This presentation was made at CAASE18, The Conference on Advancing Analysis & Simulation in Engineering. CAASE18 brought together the leading visionaries, developers, and practitioners of CAE-related technologies in an open forum, to share experiences, discuss relevant trends, discover common themes, and explore future issues.

Resource Abstract

Additive manufacturing (AM) or 3D printing is maturing rapidly as a viable solution of make optimized parts for “real engineering” applications. What started as an amateurish persuasion has now evolved into a worldwide phenomenon that is touching industries from aerospace to industrial equipment to automotive to life science to energy & process, and others. The freedom of design that is achievable using AM process is un parallel in terms of reducing structural weight, reducing material cost, generating complex shapes and connections and introducing directional properties in a component. However, understanding of AM process and utilizing process parameters to optimize a design comes with many challenges. Currently, one of the emphasize is to use physics based realistic simulation to replicate the AM process numerically and relate process parameters to the concept of functional generative design that relates design with manufacturing process.

Current work, through a typical build example, discusses an integrated numerical solution on a digital platform that involves the following.

Generative Design involving topology optimization that creates parts in context of the manufacturing process and automatically generate variants of conceptual and detailed organic shapes that helps make informed business decisions based on physics-based analytic tools.

Process planning that defines and customizes manufacturing environment including nesting parts automatically on the build tray, designing and generating optimal support structures, and creating machine specific slicing and scan path which is ready for print.

Process simulation that automatically includes machine inputs for energy, material and supports into the simulation at layer, part and build levels for any additive manufacturing process and accurately predicts part distortions, residual stresses and as-built material behavior.

Finally, the platform involves post processing to perform shape optimization where simulation is used to guide support-structure strategy for enhanced build yield, compensate distortion effects without the need to redesign the product tooling, produce high-quality morphed surface geometry with unchanged topology, and perform final in-service performance validations of manufactured part.