1-20 of 92
Machine learning
Close
Save search
Follow your search
Access your saved searches in your account
Would you like to receive an alert when new items match your search?
Close Modal
Sort by
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
https://doi.org/10.1115/IMECE2022-96162
View Paper titled, Damaged Apple Detection Using Artificial Intelligence
Open the PDF for Damaged Apple Detection Using Artificial Intelligence in another window
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
https://doi.org/10.1115/IMECE2022-94680
View Paper titled, A Digital Twin Framework for Mechanical Testing Powered by Machine Learning
Open the PDF for A Digital Twin Framework for Mechanical Testing Powered by Machine Learning in another window
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
https://doi.org/10.1115/IMECE2022-95522
View Paper titled, Accelerated Structural Design of Cellular Materials for Compressive Deformation Using a Machine-Learning
Open the PDF for Accelerated Structural Design of Cellular Materials for Compressive Deformation Using a Machine-Learning in another window
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
https://doi.org/10.1115/IMECE2022-96262
View Paper titled, Unsupervised Machine Learning Algorithms for Analysis of Low Velocity Impact Damage in Composite Structures From CT Image Data
Open the PDF for Unsupervised Machine Learning Algorithms for Analysis of Low Velocity Impact Damage in Composite Structures From CT Image Data in another window
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
https://doi.org/10.1115/IMECE2022-95341
View Paper titled, Machine Learning Potentials for Graphene
Open the PDF for Machine Learning Potentials for Graphene in another window
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
https://doi.org/10.1115/IMECE2022-93668
View Paper titled, Predicting Motion of Engine-Ingested Particles Using Deep Neural Networks
Open the PDF for Predicting Motion of Engine-Ingested Particles Using Deep Neural Networks in another window
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
https://doi.org/10.1115/IMECE2022-95195
View Paper titled, Uncertainty Quantification in Material Properties of Additively Manufactured Materials for Application in Topology Optimization
Open the PDF for Uncertainty Quantification in Material Properties of Additively Manufactured Materials for Application in Topology Optimization in another window
Topics: Additive manufacturing, Machine learning, Materials properties, Topology optimization, Uncertainty quantification, Elastic moduli, Chaos, Young's modulus, Algorithms, Design
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
https://doi.org/10.1115/IMECE2022-96817
View Paper titled, Aerodynamic Analysis of a Car Based on Computational Fluid Dynamics and Machine Learning
Open the PDF for Aerodynamic Analysis of a Car Based on Computational Fluid Dynamics and Machine Learning in another window
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
https://doi.org/10.1115/IMECE2022-97121
View Paper titled, 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
Open the PDF for 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 in another window
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
https://doi.org/10.1115/IMECE2022-94564
View Paper titled, Machine-Learning Approach to Modeling Oxidation of Toluene in a Bubble Column Reactor
Open the PDF for Machine-Learning Approach to Modeling Oxidation of Toluene in a Bubble Column Reactor in another window
Topics: Bubbles, Machine learning, Modeling, Oxidation
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
https://doi.org/10.1115/IMECE2022-95632
View Paper titled, Experimental Investigation on the Use of Vibration Signals Combined With Supervised Classification to Predict Radial Load Condition in Roller Element Bearings
Open the PDF for Experimental Investigation on the Use of Vibration Signals Combined With Supervised Classification to Predict Radial Load Condition in Roller Element Bearings in another window
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
https://doi.org/10.1115/IMECE2022-95672
View Paper titled, A Comprehensive Assessment of Gearbox Tooth Faults Based on Dynamic Modelling and Machine Learning
Open the PDF for A Comprehensive Assessment of Gearbox Tooth Faults Based on Dynamic Modelling and Machine Learning in another window
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
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
Open the 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 in another window
Topics: Accelerometers, Artificial neural networks, Ball bearings, Damage, Machine learning, Rotors, Sensors, Signals, Vibration
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
https://doi.org/10.1115/IMECE2022-94850
View Paper titled, A Machine Learning Framework for Physics-Based Multi-Fidelity Modeling and Health Monitoring for a Composite Wing
Open the PDF for A Machine Learning Framework for Physics-Based Multi-Fidelity Modeling and Health Monitoring for a Composite Wing in another window
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
https://doi.org/10.1115/IMECE2022-95543
View Paper titled, Intelligent Modelling and Machining Characteristics of Hybrid Machining for Hybrid Metal Matrix Composites
Open the PDF for Intelligent Modelling and Machining Characteristics of Hybrid Machining for Hybrid Metal Matrix Composites in another window
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
https://doi.org/10.1115/IMECE2022-89743
View Paper titled, Computer Vision and Machine Learning to Create an Advanced Pick-and-Place Robotic Operation Using Industry 4.0 Trends
Open the PDF for Computer Vision and Machine Learning to Create an Advanced Pick-and-Place Robotic Operation Using Industry 4.0 Trends in another window
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
https://doi.org/10.1115/IMECE2022-96166
View Paper titled, Thin Steel Plate Surface Rust Recognition Using Processing Light Measurement for Reduction of Laser Cutting Defect False Recognition
Open the PDF for Thin Steel Plate Surface Rust Recognition Using Processing Light Measurement for Reduction of Laser Cutting Defect False Recognition in another window
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
https://doi.org/10.1115/IMECE2022-96092
View Paper titled, Industry 4.0 Trends in Intelligent Manufacturing Automation Exploring Machine Learning
Open the PDF for Industry 4.0 Trends in Intelligent Manufacturing Automation Exploring Machine Learning in another window
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
https://doi.org/10.1115/IMECE2022-94000
View Paper titled, Analysis of 3D Printing Performance Using Machine Learning Techniques
Open the PDF for Analysis of 3D Printing Performance Using Machine Learning Techniques in another window
Topics: Additive manufacturing, Artificial neural networks, Machine learning, Particle swarm optimization
Proceedings Papers

Uncertainty Quantification on Galvanic Corrosion Based on Adaptive Surrogate Modeling

Parth Bansal, Zhuoyuan Zheng, Yumeng Li
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, V009T12A005, October 30–November 3, 2022
Paper No: IMECE2022-95333
https://doi.org/10.1115/IMECE2022-95333
View Paper titled, Uncertainty Quantification on Galvanic Corrosion Based on Adaptive Surrogate Modeling
Open the PDF for Uncertainty Quantification on Galvanic Corrosion Based on Adaptive Surrogate Modeling in another window
Topics: Corrosion, Electrolytic corrosion, Machine learning, Metamodeling, Physics, Resilience, Uncertainty quantification