5 research outputs found
Accelerated Discovery of 3D Printing Materials Using Data-Driven Multi-Objective Optimization
Additive manufacturing has become one of the forefront technologies in
fabrication, enabling new products impossible to manufacture before. Although
many materials exist for additive manufacturing, they typically suffer from
performance trade-offs preventing them from replacing traditional manufacturing
techniques. Current materials are designed with inefficient human-driven
intuition-based methods, leaving them short of optimal solutions. We propose a
machine learning approach to accelerate the discovery of additive manufacturing
materials with optimal trade-offs in mechanical performance. A multi-objective
optimization algorithm automatically guides the experimental design by
proposing how to mix primary formulations to create better-performing
materials. The algorithm is coupled with a semi-autonomous fabrication platform
to significantly reduce the number of performed experiments and overall time to
solution. Without any prior knowledge of the primary formulations, the proposed
methodology autonomously uncovers twelve optimal composite formulations and
enlarges the discovered performance space 288 times after only 30 experimental
iterations. This methodology could easily be generalized to other material
formulation problems and enable completely automated discovery of a wide
variety of material designs
Polygrammar: Grammar for Digital Polymer Representation and Generation
Polymers are widely studied materials with diverse properties and applications determined by molecular structures. It is essential to represent these structures clearly and explore the full space of achievable chemical designs. However, existing approaches cannot offer comprehensive design models for polymers because of their inherent scale and structural complexity. Here, a parametric, context-sensitive grammar designed specifically for polymers (PolyGrammar) is proposed. Using the symbolic hypergraph representation and 14 simple production rules, PolyGrammar can represent and generate all valid polyurethane structures. An algorithm is presented to translate any polyurethane structure from the popular Simplified Molecular-Input Line-entry System (SMILES) string format into the PolyGrammar representation. The representative power of PolyGrammar is tested by translating a dataset of over 600 polyurethane samples collected from the literature. Furthermore, it is shown that PolyGrammar can be easily extended to other copolymers and homopolymers. By offering a complete, explicit representation scheme and an explainable generative model with validity guarantees, PolyGrammar takes an essential step toward a more comprehensive and practical system for polymer discovery and exploration. As the first bridge between formal languages and chemistry, PolyGrammar also serves as a critical blueprint to inform the design of similar grammars for other chemistries, including organic and inorganic molecules
Closed-Loop Control of Direct Ink Writing via Reinforcement Learning
Enabling additive manufacturing to employ a wide range of novel, functional
materials can be a major boost to this technology. However, making such
materials printable requires painstaking trial-and-error by an expert operator,
as they typically tend to exhibit peculiar rheological or hysteresis
properties. Even in the case of successfully finding the process parameters,
there is no guarantee of print-to-print consistency due to material differences
between batches. These challenges make closed-loop feedback an attractive
option where the process parameters are adjusted on-the-fly. There are several
challenges for designing an efficient controller: the deposition parameters are
complex and highly coupled, artifacts occur after long time horizons,
simulating the deposition is computationally costly, and learning on hardware
is intractable. In this work, we demonstrate the feasibility of learning a
closed-loop control policy for additive manufacturing using reinforcement
learning. We show that approximate, but efficient, numerical simulation is
sufficient as long as it allows learning the behavioral patterns of deposition
that translate to real-world experiences. In combination with reinforcement
learning, our model can be used to discover control policies that outperform
baseline controllers. Furthermore, the recovered policies have a minimal
sim-to-real gap. We showcase this by applying our control policy in-vivo on a
single-layer, direct ink writing printer
Accelerated discovery of 3D printing materials using data-driven multiobjective optimization
Machine learning can aid the discovery of useful 3D printing material formulations.</jats:p
Highly Nonlinear Chalcogenide Glass Waveguides for All-optical Signal Processing
I describe the development of highly nonlinear chalcogenide glass waveguides for photonics and their application as nonlinear optical devices for high speed processing and monitoring of telecommunications signals.Accepted versio