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Proc. ASME. IMECE2022, Volume 9: Mechanics of Solids, Structures, and Fluids; Micro- and Nano-Systems Engineering and Packaging; Safety Engineering, Risk, and Reliability Analysis; Research Posters, V009T14A013, October 30–November 3, 2022

Paper No: IMECE2022-94192

DOI: https://doi.org/10.1115/IMECE2022-94192

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Topics: Artificial neural networks, Surface finishing, Surface texture, Machining, Computer-aided design, Stress concentration, Surface roughness, Water jet cutting, Aluminum alloys, Artificial intelligence

Proceedings Papers

Sparse Data Recovery Algorithm Based on BP Neural Network for Ultrasonic Guided Wave Imaging

Xiaocen Wang, Min Lin, Jian Li, Dingpeng Wang, Yang Liu

Proc. ASME. IMECE2022, Volume 1: Acoustics, Vibration, and Phononics, V001T01A023, October 30–November 3, 2022

Paper No: IMECE2022-96700

DOI: https://doi.org/10.1115/IMECE2022-96700

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Topics: Algorithms, Artificial neural networks, Imaging, Waves

Proceedings Papers

A Machine Learning Framework for Physics-Based Multi-Fidelity Modeling and Health Monitoring for a Composite Wing

Gaurav Makkar, Cameron Smith, George Drakoulas, Fotis Kopsaftopoulos, Farhan Gandhi

Proc. ASME. IMECE2022, Volume 1: Acoustics, Vibration, and Phononics, V001T01A008, October 30–November 3, 2022

Paper No: IMECE2022-94850

DOI: https://doi.org/10.1115/IMECE2022-94850

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Topics: Artificial neural networks, Composite materials, Finite element analysis, Machine learning, Modeling, Physics, Structural health monitoring, Wings, Damage, Unmanned aerial vehicles

Proceedings Papers

Fast and Robust Damage Imaging With a Cascaded Deep Learning Technique

Junkai Tong, Min Lin, Jian Li, Xiaocen Wang, Guoan Chu, Yang Liu

Proc. ASME. IMECE2022, Volume 1: Acoustics, Vibration, and Phononics, V001T01A022, October 30–November 3, 2022

Paper No: IMECE2022-96652

DOI: https://doi.org/10.1115/IMECE2022-96652

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Topics: Artificial neural networks, Corrosion, Damage, Imaging, Waves

Proceedings Papers

Intelligent Modelling and Machining Characteristics of Hybrid Machining for Hybrid Metal Matrix Composites

Janvita Reddy, Ram Singar Yadav

Proc. ASME. IMECE2022, Volume 2A: Advanced Manufacturing, V02AT02A043, October 30–November 3, 2022

Paper No: IMECE2022-95543

DOI: https://doi.org/10.1115/IMECE2022-95543

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Topics: Machining, Metal matrix composites, Modeling, Algorithms, Grinding, Machine learning, Artificial intelligence, Artificial neural networks, Composite materials, Diamonds

Proceedings Papers

An Image-Based Convolutional Neural Network Platform for the Prediction of the Porosity of Composite Bone Scaffolds, Fabricated Using Material Extrusion Additive Manufacturing

Joshua Blatt, Jacob Kirkendoll, Paavana Krishna Mandava, Zachary Preston, Robert Joyce, Roozbeh (Ross) Salary

Proc. ASME. IMECE2022, Volume 2A: Advanced Manufacturing, V02AT02A001, October 30–November 3, 2022

Paper No: IMECE2022-95044

DOI: https://doi.org/10.1115/IMECE2022-95044

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Topics: Additive manufacturing, Artificial neural networks, Bone, Composite materials, Extruding, Porosity

Proceedings Papers

Modeling the Interplay Between Process Parameters and Part Attributes in Additive Manufacturing Process With Artificial Neural Network

Jayanta Deb, Nazmul Ahsan, Sharmin Majumder

Proc. ASME. IMECE2022, Volume 2A: Advanced Manufacturing, V02AT02A015, October 30–November 3, 2022

Paper No: IMECE2022-95120

DOI: https://doi.org/10.1115/IMECE2022-95120

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Topics: Additive manufacturing, Artificial neural networks, Modeling

Proceedings Papers

An Artificial Neural Network for Parametric Analysis of Metallic Additive Manufacturing Using Discrete Element Method

Yuxuan Wu, Sirish Namilae

Proc. ASME. IMECE2022, Volume 2B: Advanced Manufacturing, V02BT02A003, October 30–November 3, 2022

Paper No: IMECE2022-95117

DOI: https://doi.org/10.1115/IMECE2022-95117

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Topics: Additive manufacturing, Artificial neural networks, Discrete element methods, Metals

Proceedings Papers

Analysis of 3D Printing Performance Using Machine Learning Techniques

Kantu Thomas Kabengele, Lagouge Kwanda Tartibu, Isaac Oyeyemi Olayode

Proc. ASME. IMECE2022, Volume 2B: Advanced Manufacturing, V02BT02A002, October 30–November 3, 2022

Paper No: IMECE2022-94000

DOI: https://doi.org/10.1115/IMECE2022-94000

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Topics: Additive manufacturing, Artificial neural networks, Machine learning, Particle swarm optimization

Proceedings Papers

Damaged Apple Detection Using Artificial Intelligence

Sathish K. Gurupatham, Caleb Bailey

Proc. ASME. IMECE2022, Volume 2B: Advanced Manufacturing, V02BT02A045, October 30–November 3, 2022

Paper No: IMECE2022-96162

DOI: https://doi.org/10.1115/IMECE2022-96162

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Topics: Artificial intelligence, Machine learning, Artificial neural networks, Algorithms, Mechanical engineering, Quality control, Assembly lines, Brain, Errors, Machine components

Proceedings Papers

In-Process Intelligent Inspection of the Specimen Using Machine Vision

Adarsh Mahor, Ram Singar Yadav

Proc. ASME. IMECE2022, Volume 2B: Advanced Manufacturing, V02BT02A057, October 30–November 3, 2022

Paper No: IMECE2022-95347

DOI: https://doi.org/10.1115/IMECE2022-95347

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Topics: Inspection, Machinery, Algorithms, Dimensions, Manufacturing, Assembly lines, Computers, Shapes, Artificial neural networks, Conveyor belts

Proceedings Papers

Accelerated Structural Design of Cellular Materials for Compressive Deformation Using a Machine-Learning

Jinlan Song, Aoi Takagi, Genki Mitsuhashi, Kohei Saito, Kazuma Ogata, Takeru Miyagawa, Akio Yonezu

Proc. ASME. IMECE2022, Volume 3: Advanced Materials: Design, Processing, Characterization and Applications; Advances in Aerospace Technology, V003T03A017, October 30–November 3, 2022

Paper No: IMECE2022-95522

DOI: https://doi.org/10.1115/IMECE2022-95522

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Topics: Artificial neural networks, Deformation, Equipment performance, Machine learning, Structural design

Proceedings Papers

On the Accuracy and Efficiency of Convolutional Neural Networks for Element-Wise Refinement of FEM Models

Marco Petrolo, Pierluigi Iannotti, Alfonso Pagani, Erasmo Carrera

Proc. ASME. IMECE2022, Volume 3: Advanced Materials: Design, Processing, Characterization and Applications; Advances in Aerospace Technology, V003T04A013, October 30–November 3, 2022

Paper No: IMECE2022-93995

DOI: https://doi.org/10.1115/IMECE2022-93995

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Topics: Artificial neural networks, Finite element analysis, Finite element methods, Finite element model

Proceedings Papers

Investigation of Mechanical Properties of Combinatorial Ti-Cu Film Using MD Simulation With Neural Network Potential

Takeru Miyagawa, Yugo Sakai, Akio Yonezu, Kazuki Mori, Nobuhiko Kato, Keiji Ishibashi

Proc. ASME. IMECE2022, Volume 3: Advanced Materials: Design, Processing, Characterization and Applications; Advances in Aerospace Technology, V003T03A019, October 30–November 3, 2022

Paper No: IMECE2022-94934

DOI: https://doi.org/10.1115/IMECE2022-94934

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Topics: Alloys, Artificial neural networks, Copper, Mechanical properties, Molecular dynamics simulation, Titanium

Proceedings Papers

Machine Learning Potentials for Graphene

Akash Singh, Yumeng Li

Proc. ASME. IMECE2022, Volume 3: Advanced Materials: Design, Processing, Characterization and Applications; Advances in Aerospace Technology, V003T03A036, October 30–November 3, 2022

Paper No: IMECE2022-95341

DOI: https://doi.org/10.1115/IMECE2022-95341

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Topics: Artificial neural networks, Density functional theory, Graphene, Machine learning, Molecular dynamics simulation

Proceedings Papers

Predicting Motion of Engine-Ingested Particles Using Deep Neural Networks

Travis Bowman, Cairen Miranda, John Palmore, Jr.

Proc. ASME. IMECE2022, Volume 3: Advanced Materials: Design, Processing, Characterization and Applications; Advances in Aerospace Technology, V003T04A007, October 30–November 3, 2022

Paper No: IMECE2022-93668

DOI: https://doi.org/10.1115/IMECE2022-93668

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Topics: Artificial neural networks, Engines, Particulate matter, Flow (Dynamics), Gas turbines, Ice, Fluids, Rotation, Abrasion, Aviation

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A Data Driven Analysis on the Energy Performance and Efficiency of Water Treatment Plants

Application of Artificial Neural Network to Predict the Performance of Thermoelectric Power Plants at Design Conditions

A Robust Hybrid Machine Learning-Based Modeling Technique for Wind Power Production Estimates

Deploying Machine Learning (ML) for Improving Reliability and Resiliency of Thermal Energy Storage (TES) Platforms by Leveraging Phase Change Materials (PCM) for Sustainability Applications and Mitigating Food-Energy-Water (FEW) Nexus

Artificial Neural Network for Automatic Prediction of the Surface Finishing via Classification of the Surface Texture

Sparse Data Recovery Algorithm Based on BP Neural Network for Ultrasonic Guided Wave Imaging

A Machine Learning Framework for Physics-Based Multi-Fidelity Modeling and Health Monitoring for a Composite Wing

Fast and Robust Damage Imaging With a Cascaded Deep Learning Technique

Intelligent Modelling and Machining Characteristics of Hybrid Machining for Hybrid Metal Matrix Composites

An Image-Based Convolutional Neural Network Platform for the Prediction of the Porosity of Composite Bone Scaffolds, Fabricated Using Material Extrusion Additive Manufacturing

Modeling the Interplay Between Process Parameters and Part Attributes in Additive Manufacturing Process With Artificial Neural Network

An Artificial Neural Network for Parametric Analysis of Metallic Additive Manufacturing Using Discrete Element Method

Analysis of 3D Printing Performance Using Machine Learning Techniques

Damaged Apple Detection Using Artificial Intelligence

In-Process Intelligent Inspection of the Specimen Using Machine Vision

Accelerated Structural Design of Cellular Materials for Compressive Deformation Using a Machine-Learning

On the Accuracy and Efficiency of Convolutional Neural Networks for Element-Wise Refinement of FEM Models

Investigation of Mechanical Properties of Combinatorial Ti-Cu Film Using MD Simulation With Neural Network Potential

Machine Learning Potentials for Graphene

Predicting Motion of Engine-Ingested Particles Using Deep Neural Networks

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