[We] introduced the topic of injection molding process simulation and the influence of the manufacturing process on structural analysis. The strength and stiffness of a part can be inaccurately represented if the manufacturing process conditions are not properly considered. This results in a different calculation of system natural frequencies or improper estimation of the energy absorbing characteristics. We continue on this topic, extending the scope to advanced technologies available in the Altair Partner Alliance (APA) to help solve the problem of proper design validation with fiber reinforced plastics.
With the growing interest in additive manufacturing in the aerospace industry, there is a desire to accurately simulate the behavior of components made by this process. The layer by layer print process appears to create a morphology that is different from that from conventional manufacturing processes. This can have dramatic impact on the material properties, which in turn, can affect how the material is modeled in simulation. We tested an additively manufactured metal part for mechanical properties and validated the material model used in a linear static simulation.
Notwithstanding the increasing demand for polymeric materials in an
extraordinary variety of applications, the engineers have often only limited tools suitable for
the design of parts made of polymers, both in terms of mathematical models and reliable
material data, which together constitute the basis for a finite-elements based design.
Within this context, creep modelling constitutes a clear example of the needs for a more
refined approach. An accurate prediction of the creep behaviour of polymers would definitely
lead to a more refined design and thus to a better performance of the polymeric components.
However, a limited number of models is available within the f.e. codes, and when the model
complexity increases, it becomes sometimes difficult fitting the models parameters to the
experimental data.
In order to predict the polymer creep behaviour, this paper proposes a solution based on
artificial neural networks, where the experimental creep curves are used to determine the
parameters of a neural network which is then simply implemented in an Abaqus user
subroutine.
This allows to avoid the implementation of a complex material law and also the difficulties
related to match the experimental data to the model parameters, keeping easily into account
the dependence on stress and temperature.
After a discussion of the selection of the appropriate network and its parameters, an example
of the application of this approach to polyolefins in a simplified test case is presented.
Finite-element analysis and injection-molding simulation are two technologies that are seeing widespread use today in the design of plastic components. Limitations exist in our ability to mathematically describe the complexity of polymer behavior to these software packages. Material models commonly used in finite-element analysis were not designed for plastics, making it difficult to correctly describe non-linear behavior and plasticity of these complex materials. Time-based viscoelastic phenomena further complicate analysis. Dealing with fiber fillers brings yet another layer of complexity. It is vital to the plastics engineer to comprehend these gaps in order to make good design decisions. Approaches to understanding and dealing with these challenges, including practical strategies for everyday use, will be discussed.
Thermoplastic materials are one of the largest categories of materials to be injection molded. Simulation of the injection molding process requires sophisticated and exact material properties to be measured. This presentation will discuss the testing required to characterize a material for use in SIGMASOFT, as well as the significance of material model parameters. Differences in testing methodology for amorphous and semi-crystalline polymers will be covered, along with step-by-step implementation into the software to produce a successful injection molding simulation simulation.
It has long been desired to quantify the accuracy of simulation results. Through developments in digital image correlation (DIC) techniques, it is now possible to quantify the deviation between simulation and real life experimentation. In this paper, three-dimension DIC measurements of deformed parts are compared to deformed surfaces predicted in simulation. Using DIC, it is possible to import deformed surface elements from simulation and map the magnitude of deviation from the measurements of the actual deformed shape.
LS-DYNA software contains a wealth of material models that allow for the simulation of transient phenomena. The Matereality® CAE Modeler is a generalized pre-processor software used to convert material property data into material parameters for different material models used in CAE. In a continuation of previously presented work, we discuss the extension of the CAE Modeler software to commonly used material models beyond MAT_024. Software enhancements include advanced point picking to perform extrapolations beyond the tested data, as well as the ability to fine-tune the material models while scrutinizing the trends shown in the underlying raw data. Advanced modeling features include the ability to tune the rate dependency as well as the initial response. Additional material models that are quite complex and difficult to calibrate are supported, including those for hyperelastic and viscoelastic behavior. As before, the written material cards are directly readable into the LS-DYNA software, but now they can also be stored and catalogued in a material card library for later reuse.
Plastics appeared as design materials of choice about 30 years ago. They brought with them huge design challenges because their multi-variable, non-linear nature was not well understood by engineers trained to work in a linear elastic world. We outline a 20 year journey accompanying our customers in their efforts to understand and simulate these remarkable materials to produce the highly reliable plastic products of today. We discuss challenges related to processes such as injection molding vs. blow-molding; coping with filled plastics; the difficulties of modeling polymers for crash applications. We include our latest findings related to volumetric yield in polymers and its relationship to failure. We describe the material database technology that was created to store this kind of multi-variable data and the analytical tools created to help the CAE engineer understand and use plastics material data.
