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Proceedings Papers

Integrated Computational Materials Engineering With Monotonic Gaussian Processes

Anh Tran, Kathryn Maupin, Theron Rodgers

Proc. ASME. IDETC-CIE2022, Volume 2: 42nd Computers and Information in Engineering Conference (CIE), V002T02A038, August 14–17, 2022

Paper No: DETC2022-89213

DOI: https://doi.org/10.1115/DETC2022-89213

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Topics: Interpolation, Machine learning, Materials science, Physics, Trains

Proceedings Papers

Surrogate Models and Time Series for Flow Prediction on the Red River Dam Network

Reza Alizadeh, Janet K. Allen, Farrokh Mistree

Proc. ASME. IDETC-CIE2022, Volume 3B: 48th Design Automation Conference (DAC), V03BT03A023, August 14–17, 2022

Paper No: DETC2022-88163

DOI: https://doi.org/10.1115/DETC2022-88163

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Topics: Artificial neural networks, Dams, Flow (Dynamics), Machine learning, Metamodeling, Rivers, Time series

Proceedings Papers

Uncertainty Quantification of Physics-Based Label-Free Deep Learning and Probabilistic Prediction of Extreme Events

Huiru Li, Jianhua Yin, Xiaoping Du

Proc. ASME. IDETC-CIE2022, Volume 3B: 48th Design Automation Conference (DAC), V03BT03A001, August 14–17, 2022

Paper No: DETC2022-88277

DOI: https://doi.org/10.1115/DETC2022-88277

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Topics: Physics, Reliability, Uncertainty, Uncertainty quantification, Artificial neural networks, Errors, Machine learning, Design, Linkages, Regression models

Proceedings Papers

Automated and Customized CAD Drawings by Utilizing Machine Learning Algorithms: A Case Study

Javier Villena Toro, Mehdi Tarkian

Proc. ASME. IDETC-CIE2022, Volume 3B: 48th Design Automation Conference (DAC), V03BT03A040, August 14–17, 2022

Paper No: DETC2022-88971

DOI: https://doi.org/10.1115/DETC2022-88971

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Topics: Algorithms, Computer-aided design, Engineering drawings, Machine learning, Artificial neural networks, Computer software, Dimensions, Geometry, Personnel management, Trains

Proceedings Papers

Generative Adversarial Design Analysis of Non-Convexity in Topology Optimization

Nathan Hertlein, Andrew Gillman, Philip R. Buskohl

Proc. ASME. IDETC-CIE2022, Volume 3B: 48th Design Automation Conference (DAC), V03BT03A044, August 14–17, 2022

Paper No: DETC2022-89997

DOI: https://doi.org/10.1115/DETC2022-89997

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Topics: Design, Optimization, Topology, Filtration, Boundary-value problems, Filters, Fluctuations (Physics), Machine learning, Performance

Proceedings Papers

Design Target Achievement Index: A Differentiable Metric to Enhance Deep Generative Models in Multi-Objective Inverse Design

Lyle Regenwetter, Faez Ahmed

Proc. ASME. IDETC-CIE2022, Volume 3B: 48th Design Automation Conference (DAC), V03BT03A046, August 14–17, 2022

Paper No: DETC2022-91344

DOI: https://doi.org/10.1115/DETC2022-91344

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Topics: Design, Algorithms, Bicycles, Machine learning

Proceedings Papers

Short Paper: An Initial Investigation of the Correlations Between the Quality of Engineering Assignments and Task-Independent Features

Wanyu Xu, Daniel A. McAdams, Andreas A. Polycarpou

Proc. ASME. IDETC-CIE2022, Volume 4: 19th International Conference on Design Education (DEC), V004T04A011, August 14–17, 2022

Paper No: DETC2022-91031

DOI: https://doi.org/10.1115/DETC2022-91031

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Topics: Design, Design education, Errors, Machine learning, Mechanical engineering, Teaching

Proceedings Papers

Teaching Machine Learning for Analysis and Design to Mechanical Engineering Students

Xiaoping Du, Jianhua Yin

Proc. ASME. IDETC-CIE2022, Volume 4: 19th International Conference on Design Education (DEC), V004T04A007, August 14–17, 2022

Paper No: DETC2022-87482

DOI: https://doi.org/10.1115/DETC2022-87482

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Topics: Design, Machine learning, Mechanical engineering, Students, Teaching, Education, Machining

Proceedings Papers

Hey, AI! Can You See What I See? Multimodal Transfer Learning-Based Design Metrics Prediction for Sketches With Text Descriptions

Binyang Song, Scarlett Miller, Faez Ahmed

Proc. ASME. IDETC-CIE2022, Volume 6: 34th International Conference on Design Theory and Methodology (DTM), V006T06A017, August 14–17, 2022

Paper No: DETC2022-91269

DOI: https://doi.org/10.1115/DETC2022-91269

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Topics: Design, Creativity, Computational methods, Engineering design, Innovation, Joining, Machine learning

Proceedings Papers

TRIZ Mapping and Novelty Detection of Engineering Design Patents Using Machine Learning

Sawyer Hall, Calahan Mollan, Vijitashwa Pandey, Zissimos Mourelatos

Proc. ASME. IDETC-CIE2022, Volume 6: 34th International Conference on Design Theory and Methodology (DTM), V006T06A044, August 14–17, 2022

Paper No: DETC2022-89746

DOI: https://doi.org/10.1115/DETC2022-89746

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Topics: Engineering design, Machine learning, Patents, Vehicle brakes

Proceedings Papers

Triggered: Using Human-AI Dialogue for Problem Understanding in Collaborative Design

Anne Arzberger, Vera van der Burg, Senthil Chandrasegaran, Peter Lloyd

Proc. ASME. IDETC-CIE2022, Volume 6: 34th International Conference on Design Theory and Methodology (DTM), V006T06A038, August 14–17, 2022

Paper No: DETC2022-89273

DOI: https://doi.org/10.1115/DETC2022-89273

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Topics: Design, Artificial intelligence, Engineers, Generators, Machine learning, Space, Trains

Proceedings Papers

Anomaly Detection of Laser Powder Bed Fusion Melt Pool Images Using Combined Unsupervised and Supervised Learning Methods

Matthew M. Sato, Vivian Wen Hui Wong, Kincho H. Law, Ho Yeung, Zhuo Yang, Brandon Lane, Paul Witherell

Proc. ASME. IDETC-CIE2022, Volume 2: 42nd Computers and Information in Engineering Conference (CIE), V002T02A070, August 14–17, 2022

Paper No: DETC2022-88313

DOI: https://doi.org/10.1115/DETC2022-88313

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Topics: Additive manufacturing, Artificial neural networks, Lasers, Algorithms, Machine learning, Machinery, Manufacturing, Metals, Process control, Shapes

Proceedings Papers

Multi-Fidelity Physics-Constrained Neural Networks With Minimax Architecture for Materials Modeling

Dehao Liu, Pranav Pusarla, Yan Wang

Proc. ASME. IDETC-CIE2022, Volume 2: 42nd Computers and Information in Engineering Conference (CIE), V002T02A011, August 14–17, 2022

Paper No: DETC2022-91219

DOI: https://doi.org/10.1115/DETC2022-91219

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Topics: Artificial neural networks, Machine learning, Modeling, Optimization, Physics

Proceedings Papers

Reinforcement Learning As an Alternative for Parameter Prediction In Design for Sheet Bulk Metal Forming

Fabian Dworschak, Christopher Sauer, Benjamin Schleich, Sandro Wartzack

Proc. ASME. IDETC-CIE2022, Volume 2: 42nd Computers and Information in Engineering Conference (CIE), V002T02A001, August 14–17, 2022

Paper No: DETC2022-89073

DOI: https://doi.org/10.1115/DETC2022-89073

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Topics: Design, Metalworking, Reinforcement learning, Genetic algorithms, Machine learning, Manufacturing, Pareto optimization, Probes

Proceedings Papers

Multi-Mission Engineering With Zero Trust: A Modeling Methodology and Application to Contested Offshore Wind Farms

Douglas L. Van Bossuyt, Britta Hale, Ryan M. Arlitt, Nikolaos Papakonstantinou

Proc. ASME. IDETC-CIE2022, Volume 2: 42nd Computers and Information in Engineering Conference (CIE), V002T02A058, August 14–17, 2022

Paper No: DETC2022-90067

DOI: https://doi.org/10.1115/DETC2022-90067

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Topics: Modeling, Ocean engineering, Wind farms, Decision making, Silicon-on-sapphire, Economics , Machine learning, Defense industry, Maintenance, Risk

Proceedings Papers

A Constraint-Handling Technique for Parametric Optimization and Control Co-Design

Ying-Kuan Tsai, Richard J. Malak, Jr.

Proc. ASME. IDETC-CIE2022, Volume 3A: 48th Design Automation Conference (DAC), V03AT03A009, August 14–17, 2022

Paper No: DETC2022-89957

DOI: https://doi.org/10.1115/DETC2022-89957

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Topics: Design, Optimization, Genetic algorithms, Pendulums, Support vector machines, Algorithms, Control equipment, Control systems, Dynamics (Mechanics), Machine learning

Proceedings Papers

t-METASET: Task-Aware Generation of Metamaterial Datasets by Diversity-Based Active Learning

Doksoo Lee, Yu-Chin Chan, Wei (Wayne) Chen, Liwei Wang, Wei Chen

Proc. ASME. IDETC-CIE2022, Volume 3A: 48th Design Automation Conference (DAC), V03AT03A011, August 14–17, 2022

Paper No: DETC2022-87653

DOI: https://doi.org/10.1115/DETC2022-87653

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Topics: Metamaterials, Design, Data acquisition, Shapes, Data-driven design, Architecture, Machine learning

Proceedings Papers

Data Augmentation of Engineering Drawings for Data-Driven Component Segmentation

Wentai Zhang, Quan Chen, Can Koz, Liuyue Xie, Amit Regmi, Soji Yamakawa, Tomotake Furuhata, Kenji Shimada, Levent Burak Kara

Proc. ASME. IDETC-CIE2022, Volume 3A: 48th Design Automation Conference (DAC), V03AT03A016, August 14–17, 2022

Paper No: DETC2022-91043

DOI: https://doi.org/10.1115/DETC2022-91043

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Topics: Engineering drawings, Image segmentation, Computers, Machinery, Design, Dimensions, Machine learning, Manufacturing

Proceedings Papers

A Study of Machine Learning Framework for Enabling Early Defect Detection in Wire Arc Additive Manufacturing Processes

Nowrin Akter Surovi, Shaista Hussain, Gim Song Soh

Proc. ASME. IDETC-CIE2022, Volume 3A: 48th Design Automation Conference (DAC), V03AT03A002, August 14–17, 2022

Paper No: DETC2022-89164

DOI: https://doi.org/10.1115/DETC2022-89164

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Topics: Additive manufacturing, Flaw detection, Machine learning, Wire, Acoustics, Printing, Support vector machines, Pressure

Proceedings Papers

Multi-Scale Topology Optimization With Neural Network-Assisted Optimizer

Sina Rastegarzadeh, Jun Wang, Jida Huang

Proc. ASME. IDETC-CIE2022, Volume 2: 42nd Computers and Information in Engineering Conference (CIE), V002T02A041, August 14–17, 2022

Paper No: DETC2022-89538

DOI: https://doi.org/10.1115/DETC2022-89538

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Topics: Optimization, Topology, Design, Machine learning, Resolution (Optics), Structural design, Artificial neural networks, Density, Filters, Interpolation

2 3 4 5

NARROW

Integrated Computational Materials Engineering With Monotonic Gaussian Processes

Surrogate Models and Time Series for Flow Prediction on the Red River Dam Network

Uncertainty Quantification of Physics-Based Label-Free Deep Learning and Probabilistic Prediction of Extreme Events

Automated and Customized CAD Drawings by Utilizing Machine Learning Algorithms: A Case Study

Generative Adversarial Design Analysis of Non-Convexity in Topology Optimization

Design Target Achievement Index: A Differentiable Metric to Enhance Deep Generative Models in Multi-Objective Inverse Design

Short Paper: An Initial Investigation of the Correlations Between the Quality of Engineering Assignments and Task-Independent Features

Teaching Machine Learning for Analysis and Design to Mechanical Engineering Students

Hey, AI! Can You See What I See? Multimodal Transfer Learning-Based Design Metrics Prediction for Sketches With Text Descriptions

TRIZ Mapping and Novelty Detection of Engineering Design Patents Using Machine Learning

Triggered: Using Human-AI Dialogue for Problem Understanding in Collaborative Design

Anomaly Detection of Laser Powder Bed Fusion Melt Pool Images Using Combined Unsupervised and Supervised Learning Methods

Multi-Fidelity Physics-Constrained Neural Networks With Minimax Architecture for Materials Modeling

Reinforcement Learning As an Alternative for Parameter Prediction In Design for Sheet Bulk Metal Forming

Multi-Mission Engineering With Zero Trust: A Modeling Methodology and Application to Contested Offshore Wind Farms

A Constraint-Handling Technique for Parametric Optimization and Control Co-Design

t-METASET: Task-Aware Generation of Metamaterial Datasets by Diversity-Based Active Learning

Data Augmentation of Engineering Drawings for Data-Driven Component Segmentation

A Study of Machine Learning Framework for Enabling Early Defect Detection in Wire Arc Additive Manufacturing Processes

Multi-Scale Topology Optimization With Neural Network-Assisted Optimizer

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