simulation," SAE Technical Paper 2020-01-1071, 2020, https://doi.org/10.4271/2020-01-1071.Data Sets - Support Documents
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References
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