Damage Model and Progressive Failure Analyses for Filament Wound Composite Laminates
Source abstract
Abstract
Recent improvements in manufacturing processes and materials properties associated with excellent mechanical characteristics and low weight have made composite materials very attractive for application on civil aircraft structures. However, even new designs are still very conservative, because the composite failure phenomenon is very complex. Several failure criteria and theories have been developed to describe the damage process and how it evolves, but the solution of the problem is still open. Moreover, modern filament winding techniques have been used to produce a wide variety of structural shapes not only cylindrical parts, but also “flat” laminates. Therefore, this work presents the development of a damage model and its application to simulate the progressive failure of flat composite laminates made using a filament winding process. The damage model was implemented as a UMAT (User Material Subroutine), in ABAQUSTM Finite Element (FE) framework. Progressive failure analyses were carried out using FE simulation in order to simulate the failure of flat filament wound composite structures under different loading conditions. In addition, experimental tests were performed in order to identify parameters related to the material model, as well as to evaluate both the potential and the limitations of the model. The difference between numerical and the average experimental results in a four point bending set-up is only 1.6 % at maximum load amplitude. Another important issue is that the model parameters are not so complicated to be identified. This characteristic makes this model very attractive to be applied in an industrial environment.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Mechanical Behavior of Composites · Composite Structure Analysis and Optimization · Structural Analysis of Composite Materials
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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