Siemens AM Network

When it comes to predicting the performance of additively manufactured parts, Siemens has you covered. We’ve been working on a new multiscale analysis tool that can help our customers predict the durability of a 3D printed part. While the simulation of homogeneous materials and some heterogeneous materials, like metals, is well established and robust, analysis of newer more advanced heterogeneous materials is lagging. In general, simulation using conventional, standard FEA techniques cannot simulate these materials without extensive up-front calibration. Therefore, usage of these materials results in increased time to market and increased costs. In this case, a local scale model of the microstructure is combined with a global scale model of the part being analyzed. These two analyses are performed simultaneously and the results from one model affect the behavior of the other. This method of solving each domain individually and then linking the results is orders of magnitude faster than solving for the entire model at the resolution necessary to capture the relevant details.

For many companies, design for AM extends no further than structural topology optimization—developing the lightest weight part within a design envelope to meet structural load requirements. The problem is, most parts must fulfill a host of engineering and business objectives, from fluid flow, thermal and vibration requirements to cost demands. It’s one thing to optimize a part for one objective, but how can you optimize for several objectives all at once—while meeting your deadline? HEEDS is a design space exploration solution that automates the process of finding the design concepts that best achieve a multiplicity of performance requirements. The software can run through hundreds of designs in a matter of minutes. And it helps users visualize design performance trade-offs between competing objectives and constraints, with various plots, tables, and graphs. With an extensive list of developed interfaces to commercial CAD, CAE and other tools, HEEDS quickly and easily integrates many technologies without the need for custom scripting.

Predicting the durability of a printed part is necessary for quality AM production. Siemens Digital Industries Software (DI SW) has developed a new approach to assess the fatigue performance of 3D printed components by leveraging the power of machine learning. The goal is to improve the understanding of the printed material structure as well as to predict and optimize the performance of the printed product.