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Proceedings Papers

Generating Large Datasets of Simplified Automotive Body-in-White Structures to Predict Springback Using Machine Learning

Abhishek Lokesh Bolar, Ibraheem Alawadhi, Satchit Ramnath, Prakash Kumar, Yannis Korkolis, Joseph K. Davidson, Jami J. Shah
Proc. ASME. IDETC-CIE2023, Volume 2: 43rd Computers and Information in Engineering Conference (CIE), V002T02A042, August 20–23, 2023
Publisher: American Society of Mechanical Engineers
Paper No: DETC2023-116842
https://doi.org/10.1115/DETC2023-116842
View Paper titled, Generating Large Datasets of Simplified Automotive Body-in-White Structures to Predict Springback Using Machine Learning
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Topics: Finite element analysis, Joining, Machine learning, Metal stamping, Sheet metal, Workflow, Manufacturing, Design, Residual stresses, Simulation
Proceedings Papers

Multi-Modal Machine Learning for Vehicle Rating Predictions Using Image, Text, and Parametric Data

Hanqi Su, Binyang Song, Faez Ahmed
Proc. ASME. IDETC-CIE2023, Volume 2: 43rd Computers and Information in Engineering Conference (CIE), V002T02A089, August 20–23, 2023
Publisher: American Society of Mechanical Engineers
Paper No: DETC2023-115076
https://doi.org/10.1115/DETC2023-115076
View Paper titled, Multi-Modal Machine Learning for Vehicle Rating Predictions Using Image, Text, and Parametric Data
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Topics: Machine learning, Vehicles
Proceedings Papers

How to Encode Microstructure in Machine Learning: A Comparison Study

Yulun Wu, Yumeng Li
Proc. ASME. IDETC-CIE2023, Volume 3B: 49th Design Automation Conference (DAC), V03BT03A032, August 20–23, 2023
Publisher: American Society of Mechanical Engineers
Paper No: DETC2023-116704
https://doi.org/10.1115/DETC2023-116704
View Paper titled, How to Encode Microstructure in Machine Learning: A Comparison Study
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Topics: Artificial neural networks, Encryption, Machine learning
Proceedings Papers

Multi-Task Multi-Fidelity Machine Learning for Reliability-Based Design With Partially Observed Information

Yanwen Xu, Hao Wu, Zheng Liu, Pingfeng Wang
Proc. ASME. IDETC-CIE2023, Volume 3B: 49th Design Automation Conference (DAC), V03BT03A036, August 20–23, 2023
Publisher: American Society of Mechanical Engineers
Paper No: DETC2023-117032
https://doi.org/10.1115/DETC2023-117032
View Paper titled, Multi-Task Multi-Fidelity Machine Learning for Reliability-Based Design With Partially Observed Information
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Topics: Design, Event history analysis, Machine learning, Optimization, Reliability
Proceedings Papers

Machine Learning-Based Model Bias Correction by Fusing CAE Data With Test Data for Vehicle Crashworthiness

Jice Zeng, Ying Zhao, Guosong Li, Zhenyan Gao, Yang Li, Saeed Barbat, Zhen Hu
Proc. ASME. IDETC-CIE2023, Volume 3B: 49th Design Automation Conference (DAC), V03BT03A067, August 20–23, 2023
Publisher: American Society of Mechanical Engineers
Paper No: DETC2023-114999
https://doi.org/10.1115/DETC2023-114999
View Paper titled, Machine Learning-Based Model Bias Correction by Fusing CAE Data With Test Data for Vehicle Crashworthiness
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Topics: Computer-aided engineering, Crashworthiness, Machinery, Vehicles, Displacement, Dynamic analysis, Physics, Computer simulation, Data fusion, Elastic constants
Proceedings Papers

Quantification Model Uncertainty of Label-Free Machine Learning for Multidisciplinary Systems Analysis

Huiru Li, Jitesh H. Panchal, Xiaoping Du
Proc. ASME. IDETC-CIE2023, Volume 3B: 49th Design Automation Conference (DAC), V03BT03A045, August 20–23, 2023
Publisher: American Society of Mechanical Engineers
Paper No: DETC2023-112948
https://doi.org/10.1115/DETC2023-112948
View Paper titled, Quantification Model Uncertainty of Label-Free Machine Learning for Multidisciplinary Systems Analysis
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Topics: Artificial neural networks, Machine learning, Systems analysis, Uncertainty, Errors, Engineering disciplines, Combustion, Design, Engineering systems and industry applications, Uncertainty quantification
Proceedings Papers

The Generation of Novel Art Using Collaborative ML Models

Ada-Rhodes Short
Proc. ASME. IDETC-CIE2023, Volume 3B: 49th Design Automation Conference (DAC), V03BT03A065, August 20–23, 2023
Publisher: American Society of Mechanical Engineers
Paper No: DETC2023-116825
https://doi.org/10.1115/DETC2023-116825
View Paper titled, The Generation of Novel Art Using Collaborative ML Models
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Topics: Brain, Design automation, Diffusion (Physics), Machine learning, Nervous system, Robotics
Proceedings Papers

Accounting for Model Uncertainty in Machine Learning Assisted Mechanical Design

Xiaoping Du
Proc. ASME. IDETC-CIE2023, Volume 3B: 49th Design Automation Conference (DAC), V03BT03A068, August 20–23, 2023
Publisher: American Society of Mechanical Engineers
Paper No: DETC2023-116622
https://doi.org/10.1115/DETC2023-116622
View Paper titled, Accounting for Model Uncertainty in Machine Learning Assisted Mechanical Design
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Topics: Accounting, Design, Design engineering, Machine learning, Uncertainty
Proceedings Papers

Enhancing Design Guidelines for Metal Powder Bed Fusion: Analyzing Geometric Features to Improve Part Quality

Jannatul Bushra, Hannah D. Budinoff, Pablo Luna Falcon, Marat Latypov
Proc. ASME. IDETC-CIE2023, Volume 5: 28th Design for Manufacturing and the Life Cycle Conference (DFMLC), V005T05A011, August 20–23, 2023
Publisher: American Society of Mechanical Engineers
Paper No: DETC2023-117019
https://doi.org/10.1115/DETC2023-117019
View Paper titled, Enhancing Design Guidelines for Metal Powder Bed Fusion: Analyzing Geometric Features to Improve Part Quality
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Topics: Design, Geometry, Metal powders, Additive manufacturing, Engineering simulation, Errors, Finite element analysis, Machine learning, Product design, Shapes
Proceedings Papers

Predicting the Quantity of Recycled End-of-Life Products Using a Hybrid SVR-Based Model

Hanbing Xia, Ji Han, Jelena Milisavljevic-Syed
Proc. ASME. IDETC-CIE2023, Volume 5: 28th Design for Manufacturing and the Life Cycle Conference (DFMLC), V005T05A009, August 20–23, 2023
Publisher: American Society of Mechanical Engineers
Paper No: DETC2023-114718
https://doi.org/10.1115/DETC2023-114718
View Paper titled, Predicting the Quantity of Recycled End-of-Life Products Using a Hybrid SVR-Based Model
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Topics: Algorithms, Approximation, Chaos, Design, Diffusion (Physics), Logistics, Machine learning, Particle swarm optimization, Recycling, Resource allocation
Proceedings Papers

Conceptual Design Generation Using Large Language Models

Kevin Ma, Daniele Grandi, Christopher McComb, Kosa Goucher-Lambert
Proc. ASME. IDETC-CIE2023, Volume 6: 35th International Conference on Design Theory and Methodology (DTM), V006T06A021, August 20–23, 2023
Publisher: American Society of Mechanical Engineers
Paper No: DETC2023-116838
https://doi.org/10.1115/DETC2023-116838
View Paper titled, Conceptual Design Generation Using Large Language Models
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Topics: Conceptual design, Crowdsourcing, Design, Natural language processing, Machine learning
Proceedings Papers

An Invariant Representation of Coupler Curves Using a Variational Autoencoder: Application to Path Synthesis of Four-Bar Mechanisms

Anar Nurizada, Anurag Purwar
Proc. ASME. IDETC-CIE2023, Volume 8: 47th Mechanisms and Robotics Conference (MR), V008T08A022, August 20–23, 2023
Publisher: American Society of Mechanical Engineers
Paper No: DETC2023-116892
https://doi.org/10.1115/DETC2023-116892
View Paper titled, An Invariant Representation of Coupler Curves Using a Variational Autoencoder: Application to Path Synthesis of Four-Bar Mechanisms
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Topics: Artificial neural networks, Conceptual design, Dimensions, Linkages, Machine learning, Machinery, Robots, Wavelets
Proceedings Papers

Physics Guided Machine Learning Approach to Safe Quasi-Static Impact Situations in Human-Robot Collaboration Following the Power and Force Limiting Method of the ISO/TS 15066 Standard

Nemanja Kovinčić, Hubert Gattringer, Andreas Müller, Mathias Brandstötter
Proc. ASME. IDETC-CIE2023, Volume 10: 19th International Conference on Multibody Systems, Nonlinear Dynamics, and Control (MSNDC), V010T10A024, August 20–23, 2023
Publisher: American Society of Mechanical Engineers
Paper No: DETC2023-109517
https://doi.org/10.1115/DETC2023-109517
View Paper titled, Physics Guided Machine Learning Approach to Safe Quasi-Static Impact Situations in Human-Robot Collaboration Following the Power and Force Limiting Method of the ISO/TS 15066 Standard
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Topics: Human-robot collaboration, Machine learning, Physics, Robots, Safety
Proceedings Papers

Overview of Design Considerations for Data-Driven Time Stepping Schemes Applied to Non-Linear Mechanical Systems

Tomas Slimak, Andreas Zwölfer, Bojidar Todorov, Daniel J. Rixen
Proc. ASME. IDETC-CIE2023, Volume 10: 19th International Conference on Multibody Systems, Nonlinear Dynamics, and Control (MSNDC), V010T10A021, August 20–23, 2023
Publisher: American Society of Mechanical Engineers
Paper No: DETC2023-112087
https://doi.org/10.1115/DETC2023-112087
View Paper titled, Overview of Design Considerations for Data-Driven Time Stepping Schemes Applied to Non-Linear Mechanical Systems
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Topics: Artificial neural networks, Design, Machine learning, Physics, System dynamics, Algorithms, Brain, Dynamics (Mechanics), Multibody dynamics, Noise (Sound)
Proceedings Papers

A Combined Simple/Complex Terramechanics Representation Part 1: Using Machine Learning to Identify DEM Soil Properties From Bevameter Test Data

Ben Thornton, Eric Pesheck, Paramsothy Jayakumar
Proc. ASME. IDETC-CIE2023, Volume 10: 19th International Conference on Multibody Systems, Nonlinear Dynamics, and Control (MSNDC), V010T10A019, August 20–23, 2023
Publisher: American Society of Mechanical Engineers
Paper No: DETC2023-117123
https://doi.org/10.1115/DETC2023-117123
View Paper titled, A Combined Simple/Complex Terramechanics Representation Part 1: Using Machine Learning to Identify DEM Soil Properties From Bevameter Test Data
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Topics: Discrete element methods, Machine learning, Mechanical admittance, Pareto optimization, Soil
Proceedings Papers

Machine Learning Based Deflection Prediction and Inverse Design for Discrete Variable Stiffness Units

Jiaming Fu, Qianyu Guo, Dongming Gan
Proc. ASME. IDETC-CIE2023, Volume 8: 47th Mechanisms and Robotics Conference (MR), V008T08A043, August 20–23, 2023
Publisher: American Society of Mechanical Engineers
Paper No: DETC2023-117322
https://doi.org/10.1115/DETC2023-117322
View Paper titled, Machine Learning Based Deflection Prediction and Inverse Design for Discrete Variable Stiffness Units
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Topics: Compliant mechanisms, Deflection, Design, Machine learning, Modeling, Stiffness
Proceedings Papers

Supporting Condition-Based Maintenance for Rotary Systems Under Multiple Fault Scenarios

Larry Marshall, Jr., David Jensen, Han Hu
Proc. ASME. IDETC-CIE2023, Volume 2: 43rd Computers and Information in Engineering Conference (CIE), V002T02A075, August 20–23, 2023
Publisher: American Society of Mechanical Engineers
Paper No: DETC2023-116470
https://doi.org/10.1115/DETC2023-116470
View Paper titled, Supporting Condition-Based Maintenance for Rotary Systems Under Multiple Fault Scenarios
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Topics: Maintenance, Machinery, Flaw detection, Algorithms, Ball bearings, Bearings, Decision making, Engineering prototypes, Fault diagnosis, Machine learning
Proceedings Papers

A Physics-Informed Action Selection Framework for Robotic Heating

Neel Dhanaraj, Omey M. Manyar, Vihan Krishnan, Satyandra K. Gupta
Proc. ASME. IDETC-CIE2023, Volume 2: 43rd Computers and Information in Engineering Conference (CIE), V002T02A074, August 20–23, 2023
Publisher: American Society of Mechanical Engineers
Paper No: DETC2023-116329
https://doi.org/10.1115/DETC2023-116329
View Paper titled, A Physics-Informed Action Selection Framework for Robotic Heating
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Topics: Heating, Physics, Robotics, Machine learning, Robots, Composite materials, Manufacturing, Modeling, Simulation, Temperature
Proceedings Papers

Scalable Probabilistic Modeling and Machine Learning With Dimensionality Reduction for Expensive High-Dimensional Problems

Lele Luan, Nesar Ramachandra, Sandipp Krishnan Ravi, Anindya Bhaduri, Piyush Pandita, Prasanna Balaprakash, Mihai Anitescu, Changjie Sun, Liping Wang
Proc. ASME. IDETC-CIE2023, Volume 2: 43rd Computers and Information in Engineering Conference (CIE), V002T02A011, August 20–23, 2023
Publisher: American Society of Mechanical Engineers
Paper No: DETC2023-110704
https://doi.org/10.1115/DETC2023-110704
View Paper titled, Scalable Probabilistic Modeling and Machine Learning With Dimensionality Reduction for Expensive High-Dimensional Problems
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Topics: Artificial neural networks, Machine learning, Metamodeling, Modeling
Proceedings Papers

Additive Gaussian Process Regression for Metamodeling in High-Dimensional Problems

Anh Tran, Kathryn Maupin, Ankush Kumar Mishra, Zhen Hu, Chao Hu
Proc. ASME. IDETC-CIE2023, Volume 2: 43rd Computers and Information in Engineering Conference (CIE), V002T02A012, August 20–23, 2023
Publisher: American Society of Mechanical Engineers
Paper No: DETC2023-116555
https://doi.org/10.1115/DETC2023-116555
View Paper titled, Additive Gaussian Process Regression for Metamodeling in High-Dimensional Problems
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Topics: Approximation, Design, Dimensions, Engineering systems and industry applications, Machine learning, Metamodeling, Optimization, Sensitivity analysis, Uncertainty quantification