Versatile Additive Manufacturing at Oregon State

Welcome To

Versatile Advanced Manufacturing Lab at Oregon State

Our Lab’s Goal is to

Create functional devices using intelligent additive manufacturing so that we can tackle big-picture, real-world engineering problems like climate change, infrastructure, and biomedicine.

Additive Manufacturing

AI-driven 3D printing
“Digital Twin” development using Machine Vision

Smart/Active Materials

Liquid Crystal Elastomers (LCE)
Data-driven predictive modelling of shape transformation

Multi-Material Design

Multi-material composites
AI-driven computational mechanics

AI-Assisted Additive Manufacturing

In this work, we demonstrate the use of an ML-driven approach for real-time material extrusion print-parameter optimization through in-situ monitoring of printed line geometry. To do this, we use deep invertible neural networks (INNs) which can solve both forward and inverse, or optimization, problems using a single network. By combining in-situ computer vision and deep INNs, the printing parameters can be autonomously optimized to print a target line width in 1.2 s. Furthermore, defects that occur during printing can be rapidly identified and corrected autonomously. The methods developed and presented in this work eliminate user-intensive, time-consuming, and iterative parameter discovery approaches that currently limit accelerated implementation of extrusion-based AM processes.

Smart Material Design

In this work, 4D printing is leveraged to create an LCE-SMP composite that can achieve not only rapid and reversible shape changes, but also cooling-rate regulated tunable shape morphing. The latter is achieved by harnessing the distinct time-dependent thermomechanical properties of LCEs and SMPs. Furthermore, the composite has a high stiffness at low temperature to support heavy loads. The LCE-SMP composite hence offers a novel approach to achieve tunable shape morphing for future engineering applications.

AI-driven Mechanical Modelling & Design

AM provides access to a wide range of printable materials, where precise spatial control over structured porosity can be modulated during the fabrication process enabling the production of foam replacement structures (FRS). Current approaches for designing FRS are based on intuitive understanding of their properties or an extensive number of finite element method (FEM) simulations. These approaches, however, are computationally expensive and time consuming. Therefore, in this work, we present a novel methodology for determining the mechanical compression response of direct ink write (DIW) 3D printed FRS using a simple cross-sectional image. By obtaining measurement data for a relatively small number of samples, an artificial neural network (ANN) was trained, and a computer vision algorithm was used to make inferences about foam compression characteristics from a single cross-sectional image. Finally, a genetic algorithm (GA) was used to solve the inverse design problem, generating the AM printing parameters that an engineer should use to achieve a desired compression response from a DIW printed FRS. The methods developed herein present an avenue for entirely autonomous design and analysis of additively manufactured structures using artificial intelligence.