Topology optimization provides the best structural configuration of a design for an objective subject to one or more constraints. To generate structurally optimal designs for additive or advanced manufacturing, we are developing new multiphysics and multiobjective methodologies and leveraging existing strategies. 

To ensure a design is manufacturable, modeling the process is pertinent to first investigate the response of the structure during and after printing (deformation, residual stress). Beyond this, the process responses can be captured within the structural design methodology to mitigate severe manufacturing defects during and after production.  

With the advances in additive manufacturing technologies to produce structurally complex yet functional features, lattice/porous/infill structures have become a viable means for design applications that cover lightweight, composites/meta-materials, bone scaffolds, implants, impact absorbers, etc. 

A key objective in the multifunctional structural design and additive manufacturing lab is the development of software tools (mainly open source) that can aid teaching and research. Our goal is to make several nascent design techniques available to researchers, teachers, engineers, and designers to ensure the diffusion of knowledge and obtain feedback for technology enhancement. 

Convolutional Neural and Generative Adversarial Networks (CNN and GANs) have been investigated to upscale the use of topology optimization. While there is room for improvement in that area, we aim to develop process data-driven surrogate AI models in topology optimization.