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Proceedings Papers

Using Machine Learning Methods Towards Identifying College Campus Load Profiles and Energy Storage Application for Reducing Peak Energy Demand From the Utility Grid

Christopher J. Sweeny, Jackson R. Smith, Afsaneh Ghanavati, James R. McCusker
Proc. ASME. IMECE2022, Volume 6: Energy, V006T08A062, October 30–November 3, 2022
Paper No: IMECE2022-94830
DOI: https://doi.org/10.1115/IMECE2022-94830
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Topics: Energy storage, Machine learning, Principal component analysis, Stress, Energy consumption, Algorithms, Residential construction, Structures, Matlab, State estimation
Proceedings Papers

Aerodynamic Analysis of a Car Based on Computational Fluid Dynamics and Machine Learning

Xingchuan Ma
Proc. ASME. IMECE2022, Volume 8: Fluids Engineering; Heat Transfer and Thermal Engineering, V008T10A010, October 30–November 3, 2022
Paper No: IMECE2022-96817
DOI: https://doi.org/10.1115/IMECE2022-96817
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Topics: Computational fluid dynamics, Design, Machine learning
Proceedings Papers

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

Pinjala Sai Sudhir, Gangchen Ren, Aditya Chuttar, Nandan Shettigar, Debjyoti Banerjee
Proc. ASME. IMECE2022, Volume 8: Fluids Engineering; Heat Transfer and Thermal Engineering, V008T11A007, October 30–November 3, 2022
Paper No: IMECE2022-97121
DOI: https://doi.org/10.1115/IMECE2022-97121
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Topics: Artificial neural networks, Food products, Machine learning, Phase change materials, Reliability, Resilience, Sustainability, Thermal energy storage, Water, Temperature
Proceedings Papers

Machine-Learning Approach to Modeling Oxidation of Toluene in a Bubble Column Reactor

Raihan Tayeb, Yuwen Zhang
Proc. ASME. IMECE2022, Volume 8: Fluids Engineering; Heat Transfer and Thermal Engineering, V008T11A006, October 30–November 3, 2022
Paper No: IMECE2022-94564
DOI: https://doi.org/10.1115/IMECE2022-94564
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Topics: Bubbles, Machine learning, Modeling, Oxidation
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

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

Computer Vision and Machine Learning to Create an Advanced Pick-and-Place Robotic Operation Using Industry 4.0 Trends

David Guerra-Zubiaga, Angelicia Franklin, Diego Escobar-Escobar, Timothey Lemley, Neeyaz Hariri, Jeremy Plattel, Chan Ham
Proc. ASME. IMECE2022, Volume 2A: Advanced Manufacturing, V02AT02A010, October 30–November 3, 2022
Paper No: IMECE2022-89743
DOI: https://doi.org/10.1115/IMECE2022-89743
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Topics: Computers, Grippers, Machine learning, Robotics, Robots
Proceedings Papers

Thin Steel Plate Surface Rust Recognition Using Processing Light Measurement for Reduction of Laser Cutting Defect False Recognition

Mizuki Ishiguro, Shin'ichi Warisawa, Naoyasu Narita, Hironobu Miyoshi, Rui Fukui
Proc. ASME. IMECE2022, Volume 2B: Advanced Manufacturing, V02BT02A034, October 30–November 3, 2022
Paper No: IMECE2022-96166
DOI: https://doi.org/10.1115/IMECE2022-96166
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Topics: Laser cutting, Lasers, Steel, Steel corrosion, Cutting, Analytical methods, Corrosion, Irradiation (Radiation exposure), Machine learning, Plates (structures)
Proceedings Papers

Industry 4.0 Trends in Intelligent Manufacturing Automation Exploring Machine Learning

William Hoover, David A. Guerra-Zubiaga, Jeremy Banta, Kevin Wandene, Kaleb Key, Germanico Gonzalez-Badillo
Proc. ASME. IMECE2022, Volume 2B: Advanced Manufacturing, V02BT02A028, October 30–November 3, 2022
Paper No: IMECE2022-96092
DOI: https://doi.org/10.1115/IMECE2022-96092
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Topics: Digital twin, Machine learning, Manufacturing, Manufacturing automation
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

A Digital Twin Framework for Mechanical Testing Powered by Machine Learning

Müge Kahya, Cem Söyleyici, Mete Bakır, Hakkı Özgür Ünver
Proc. ASME. IMECE2022, Volume 3: Advanced Materials: Design, Processing, Characterization and Applications; Advances in Aerospace Technology, V003T04A008, October 30–November 3, 2022
Paper No: IMECE2022-94680
DOI: https://doi.org/10.1115/IMECE2022-94680
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Topics: Digital twin, Machine learning, Mechanical testing
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

Unsupervised Machine Learning Algorithms for Analysis of Low Velocity Impact Damage in Composite Structures From CT Image Data

Olesya I. Zhupanska, Pavlo A. Krokhmal
Proc. ASME. IMECE2022, Volume 3: Advanced Materials: Design, Processing, Characterization and Applications; Advances in Aerospace Technology, V003T04A012, October 30–November 3, 2022
Paper No: IMECE2022-96262
DOI: https://doi.org/10.1115/IMECE2022-96262
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Topics: Algorithms, Composite materials, Computerized tomography, Damage, Image segmentation, Machine learning
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
Proceedings Papers

Uncertainty Quantification in Material Properties of Additively Manufactured Materials for Application in Topology Optimization

Zahra Kazemi, Craig A. Steeves
Proc. ASME. IMECE2022, Volume 3: Advanced Materials: Design, Processing, Characterization and Applications; Advances in Aerospace Technology, V003T04A009, October 30–November 3, 2022
Paper No: IMECE2022-95195
DOI: https://doi.org/10.1115/IMECE2022-95195
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Topics: Additive manufacturing, Machine learning, Materials properties, Topology optimization, Uncertainty quantification, Elastic moduli, Chaos, Young's modulus, Algorithms, Design
Proceedings Papers

Experimental Investigation on the Use of Vibration Signals Combined With Supervised Classification to Predict Radial Load Condition in Roller Element Bearings

Issam Abu-Mahfouz, Amit Banerjee, Esfakur Rahman
Proc. ASME. IMECE2022, Volume 5: Dynamics, Vibration, and Control, V005T07A059, October 30–November 3, 2022
Paper No: IMECE2022-95632
DOI: https://doi.org/10.1115/IMECE2022-95632
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Topics: Bearings, Machine learning, Rollers, Signals, Stress, Support vector machines, Vibration, Wavelets
Proceedings Papers

A Comprehensive Assessment of Gearbox Tooth Faults Based on Dynamic Modelling and Machine Learning

Vikash Kumar, Subrata Mukherjee, Sanjeev Kumar, Somnath Sarangi
Proc. ASME. IMECE2022, Volume 5: Dynamics, Vibration, and Control, V005T07A060, October 30–November 3, 2022
Paper No: IMECE2022-95672
DOI: https://doi.org/10.1115/IMECE2022-95672
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Topics: Dynamic modeling, Fault diagnosis, Machine learning, Mechanical drives, Signals
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
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Topics: Accelerometers, Artificial neural networks, Ball bearings, Damage, Machine learning, Rotors, Sensors, Signals, Vibration