Evaluating the Potential for Remote In-Process Monitoring of Tool Wear in Friction Stir Welding of Stainless Steel

Author:

Gibson Brian T.1,Tang Wei2,Peterson Artie G.3,Feng Zhili4,Frederick Gregory J.5

Affiliation:

1. Mem. ASME Materials Science and Technology Division, Oak Ridge National Laboratory, One Bethel Valley Road, Oak Ridge, TN 37831-6097 e-mail: gibsonbt@ornl.gov

2. Materials Science and Technology Division, Oak Ridge National Laboratory, One Bethel Valley Road, Oak Ridge, TN 37831-6096 e-mail: tangw@ornl.gov

3. Consultant Charlotte, NC 28262

4. Materials Science and Technology Division, Oak Ridge National Laboratory, One Bethel Valley Road, Oak Ridge, TN 37831-6096 e-mail: fengz@ornl.gov

5. Weld Repair Technology Center, Electric Power Research Institute, 1300 W W.T. Harris Boulevard, Charlotte, NC 28262 e-mail: gfrederi@epri.com

Publisher

ASME International

Subject

Industrial and Manufacturing Engineering,Computer Science Applications,Mechanical Engineering,Control and Systems Engineering

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1. Artificial Intelligence-based Recognition of Wear and Corrosion on Steel Surfaces;2023 4th International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE);2023-08-25

2. Numerically Trained Ultrasound AI for Monitoring Tool Degradation;Advanced Intelligent Systems;2022-01-13

3. Sustainability of Fusion and Solid-State Welding Process in the Era of Industry 4.0;Handbook of Smart Materials, Technologies, and Devices;2022

4. Sustainability of Fusion and Solid-State Welding Process in the Era of Industry 4.0;Handbook of Smart Materials, Technologies, and Devices;2021-12-18

5. Morphological and microstructural investigation of the non-planar interface formed in solid-state metal additive manufacturing by additive friction stir deposition;Additive Manufacturing;2020-10

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