Tool Wear Characterization and Monitoring with Hierarchical Spatio-Temporal Models for Micro-Friction Stir Welding

Friction Stir Welding (FSW) and Friction Stir Processing (FSP) are solid-state joining and material processing techniques that have garnered considerable attention for their versatility and industrial applicability. In the present work, FSP was performed on AA 6056 T4, dealing with the issue of monitoring tool wear and assessing its impact on the process. The impact of tool wear on power requirements was analyzed, and it was expanded the understanding of tool behavior and its implications for the overall process performance. Specifically, variations in energy consumption, temperatures, and vibrations are observed with changing tool conditions. Further insights are provided by analyzing the microhardness and the pin volume ratio, which show distinct trends as the tool wears. Two tool maintenance ways are proposed, that are cleaning the tool with a sodium hydroxide solution and increasing the tool’s rotational speed. Both the strategies exhibit the potential to partially restore the tool’s initial characteristics. This study highlights the critical importance of assessing tool condition, energy consumption, and process sustainability, particularly in industrial settings where material processing requires efficiency and quality assurance.

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Applications of machine learning in friction stir welding: Prediction of joint properties, real-time control and tool failure diagnosis

Elsheikh

2023

Engineering Applications of Artificial Intelligence

116

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Tool Wear Characterization and Monitoring with Hierarchical Spatio-Temporal Models for Micro-Friction Stir Welding

Cited by 10 publications

References 40 publications

Multi-objective optimization using Taguchi based grey relational analysis in friction stir welding for dissimilar aluminium alloy

Multi-objective optimization using Taguchi based grey relational analysis in friction stir welding for dissimilar aluminium alloy

Energy Consumption and Tool Condition in Friction Stir Processing of Aluminum Alloys

Applications of machine learning in friction stir welding: Prediction of joint properties, real-time control and tool failure diagnosis

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