The fatigue characteristics of glass fiber reinforced plastic (GFRP) composites were studied under repeated loads using the finite element method (FEM). To realize the material characteristics of GFRP composites, Digimat, a mean-field homogenization tool, was employed. Additionally, the micro-structures and material models of GFRP composites were defined with it to predict the fatigue behavior of composites more realistically. Specifically, the fatigue characteristics of polybutylene terephthalate with short fiber fractions of 30wt% were investigated with respect to fiber orientation, stress ratio, and thickness. The injection analysis was conducted using Moldflow software to obtain the information on fiber orientations. It was mapped over FEM concerned with fatigue specimens. LS-DYNA, a typical finite element commercial software, was used in the coupled analysis of Digimat to calculate the stress amplitude of composites. FEMFAT software consisting of various numerical material models was used to predict the fatigue life. The results of coupled analysis of linear and nonlinear material models of Digimat were analyzed to identify the fatigue characteristics of GFRP composites using FEMFAT. Neuber’s rule was applied to the linear material model to analyze the fatigue behavior in LCF regimen. Additionally, to evaluate the morphological and mechanical structure of GFRP composites, the coupled and fatigue analysis were conducted in terms of thickness.

Whole body fatigue detection is an important phenomenon and the factors contributing to whole body fatigue can be controlled if a mathematical model is available for its assessment. This research study aims at developing a model that categorizes whole body exertion into fatigued and non-fatigued states based on physiological and perceived variables. For this purpose, logistic regression was used to categorize the fatigued and non-fatigued subject as dichotomous variable. Normalized mean power frequency of eight muscles from 25 subjects was taken as physiological variable along with the heart rate while Borg scale ratings were taken as perceived variables. The logit function was used to develop the logistic regression model. The coefficients of all the variables were found and significance level was checked. The detection accuracy of the model for fatigued and non-fatigues subjects was 83% and 95% respectively. It was observed that the mean power frequency of anterior deltoid and the Borg scale ratings of upper and lower extremities were significant in predicting the whole body fatigued when evaluated dichotomously (p < 0.05). The findings can help in better understanding of the importance of combined physiological and perceived exertion in designing the rest breaks for workers involved in squat lifting tasks in industrial as well as health sectors.