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Shape Prediction for Laser Peen Forming of Fiber Metal Laminates by Experimentally Determined Eigenstrain
- Source :
- Journal of Manufacturing Science and Engineering. 139
- Publication Year :
- 2016
- Publisher :
- ASME International, 2016.
-
Abstract
- Laser peen forming (LPF) is a promising method to fabricate fiber metal laminates (FMLs) with its design flexibility to produce complex shapes. Eigenstrain-based modeling is a helpful method to predict deformation after LPF, while determining eigenstrain is very difficult because of its complex constituents and high-dynamic loading of process. An effective experiment-based method is proposed in this work to obtain eigenstrain induced by LPF in metal layers of FMLs. An analytical beam model is developed to relate the deflection profile generated by specific scanning strategy to equivalent bending moment. Based on the determined bending moment from the measured deflection profiles, the generated eigenstrain can be inversely calculated by the proposed beam model describing the relationship between the eigenstrain and the bending moment. Chemical etching to remove sheets layer by layer is used to obtain the relaxed deflection profile to calculate the eigenstrain in each metal layer. Furthermore, an approximate model of plate is established to predict deformation after LPF based on determined eigenstrain. The results show that the predictive deformed shape agrees very well with both experiments and finite model prediction.
- Subjects :
- Materials science
business.industry
Mechanical Engineering
02 engineering and technology
Eigenstrain
Structural engineering
Deformation (meteorology)
021001 nanoscience & nanotechnology
Laser
Isotropic etching
Industrial and Manufacturing Engineering
Computer Science Applications
law.invention
Metal
020303 mechanical engineering & transports
0203 mechanical engineering
Control and Systems Engineering
law
Deflection (engineering)
visual_art
visual_art.visual_art_medium
Fiber
Composite material
0210 nano-technology
business
Subjects
Details
- ISSN :
- 15288935 and 10871357
- Volume :
- 139
- Database :
- OpenAIRE
- Journal :
- Journal of Manufacturing Science and Engineering
- Accession number :
- edsair.doi...........0a0623e5c22c6982c071d721ded7edf9
- Full Text :
- https://doi.org/10.1115/1.4034891