Volumes | International Design Engineering Technical Conferences and Computers and Information in Engineering Conference | American Society of Mechanical Engineers Digital Collection

Topics: Topology optimization , Design , Manufacturing , Optimization , Additive manufacturing , Artificial neural networks , Compliance , Innovation , Materials properties , Stiffness

Novel AI or ML Frameworks for Design or Systems Science

Automated Functional Basis Modeling Using Multi-Label Machine Learning on 3D Models

Zachariah A. Connor, Shital Sable, Matthew I. Campbell, Robert B. Stone

IDETC-CIE 2024; V03AT03A011https://doi.org/10.1115/DETC2024-138095

Abstract

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Topics: Machine learning , Modeling , Three-dimensional models , Algorithms , Trains , Computer-aided design , Convolutional neural networks , Engineering systems and industry applications , Engineers , Generators

Towards Domain-Adaptive, Resolution-Free 3D Topology Optimization With Neural Implicit Fields

Amin Heyrani Nobari, Lyle Regenwetter, Faez Ahmed

IDETC-CIE 2024; V03AT03A012https://doi.org/10.1115/DETC2024-142135

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Topics: Resolution (Optics) , Topology optimization , Topology , Shapes , Engineering design , Generative artificial intelligence , Graphics processing units , Innovation , Optimization , Trains

Exploring CausalWorld: Enhancing Robotic Manipulation via Knowledge Transfer and Curriculum Learning

Xinrui Wang, Yan Jin

IDETC-CIE 2024; V03AT03A013https://doi.org/10.1115/DETC2024-143674

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Topics: Knowledge transfer , Reinforcement learning , Robotics

Towards Physically Talented Aerial Robots With Intelligent Swarm Behavior Thereof: an Efficient Co-Design Approach

Prajit KrisshnaKumar, Steve Paul, Hemanth Manjunatha, Mary Corra, Ehsan Esfahani, Souma Chowdhury

IDETC-CIE 2024; V03AT03A014https://doi.org/10.1115/DETC2024-146399

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Topics: Design , Reinforcement learning , Robots

Data-Driven Design

Automatic Knowledge Extraction for Decision Support in the Structural Design Process

Sonja Schlenz, Simon Mößner, Carl Henrik Ek, Fabian Duddeck

IDETC-CIE 2024; V03AT03A015https://doi.org/10.1115/DETC2024-132122

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Topics: Structural design , Databases , Engineers , Simulation , Automobiles , Computer-aided engineering , Data mining , Design automation , Design methodology , Finite element analysis

DesignQA: Benchmarking Multimodal Large Language Models on Questions Grounded in Engineering Documentation

Anna C. Doris, Daniele Grandi, Ryan Tomich, Md Ferdous Alam, Hyunmin Cheong, Faez Ahmed

IDETC-CIE 2024; V03AT03A016https://doi.org/10.1115/DETC2024-139024

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Topics: Large language models , Design , Computer-aided design , Engineering design processes , Engineering drawings , Engineers , Student competitions

To Quantize or Not to Quantize: Effects on Generative Models for 2D Heat Sink Design

Arthur Drake, Jun Wang, Qiuyi Chen, Ardalan Nejat, James Guest, Mark Fuge

IDETC-CIE 2024; V03AT03A017https://doi.org/10.1115/DETC2024-142052

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Topics: Design , Heat sinks , Compression , Topology , Topology optimization , Boundary-value problems , Collapse , Engineering design , Engineering simulation , Engineering systems and industry applications

Automated Sub-Feature Labeling Using Prompt-Based Pretrained Language Model

Seyoung Park, Yilan Jiang, Harrison Kim

IDETC-CIE 2024; V03AT03A018https://doi.org/10.1115/DETC2024-142891

Abstract

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Topics: Batteries , Product design

DrivAerNet: A Parametric Car Dataset for Data-Driven Aerodynamic Design and Graph-Based Drag Prediction

Mohamed Elrefaie, Angela Dai, Faez Ahmed

IDETC-CIE 2024; V03AT03A019https://doi.org/10.1115/DETC2024-143593

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Topics: Computational fluid dynamics , Design , Drag (Fluid dynamics) , Aerodynamics , Automobiles , Automotive design , Convolutional neural networks , Deep learning , Engineering standards , Engineering systems and industry applications

Inverse Design With Conditional Cascaded Diffusion Models

Milad Habibi, Mark Fuge

IDETC-CIE 2024; V03AT03A020https://doi.org/10.1115/DETC2024-143607

Abstract

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Topics: Design , Diffusion (Physics) , Resolution (Optics) , Errors , Generative adversarial networks , Machine learning , Pipelines , Trains

Advancing Fluid-Based Thermal Management Systems Design: Leveraging Graph Neural Networks for Graph Regression and Efficient Enumeration Reduction

Saeid Bayat, Nastaran Shahmansouri, Satya RT Peddada, Alexander Tessier, Adrian Butscher, James T. Allison

IDETC-CIE 2024; V03AT03A021https://doi.org/10.1115/DETC2024-143660

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Topics: Design , Fluids , Graph neural networks , Optimal control , System architecture , Thermal management

Generative Design of Planar Four-Bar Motions Using Conditional Variational Autoencoder

Amro Halwah, Anar Nurizada, Anurag Purwar

IDETC-CIE 2024; V03AT03A022https://doi.org/10.1115/DETC2024-146347

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Topics: Algebra , Circuits , Dimensions , Errors , Filtration , Generative design , Kinematics , Linkages , Machine learning , Machinery

Design and Optimization of Energy Systems

Development of an Optimal Variable-Pitch Controller for Floating Axial-Flow Marine Hydrokinetic Turbines

Athul Krishna Sundarrajan, Thanh Toan Tran, Will Wiley, Hannah Ross, Daniel Zalkind, Daniel R. Herber

IDETC-CIE 2024; V03AT03A023https://doi.org/10.1115/DETC2024-141271

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Topics: Axial flow , Control equipment , Hydraulic turbines , Turbines , Control systems , Damage , Design , Engineers , Generators , Modeling

Real-Time Thermal Data Assimilation for Power Electronics at the Edge

Braden Priddy, Richard Hainey, Tyler Deese, Austin R. J. Downey, Jamil Khan, Herbert L. Ginn

IDETC-CIE 2024; V03AT03A024https://doi.org/10.1115/DETC2024-141697

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Topics: Digital twin , Electronics , Particle swarm optimization , Ships , Algorithms , Particulate matter , Sensors , Computation , Cycles , Edge computing

Optimizing Day-Ahead Market Scheduling: Policy Design for Renewable Energy Integration and Imbalance Cost Consideration

Zhetao Chen, Zhimin Xi, Hao Wang

IDETC-CIE 2024; V03AT03A025https://doi.org/10.1115/DETC2024-143074

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Topics: Charging stations , Design , Renewable energy , Sales , Algorithms , Batteries , Buses , Computer simulation , Fuel cells , Photovoltaics

Integrated Design for Wave Energy Converter Farms: Assessing Plant, Control, Layout, and Site Selection Coupling in the Presence of Irregular Waves

Saeed Azad, Suraj R. Khanal, Daniel R. Herber, Gaofeng Jia

IDETC-CIE 2024; V03AT03A026https://doi.org/10.1115/DETC2024-143179

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Topics: Design , Optimization , Wave energy converters , Waves

Adaptive Agent-Based Control for Lithium-Ion Batteries in Naval Microgrids

Jarrett Peskar, Braden Priddy, Kerry Sado, Austin R. J. Downey, Kristen Booth, Jamil Khan

IDETC-CIE 2024; V03AT03A027https://doi.org/10.1115/DETC2024-143181

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Topics: Distributed power generation , Lithium-ion batteries , Microgrids , Storage , Batteries , Stress , Generators , Temperature , Lithium , Power grids

Physics-Informed Data-Driven Approaches to Electric Vehicle Battery State-of-Health Prediction: Comparison of Parallel and Series Configurations

Yixin Zhao, Karl R. Haapala, Arun Natarajan, Sara Behdad

IDETC-CIE 2024; V03AT03A028https://doi.org/10.1115/DETC2024-143546

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Topics: Artificial neural networks , Electric vehicle batteries , Physics

Integrating Cost, Reliability, and Renewable Energy for Robust Microgrid Design and Operations

Samuel Ashmore, Philip Odonkor

IDETC-CIE 2024; V03AT03A029https://doi.org/10.1115/DETC2024-143774

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Topics: Design , Microgrids , Reliability , Renewable energy , Resilience

Experimentally Supported Design Optimization of Marine Hydrokinetic Turbine Systems With Adaptive Duct Contraction Control

Austin L. Griffin, Yong Hoon Lee

IDETC-CIE 2024; V03AT03A030https://doi.org/10.1115/DETC2024-143861

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Topics: Design , Ducts , Hydraulic turbines , Optimization

Data-Driven Modeling Adaptive Aerostructures

James Roetzer, Hunter Boik, Ben Janke, Xingjie Li, John Hall

IDETC-CIE 2024; V03AT03A031https://doi.org/10.1115/DETC2024-145979

Abstract

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Topics: Modeling , Wind energy , Turbines , Errors , Optimization , Artificial neural networks , Blades , Engineering simulation , Machinery , Particle swarm optimization

Design for Additive Manufacturing

Evolved Structures: Primification Using Load Lines

Elliot Sadler, Ryan McClelland, Nhut Ho, Bingbing Li

IDETC-CIE 2024; V03AT03A032https://doi.org/10.1115/DETC2024-131819

Abstract

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Topics: Generative design , Stress , Geometry , Testing , Workflow , Finite element analysis , Carbon fibers , Engineers , Manufacturing , Shapes

A Graph Algorithm for the Design of Functionally Graded Alloy Components

Marshall D. Allen, Raymundo Arróyave, Richard Malak

IDETC-CIE 2024; V03AT03A033https://doi.org/10.1115/DETC2024-141977

Abstract

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Topics: Algorithms , Alloys , Design , Metals , Additive manufacturing , Cracking (Materials) , Fracture (Process) , Geometry , Ocean engineering , Solidification

Studying Changes to the Additive Manufacturability of Design Solutions When Prepared and Simulated in Immersive Virtual Reality

Jayant Mathur, Scarlett R. Miller, Timothy W. Simpson, Nicholas A. Meisel

IDETC-CIE 2024; V03AT03A034https://doi.org/10.1115/DETC2024-143204

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Topics: Additive manufacturing , Design , Simulation , Virtual reality

Multi-Objective Surrogate Optimization of Process Parameters for Additive Manufacturing With Applications in Laser Powder Bed Fusion

Betelhiem N. Mengesha, Vikrant Aute, Davis J. McGregor, Shapour Azarm

IDETC-CIE 2024; V03AT03A035https://doi.org/10.1115/DETC2024-143279

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Topics: Additive manufacturing , Lasers , Metamodeling , Optimization , Porosity

Inverse-Prediction of Material Property in Fused Filament Fabrication/Fused Deposition Modeling

Junghun Lee, Hadiza Yusuf, Conrad Tucker

IDETC-CIE 2024; V03AT03A036https://doi.org/10.1115/DETC2024-143840

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Topics: Manufacturing , Materials properties , Modeling , Printing , Geometry , Yield strength , Young's modulus , Additive manufacturing , Computer-aided design , Deep learning