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Proceedings Papers

A Fast and Cost-Effective Imaging System for Fine-Scale Tool Condition Monitoring in Ultrasonic Metal Welding

Zhiqiao Dong, Qianmeng Chen, Kuan-Chieh Lu, Chenhui Shao
Proc. ASME. MSEC2023, Volume 2: Manufacturing Equipment and Automation; Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability, V002T07A008, June 12–16, 2023
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2023-104906
https://doi.org/10.1115/MSEC2023-104906
View Paper titled, A Fast and Cost-Effective Imaging System for Fine-Scale Tool Condition Monitoring in Ultrasonic Metal Welding
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Topics: Condition monitoring, Imaging, Maintenance, Metals, Metrology, Welding
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

Anomaly Scoring Model for Diagnosis on Machine Condition and Health Management

Byung Gun Joung, Zhongtian Li, John W. Sutherland
Proc. ASME. MSEC2022, Volume 2: Manufacturing Processes; Manufacturing Systems, V002T06A017, June 27–July 1, 2022
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2022-85459
https://doi.org/10.1115/MSEC2022-85459
View Paper titled, Anomaly Scoring Model for Diagnosis on Machine Condition and Health Management
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Topics: Machine learning, Machinery, Manufacturing, Pulse width modulation, Maintenance, Manufacturing systems, Failure, Vibration, Artificial intelligence, Compressors
Proceedings Papers

Research on Condition Monitoring Technology of Automobile Parts Intelligent Production Line Based on Cyber Physical System

Yifei Wang, Zhiwen Xia, Kexin Yang, Lijun Jin
Proc. ASME. MSEC2022, Volume 2: Manufacturing Processes; Manufacturing Systems, V002T06A031, June 27–July 1, 2022
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2022-87425
https://doi.org/10.1115/MSEC2022-87425
View Paper titled, Research on Condition Monitoring Technology of Automobile Parts Intelligent Production Line Based on Cyber Physical System
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Topics: Assembly lines, Automobiles, Condition monitoring, Cyber-physical systems, Signals
Proceedings Papers

A Flexible Similarity Based Algorithm for Tool Condition Monitoring

Ben Stuhr, Rui Liu
Proc. ASME. MSEC2021, Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability, V002T06A012, June 21–25, 2021
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2021-62021
https://doi.org/10.1115/MSEC2021-62021
View Paper titled, A Flexible Similarity Based Algorithm for Tool Condition Monitoring
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Topics: Algorithms, Condition monitoring, Manufacturing, Signals
Proceedings Papers

Tool Condition Monitoring Method Based on Generative Adversarial Network for Data Augmentation

Yongqing Wang, Mengmeng Niu, Kuo Liu, Honghui Wang, Mingrui Shen, Bo Qin
Proc. ASME. MSEC2021, Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability, V002T06A024, June 21–25, 2021
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2021-63576
https://doi.org/10.1115/MSEC2021-63576
View Paper titled, Tool Condition Monitoring Method Based on Generative Adversarial Network for Data Augmentation
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Topics: Condition monitoring
Proceedings Papers

A Novel Edge Computing Based Architecture for Intelligent Tool Condition Monitoring

Rui Liu
Proc. ASME. MSEC2020, Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability, V002T07A015, September 3, 2020
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2020-8499
https://doi.org/10.1115/MSEC2020-8499
View Paper titled, A Novel Edge Computing Based Architecture for Intelligent Tool Condition Monitoring
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Topics: Calibration, Condition monitoring, Manufacturing
Proceedings Papers

A Broad Transfer Learning Algorithm for Classification of Bearing Faults

Guokai Liu, Liang Gao, Weiming Shen, Andrew Kusiak
Proc. ASME. MSEC2020, Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability, V002T07A028, September 3, 2020
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2020-8312
https://doi.org/10.1115/MSEC2020-8312
View Paper titled, A Broad Transfer Learning Algorithm for Classification of Bearing Faults
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Topics: Algorithms, Bearings, Fault diagnosis, Condition monitoring, Manufacturing industry
Proceedings Papers

Class Incremental Fault Detection and Diagnosis With Unlabeled Condition Monitoring Data

Xiaomeng Peng, Xiaoning Jin, Shiming Duan, Chaitanya Sankavaram
Proc. ASME. MSEC2020, Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability, V002T07A031, September 3, 2020
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2020-8431
https://doi.org/10.1115/MSEC2020-8431
View Paper titled, Class Incremental Fault Detection and Diagnosis With Unlabeled Condition Monitoring Data
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Topics: Condition monitoring, Flaw detection
Proceedings Papers

Experimental Investigation of Charged Particles Emission in Machining: Towards Process Monitoring

Jagadeesh Govindaraj, Sathyan Subbiah
Proc. ASME. MSEC2018, Volume 4: Processes, V004T03A071, June 18–22, 2018
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2018-6670
https://doi.org/10.1115/MSEC2018-6670
View Paper titled, Experimental Investigation of Charged Particles Emission in Machining: Towards Process Monitoring
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Topics: Emissions, Machining, Particulate matter, Process monitoring, Deformation, Sensors, Carbide cutting tools, Condition monitoring, Coolants, Copper
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

Feature Selection for Helicopter Swashplate Bearing Fault Diagnosis

Yong Wang, Lin Li
Proc. ASME. MSEC2017, Volume 3: Manufacturing Equipment and Systems, V003T04A058, June 4–8, 2017
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2017-2643
https://doi.org/10.1115/MSEC2017-2643
View Paper titled, Feature Selection for Helicopter Swashplate Bearing Fault Diagnosis
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Topics: Bearings, Fault diagnosis, Feature selection, Ball bearings, Algorithms, Condition monitoring, Feature extraction, Maintenance, Quality control, Signals
Proceedings Papers

Broaching Tool Degradation Characterization Based on Functional Descriptors

Wenmeng Tian, Lee J. Wells, Jaime A. Camelio
Proc. ASME. MSEC2016, Volume 2: Materials; Biomanufacturing; Properties, Applications and Systems; Sustainable Manufacturing, V002T04A030, June 27–July 1, 2016
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2016-8781
https://doi.org/10.1115/MSEC2016-8781
View Paper titled, Broaching Tool Degradation Characterization Based on Functional Descriptors
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Topics: Computation, Condition monitoring, Cutting, Filtration, Fittings, Image processing, Machine tools, Machinery, Machining, Manufacturing
Proceedings Papers

Heuristic Feature Selection for Shaving Tool Wear Classification

Yong Wang, Adam J. Brzezinski, Xianli Qiao, Jun Ni
Proc. ASME. MSEC2016, Volume 2: Materials; Biomanufacturing; Properties, Applications and Systems; Sustainable Manufacturing, V002T04A040, June 27–July 1, 2016
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2016-8547
https://doi.org/10.1115/MSEC2016-8547
View Paper titled, Heuristic Feature Selection for Shaving Tool Wear Classification
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Topics: Feature selection, Wear, Accelerometers, Condition monitoring, Feature extraction, Machinery, Signals
Proceedings Papers

The Current State of Sensing, Health Management, and Control for Small-to-Medium-Sized Manufacturers

Moneer Helu, Brian Weiss
Proc. ASME. MSEC2016, Volume 2: Materials; Biomanufacturing; Properties, Applications and Systems; Sustainable Manufacturing, V002T04A007, June 27–July 1, 2016
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2016-8783
https://doi.org/10.1115/MSEC2016-8783
View Paper titled, The Current State of Sensing, Health Management, and Control for Small-to-Medium-Sized Manufacturers
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Topics: Condition monitoring, Maintenance, Manufacturing, National Institute of Standards and Technology
Proceedings Papers

Sensorless Monitoring of Cutting Force Variation With Fractured Tool Under Heavy Cutting Condition

Yuki Yamada, Yasuhiro Kakinuma, Takamichi Ito, Jun Fujita, Hirohiko Matsuzaki
Proc. ASME. MSEC2016, Volume 3: Joint MSEC-NAMRC Symposia, V003T08A025, June 27–July 1, 2016
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2016-8820
https://doi.org/10.1115/MSEC2016-8820
View Paper titled, Sensorless Monitoring of Cutting Force Variation With Fractured Tool Under Heavy Cutting Condition
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Topics: Cutting, Fracture (Materials), Fracture (Process), Condition monitoring, Engines, Friction, Machine tools, Milling, Motors, Signal processing
Proceedings Papers

Tool Wear Monitoring for Ultrasonic Metal Welding of Lithium-Ion Batteries

Chenhui Shao, Tae Hyung Kim, S. Jack Hu, Jionghua (Judy) Jin, Jeffrey A. Abell, J. Patrick Spicer
Proc. ASME. MSEC2015, Volume 2: Materials; Biomanufacturing; Properties, Applications and Systems; Sustainable Manufacturing, V002T05A022, June 8–12, 2015
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2015-9428
https://doi.org/10.1115/MSEC2015-9428
View Paper titled, Tool Wear Monitoring for Ultrasonic Metal Welding of Lithium-Ion Batteries
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Topics: Lithium-ion batteries, Metals, Wear, Welding, Algorithms, Batteries, Condition monitoring, Geometry, Joining, Manufacturing
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

Multi-Scale Surface Characterization and Control Based High Density Measurements

Xin Weng, Xiaoning Jin, Jun Ni
Proc. ASME. MSEC2014, Volume 1: Materials; Micro and Nano Technologies; Properties, Applications and Systems; Sustainable Manufacturing, V001T04A044, June 9–13, 2014
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2014-4122
https://doi.org/10.1115/MSEC2014-4122
View Paper titled, Multi-Scale Surface Characterization and Control Based High Density Measurements
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Topics: Density, Surface characterization, Metrology, Process monitoring, Shapes, Surface quality, Condition monitoring, Delays, Dimensions, Errors