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Proceedings Papers

Stochastically-Trained Physics-Informed Neural Networks: Application to Thermal Analysis in Metal Laser Powder Bed Fusion

Justin Pierce, Glen Williams, Timothy W. Simpson, Nicholas A. Meisel, Christopher McComb

Proc. ASME. IDETC-CIE2021, Volume 3A: 47th Design Automation Conference (DAC), V03AT03A004, August 17–19, 2021

Paper No: DETC2021-70557

DOI: https://doi.org/10.1115/DETC2021-70557

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Topics: Artificial neural networks, Lasers, Metals, Physics, Thermal analysis, Engineering simulation, Machine learning, Manufacturing, Simulation, Transients (Dynamics)

Proceedings Papers

A Graph Neural Network Approach for Product Relationship Prediction

Faez Ahmed, Yaxin Cui, Yan Fu, Wei Chen

Proc. ASME. IDETC-CIE2021, Volume 3A: 47th Design Automation Conference (DAC), V03AT03A036, August 17–19, 2021

Paper No: DETC2021-69462

DOI: https://doi.org/10.1115/DETC2021-69462

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Topics: Artificial neural networks, Artificial intelligence, Automobiles, Design, Drugs, Engineering systems and industry applications, Machine learning, Modeling, Network analysis, Optimization

Proceedings Papers

Data-Driven Design via Scalable Gaussian Processes for Multi-Response Big Data With Qualitative Factors

Liwei Wang, Suraj Yerramilli, Akshay Iyer, Daniel Apley, Ping Zhu, Wei Chen

Proc. ASME. IDETC-CIE2021, Volume 3A: 47th Design Automation Conference (DAC), V03AT03A018, August 17–19, 2021

Paper No: DETC2021-71570

DOI: https://doi.org/10.1115/DETC2021-71570

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Topics: Design, Metamodeling, Optimization, Topology, Machine learning, Architecture, Blocks (Building materials), Compliant mechanisms, Metamaterials, Modeling

Proceedings Papers

Can Machine Learning Tools Support the Identification of Sustainable Design Leads From Product Reviews? Opportunities and Challenges

Michael Saidani, Harrison Kim, Bernard Yannou

Proc. ASME. IDETC-CIE2021, Volume 3A: 47th Design Automation Conference (DAC), V03AT03A005, August 17–19, 2021

Paper No: DETC2021-70613

DOI: https://doi.org/10.1115/DETC2021-70613

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Topics: Design, Green design, Machine learning, Natural language processing

Proceedings Papers

Physics-Informed Machine Learning for Degradation Diagnostics of Lithium-Ion Batteries

Adam Thelen, Yu Hui Lui, Sheng Shen, Simon Laflamme, Shan Hu, Chao Hu

Proc. ASME. IDETC-CIE2021, Volume 3A: 47th Design Automation Conference (DAC), V03AT03A041, August 17–19, 2021

Paper No: DETC2021-71407

DOI: https://doi.org/10.1115/DETC2021-71407

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Topics: Lithium-ion batteries, Machine learning, Physics

Proceedings Papers

BIKED: A Dataset and Machine Learning Benchmarks for Data-Driven Bicycle Design

Lyle Regenwetter, Brent Curry, Faez Ahmed

Proc. ASME. IDETC-CIE2021, Volume 3A: 47th Design Automation Conference (DAC), V03AT03A019, August 17–19, 2021

Paper No: DETC2021-71681

DOI: https://doi.org/10.1115/DETC2021-71681

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Topics: Bicycles, Design, Machine learning, Design methodology, Manufacturing

Proceedings Papers

A Machine Learning Method for State of Charge Estimation in Lead-Acid Batteries for Heavy-Duty Vehicles

Sara Luciani, Stefano Feraco, Angelo Bonfitto, Andrea Tonoli, Nicola Amati, Maurizio Quaggiotto

Proc. ASME. IDETC-CIE2021, Volume 1: 23rd International Conference on Advanced Vehicle Technologies (AVT), V001T01A017, August 17–19, 2021

Paper No: DETC2021-68469

DOI: https://doi.org/10.1115/DETC2021-68469

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Topics: Artificial neural networks, Construction equipment, Genetic algorithms, Lead-acid batteries, Machine learning

Proceedings Papers

An Application of Machine Learning to Predict Stiffness Discrimination Thresholds Using Haptics

Ernur Karadoğan

Proc. ASME. IDETC-CIE2021, Volume 2: 41st Computers and Information in Engineering Conference (CIE), V002T02A078, August 17–19, 2021

Paper No: DETC2021-69337

DOI: https://doi.org/10.1115/DETC2021-69337

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Topics: Haptics, Machine learning, Stiffness, Computers, Surface roughness, Virtual reality

Proceedings Papers

A Zero-Trust Methodology for Security of Complex Systems With Machine Learning Components

Britta Hale, Douglas L. Van Bossuyt, Nikolaos Papakonstantinou, Bryan O’Halloran

Proc. ASME. IDETC-CIE2021, Volume 2: 41st Computers and Information in Engineering Conference (CIE), V002T02A067, August 17–19, 2021

Paper No: DETC2021-70442

DOI: https://doi.org/10.1115/DETC2021-70442

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Topics: Complex systems, Machine learning, Security, Defense industry, Unmanned aerial vehicles, Algorithms, Artificial neural networks, Decision making, Machinery, Safety

Proceedings Papers

Optimal Release Planning Using Machine Learning and Linear Integer Programming for Ideas in a Crowdsourcing Platform

Nour J. Absi-Halabi, Ali A. Yassine

Proc. ASME. IDETC-CIE2021, Volume 2: 41st Computers and Information in Engineering Conference (CIE), V002T02A026, August 17–19, 2021

Paper No: DETC2021-68177

DOI: https://doi.org/10.1115/DETC2021-68177

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Topics: Crowdsourcing, Integer programming, Machine learning, Product development

Proceedings Papers

In-Situ Laser-Based Process Monitoring and In-Plane Surface Anomaly Identification for Additive Manufacturing Using Point Cloud and Machine Learning

Jiaqi Lyu, Javid Akhavan Taheri Boroujeni, Souran Manoochehri

Proc. ASME. IDETC-CIE2021, Volume 2: 41st Computers and Information in Engineering Conference (CIE), V002T02A030, August 17–19, 2021

Paper No: DETC2021-69436

DOI: https://doi.org/10.1115/DETC2021-69436

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Topics: Additive manufacturing, Lasers, Machine learning, Process monitoring

Proceedings Papers

Sketch-Based Mechanism Simulation Using Machine Learning

Anar Nurizada, Anurag Purwar

Proc. ASME. IDETC-CIE2021, Volume 2: 41st Computers and Information in Engineering Conference (CIE), V002T02A034, August 17–19, 2021

Paper No: DETC2021-72149

DOI: https://doi.org/10.1115/DETC2021-72149

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Topics: Linkages, Machine learning, Simulation

Proceedings Papers

Design Form and Function Prediction From a Single Image

Kristen M. Edwards, Vaishnavi L. Addala, Faez Ahmed

Proc. ASME. IDETC-CIE2021, Volume 2: 41st Computers and Information in Engineering Conference (CIE), V002T02A032, August 17–19, 2021

Paper No: DETC2021-71853

DOI: https://doi.org/10.1115/DETC2021-71853

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Topics: Design, Aircraft, Machine learning, Machinery, Brain, Computational fluid dynamics, Computer simulation, Drag (Fluid dynamics), Engineering prototypes, Engineering simulation

Proceedings Papers

Data Augmentation for Roller Bearing Health Indicator Estimation Using Multi-Channel Frequency Data Representations

Jacob Hendriks, Patrick Dumond

Proc. ASME. IDETC-CIE2021, Volume 10: 33rd Conference on Mechanical Vibration and Sound (VIB), V010T10A011, August 17–19, 2021

Paper No: DETC2021-66701

DOI: https://doi.org/10.1115/DETC2021-66701

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Topics: Roller bearings, Bearings, Noise (Sound), Failure, Accelerometers, Machine learning, Service life (Equipment), Spectra (Spectroscopy), Vibration

Proceedings Papers

CreativeGAN: Editing Generative Adversarial Networks for Creative Design Synthesis

Amin Heyrani Nobari, Muhammad Fathy Rashad, Faez Ahmed

Proc. ASME. IDETC-CIE2021, Volume 3A: 47th Design Automation Conference (DAC), V03AT03A002, August 17–19, 2021

Paper No: DETC2021-68103

DOI: https://doi.org/10.1115/DETC2021-68103

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Topics: Creativity, Design, Bicycles, Innovation, Artificial neural networks, Design automation, Design methodology, Engineering disciplines, Image segmentation, Machine learning

Proceedings Papers

Using Physics-Informed Generative Adversarial Networks to Perform Super-Resolution for Multiphase Fluid Simulations

Matthew Li, Christopher McComb

Proc. ASME. IDETC-CIE2021, Volume 3A: 47th Design Automation Conference (DAC), V03AT03A001, August 17–19, 2021

Paper No: DETC2021-66719

DOI: https://doi.org/10.1115/DETC2021-66719

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Topics: Engineering simulation, Fluids, Physics, Resolution (Optics), Simulation, Fluid dynamics, Computational fluid dynamics, Engineering design, Machine learning, Turbulence

Proceedings Papers

A Study on the Acoustic Signal Based Frameworks for the Real-Time Identification of Geometrically Defective Wire Arc Bead

Nowrin Akter Surovi, Audelia G. Dharmawan, Gim Song Soh

Proc. ASME. IDETC-CIE2021, Volume 3A: 47th Design Automation Conference (DAC), V03AT03A003, August 17–19, 2021

Paper No: DETC2021-69573

DOI: https://doi.org/10.1115/DETC2021-69573

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Topics: Acoustics, Signals, Wire, Artificial neural networks, Bronze, Stainless steel, Additive manufacturing, Machine learning, Principal component analysis, Sensors

Proceedings Papers

Deep Learning and Machine Learning Techniques to Classify Electrical and Electronic Equipment

Shuaizhou Hu, Xinyao Zhang, Hao-yu Liao, Xiao Liang, Minghui Zheng, Sara Behdad

Proc. ASME. IDETC-CIE2021, Volume 5: 26th Design for Manufacturing and the Life Cycle Conference (DFMLC), V005T05A029, August 17–19, 2021

Paper No: DETC2021-71403

DOI: https://doi.org/10.1115/DETC2021-71403

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Topics: Electronic equipment, Laptop computers, Machine learning

Proceedings Papers

Machine Learning to Predict Medical Devices Repair and Maintenance Needs

Hao-yu Liao, Karthik Boregowda, Willie Cade, Sara Behdad

Proc. ASME. IDETC-CIE2021, Volume 5: 26th Design for Manufacturing and the Life Cycle Conference (DFMLC), V005T05A012, August 17–19, 2021

Paper No: DETC2021-71333

DOI: https://doi.org/10.1115/DETC2021-71333

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Topics: Machine learning, Maintenance, Medical devices

Proceedings Papers

Using Machine Learning for the Classification of the Remaining Useful Cycles in Lithium-Ion Batteries

Harry Coutts, Qing Wang

Proc. ASME. IDETC-CIE2021, Volume 5: 26th Design for Manufacturing and the Life Cycle Conference (DFMLC), V005T05A014, August 17–19, 2021

Paper No: DETC2021-69647

DOI: https://doi.org/10.1115/DETC2021-69647

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Topics: Cycles, Lithium-ion batteries, Machine learning, Batteries, Algorithms, Chemistry, User interfaces, Economics , Lithium, Renewable energy

2 3 4 5

NARROW

Stochastically-Trained Physics-Informed Neural Networks: Application to Thermal Analysis in Metal Laser Powder Bed Fusion

A Graph Neural Network Approach for Product Relationship Prediction

Data-Driven Design via Scalable Gaussian Processes for Multi-Response Big Data With Qualitative Factors

Can Machine Learning Tools Support the Identification of Sustainable Design Leads From Product Reviews? Opportunities and Challenges

Physics-Informed Machine Learning for Degradation Diagnostics of Lithium-Ion Batteries

BIKED: A Dataset and Machine Learning Benchmarks for Data-Driven Bicycle Design

A Machine Learning Method for State of Charge Estimation in Lead-Acid Batteries for Heavy-Duty Vehicles

An Application of Machine Learning to Predict Stiffness Discrimination Thresholds Using Haptics

A Zero-Trust Methodology for Security of Complex Systems With Machine Learning Components

Optimal Release Planning Using Machine Learning and Linear Integer Programming for Ideas in a Crowdsourcing Platform

In-Situ Laser-Based Process Monitoring and In-Plane Surface Anomaly Identification for Additive Manufacturing Using Point Cloud and Machine Learning

Sketch-Based Mechanism Simulation Using Machine Learning

Design Form and Function Prediction From a Single Image

Data Augmentation for Roller Bearing Health Indicator Estimation Using Multi-Channel Frequency Data Representations

CreativeGAN: Editing Generative Adversarial Networks for Creative Design Synthesis

Using Physics-Informed Generative Adversarial Networks to Perform Super-Resolution for Multiphase Fluid Simulations

A Study on the Acoustic Signal Based Frameworks for the Real-Time Identification of Geometrically Defective Wire Arc Bead

Deep Learning and Machine Learning Techniques to Classify Electrical and Electronic Equipment

Machine Learning to Predict Medical Devices Repair and Maintenance Needs

Using Machine Learning for the Classification of the Remaining Useful Cycles in Lithium-Ion Batteries

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