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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

Paper No: MSEC2022-85459

DOI: https://doi.org/10.1115/MSEC2022-85459

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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

Paper No: MSEC2022-87425

DOI: https://doi.org/10.1115/MSEC2022-87425

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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

Paper No: MSEC2021-62021

DOI: https://doi.org/10.1115/MSEC2021-62021

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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

Paper No: MSEC2021-63576

DOI: https://doi.org/10.1115/MSEC2021-63576

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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

Paper No: MSEC2020-8499

DOI: https://doi.org/10.1115/MSEC2020-8499

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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

Paper No: MSEC2020-8312

DOI: https://doi.org/10.1115/MSEC2020-8312

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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

Paper No: MSEC2020-8431

DOI: https://doi.org/10.1115/MSEC2020-8431

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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

Paper No: MSEC2018-6670

DOI: https://doi.org/10.1115/MSEC2018-6670

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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

Paper No: MSEC2018-6680

DOI: https://doi.org/10.1115/MSEC2018-6680

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Topics: Artificial neural networks, Condition monitoring, Milling, Support vector machines, Wear, Machining, Cutting tools, Monitoring systems, Signals

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

Paper No: MSEC2017-2643

DOI: https://doi.org/10.1115/MSEC2017-2643

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Topics: Bearings, Fault diagnosis, Feature selection, Ball bearings, Algorithms, Condition monitoring, Feature extraction, Maintenance, Quality control, 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

Paper No: MSEC2017-3069

DOI: https://doi.org/10.1115/MSEC2017-3069

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Topics: Cutting, Machine learning, Milling, Process monitoring, Signals, Wear, Algorithms, Cutting tools, Failure, Support vector machines

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

Paper No: MSEC2016-8783

DOI: https://doi.org/10.1115/MSEC2016-8783

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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

Paper No: MSEC2016-8820

DOI: https://doi.org/10.1115/MSEC2016-8820

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Topics: Cutting, Fracture (Materials), Fracture (Process), Condition monitoring, Engines, Friction, Machine tools, Milling, Motors, Signal processing

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

Paper No: MSEC2016-8781

DOI: https://doi.org/10.1115/MSEC2016-8781

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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

Paper No: MSEC2016-8547

DOI: https://doi.org/10.1115/MSEC2016-8547

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Topics: Feature selection, Wear, Accelerometers, Condition monitoring, Feature extraction, Machinery, Signals

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

Paper No: MSEC2015-9428

DOI: https://doi.org/10.1115/MSEC2015-9428

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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

Paper No: MSEC2015-9346

DOI: https://doi.org/10.1115/MSEC2015-9346

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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

Paper No: MSEC2014-4122

DOI: https://doi.org/10.1115/MSEC2014-4122

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Topics: Density, Surface characterization, Metrology, Process monitoring, Shapes, Surface quality, Condition monitoring, Delays, Dimensions, Errors

Proceedings Papers

A Study on Condition Monitoring and Diagnosis of Injection Molding Process Using Probabilistic Neural Network Method

Dae Seong Baek, Chengjun Li, Jung Soo Nam, Cho Rok Na, Myungho Kim, Byungohk Rhee, Sangwon Lee

Proc. ASME. MSEC2014, Volume 1: Materials; Micro and Nano Technologies; Properties, Applications and Systems; Sustainable Manufacturing, V001T04A039, June 9–13, 2014

Paper No: MSEC2014-4058

DOI: https://doi.org/10.1115/MSEC2014-4058

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Topics: Artificial neural networks, Condition monitoring, Injection molding, Pressure, Cavities, Signals, Nozzles, Screws, Feature extraction, Modeling

Proceedings Papers

Multi-Zone Proportional Hazard Model for a Multi-Stage Degradation Process

Lin Li, Zeyi Sun, Xinwei Xu, Kaifu Zhang

Proc. ASME. MSEC2013, Volume 2: Systems; Micro and Nano Technologies; Sustainable Manufacturing, V002T02A013, June 10–14, 2013

Paper No: MSEC2013-1113

DOI: https://doi.org/10.1115/MSEC2013-1113

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Topics: Hazards, Condition monitoring, Decision making, Machinery, Maintenance, Modeling, Reliability, Service life (Equipment)

NARROW

Anomaly Scoring Model for Diagnosis on Machine Condition and Health Management

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

A Flexible Similarity Based Algorithm for Tool Condition Monitoring

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

A Novel Edge Computing Based Architecture for Intelligent Tool Condition Monitoring

A Broad Transfer Learning Algorithm for Classification of Bearing Faults

Class Incremental Fault Detection and Diagnosis With Unlabeled Condition Monitoring Data

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

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

Feature Selection for Helicopter Swashplate Bearing Fault Diagnosis

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

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

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

Broaching Tool Degradation Characterization Based on Functional Descriptors

Heuristic Feature Selection for Shaving Tool Wear Classification

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

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

Multi-Scale Surface Characterization and Control Based High Density Measurements

A Study on Condition Monitoring and Diagnosis of Injection Molding Process Using Probabilistic Neural Network Method

Multi-Zone Proportional Hazard Model for a Multi-Stage Degradation Process

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