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Proc. ASME. MSEC2019, Volume 1: Additive Manufacturing; Manufacturing Equipment and Systems; Bio and Sustainable Manufacturing, V001T02A009, June 10–14, 2019

Paper No: MSEC2019-2898

DOI: https://doi.org/10.1115/MSEC2019-2898

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Topics: Coating processes, Coatings, Gages, Measurement systems, Sprays

Proceedings Papers

Ultrasonic Welding of Soft Polymer and Metal: A Preliminary Study

Yuquan Meng, Dingyu Peng, Qasim Nazir, Gowtham Kuntumalla, Manjunath C. Rajagopal, Ho Chan Chang, Hanyang Zhao, Sreenath Sundar, Placid M. Ferreira, Sanjiv Sinha, Nenad Miljkovic, Srinivasa M. Salapaka, Chenhui Shao

Proc. ASME. MSEC2019, Volume 2: Processes; Materials, V002T03A083, June 10–14, 2019

Paper No: MSEC2019-2938

DOI: https://doi.org/10.1115/MSEC2019-2938

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Topics: Copper, Heat exchangers, Metals, Polymers, Ultrasonic welding

Proceedings Papers

Experimental and Statistical Study on Magnetic-Field Assisted Finishing of Mold Steel Using Nano-Scale Solid Lubricant and Abrasive Particles

Pil-Ho Lee, Haseung Chung, Patrick Steven McCormick, Patrick Kwon, Hoa Nguyen, Yuhang Yang, Chenhui Shao

Proc. ASME. MSEC2018, Volume 3: Manufacturing Equipment and Systems, V003T02A012, June 18–22, 2018

Paper No: MSEC2018-6544

DOI: https://doi.org/10.1115/MSEC2018-6544

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Topics: Finishing, Lubricants, Magnetic fields, Nanoscale phenomena, Particulate matter, Steel, Surface quality, Iron, Iron alloys, Statistics

Proceedings Papers

High-Performance Computing Based Big Data Analytics for Smart Manufacturing

Yuhang Yang, Y. Dora Cai, Qiyue Lu, Yifang Zhang, Seid Koric, Chenhui Shao

Proc. ASME. MSEC2018, Volume 3: Manufacturing Equipment and Systems, V003T02A013, June 18–22, 2018

Paper No: MSEC2018-6602

DOI: https://doi.org/10.1115/MSEC2018-6602

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Topics: Manufacturing, Manufacturing industry, Decision making, Engineering prototypes, Workflow

Journal Articles

Spatial Interpolation for Periodic Surfaces in Manufacturing Using a Bessel Additive Variogram Model

Yuhang Yang, Chenhui Shao

J. Manuf. Sci. Eng. June 2018, 140(6): 061001.

Paper No: MANU-17-1679

DOI: https://doi.org/10.1115/1.4039199

Published Online: March 7, 2018

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Topics: Interpolation, Manufacturing, Modeling, Simulation

Journal Articles

Dynamic Sampling Design for Characterizing Spatiotemporal Processes in Manufacturing

Chenhui Shao, Jionghua (Judy) Jin, S. Jack Hu

J. Manuf. Sci. Eng. October 2017, 139(10): 101002.

Paper No: MANU-16-1678

DOI: https://doi.org/10.1115/1.4036347

Published Online: August 24, 2017

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Topics: Design, Manufacturing, Metals, Spatiotemporal phenomena, Welding, Errors

Proceedings Papers

Dynamic Sampling Design for Characterizing Spatiotemporal Processes in Manufacturing

Chenhui Shao, Jionghua (Judy) Jin, S. Jack Hu

Proc. ASME. MSEC2017, Volume 3: Manufacturing Equipment and Systems, V003T04A004, June 4–8, 2017

Paper No: MSEC2017-2695

DOI: https://doi.org/10.1115/MSEC2017-2695

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Topics: Design, Manufacturing, Spatiotemporal phenomena, Algorithms, Genetic algorithms, Integer programming, Kalman filters, Machining, Measurement systems, Metals

Proceedings Papers

Analysis of Friction Stir Riveting Processes: A Review

Haris Ali Khan, Jingjing Li, Chenhui Shao

Proc. ASME. MSEC2017, Volume 1: Processes, V001T02A003, June 4–8, 2017

Paper No: MSEC2017-2700

DOI: https://doi.org/10.1115/MSEC2017-2700

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Topics: Friction, Riveting, Mechanical properties, Corrosion, Fatigue, Joining

Journal Articles

Analyses of Friction Stir Riveting Processes: A Review

Haris Ali Khan, Jingjing Li, Chenhui Shao

J. Manuf. Sci. Eng. September 2017, 139(9): 090801.

Paper No: MANU-17-1101

DOI: https://doi.org/10.1115/1.4036909

Published Online: July 18, 2017

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Topics: Friction, Joining, Riveting, Rivets, Mechanical properties, Heat

eBook Chapter

Process Monitoring Using Online Sensor Signals

Book: Ultrasonic Welding of Lithium-Ion Batteries

Publisher: ASME Press

Published: 2017

DOI: https://doi.org/10.1115/1.861257_ch8

ISBN: 9780791861257

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Topics: Process monitoring, Sensors, Signals

eBook Chapter

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

Book: Ultrasonic Welding of Lithium-Ion Batteries

Publisher: ASME Press

Published: 2017

DOI: https://doi.org/10.1115/1.861257_ch9

ISBN: 9780791861257

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PDF

Topics: Lithium-ion batteries, Metals, Wear, Welding

Journal Articles

Improving Machined Surface Shape Prediction by Integrating Multi-Task Learning With Cutting Force Variation Modeling

Chenhui Shao, Jie Ren, Hui Wang, Jionghua (Judy) Jin, S. Jack Hu

J. Manuf. Sci. Eng. January 2017, 139(1): 011014.

Paper No: MANU-16-1244

DOI: https://doi.org/10.1115/1.4034592

Published Online: September 29, 2016

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Topics: Cutting, Modeling, Shapes, Machining, Algorithms, Engines, Manufacturing, Physics, Resolution (Optics)

Journal Articles

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

J. Manuf. Sci. Eng. May 2016, 138(5): 051005.

Paper No: MANU-15-1087

DOI: https://doi.org/10.1115/1.4031677

Published Online: November 18, 2015

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Topics: Wear, Welding, Metals, Algorithms, Lithium-ion batteries

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

Characterization of Ultrasonic Metal Welding by Correlating Online Sensor Signals With Weld Attributes

S. Shawn Lee, Chenhui Shao, Tae Hyung Kim, S. Jack Hu, Elijah Kannatey-Asibu, Wayne W. Cai, J. Patrick Spicer, Jeffrey A. Abell

Proc. ASME. MSEC2014, Volume 2: Processing, V002T02A002, June 9–13, 2014

Paper No: MSEC2014-4139

DOI: https://doi.org/10.1115/MSEC2014-4139

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Topics: Metals, Sensors, Signals, Welding, Ultrasonic welding, Manufacturing, Algorithms, Batteries, Density, Displacement

Journal Articles

Characterization of Ultrasonic Metal Welding by Correlating Online Sensor Signals With Weld Attributes

S. Shawn Lee, Chenhui Shao, Tae Hyung Kim, S. Jack Hu, Elijah Kannatey-Asibu, Wayne W. Cai, J. Patrick Spicer, Jeffrey A. Abell

J. Manuf. Sci. Eng. October 2014, 136(5): 051019.

Paper No: MANU-14-1088

DOI: https://doi.org/10.1115/1.4028059

Published Online: August 12, 2014

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Topics: Signals, Welding, Sensors, Metals, Displacement

Journal citation

NARROW

Corrosion Modeling and Prognosis of the Al-Fe Self-Pierce Riveting Joints

Joining Techniques for Novel Metal Polymer Hybrid Heat Exchangers

Influence of Coating Spray on Surface Measurement Using 3D Optical Scanning Systems

Ultrasonic Welding of Soft Polymer and Metal: A Preliminary Study

Experimental and Statistical Study on Magnetic-Field Assisted Finishing of Mold Steel Using Nano-Scale Solid Lubricant and Abrasive Particles

High-Performance Computing Based Big Data Analytics for Smart Manufacturing

Spatial Interpolation for Periodic Surfaces in Manufacturing Using a Bessel Additive Variogram Model

Dynamic Sampling Design for Characterizing Spatiotemporal Processes in Manufacturing

Dynamic Sampling Design for Characterizing Spatiotemporal Processes in Manufacturing

Analysis of Friction Stir Riveting Processes: A Review

Analyses of Friction Stir Riveting Processes: A Review

Process Monitoring Using Online Sensor Signals

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

Improving Machined Surface Shape Prediction by Integrating Multi-Task Learning With Cutting Force Variation Modeling

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

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

Characterization of Ultrasonic Metal Welding by Correlating Online Sensor Signals With Weld Attributes

Characterization of Ultrasonic Metal Welding by Correlating Online Sensor Signals With Weld Attributes

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