Posts Tagged "SIMULIA"

Additive manufacturing is not a new technology – it was introduced in the manufacturing industry in late 80s for very niche applications. Stereolithography, a variant of additive manufacturing, was introduced in 1986 for rapid prototyping applications; however, its true potential remained hidden for a long time. Additive manufacturing primarily refers to methods of creating a part or a tool using a layered approach. As a still-evolving technology, it now covers a family of processes such as material extrusion, material jetting, direct energy deposition, power bed fusion, and more.

Additive manufacturing expands design possibilities by eliminating many manufacturing constraints. Contrary to rapid prototyping and 3D printing, there has been a shift of focus to functional requirements in additive manufacturing; however, these functional requirements may deviate from what is expected due to many factors typical of an additive manufacturing process.

  • Change in material properties: Mechanical and thermal properties of a manufactured part differ from raw material properties. This happens due to material phase change which is typical to most additive manufacturing applications.
  • Cracking and failure: The process itself generates lots of heat that produces residual stresses due to thermal expansion. These stresses can cause cracks in material during manufacturing.
  • Distortion: Thermal stresses can lead to distortion that can make the part unusable.

The additive manufacturing process is not certifiable yet, which is a major barrier in widespread adoption of these processes commercially. The ASTM F42 committee is working on defining AM standards with respect to materials, machines, and process variables.

The role of Simulation in additive manufacturing

  • Functional design: The first objective is to generate a suitable design that meets functional requirements, then subsequently improve the design through optimization methodologies that work in parallel with simulation.
  • Generate a lattice structure: Many of the parts manufactured through AM have a lattice structure instead of a full continuum. One objective of simulation in AM is to generate a lattice structure and optimize it using sizing optimization.
  • Calibrate material: As mentioned before, the material properties of a final part can differ substantially from that of the raw material. The next objective is to capture the phase transformation process through multi-scale material modeling.
  • Optimize the AM process: Unwanted residual stresses and distortions can develop in the process. It is necessary to accurately capture these physical changes to minimize the gap between the as-designed and as-manufactured part specs.
  • In service performance: Evaluate how the manufactured part will perform under real life service loads with respect to stiffness, fatigue, etc.

 

Now let’s discuss each of these objectives in more detail, with respect to SIMULIA. […]

I mentioned the process automation concept of ISight in a previous simulation automation blog. ISight is an open source code simulation automation and parametric optimization tool to create workflows that automate the repetitive process of model update and job submission with certain objectives associated with it. The objective could be achievement of an optimal design through any of the available techniques in ISight: Design of experiments, optimization, Monte Carlo simulation or Six Sigma. In this blog post, I will be discussing various value added algorithms in DOE technique; I will discuss other techniques in future blogs.

Why design of experiments

Real life engineering models are associated with multiple design variables and with multiple responses. There are two ways to evaluate the effect of change in design variable on response: Vary one at a time (VOAT) approach or Design of experiments (DOE) approach. The VOAT approach is not viable because:

  • This approach ignores interactions among design variables, averaged and non-linear effects.
  • In models associated with large FE entities, each iteration is very expensive. VOAT does not offer the option of creating high fidelity models with a manageable number of iterations.

With the DOE approach, user can study the design space efficiently, can manage multi dimension design space and can select design points intelligently vs. manual guessing. The objective of any DOE technique is to generate an experimental matrix using formal proven methods. The matrix explores design space and each technique creates a design matrix differently. There are multiple techniques which will be discussed shortly and they are classified into two broad configurations:

  • Configuration 1: User defines the number of levels and their values for each design variable. The chosen technique and number of variables determines number of experiments.
  • Configuration 2: User defines the number of experiments and design variables range.

Box-Behnken Technique

This is a three level factorial design consisting of orthogonal blocks that excludes extreme points. Box-Behnken designs are typically used to estimate the coefficients of a second-degree polynomial. The designs either meet, or approximately meet, the criterion of rotatability. Since Box-Behnken designs do not include any extreme (corner) point, these designs are particularly useful in cases where the corner points are either numerically unstable or infeasible. Box-Behnken designs are available only for three to twenty-one factors.untitled

Central Composite Design Technique […]

Our SIMULIA user community has been using the conventional analysis and portfolio tokens for a while now. These tokens are primarily used to access the Abaqus CAE pre-processor, Abaqus solver, and the Abaqus viewer. The analysis configuration offers Abaqus solver licenses in the form of tokens, and Abaqus CAE as well as Abaqus viewer as interactive seats. The portfolio configuration offers all three components of Abaqus, i.e. the solver itself, Abaqus CAE as well as Abaqus viewer as tokens.

                                                                                                                                                      IS SIMULIA = only ABAQUS!

The new equation has been EXTENDED

                                                                                                                                   SIMULIA = ABAQUS + ISIGHT + TOSCA + FESAFE

The overall simulation offerings from Dassault Systèmes go way beyond Abaqus finite element simulations. The functionalities now include process automation, parametric optimizations, topology optimization, fatigue estimation, and many more. And starting from Abaqus release 6.13-2, all these additional capabilities are included in a single licensing scheme called extended tokens. Here is an overview of these additional SIMULIA products.extended-products

ISIGHT

ISight is an open desktop solution for creating flexible simulation process flows, consisting of a variety of applications, to automate the exploration of design alternatives, identify optimal performance parameters, and integrate added-value systems. The simulation process flows created from ISight can include multiple third party simulation components such as Ansys, LS-DYNA, Nastran, Mathcad as well as general purpose components such as Matlab, excel, calculator, and many more. It offers advanced parametric optimization, Design of experiments and Six Sigma techniques. Moreover, the vast amount of Simulation output data generated by such techniques can be managed effectively using the post processing runtime gateways of ISight. It’s rightly called a Simulation Robot.

ISight-image

 

TOSCA

Tosca is a general purpose optimization solution for designing high performance light weighted structures. As fuel economy continues to be the most important design factor in the transportation and aviation industries, designing lightweighted components and assemblies will remain a top priority, and Tosca can really help to achieve those objectives. […]

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