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Proceedings Papers

Brief Paper: Multi-Task Brain-Inspired Learning for Interlinking Machining Dynamics With Parts Geometrical Deviations

Danny Hoang, Hanning Chen, Mohsen Imani, Ruimin Chen, Farhad Imani
Proc. ASME. MSEC2024, Volume 2: Manufacturing Equipment and Automation; Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality & Reliability, V002T05A012, June 17–21, 2024
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2024-125435
https://doi.org/10.1115/MSEC2024-125435
View Paper titled, Brief Paper: Multi-Task Brain-Inspired Learning for Interlinking Machining Dynamics With Parts Geometrical Deviations
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Topics: Brain, Computers, Dynamics (Mechanics), Machining, Manufacturing, Computer numerical control machine tools, Product quality, Chatter, Machine learning, Surface roughness
Proceedings Papers

Artificial Intelligence-Based Design of Process Parameters in Laser Powder Bed Fusion of Ti-6Al-4V for Desired Solidification Structure

Ehsan Vaghefi, Elham Mirkoohi
Proc. ASME. MSEC2023, Volume 1: Additive Manufacturing; Advanced Materials Manufacturing; Biomanufacturing; Life Cycle Engineering, V001T01A013, June 12–16, 2023
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2023-104463
https://doi.org/10.1115/MSEC2023-104463
View Paper titled, Artificial Intelligence-Based Design of Process Parameters in Laser Powder Bed Fusion of Ti-6Al-4V for Desired Solidification Structure
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Topics: Additive manufacturing, Design, Lasers, Machine learning, Solidification, Temperature, Temperature gradient, Errors, Finite element analysis, Finite element model
Proceedings Papers

Review of Recent Methods and Learning Techniques in Prediction of Tool Life

Ulfa Fairuz Izdihar, Brijesh Patel, Zih Fong Huang, Chih-Ho Yeh, Po Ting Lin
Proc. ASME. MSEC2023, Volume 2: Manufacturing Equipment and Automation; Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability, V002T09A002, June 12–16, 2023
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2023-101060
https://doi.org/10.1115/MSEC2023-101060
View Paper titled, Review of Recent Methods and Learning Techniques in Prediction of Tool Life
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Topics: Alloy steel, Chatter, Computer numerical control machine tools, Condition monitoring, Cutting, Cutting tools, Data acquisition, Drilling, Failure, Feature extraction
Proceedings Papers

Support Vector Machines for Classification of Direct Energy Deposition Standoff Distance for Improved Process Control

Zoe Alexander, Nathan DeVol, Molly Emig, Kyle Saleeby, Thomas Feldhausen, Thomas Kurfess, Katherine Fu, Christopher Saldana
Proc. ASME. MSEC2022, Volume 1: Additive Manufacturing; Biomanufacturing; Life Cycle Engineering; Manufacturing Equipment and Automation; Nano/Micro/Meso Manufacturing, V001T01A018, June 27–July 1, 2022
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2022-85382
https://doi.org/10.1115/MSEC2022-85382
View Paper titled, Support Vector Machines for Classification of Direct Energy Deposition Standoff Distance for Improved Process Control
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Topics: Process control, Support vector machines
Proceedings Papers

Forecasting Repair and Maintenance Services of Medical Devices Using Support Vector Machine

Hao-yu Liao, Willie Cade, Sara Behdad
Proc. ASME. MSEC2021, Volume 1: Additive Manufacturing; Advanced Materials Manufacturing; Biomanufacturing; Life Cycle Engineering; Manufacturing Equipment and Automation, V001T04A008, June 21–25, 2021
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2021-63966
https://doi.org/10.1115/MSEC2021-63966
View Paper titled, Forecasting Repair and Maintenance Services of Medical Devices Using Support Vector Machine
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Topics: Machine learning, Maintenance, Medical devices, Support vector machines
Proceedings Papers

Edge Computing Accelerated Defect Classification Based on Deep Convolutional Neural Network With Application in Rolling Image Inspection

Jiayu Huang, Nurretin Sergin, Akshay Dua, Erfan Bank Tavakoli, Hao Yan, Fengbo Ren, Feng Ju
Proc. ASME. MSEC2020, Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability, V002T07A037, September 3, 2020
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2020-8261
https://doi.org/10.1115/MSEC2020-8261
View Paper titled, Edge Computing Accelerated Defect Classification Based on Deep Convolutional Neural Network With Application in Rolling Image Inspection
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Topics: Artificial neural networks, Inspection, Algorithms, Flaw detection, Machine learning, Manufacturing systems, Support vector machines, Visualization
Proceedings Papers

On Transfer Learning of Traditional Frequency and Time Domain Features in Turning

Melih C. Yesilli, Firas A. Khasawneh
Proc. ASME. MSEC2020, Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability, V002T07A027, September 3, 2020
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2020-8274
https://doi.org/10.1115/MSEC2020-8274
View Paper titled, On Transfer Learning of Traditional Frequency and Time Domain Features in Turning
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Topics: Accelerometers, Algorithms, Chatter, Cutting, Fast Fourier transforms, Machine learning, Manufacturing, Signal processing, Signals, Spectral energy distribution
Proceedings Papers

Fault Diagnosis of Timed Event Systems: An Exploration of Machine Learning Methods

Joseph Cohen, Baoyang Jiang, Jun Ni
Proc. ASME. MSEC2020, Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability, V002T07A029, September 3, 2020
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2020-8360
https://doi.org/10.1115/MSEC2020-8360
View Paper titled, Fault Diagnosis of Timed Event Systems: An Exploration of Machine Learning Methods
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Topics: Fault diagnosis, Machine learning, Principal component analysis, Programmable controllers, Algorithms, Signals, Artificial neural networks, Failure, Integrated circuit fabrication, Manufacturing
Proceedings Papers

Machine Learning Based Novelty Detection Methods for Progressive Stamping Process Health Monitoring

Tianchen Qiu, Xingjian Lai, Jun Ni
Proc. ASME. MSEC2020, Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability, V002T07A034, September 3, 2020
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2020-8496
https://doi.org/10.1115/MSEC2020-8496
View Paper titled, Machine Learning Based Novelty Detection Methods for Progressive Stamping Process Health Monitoring
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Topics: Machine learning, Machinery, Metal stamping, Support vector machines
Proceedings Papers

Degradation Assessment of Reciprocating Seal Using Support Vector Regression

Madhumitha Ramachandran, Jon Keegan, Zahed Siddique
Proc. ASME. MSEC2019, Volume 1: Additive Manufacturing; Manufacturing Equipment and Systems; Bio and Sustainable Manufacturing, V001T02A008, June 10–14, 2019
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2019-2820
https://doi.org/10.1115/MSEC2019-2820
View Paper titled, Degradation Assessment of Reciprocating Seal Using Support Vector Regression
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Topics: Support vector machines, Friction, Machinery, Failure, Leakage, Manufacturing industry, Petroleum industry, Pistons, Principal component analysis, Signals
Proceedings Papers

Audio-Based Condition Monitoring in Milling of the Workpiece Material With the Hardness Variation Using Support Vector Machines and Convolutional Neural Networks

Achyuth Kothuru, Sai Prasad Nooka, Rui Liu
Proc. ASME. MSEC2018, Volume 4: Processes, V004T03A072, June 18–22, 2018
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2018-6680
https://doi.org/10.1115/MSEC2018-6680
View Paper titled, Audio-Based Condition Monitoring in Milling of the Workpiece Material With the Hardness Variation Using Support Vector Machines and Convolutional Neural Networks
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Topics: Artificial neural networks, Condition monitoring, Milling, Support vector machines, Wear, Machining, Cutting tools, Monitoring systems, Signals
Proceedings Papers

Cutting Process Monitoring System Using Audible Sound Signals and Machine Learning Techniques: An Application to End Milling

Achyuth Kothuru, Sai Prasad Nooka, Rui Liu
Proc. ASME. MSEC2017, Volume 3: Manufacturing Equipment and Systems, V003T04A050, June 4–8, 2017
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2017-3069
https://doi.org/10.1115/MSEC2017-3069
View Paper titled, Cutting Process Monitoring System Using Audible Sound Signals and Machine Learning Techniques: An Application to End Milling
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Topics: Cutting, Machine learning, Milling, Process monitoring, Signals, Wear, Algorithms, Cutting tools, Failure, Support vector machines
Proceedings Papers

Data-Driven Thermal Comfort Prediction With Support Vector Machine

Bo Peng, Sheng-Jen Hsieh
Proc. ASME. MSEC2017, Volume 3: Manufacturing Equipment and Systems, V003T04A044, June 4–8, 2017
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2017-3003
https://doi.org/10.1115/MSEC2017-3003
View Paper titled, Data-Driven Thermal Comfort Prediction With Support Vector Machine
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Topics: Support vector machines, Machine learning, Algorithms, Climate, Sensor networks
Proceedings Papers

Pricing Method for Service-Oriented Manufacturing With Support Vector Machine

Qiunan Meng, Jian Lou, Xun Xu, Shiqiang Yu
Proc. ASME. MSEC2017, Volume 3: Manufacturing Equipment and Systems, V003T04A033, June 4–8, 2017
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2017-2752
https://doi.org/10.1115/MSEC2017-2752
View Paper titled, Pricing Method for Service-Oriented Manufacturing With Support Vector Machine
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Topics: Manufacturing, Support vector machines, Artificial neural networks, Chain, China, Design, Molding, Performance, Robustness
Proceedings Papers

Data-Driven Prognostics Using Random Forests: Prediction of Tool Wear

Dazhong Wu, Connor Jennings, Janis Terpenny, Robert Gao, Soundar Kumara
Proc. ASME. MSEC2017, Volume 3: Manufacturing Equipment and Systems, V003T04A048, June 4–8, 2017
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2017-2679
https://doi.org/10.1115/MSEC2017-2679
View Paper titled, Data-Driven Prognostics Using Random Forests: Prediction of Tool Wear
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Topics: Wear, Artificial neural networks, Manufacturing systems, Artificial intelligence, Algorithms, Failure, Machine learning, Machinery, Maintenance, Manufacturing
Proceedings Papers

Sensor-Based Online Process Fault Detection in Additive Manufacturing

Prahalad K. Rao, Jia (Peter) Liu, David Roberson, Zhenyu (James) Kong
Proc. ASME. MSEC2015, Volume 2: Materials; Biomanufacturing; Properties, Applications and Systems; Sustainable Manufacturing, V002T04A010, June 8–12, 2015
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2015-9389
https://doi.org/10.1115/MSEC2015-9389
View Paper titled, Sensor-Based Online Process Fault Detection in Additive Manufacturing
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Topics: Additive manufacturing, Flaw detection, Sensors, Accelerometers, Accuracy and precision, Artificial neural networks, Decision making, Failure, Failure mechanisms, Machinery
Proceedings Papers

Cutting Tool Condition Monitoring and Prediction Based on Dynamic Data Driven Approaches

Zhenhua Wu
Proc. ASME. MSEC2015, Volume 1: Processing, V001T02A110, June 8–12, 2015
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2015-9346
https://doi.org/10.1115/MSEC2015-9346
View Paper titled, Cutting Tool Condition Monitoring and Prediction Based on Dynamic Data Driven Approaches
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Topics: Condition monitoring, Cutting tools, Wear, Cutting, Errors, Signals, Acoustic emissions, Artificial neural networks, Filters, Machining
Proceedings Papers

Predicting Subjective Responses From Human Motion: Application to Vehicle Ingress Assessment

Hadi I. Masoud, Matthew P. Reed, Kamran Paynabar, Jionghua (Judy) Jin, Ksenia K. Kozak, Nanxin Wang, Jian Wan, Gianna Gomez-Levi
Proc. ASME. MSEC2014, Volume 1: Materials; Micro and Nano Technologies; Properties, Applications and Systems; Sustainable Manufacturing, V001T04A009, June 9–13, 2014
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2014-4039
https://doi.org/10.1115/MSEC2014-4039
View Paper titled, Predicting Subjective Responses From Human Motion: Application to Vehicle Ingress Assessment
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Topics: B-splines, Design, Filters, Modeling, Support vector machines, Vehicles
Proceedings Papers

A Comparative Study of Metamodeling Techniques for Predictive Process Control of Welding Applications

G. Ray Ely, Carolyn Conner Seepersad
Proc. ASME. MSEC2009, ASME 2009 International Manufacturing Science and Engineering Conference, Volume 1, 395-407, October 4–7, 2009
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2009-84189
https://doi.org/10.1115/MSEC2009-84189
View Paper titled, A Comparative Study of Metamodeling Techniques for Predictive Process Control of Welding Applications
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Topics: Process control, Welding, Modeling, Physics, Computer software, Construction, Geometry, Polynomials, Real-time control, Splines