A5081062188- Manufacturing Process and Optimization
- Additive Manufacturing and 3D Printing Technologies
- Topology Optimization in Engineering
- Cellular and Composite Structures
- Model Reduction and Neural Networks
- Industrial Vision Systems and Defect Detection
- Additive Manufacturing Materials and Processes
- Modular Robots and Swarm Intelligence
- Advanced Materials and Mechanics
- Structural Health Monitoring Techniques
- Probabilistic and Robust Engineering Design
- Anomaly Detection Techniques and Applications
- Hydraulic and Pneumatic Systems
- Dynamics and Control of Mechanical Systems
- Vibration Control and Rheological Fluids
- Welding Techniques and Residual Stresses
- Design Education and Practice
- Radiative Heat Transfer Studies
- Structural Analysis and Optimization
- Fault Detection and Control Systems
- Modeling and Simulation Systems
- Neuroscience and Neural Engineering
- Bladed Disk Vibration Dynamics
- Transportation Safety and Impact Analysis
- Machine Learning in Materials Science
Sandia National Laboratories
2020-2024
Center for Integrated Nanotechnologies
2021-2023
Sandia National Laboratories California
2020-2022
Clemson University
2014-2020
Metamaterials, otherwise known as architected or programmable materials, enable designers to tailor mesoscale topology and shape achieve unique material properties that are not present in nature. Additionally, with the recent proliferation of additive manufacturing tools across industrial sectors, ability readily fabricate geometrically complex metamaterials is now possible. However, many high-performance applications involving multi-physics interactions, design novel lattice still...
Metamaterials derive their unusual properties from architected structure, which generally consists of a repeating unit cell designed to perform particular function. However, existing metamaterials are, with few exceptions, physically continuous throughout volume, and thus cannot take advantage multi-body behavior or contact interactions. Here we introduce the concept interpenetrating lattices, where two more lattices interlace through same volume without any direct connection each other....
Mechanical metamaterials are artificial materials with unique global properties due to the structural geometry and material composition of their unit cell. Typically, mechanical metamaterial cells designed such that, when tessellated, they exhibit as zero or negative Poisson's ratio stiffness. Beyond these applications, can be used achieve tailorable nonlinear deformation responses. Computational methods gradient-based topology optimization (TO) size/shape (SSO) implemented design...
The nonlinearities present in structural systems are often found isolated regions within the structure, such as those containing joints or interfaces. However, despite localized nature of these their presence serves to couple together modes underlying linear system and significantly complicate development appropriate reduced-order models; have a global effect on dynamics system. Further, evolving health can arise from accumulating damage, with distinct observed healthy state. work develops...
Abstract This paper presents the challenges and solutions encountered while designing then printing functionally gradient material (FGM) objects using an off shelf fused deposition modeling (FDM) 3D printer. The printer, Big Builder Dual-Feed Extruder from 3dprinter4u, Noordwijkerhout, Netherlands, has unique design of extruding two different filaments out one nozzle. By controlling rate at which are pulled into melt chamber, FGM can be printed. Software associated with process planning...
Advances in machine learning algorithms and increased computational efficiencies give engineers new capabilities tools to apply engineering design. Machine models can approximate complex functions and, therefore, be useful for various tasks the design workflow. This paper investigates using reinforcement (RL), a subset of that teaches an agent complete task through accumulating experiences interactive environment, automate designing 2D discretized topologies. RL agents use past learn...
Abstract We propose coupling a physics-based reduction framework with suited response decomposition technique to derive component-oriented (COR) approach, which is suitable for assembly systems featuring localized nonlinearities. Dependencies on influencing parameters are injected into the reduced-order model (ROM), thus ensuring robustness and validity over domain of parametric inputs, while capturing nonlinear effects. The implemented approach employs individual component modes capture...
Abstract Reduced Order Models (ROMs) are of considerable importance in many areas engineering which computational time presents difficulties. Established approaches employ projection-based reduction, such as Proper Orthogonal Decomposition. The limitation the linear nature operators is typically tackled via a library local reduction subspaces, requires assembly numerous ROMs to address parametric dependencies. Our work attempts define more generalisable mapping between inputs and reduced...
Mechanical metamaterials are regularly implemented in engineering applications due to their unique properties derived from structural geometry and material composition. This study incorporates deep reinforcement learning, a subset of machine learning that teaches an agent complete task through interactive experiences, into mechanical metamaterial design. The approach creates design environment for the iteratively construct with tailorable deformation hysteretic characteristics. Validation...
With the proliferation of additive manufacturing and 3D printing technologies, a broader palette material properties can be elicited from cellular solids, also known as metamaterials, architected foams, programmable materials, or lattice structures. Metamaterials are designed optimized under assumption perfect geometry homogeneous underlying base material. Yet in practice real lattices contain thousands even millions complex features, each with imperfections shape constituency. While role...
Abstract Future machine learning strategies for materials process optimization will likely replace human capital-intensive artisan research with autonomous and/or accelerated approaches. Such automation enables multimodal characterization that simultaneously minimizes errors, lowers costs, enhances statistical sampling, and allows scientists to allocate their time critical thinking instead of repetitive manual tasks. Previous acceleration efforts synthesize evaluate have often employed...
Abstract In this work, a novel approach is introduced for accelerating the solution of structural dynamics problems in presence localised phenomena, such as cracks. For category problems, conventional projection‐based Model Order Reduction (MOR) methods are either limited with respect to range system configurations that can be represented or require frequent solutions Full (FOM) update low‐dimensional spaces, which represented. proposed approach, constructed healthy structure, enriched...
Abstract Structural nonlinearities are often spatially localized, such joints and interfaces, localized damage, or isolated connections, in an otherwise linearly behaving system. Quinn Brink (2021, “Global System Reduction Order Modeling for Localized Feature Inclusion,” ASME J. Vib. Acoust., 143(4), p. 041006.) modeled this nonlinearity as a deviatoric force component. In other previous work (Najera-Flores, D. A., Quinn, D., Garland, Vlachas, K., Chatzi, E., Todd, M. 2023, “A...
We present a deep learning approach for quantifying and localizing ex-situ porosity within Laser Powder Bed Fusion fabricated samples utilizing in-situ thermal image monitoring data. Our goal is to build the real time map of parts based on images acquired during build. The quantification task builds upon established Convolutional Neural Network model architecture predict pore count localization leverages spatial temporal attention mechanisms novel Video Vision Transformer indicate areas...
Designers can involve users in the design process. The challenge lies reaching multiple and finding best way to use their input Affordance based (ABD) is a method that focuses part on perceived or existing interactions between user artifact. shape physical characteristics of product enable perceive some its affordances. goal this research ABD, along with an optimization tool, evolve products toward better solutions using from users. A web application has been developed evolves concepts...