Publications

To describe a new method for the automatic generation of process parameters for fused filament fabrication (FFF) across varying machines and materials. We use an instrumented extruder to fit a function that maps nozzle pressures across varying flow rates and temperatures for a given machine and material configuration. We then develop a method to extract real parameters for flow rate and temperature using relative pressures and temperature offsets. Our method allows us to successfully find process parameters, using one set of input parameters, across all of the machine and material configurations that we tested, even in materials that we had never printed before. Rather than using direct parameters in FFF printing, which is time-consuming to tune and modify, it is possible to deploy machine-generated data that captures the fundamental phenomenology of FFF to automatically select parameters.

There has been a proliferation of particle systems developed to model complex systems. These are attractive because they are mesh-free, avoiding issues associated with solver remeshing and convergence. They have however fragmented into niches, using increasingly complex particles that introduce internal degrees of freedom and external solver coupling. We show that, contrary to prior assumptions in the literature, memoryless isotropic point particles can model material properties including anisotropy, hysteresis, and failure solely through the statistics of their distributions. The resulting models offer compact code that is straightforward to accelerate and port, can span between micro- and macro-structure, require few parameters to set up a simulation, and unlike high-dimensional machine learning models they use a low-dimensional representation that can be efficiently learned. Rather than deriving them as approximations to either molecular dynamics or partial differential equations we investigate how these models can be found directly, and illustrate this with both qualitative comparisons of phenomenology and quantitative comparisons of predictions.

Calibration of highly dynamic multi-physics manufacturing processes such as electrohydrodynamics-based additive manufacturing (AM) technologies (E-jet printing) is still performed by labor-intensive trial-and-error practices. Such practices have hindered the broad adoption of these technologies, demanding a new paradigm of self-calibrating E-jet printing machines. Here we develop an end-to-end physics-informed Bayesian learning framework (GPJet) which can learn the jet process dynamics with minimum experimental cost. GPJet consists of three modules: the machine vision module, the physics-based modeling module, and the machine learning (ML) module. GPJet was tested on a virtual E-jet printing machine with in-process jet monitoring capabilities. Our results show that the Machine Vision module can extract high-fidelity jet features in real-time from video data using an automated parallelized computer vision workflow. The Machine Vision module, combined with the Physics-based modeling module, can also act as closed-loop sensory feedback to the Machine Learning module of high- and low-fidelity data. This work extends the application of intelligent AM machines to more complex working conditions while reducing cost and increasing computational efficiency.

This chapter presents how 3D printing biopolymers can enable a circular economy. We focus on 3D printing as a technology for distributed production, how the use of biopolymers from second and third-generation biomass such as food waste and algae can ensure that sourcing is regenerative and non-extractive, and the need for comprehensive life-cycle assessment. The technical means by which 3D printing can be adapted to and can add functionality to biopolymers is discussed. A case study on extracting chitin-cellulose biopolymer formulations from local biomass is provided, offering a view toward regenerative production for a circular economy. The future of 3D printing biopolymers for the circular economy is discussed, with an emphasis on improving material performance and developing scalable regenerative supply chains.

In this study, we present a method to construct meter-scale deformable structures for underwater robotic applications by discretely assembling mechanical metamaterials. We address the challenge of scaling up nature-like deformable structures while remaining structurally efficient by combining rigid and compliant facets to form custom unit cells that assemble into lattices. The unit cells generate controlled local anisotropies that architect the global deformation of the robotic structure. The resulting flexibility allows better unsteady flow control that enables highly efficient propulsion and optimized force profile manipulations. We demonstrate the utility of this approach in two models. The first is a morphing beam snake-like robot that can generate thrust at specific anguilliform swimming parameters. The second is a morphing surface hydrofoil that, when compared with a rigid wing at the same angles of attack (AoAs), can increase the lift coefficient up to 0.6. In addition, in lower AoAs, the  ratio improves by 5 times, whereas in higher angles it improves by 1.25 times. The resulting hydrodynamic performance demonstrates the potential to achieve accessible, scalable, and simple to design and assemble morphing structures for more efficient and effective future ocean exploration and exploitation.