1. Stochastic prediction of apparent compressive stiffness of selective laser sintered lattice structure with geometrical imperfection and uncertainty in material property
- Author
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Hideo Takizawa, Pin Wen, Satoru Matsunaga, Shinichi Abe, Kendo Odaka, and Naoki Takano
- Subjects
Materials science ,Perturbation (astronomy) ,Parameterized complexity ,02 engineering and technology ,Crystal structure ,engineering.material ,Homogenization (chemistry) ,law.invention ,0203 mechanical engineering ,law ,medicine ,General Materials Science ,Maraging steel ,Civil and Structural Engineering ,business.industry ,Mechanical Engineering ,Stiffness ,Structural engineering ,021001 nanoscience & nanotechnology ,Condensed Matter Physics ,Laser ,Selective laser sintering ,020303 mechanical engineering & transports ,Mechanics of Materials ,engineering ,medicine.symptom ,0210 nano-technology ,business - Abstract
Aiming at the future applications of a lattice structure manufactured by selective laser sintering for bone scaffold in the biomedical field, by putting highlight on the possible imperfections influenced by different manufacturing strategies, a numerical prediction method was studied and compared with compressive test results. To consider the geometrical imperfections as well as the uncertainty in sintered material property, the first-order perturbation based stochastic homogenization (FPSH) method was employed. To be able to compare products made by three different printing service companies, maraging steel was used in this study, which was commonly available. Several geometrical imperfections were categorized into kink, notch and hole, and representative dimensions were parameterized and measured statistically using micro-CT images. They were also correlated to the building direction and support types. The numerical prediction revealed the reduction of stiffness in a stochastic way qualitatively. The constructed database of the geometrical imperfections and the FPSH method will be useful in the next occasion of manufacturing unexperienced product.
- Published
- 2017
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