Schmidt, H.B. (2024, May). Development of effective FSW models for training AI. 13th International Symposium.
Schmidt H B. "Development of effective FSW models for training AI". 13th International Symposium (May.2024).
Schmidt, H B. "Development of effective FSW models for training AI". 13th International Symposium (May.2024).
Schmidt H.B.Development of effective FSW models for training AI. 13th International Symposium. 2024 May; .
Schmidt 2024, 'Development of effective FSW models for training AI', 13th International Symposium. Available from: https://www.twi-global.com/technical-knowledge/fsw-symposium-papers/FSWSymposia-202405-5Paper01.pdf.
Schmidt HB. Development of effective FSW models for training AI. 13th International Symposium. 2024;. https://www.twi-global.com/technical-knowledge/fsw-symposium-papers/FSWSymposia-202405-5Paper01.pdf.
Schmidt HB . Development of effective FSW models for training AI. 13th International Symposium. 2024 May;. https://www.twi-global.com/technical-knowledge/fsw-symposium-papers/FSWSymposia-202405-5Paper01.pdf.
Development of effective FSW models for training AI
- Schmidt H.B.
- Schmidt H.B.
May 2024
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Artificial intelligence (Al) and machine learning (ML) are major drivers for improving quality of manufacturing process including Friction Stir welding. The specific parameter that has 100% effect on the quality of FSW has not yet been identified, however, it should more likely be envisioned that a set of parameters are decisive for the outcome of a weld, and its quality. Some of these parameters are controlled via the welding parameters, such as rotation and traverse speed. And some parameters are controlled indirectly such as tolerances and environment conditions.
13th International Symposium, 21-23 May 2024, Session 5: Plenary II, Paper 01
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