Energy Absorption of Metal-FRP Hybrid Square Tubes

Lower-cost manufacturing methods have increased the anticipation for economical mass production of vehicles manufactured from composite materials. One of the potential applications of composite materials in vehicles is in energy-absorbing components such as hollow shells and struts (these components may be in the form of circular cylindrical shells, square and rectangular tubes, conical shells, and frusta). However, constructions which result in brittle fracture of the composite tubes in the form of circumferential or longitudinal corner crack propagation may lead to unstable collapse failure mode and concomitant very low energy absorption. As a result, metal-composite hollow tubes have been developed that combine the benefits of stable ductile collapse of the metal (which can absorb crushing energy in a controlled manner) and the high strength-to-weight ratio of the composites. The relative and absolute thicknesses of metal or FRP section has a substantial effect on energy absorption of the hybrid tubes. In particular, likelihood of delamination occurrence raises with increase in FRP thickness. This can reduce the energy absorption capability of the metal-FRP hybrid tubes. Additionally, adding a very thick FRP section may result in a global buckling failure mode (rather than local folding). Until now, there are no studies specifically addressing the effect of FRP thickness on energy absorption of hybrid tubes. In this study, the effects of fiber orientation and FRP thickness (the number of layers) on the energy absorption of S2-glass/epoxy-304 stainless steel square tubes were experimentally investigated. In addition, a new geometrical trigger was demonstrated which has positive effects on the collapse modes, delamination in the FRP, and the crush load efficiency of the hybrid tube. To complete this study, a new methodology including the combination of experimental results, numerical modeling, and a multi-objective optimization process was introduced to obtain the best combination of design variables for hybrid metal-composite tubes for crashworthiness applications. The experimental results for the S2 glass/epoxy-304 stainless steel square tubes with different configurations tested under quasi-static compression loading were used to validate numerical models implemented in LS-DYNA software. The models were able to capture progressive failure mechanisms of the hybrid tubes. In addition, the effects of the design variables on the energy absorption and failure modes of the hybrid tubes were explained. Subsequently, the results from the numerical models were used to obtain optimum crashworthiness functions. The load efficiency factor (the ratio of mean crushing load to maximum load) and ratio between the difference of mean crushing load of hybrid and metal tube and thickness of the FRP section were introduced as objective functions. To connect the variables and the functions, back-propagation artificial neural networks (ANN) were used. The Non-dominated Sorting Genetic Algorithm–II (NSGAII) was applied to the constructed ANNs to obtain optimal results. The results were presented in the form of Pareto frontiers to help designers choose optimized configurations based on their manufacturing limitations. Such restrictions may include, but are not limited to, cost (related to the number of layers), laminate architecture (fiber orientation and stacking sequence) which can be constrained by the manufacturing techniques (i.e. filament winding) and thickness (as an example of physical constraints).

Name thesis:degree_name

Ph. D.

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Engineering Mechanics

Department dc:contributor.department

Engineering Science and Mechanics

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2017

Author dc:creator

• Kalhor, Roozbeh

Chair dc:contributor.committeechair

• Case, Scott W.

Committee members dc:contributor.committeemember

• Al-Haik, Marwan
• Ross, Shane D.
• Seidel, Gary D.

• Energy absorption
• Axial crushing
• Metal-composite square hybrid tubes
• Neural networks
• Multi-objective optimization

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Dc Identifier Other

vt_gsexam:9497

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/74960