Study of the Stress Concentration Factor in PPS/C Type Composite Using FEA and Analytical Method

<div class="section abstract"><div class="htmlview paragraph">The use of parts with notches or some geometric discontinuity is common in the industrial field. In the aerospace industry, it is common to use components made of composite materials with holes for fixing components. Thus, understanding the behavior of these materials, especially when they present holes or geometries that act as stress concentrators, becomes crucial to assess the possible reduction in strength due to presence of these notches. This study aims to determine the stress concentration factor in circular-hole composite laminates made of PPS (Polyphenylene Sulfide) with 5 HS carbon fiber. For determining stress concentration factor, analytical methods using the point stress criterion, computational numerical simulation through FEA (finite element analysis), and experimental validation of proposed model were used. Mechanical tests of specimens with dimensions adapted from ASTM D3039 standard were performed, which were instrumented using strain gauges in the transverse and longitudinal positions near the hole. The acquisition of specific strains was carried out through the Quantum X-HBM, which served to validate the computational simulation model. The modeling and analysis of stresses and strains were done using Ansys Workbench software. The proposed model was made using the PLANE183 element, which supports multilayer materials. Paths were also created in the locations where the strain gauges were mounted to validate the strain values obtained in the tests and thus validate the model. After validation of reference model, with a ratio between hole diameter and width of specimen (D/W) equal to 0.4, models were made by varying the D/W ratio from 0.1 to 0.5. The values SCF obtained from the numerical simulations were close to those obtained analytically, with this difference being approximately 3%. The SCF decreases as the D/W ratio increases, varying from 4.6 to 3.3 using the data from simulations and from 4.6 to 3.4 by analytical method.</div></div>