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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
View Paper titled, An Artificial Neural Network for Parametric Analysis of Metallic Additive Manufacturing Using Discrete Element Method
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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
View Paper titled, Analysis of 3D Printing Performance Using Machine Learning Techniques
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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
View Paper titled, Damaged Apple Detection Using Artificial Intelligence
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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
View Paper titled, In-Process Intelligent Inspection of the Specimen Using Machine Vision
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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
View Paper titled, Accelerated Structural Design of Cellular Materials for Compressive Deformation Using a Machine-Learning
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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
View Paper titled, On the Accuracy and Efficiency of Convolutional Neural Networks for Element-Wise Refinement of FEM Models
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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
View Paper titled, Investigation of Mechanical Properties of Combinatorial Ti-Cu Film Using MD Simulation With Neural Network Potential
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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
View Paper titled, Machine Learning Potentials for Graphene
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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
View Paper titled, Predicting Motion of Engine-Ingested Particles Using Deep Neural Networks
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Topics: Artificial neural networks, Engines, Particulate matter, Flow (Dynamics), Gas turbines, Ice, Fluids, Rotation, Abrasion, Aviation
Proceedings Papers

Implementation of ANN Modeling Techniques and Genetic Algorithm in the Diameter Prediction of MWCNTs/Epoxy Nanofibers for CFRP Structures

Pias Kumar Biswas, Pradnya Zende, Hamid Dalir, Mangilal Agarwal
Proc. ASME. IMECE2022, Volume 3: Advanced Materials: Design, Processing, Characterization and Applications; Advances in Aerospace Technology, V003T03A047, October 30–November 3, 2022
Paper No: IMECE2022-90499
DOI: https://doi.org/10.1115/IMECE2022-90499
View Paper titled, Implementation of ANN Modeling Techniques and Genetic Algorithm in the Diameter Prediction of MWCNTs/Epoxy Nanofibers for CFRP Structures
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Topics: Artificial neural networks, Carbon reinforced plastics, Epoxy resins, Genetic algorithms, Modeling, Multi-walled carbon nanotubes, Nanofibers
Proceedings Papers

Evaluation of Deep Learning Networks for Predicting Truss Topology Optimization Results

Recep M. Gorguluarslan, Gorkem Can Ates
Proc. ASME. IMECE2022, Volume 3: Advanced Materials: Design, Processing, Characterization and Applications; Advances in Aerospace Technology, V003T04A011, October 30–November 3, 2022
Paper No: IMECE2022-95870
DOI: https://doi.org/10.1115/IMECE2022-95870
View Paper titled, Evaluation of Deep Learning Networks for Predicting Truss Topology Optimization Results
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Topics: Artificial neural networks, Topology optimization, Trusses (Building)
Proceedings Papers

Development of an Artificial Neural Network (ANN) Constitutive Model for Mechanical Metamaterials

Arif Hussain, Amir Hosein Sakhaei, Mahmood Shafiee
Proc. ASME. IMECE2022, Volume 3: Advanced Materials: Design, Processing, Characterization and Applications; Advances in Aerospace Technology, V003T03A041, October 30–November 3, 2022
Paper No: IMECE2022-94049
DOI: https://doi.org/10.1115/IMECE2022-94049
View Paper titled, Development of an Artificial Neural Network (ANN) Constitutive Model for Mechanical Metamaterials
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Topics: Artificial neural networks, Constitutive equations, Metamaterials
Proceedings Papers

An Artificial Neural Network Model for Multiple Piezoelectric Actuation of Plates

Linjin Jiang, Pengcheng Yu, Mu Fan
Proc. ASME. IMECE2022, Volume 5: Dynamics, Vibration, and Control, V005T07A082, October 30–November 3, 2022
Paper No: IMECE2022-94673
DOI: https://doi.org/10.1115/IMECE2022-94673
View Paper titled, An Artificial Neural Network Model for Multiple Piezoelectric Actuation of Plates
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Topics: Artificial neural networks, Finite element analysis, Plates (structures), Simulation
Proceedings Papers

A Neural Network-Augmented Bayesian Approach to Uncertain Parameter Estimation in Nonlinear Dynamic Systems

Roja Zakeri, Praveen Shankar
Proc. ASME. IMECE2022, Volume 5: Dynamics, Vibration, and Control, V005T07A054, October 30–November 3, 2022
Paper No: IMECE2022-95563
DOI: https://doi.org/10.1115/IMECE2022-95563
View Paper titled, A Neural Network-Augmented Bayesian Approach to Uncertain Parameter Estimation in Nonlinear Dynamic Systems
Click here to open pdf in another window PDF for A Neural Network-Augmented Bayesian Approach to Uncertain Parameter Estimation in Nonlinear Dynamic Systems
Topics: Algorithms, Artificial neural networks, Chain, Damping, Design, Dynamic systems, Kinematics, Nonlinear dynamical systems, Nonlinear dynamics, Parameter estimation
Proceedings Papers

Long Short-Term Memory Neural Networks for Predicting Dynamic Response of Structures of High Complexities

Yabin Liao, Biswas Poudel, Priyanshu Kumar, Mark Sensemier
Proc. ASME. IMECE2022, Volume 5: Dynamics, Vibration, and Control, V005T07A065, October 30–November 3, 2022
Paper No: IMECE2022-97025
DOI: https://doi.org/10.1115/IMECE2022-97025
View Paper titled, Long Short-Term Memory Neural Networks for Predicting Dynamic Response of Structures of High Complexities
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Topics: Artificial neural networks, Dynamic response, Structural dynamics, Vibration
Proceedings Papers

Open-Loop Optimal Control for Tracking a Reference Signal With Approximate Dynamic Programming

Jorge A. Diaz, Lei Xu, Tohid Sardarmehni
Proc. ASME. IMECE2022, Volume 5: Dynamics, Vibration, and Control, V005T07A064, October 30–November 3, 2022
Paper No: IMECE2022-96769
DOI: https://doi.org/10.1115/IMECE2022-96769
View Paper titled, Open-Loop Optimal Control for Tracking a Reference Signal With Approximate Dynamic Programming
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Topics: Artificial neural networks, Dynamic programming, Optimal control, Signals
Proceedings Papers

Quadcopter Control Using Single Network Adaptive Critics

Alberto Velazquez, Lei Xu, Tohid Sardarmehni
Proc. ASME. IMECE2022, Volume 5: Dynamics, Vibration, and Control, V005T07A035, October 30–November 3, 2022
Paper No: IMECE2022-96834
DOI: https://doi.org/10.1115/IMECE2022-96834
View Paper titled, Quadcopter Control Using Single Network Adaptive Critics
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Topics: Adaptive dynamics, Artificial neural networks, Computer programming, Control equipment, Dynamics (Mechanics), Feedback, Least squares approximations, Nonlinear systems, Optimal control, Reinforcement learning
Proceedings Papers

Data-Driven Fault Detection in Composite Cylindrical Shells: Directing the Proper Orthogonal Decomposition Prospective Into an Artificial Neural Network Vision

Konstantinos N. Liontos, Ioannis T. Georgiou
Proc. ASME. IMECE2022, Volume 5: Dynamics, Vibration, and Control, V005T07A063, October 30–November 3, 2022
Paper No: IMECE2022-96255
DOI: https://doi.org/10.1115/IMECE2022-96255
View Paper titled, Data-Driven Fault Detection in Composite Cylindrical Shells: Directing the Proper Orthogonal Decomposition Prospective Into an Artificial Neural Network Vision
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Topics: Artificial neural networks, Composite materials, Damage, Flaw detection, Pipes, Principal component analysis
Proceedings Papers

Using Deep Convolutional Neural Networks (DCNN) to Learn by Sensor Signal Classification Critical Local Regions of a Physical Shaft-Rotor System: A Complex Mechanical System Example for Machine Learning Considerations

Pantelis Panagiotis Papageorgiou, Ioannis T. Georgiou
Proc. ASME. IMECE2022, Volume 5: Dynamics, Vibration, and Control, V005T07A062, October 30–November 3, 2022
Paper No: IMECE2022-96146
DOI: https://doi.org/10.1115/IMECE2022-96146
View Paper titled, Using Deep Convolutional Neural Networks (DCNN) to Learn by Sensor Signal Classification Critical Local Regions of a Physical Shaft-Rotor System: A Complex Mechanical System Example for Machine Learning Considerations
Click here to open pdf in another window PDF for Using Deep Convolutional Neural Networks (DCNN) to Learn by Sensor Signal Classification Critical Local Regions of a Physical Shaft-Rotor System: A Complex Mechanical System Example for Machine Learning Considerations
Topics: Accelerometers, Artificial neural networks, Ball bearings, Damage, Machine learning, Rotors, Sensors, Signals, Vibration
Proceedings Papers

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

Alex Callinan, Hamidreza Najafi, Aldo Fabregas, Troy Nguyen
Proc. ASME. IMECE2022, Volume 6: Energy, V006T08A039, October 30–November 3, 2022
Paper No: IMECE2022-96040
DOI: https://doi.org/10.1115/IMECE2022-96040
View Paper titled, A Data Driven Analysis on the Energy Performance and Efficiency of Water Treatment Plants
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Topics: Water treatment, Artificial neural networks, Databases, Energy consumption, Information Analysis Centers, Energy efficiency, Potential energy, Testing