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Proceedings Papers

An Active Machine Learning Approach for Spatial-Temporal Temperature Prediction of a Moving Heat Source

Mahtab Heydari, Bruce Tai
Proc. ASME. MSEC2024, Volume 2: Manufacturing Equipment and Automation; Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality & Reliability, V002T07A008, June 17–21, 2024
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2024-125289
https://doi.org/10.1115/MSEC2024-125289
View Paper titled, An Active Machine Learning Approach for Spatial-Temporal Temperature Prediction of a Moving Heat Source
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Topics: Heat, Machine learning, Temperature, Additive manufacturing, Artificial neural networks, Computer simulation, Laser cutting, Machining, Manufacturing, Trains
Proceedings Papers

Deep Learning-Based Recognition of Manufacturing Components Using Augmented Reality for Worker Training of Assembly Tasks

Sourabh Deshpande, Manish Raj Aryal, Sam Anand
Proc. ASME. MSEC2024, Volume 2: Manufacturing Equipment and Automation; Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality & Reliability, V002T07A007, June 17–21, 2024
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2024-125279
https://doi.org/10.1115/MSEC2024-125279
View Paper titled, Deep Learning-Based Recognition of Manufacturing Components Using Augmented Reality for Worker Training of Assembly Tasks
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Topics: Augmented reality, Deep learning, Manufacturing, Engines, Artificial neural networks, Computer-aided design, Computers, Additive manufacturing, Algorithms, Artificial intelligence
Proceedings Papers

Data Efficient Modeling for Prediction and Forecasting of WAAM Process Conditions

Austen Thien, Nicole Van Handel, Helen Hu, Kyle Saleeby, Christopher Saldana
Proc. ASME. MSEC2024, Volume 1: Additive Manufacturing; Advanced Materials Manufacturing; Biomanufacturing; Life Cycle Engineering, V001T01A017, June 17–21, 2024
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2024-124113
https://doi.org/10.1115/MSEC2024-124113
View Paper titled, Data Efficient Modeling for Prediction and Forecasting of WAAM Process Conditions
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Topics: Additive manufacturing, Modeling, Time series, Wire, Artificial neural networks, Density, Geometry, Resolution (Optics), Feedstock, Machine learning
Proceedings Papers

Physics-Informed Loss Functions With Explainable AI to Predict Emission in Powder Bed Fusion

Ethan Regal, Vidita Gawade, Weihong Guo
Proc. ASME. MSEC2024, Volume 2: Manufacturing Equipment and Automation; Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality & Reliability, V002T07A010, June 17–21, 2024
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2024-125898
https://doi.org/10.1115/MSEC2024-125898
View Paper titled, Physics-Informed Loss Functions With Explainable AI to Predict Emission in Powder Bed Fusion
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Topics: Emissions, Physics, Printing, Artificial neural networks, Deep learning, Errors, Metals, Stability, Transparency
Proceedings Papers

Forecasting the Range of Possible Human Hand Movement in Consumer Electronics Disassembly Using Machine Learning

Hao-Yu Liao, Yuhao Chen, Boyi Hu, Xiao Liang, Sara Behdad
Proc. ASME. MSEC2023, Volume 1: Additive Manufacturing; Advanced Materials Manufacturing; Biomanufacturing; Life Cycle Engineering, V001T04A007, June 12–16, 2023
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2023-104792
https://doi.org/10.1115/MSEC2023-104792
View Paper titled, Forecasting the Range of Possible Human Hand Movement in Consumer Electronics Disassembly Using Machine Learning
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Topics: Artificial neural networks, Electronics, Machine learning, Safety, Collaboration, Computers, Errors, Hazardous substances, Human-robot collaboration, Measurement units and standards
Proceedings Papers

Additively Manufactured NiTiHf Shape Memory Alloy Transformation Temperature Evaluation by Radial Basis Function and Perceptron Neural Networks

Hossein Abedi, Mohammadjavad Abdollahzadeh, Abdalmageed Almotari, Majed Ali, Shiva Mohajerani, Mohammad Elahinia, Ala Qattawi
Proc. ASME. MSEC2023, Volume 1: Additive Manufacturing; Advanced Materials Manufacturing; Biomanufacturing; Life Cycle Engineering, V001T01A004, June 12–16, 2023
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2023-101325
https://doi.org/10.1115/MSEC2023-101325
View Paper titled, Additively Manufactured NiTiHf Shape Memory Alloy Transformation Temperature Evaluation by Radial Basis Function and Perceptron Neural Networks
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Topics: Additive manufacturing, Artificial neural networks, Design, Lasers, Machine learning, Shape memory alloys, Temperature
Proceedings Papers

Intelligent Uncertainty Handling Using Artificial Neural Networks in a Programmatic Logic Controller-Based Automation System

Enoch Lai, Jianfeng Ma
Proc. ASME. MSEC2023, Volume 2: Manufacturing Equipment and Automation; Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability, V002T05A003, June 12–16, 2023
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2023-101825
https://doi.org/10.1115/MSEC2023-101825
View Paper titled, Intelligent Uncertainty Handling Using Artificial Neural Networks in a Programmatic Logic Controller-Based Automation System
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Topics: Artificial neural networks, Control equipment, Matlab, Programmable controllers, Uncertainty, Weight (Mass)
Proceedings Papers

Development of a Model for Prediction and Optimization of Hardness of Electrodeposited Cu/SiC Composite Using RSM and ANN-PSO

Prince Kumar Rai, Ankur Gupta
Proc. ASME. MSEC2023, Volume 2: Manufacturing Equipment and Automation; Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability, V002T06A003, June 12–16, 2023
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2023-101504
https://doi.org/10.1115/MSEC2023-101504
View Paper titled, Development of a Model for Prediction and Optimization of Hardness of Electrodeposited Cu/SiC Composite Using RSM and ANN-PSO
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Topics: Artificial neural networks, Composite materials, Copper, Electrodeposition, Optimization, Particle swarm optimization, Response surface methodology
Proceedings Papers

Real-Time Temperature Prediction of a Moving Heat Source Problem Using Machine Learning

Mahtab Heydari, Pei-Ching Kung, Bruce L. Tai, Nien-Ti Tsou
Proc. ASME. MSEC2023, Volume 2: Manufacturing Equipment and Automation; Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability, V002T06A013, June 12–16, 2023
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2023-104529
https://doi.org/10.1115/MSEC2023-104529
View Paper titled, Real-Time Temperature Prediction of a Moving Heat Source Problem Using Machine Learning
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Topics: Artificial neural networks, Finite element analysis, Heat, Machine learning, Temperature
Proceedings Papers

Submicron Motion Measurement Based on Nonlinear Identification of Diffractive Optical Features

Taiyu Su, Zhijing Zhang, Jian Xiong, Erbo Li, Qimuge Saren
Proc. ASME. MSEC2023, Volume 2: Manufacturing Equipment and Automation; Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability, V002T05A004, June 12–16, 2023
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2023-104752
https://doi.org/10.1115/MSEC2023-104752
View Paper titled, Submicron Motion Measurement Based on Nonlinear Identification of Diffractive Optical Features
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Topics: Diffraction, Displacement measurement, Motion measurement, Artificial neural networks, Degrees of freedom, Machinery, Manufacturing, Optical measurement
Proceedings Papers

A Study of the Laser Powder Bed Fusion Manufactured Surface Roughness Prediction and Optimization Based on Artificial Neural Network

Dongqing Yan, Eddie Taewan Lee, Somayeh Pasebani, Zhaoyan Fan
Proc. ASME. MSEC2023, Volume 2: Manufacturing Equipment and Automation; Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability, V002T09A005, June 12–16, 2023
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2023-102011
https://doi.org/10.1115/MSEC2023-102011
View Paper titled, A Study of the Laser Powder Bed Fusion Manufactured Surface Roughness Prediction and Optimization Based on Artificial Neural Network
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Topics: Artificial neural networks, Lasers, Optimization, Surface roughness
Proceedings Papers

A Neural Network-Based Framework of Real-Time Heat Map Generation for Surgical Hand-Held Drilling

Pei-Ching Kung, Mahtab Heydari, Bruce L. Tai
Proc. ASME. MSEC2022, Volume 1: Additive Manufacturing; Biomanufacturing; Life Cycle Engineering; Manufacturing Equipment and Automation; Nano/Micro/Meso Manufacturing, V001T02A007, June 27–July 1, 2022
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2022-85693
https://doi.org/10.1115/MSEC2022-85693
View Paper titled, A Neural Network-Based Framework of Real-Time Heat Map Generation for Surgical Hand-Held Drilling
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Topics: Artificial neural networks, Drilling, Heat, Surgery, Time-invariant systems
Proceedings Papers

Development of Autonomous Robotic Bin Picking System Using Convolutional Neural Network (CNN) Initially Trained by Human Skills

Huitaek Yun, Jinho Park, Martin Byung-Guk Jun
Proc. ASME. MSEC2022, Volume 2: Manufacturing Processes; Manufacturing Systems, V002T06A006, June 27–July 1, 2022
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2022-84712
https://doi.org/10.1115/MSEC2022-84712
View Paper titled, Development of Autonomous Robotic Bin Picking System Using Convolutional Neural Network (CNN) Initially Trained by Human Skills
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Topics: Artificial neural networks, Robotics
Proceedings Papers

Image-Based Fractographic Pattern Recognition With Cluster Analysis

Shenghan Guo, Paul Paradise, Nicole Van Handel, Dhruv Bhate
Proc. ASME. MSEC2022, Volume 2: Manufacturing Processes; Manufacturing Systems, V002T05A008, June 27–July 1, 2022
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2022-82773
https://doi.org/10.1115/MSEC2022-82773
View Paper titled, Image-Based Fractographic Pattern Recognition With Cluster Analysis
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Topics: Fractography, Fracture (Materials), Pattern recognition, Additive manufacturing, Algorithms, Artificial neural networks, Decision making, High cycle fatigue, Inspection, Scanning electron microscopy
Proceedings Papers

Surface Roughness Prediction for Additively Manufactured Ti-6Al-4V Components Based on Supervised Learning Models

Varad Maitra, Jing Shi
Proc. ASME. MSEC2022, Volume 1: Additive Manufacturing; Biomanufacturing; Life Cycle Engineering; Manufacturing Equipment and Automation; Nano/Micro/Meso Manufacturing, V001T01A013, June 27–July 1, 2022
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2022-85329
https://doi.org/10.1115/MSEC2022-85329
View Paper titled, Surface Roughness Prediction for Additively Manufactured Ti-6Al-4V Components Based on Supervised Learning Models
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Topics: Additive manufacturing, Alloys, Artificial neural networks, Lasers, Melting, Surface roughness
Proceedings Papers

Physics-Informed Artificial Intelligence for Temperature Prediction in Metal Additive Manufacturing: A Comparative Study

Suyog Ghungrad, Benjamin Gould, Sarah Wolff, Azadeh Haghighi
Proc. ASME. MSEC2022, Volume 1: Additive Manufacturing; Biomanufacturing; Life Cycle Engineering; Manufacturing Equipment and Automation; Nano/Micro/Meso Manufacturing, V001T01A008, June 27–July 1, 2022
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2022-85159
https://doi.org/10.1115/MSEC2022-85159
View Paper titled, Physics-Informed Artificial Intelligence for Temperature Prediction in Metal Additive Manufacturing: A Comparative Study
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Topics: Additive manufacturing, Artificial intelligence, Artificial neural networks, Metals, Physics, Temperature
Proceedings Papers

Multiple Sound Sensors And Fusion In Modern CNN-Based Machine State Prediction

Eunseob Kim, Huitaek Yun, Martin Byung-Guk Jun, Kyunghyun Kim, Suk Won Cha
Proc. ASME. MSEC2021, Volume 1: Additive Manufacturing; Advanced Materials Manufacturing; Biomanufacturing; Life Cycle Engineering; Manufacturing Equipment and Automation, V001T05A017, June 21–25, 2021
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2021-64036
https://doi.org/10.1115/MSEC2021-64036
View Paper titled, Multiple Sound Sensors And Fusion In Modern CNN-Based Machine State Prediction
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Topics: Artificial neural networks, Machine tools, Machinery, Sensors, Signals, Internet of things, Manufacturing, Acoustics, Bridges (Structures), Conveyor systems
Proceedings Papers

Predicting Nugget Size of Resistance Spot Welds Using Infrared Thermal Videos With Image Segmentation and Convolutional Neural Network

Shenghan Guo, Dali Wang, Jian Chen, Zhili Feng, Weihong “Grace” Guo
Proc. ASME. MSEC2021, Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability, V002T06A011, June 21–25, 2021
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2021-61775
https://doi.org/10.1115/MSEC2021-61775
View Paper titled, Predicting Nugget Size of Resistance Spot Welds Using Infrared Thermal Videos With Image Segmentation and Convolutional Neural Network
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Topics: Artificial neural networks, Image segmentation, Nondestructive evaluation, Welded joints, Welding
Proceedings Papers

Real-Time Reliability Assessment of Wind Turbine Components Using a Back-Propagation Neural Network and SCADA Data

Shenglei Du, Jingmei Guo, Lin Yi, Chen Zhang, Shi Liu
Proc. ASME. MSEC2021, Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability, V002T09A001, June 21–25, 2021
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2021-58639
https://doi.org/10.1115/MSEC2021-58639
View Paper titled, Real-Time Reliability Assessment of Wind Turbine Components Using a Back-Propagation Neural Network and SCADA Data
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Topics: Artificial neural networks, Reliability, Wind turbines
Proceedings Papers

A Deep Neural Network Model for Learning Runtime Frequency Response Function Using Sensor Measurements

Yongzhi Qu, Gregory W. Vogl, Zechao Wang
Proc. ASME. MSEC2021, Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability, V002T07A017, June 21–25, 2021
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2021-64065
https://doi.org/10.1115/MSEC2021-64065
View Paper titled, A Deep Neural Network Model for Learning Runtime Frequency Response Function Using Sensor Measurements
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Topics: Artificial neural networks, Frequency response, Neural network models, Sensors