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Journal Articles

An efficient reliability analysis method based on the improved radial basis function neural network

Dequan Zhang, Zida Zhao, Heng Ouyang, Zeping Wu, Xu Han

J. Mech. Des.

Paper No: MD-22-1411

DOI: https://doi.org/10.1115/1.4062584

Published Online: May 22, 2023

View Article titled, An efficient reliability analysis method based on the improved radial basis function neural network

Topics: Event history analysis, Radial basis function networks, Reliability, Algorithms, Artificial neural networks, Design, Design under uncertainty, Particle swarm optimization, Robots, Shapes

Journal Articles

Comparing Mechanical Neural-Network Learning Algorithms

Ryan H. Lee, Pietro Sainaghi, Jonathan B. Hopkins

J. Mech. Des. July 2023, 145(7): 071704.

Paper No: MD-22-1782

DOI: https://doi.org/10.1115/1.4062313

Published Online: May 18, 2023

View Article titled, Comparing Mechanical Neural-Network Learning Algorithms

Topics: Algorithms, Artificial neural networks, Simulation

Journal Articles

Neural Network-Assisted Design: A Study of Multiscale Topology Optimization With Smoothly Graded Cellular Structures

Sina Rastegarzadeh, Jun Wang, Jida Huang

J. Mech. Des. January 2023, 145(1): 011701.

Paper No: MD-22-1154

DOI: https://doi.org/10.1115/1.4055505

Published Online: October 7, 2022

View Article titled, Neural Network-Assisted Design: A Study of Multiscale Topology Optimization With Smoothly Graded Cellular Structures

Topics: Design, Optimization, Topology, Finite element analysis, Density, Artificial neural networks, Resolution (Optics)

Journal Articles

Artificial Intelligence Design for Ship Structures: A Variant Multiple-Input Neural Network-Based Ship Resistance Prediction

Yu Ao, Yunbo Li, Jiaye Gong, Shaofan Li

J. Mech. Des. September 2022, 144(9): 091707.

Paper No: MD-21-1483

DOI: https://doi.org/10.1115/1.4053816

Published Online: June 16, 2022

View Article titled, Artificial Intelligence Design for Ship Structures: A Variant Multiple-Input Neural Network-Based Ship Resistance Prediction

Topics: Algorithms, Artificial neural networks, Design, Geometry, Hull, Ships, Artificial intelligence

Journal Articles

Bias-Learning-Based Model Predictive Controller Design for Reliable Path Tracking of Autonomous Vehicles Under Model and Environmental Uncertainty

Lichuan Ren, Zhimin Xi

J. Mech. Des. September 2022, 144(9): 091706.

Paper No: MD-21-1676

DOI: https://doi.org/10.1115/1.4054674

Published Online: June 13, 2022

View Article titled, Bias-Learning-Based Model Predictive Controller Design for Reliable Path Tracking of Autonomous Vehicles Under Model and Environmental Uncertainty

Topics: Autonomous vehicles, Control equipment, Design, Dynamic models, Errors, Tires, Uncertainty, Vehicles, Simulation, Roads

Journal Articles

ActivationNet : Representation Learning to Predict Contact Quality of Interacting 3D Surfaces in Engineering Designs

Rishikesh Ranade, Jay Pathak

J. Mech. Des. July 2022, 144(7): 071705.

Paper No: MD-21-1136

DOI: https://doi.org/10.1115/1.4053811

Published Online: March 18, 2022

View Article titled, ActivationNet : Representation Learning to Predict Contact Quality of Interacting 3D Surfaces in Engineering Designs

Topics: Algorithms, Artificial neural networks, Design, Engineering simulation, Geometry, Machine learning, Testing, Physics, Computer-aided design, Rotation

Journal Articles

Classifying Component Function in Product Assemblies With Graph Neural Networks

Vincenzo Ferrero, Bryony DuPont, Kaveh Hassani, Daniele Grandi

J. Mech. Des. February 2022, 144(2): 021406.

Paper No: MD-21-1111

DOI: https://doi.org/10.1115/1.4052720

Published Online: November 9, 2021

View Article titled, Classifying Component Function in Product Assemblies With Graph Neural Networks

Topics: Artificial neural networks, Design, Flow (Dynamics), Manufacturing, Product design

Journal Articles

Inverse Design Framework With Invertible Neural Networks for Passive Vibration Suppression in Phononic Structures

Manaswin Oddiraju, Amir Behjat, Mostafa Nouh, Souma Chowdhury

J. Mech. Des. February 2022, 144(2): 021707.

Paper No: MD-21-1246

DOI: https://doi.org/10.1115/1.4052300

Published Online: September 23, 2021

View Article titled, Inverse Design Framework With Invertible Neural Networks for Passive Vibration Suppression in Phononic Structures

Topics: Artificial neural networks, Design, Optimization, Modeling, Metamaterials

Journal Articles

A Flow Information-Based Prediction Model Applied to the Nonaxisymmetric Hub Optimization of a Centrifugal Impeller

Cheng Ji, Zhiheng Wang, Yonghong Tang, Guang Xi

J. Mech. Des. October 2021, 143(10): 103502.

Paper No: MD-20-1829

DOI: https://doi.org/10.1115/1.4050655

Published Online: May 3, 2021

View Article titled, A Flow Information-Based Prediction Model Applied to the Nonaxisymmetric Hub Optimization of a Centrifugal Impeller

Topics: Artificial neural networks, Flow (Dynamics), Impellers, Optimization, Pressure, Design, Blades

Journal Articles

A Data-Driven Methodology to Improve Tolerance Allocation Using Product Usage Data

Kangjie Li, Yicong Gao, Hao Zheng, Jianrongg Tan

J. Mech. Des. July 2021, 143(7): 071101.

Paper No: MD-20-1604

DOI: https://doi.org/10.1115/1.4050400

Published Online: March 24, 2021

View Article titled, A Data-Driven Methodology to Improve Tolerance Allocation Using Product Usage Data

Topics: Algorithms, Design, Evaluation methods, Machinery, Manufacturing, Chain, Tolerance analysis, Maintenance, Deformation, Artificial neural networks

Journal Articles

A Parametric Level Set Method for Topology Optimization Based on Deep Neural Network

Hao Deng, Albert C. To

J. Mech. Des. September 2021, 143(9): 091702.

Paper No: MD-20-1595

DOI: https://doi.org/10.1115/1.4050105

Published Online: March 18, 2021

View Article titled, A Parametric Level Set Method for Topology Optimization Based on Deep Neural Network

Topics: Artificial neural networks, Design, Optimization, Topology

Journal Articles

Approach for Importance–Performance Analysis of Product Attributes From Online Reviews

Junegak Joung, Harrison M. Kim

J. Mech. Des. August 2021, 143(8): 081705.

Paper No: MD-20-1538

DOI: https://doi.org/10.1115/1.4049865

Published Online: February 11, 2021

View Article titled, Approach for Importance–Performance Analysis of Product Attributes From Online Reviews

Topics: Artificial neural networks, Product design, Design

Journal Articles

Deriving Design Feature Vectors for Patent Images Using Convolutional Neural Networks

Shuo Jiang, Jianxi Luo, Guillermo Ruiz-Pava, Jie Hu, Christopher L. Magee

J. Mech. Des. June 2021, 143(6): 061405.

Paper No: MD-20-1469

DOI: https://doi.org/10.1115/1.4049214

Published Online: January 29, 2021

View Article titled, Deriving Design Feature Vectors for Patent Images Using Convolutional Neural Networks

Topics: Artificial neural networks, Design, Patents, Databases

Journal Articles

Leveraging Task Modularity in Reinforcement Learning for Adaptable Industry 4.0 Automation

Qiliang Chen, Babak Heydari, Mohsen Moghaddam

J. Mech. Des. July 2021, 143(7): 071701.

Paper No: MD-20-1388

DOI: https://doi.org/10.1115/1.4049531

Published Online: January 29, 2021

View Article titled, Leveraging Task Modularity in Reinforcement Learning for Adaptable Industry 4.0 Automation

Topics: Algorithms, Artificial neural networks, Manufacturing, Reinforcement learning, Robotics, Robots, Machinery, Resilience, Inspection, Materials handling

Journal Articles

Toward a Digital Twin: Time Series Prediction Based on a Hybrid Ensemble Empirical Mode Decomposition and BO-LSTM Neural Networks

Weifei Hu, Yihan He, Zhenyu Liu, Jianrong Tan, Ming Yang, Jiancheng Chen

J. Mech. Des. May 2021, 143(5): 051705.

Paper No: MD-20-1093

DOI: https://doi.org/10.1115/1.4048414

Published Online: November 17, 2020

View Article titled, Toward a Digital Twin: Time Series Prediction Based on a Hybrid Ensemble Empirical Mode Decomposition and BO-LSTM Neural Networks

Topics: Artificial neural networks, Time series, Wind velocity, Waves

Journal Articles

Predicting Design Performance Utilizing Automated Topic Discovery

Zachary Ball, Kemper Lewis

J. Mech. Des. December 2020, 142(12): 121703.

Paper No: MD-20-1144

DOI: https://doi.org/10.1115/1.4048455

Published Online: October 7, 2020

View Article titled, Predicting Design Performance Utilizing Automated Topic Discovery

Topics: Algorithms, Artificial neural networks, Collaboration, Competencies, Design, Innovation, Modeling, Teams, Support vector machines, Engineering design

Journal Articles

Combining Direct and Indirect User Data for Calculating Social Impact Indicators of Products in Developing Countries

Bryan J. Stringham, Daniel O. Smith, Christopher A. Mattson, Eric C. Dahlin

J. Mech. Des. December 2020, 142(12): 121401.

Paper No: MD-19-1904

DOI: https://doi.org/10.1115/1.4047433

Published Online: August 4, 2020

View Article titled, Combining Direct and Indirect User Data for Calculating Social Impact Indicators of Products in Developing Countries

Topics: Pumps, Water, Artificial neural networks, Sensors, Developing nations

Journal Articles

Using Recurrent Neural Networks to Model Spatial Grammars for Design Creation

Michael A Yukish, Gary M Stump, Simon W Miller

J. Mech. Des. October 2020, 142(10): 104501.

Paper No: MD-19-1284

DOI: https://doi.org/10.1115/1.4046806

Published Online: May 8, 2020

View Article titled, Using Recurrent Neural Networks to Model Spatial Grammars for Design Creation

Topics: Artificial neural networks, Design, String

Journal Articles

Convolutional Neural Network Surrogate Models for the Mechanical Properties of Periodic Structures

Mark C. Messner

J. Mech. Des. February 2020, 142(2): 024503.

Paper No: MD-18-1635

DOI: https://doi.org/10.1115/1.4045040

Published Online: October 17, 2019

View Article titled, Convolutional Neural Network Surrogate Models for the Mechanical Properties of Periodic Structures

Topics: Artificial neural networks, Mechanical properties, Optimization, Topology, Design, Periodic structures, Simulation, Databases, Trains, Modeling

Journal Articles

A Comparative Evaluation of Supervised Machine Learning Classification Techniques for Engineering Design Applications

Conner Sharpe, Tyler Wiest, Pingfeng Wang, Carolyn Conner Seepersad

J. Mech. Des. December 2019, 141(12): 121404.

Paper No: MD-19-1180

DOI: https://doi.org/10.1115/1.4044524

Published Online: October 3, 2019

View Article titled, A Comparative Evaluation of Supervised Machine Learning Classification Techniques for Engineering Design Applications

Topics: Algorithms, Design, Engineering design, Machine learning, Optimization, Support vector machines, Artificial neural networks

Includes: Supplementary data

Journal citation

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Supplemental Material

Manuscript

An efficient reliability analysis method based on the improved radial basis function neural network

Comparing Mechanical Neural-Network Learning Algorithms

Neural Network-Assisted Design: A Study of Multiscale Topology Optimization With Smoothly Graded Cellular Structures

Artificial Intelligence Design for Ship Structures: A Variant Multiple-Input Neural Network-Based Ship Resistance Prediction

Bias-Learning-Based Model Predictive Controller Design for Reliable Path Tracking of Autonomous Vehicles Under Model and Environmental Uncertainty

ActivationNet : Representation Learning to Predict Contact Quality of Interacting 3D Surfaces in Engineering Designs

Classifying Component Function in Product Assemblies With Graph Neural Networks

Inverse Design Framework With Invertible Neural Networks for Passive Vibration Suppression in Phononic Structures

A Flow Information-Based Prediction Model Applied to the Nonaxisymmetric Hub Optimization of a Centrifugal Impeller

A Data-Driven Methodology to Improve Tolerance Allocation Using Product Usage Data

A Parametric Level Set Method for Topology Optimization Based on Deep Neural Network

Approach for Importance–Performance Analysis of Product Attributes From Online Reviews

Deriving Design Feature Vectors for Patent Images Using Convolutional Neural Networks

Leveraging Task Modularity in Reinforcement Learning for Adaptable Industry 4.0 Automation

Toward a Digital Twin: Time Series Prediction Based on a Hybrid Ensemble Empirical Mode Decomposition and BO-LSTM Neural Networks

Predicting Design Performance Utilizing Automated Topic Discovery

Combining Direct and Indirect User Data for Calculating Social Impact Indicators of Products in Developing Countries

Using Recurrent Neural Networks to Model Spatial Grammars for Design Creation

Convolutional Neural Network Surrogate Models for the Mechanical Properties of Periodic Structures

A Comparative Evaluation of Supervised Machine Learning Classification Techniques for Engineering Design Applications

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