2009
DOI: 10.1016/j.matdes.2009.03.037
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Optimisation of energy absorbing liner for equestrian helmets. Part I: Layered foam liner

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Cited by 78 publications
(39 citation statements)
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“…One homogeneous and three different multi-layered foam samples with a size of approximately 30 Â 30 Â 30 mm 3 were manufactured for this study and their characteristics are presented in the Table 2. The thickness of the three layers is equal to one third of the sample thickness.…”
Section: Experiments E Quasistatic Compressive Testsmentioning
confidence: 99%
See 1 more Smart Citation
“…One homogeneous and three different multi-layered foam samples with a size of approximately 30 Â 30 Â 30 mm 3 were manufactured for this study and their characteristics are presented in the Table 2. The thickness of the three layers is equal to one third of the sample thickness.…”
Section: Experiments E Quasistatic Compressive Testsmentioning
confidence: 99%
“…Following a first elastic stage, a great amount of energy of the impact can be dissipated by foams during the stress plateau [2]. Recently, energy absorption capacity of foams has been enhanced by using layered [3] or functionally graded [4e6] foams. Several studies have been reported on different kinds of expanded materials such as polyurethane [1], polystyrene [3e5] or syntactic epoxy [6].…”
Section: Introductionmentioning
confidence: 99%
“…Recently, in the studies by Forero Rueda et al (2009) and Cui et al (2009a), equestrian helmets have begun to be analysed with computational methods. The helmet performance was analysed not only by headform linear acceleration as in the current equestrian helmet standards (NSAI 1997(NSAI , 2005, but also in terms of the internal behaviour of the helmet liner material.…”
Section: Introductionmentioning
confidence: 99%
“…The laboratory tests were done on three impact locations on the helmet and the impact simulations were done to mimic these impact locations. The helmet model used is the same one as the one used in previous studied by the authors 17,19,20 .…”
Section: Helmet Model Validationmentioning
confidence: 99%