Improving the mechanical and physical properties of bio-composite materials involves the incorporation of plant fibers such as Jute, Hemp, Kenaf, Ramie, Sisal, Linen, etc. The existence of Diss grass (Ampelodesmos mauritanicus) in abundance in the east of Algeria especially in Khenchela region and taking into account their mechanical resistance and their low density, which justifies their choice of use in composite materials. Tensile and hardness tests for different volume fractions (from 05% to 20%) of short fibers of Diss are performed. The increase in fiber content and their treatment improves the mechanical characteristics of the composite materials. These concentration levels are added to a Polyester resin matrix. Our work relates to the study of a composite material reinforced by a vegetable fiber of which different volume ratio of short Diss fiber are considered. The results collected are purely experimental.

The behavior of thermoplastics depends on several factors, mainly time and temperature. The present work is the a study of the dependence of these materials on time. The material considered in this study is a polyamide 6. The applied behavior model is represented by the Kelvin-Voigt viscoelastic mechanism. the mathematical formulation of the equations of the model is put in place and tensile tests at different strain rates are conducted. The parameters model are then identified. These being sensitive to the strain-rate, the relation which links them with it is established by means of an exponential regression. The results test-model are presented and dicussed.

The behavior of thermoplastics depends on several factors, mainly time and temperature. The present work is focused on an analysis of the time sensitivity of the viscoelastic and viscoplastic parameters of a rheological model. The material considered in this study is a polyamide 6. The analogical model is represented by the Kelvin-Voigt viscoelastic mechanism mounted in series with a viscoplastic branch of Bingham. After a mathematical formulation of the equations governing the model, tensile tests at different strain rates are conducted. The model parameters are then identified by inverse analysis. The technique of genetic algorithms has been favored. A nonlinear dependence of these parameters on the rate of strain has been observed. The dependence function has been established by a nonlinear regression technique. The comparison of the experimental results with those obtained by the model reveals a satisfactory agreement, hence the validation of the approach adopted.