Enhancing Friction Stir Welding: Quality Machine Learning Based Friction Stir Welding Tool Condition Monitoring

Abstract

Ensuring the quality and optimizing the tool in Friction Stir Welding (FSW) process is quite complex and the solution relies on implementing Condition Monitoring. The major impact of this process yields good quality welds and cuts down the non-operational timing and cost. Condition Monitoring is the key to find a solution to the challenging problem of ensuring quality and optimizing the tool in the FSW process. The creation of a graphical user interface (GUI) and the development and comparison of several models, including Decision Tree (DT), Random Forest (RF), Light Gradient Boosted Machine (LGBM), and Extreme Gradient Boosting (XGBoost), are the main objectives of this study. By offering an uniform interface for tracking and evaluating tool condition data, GUI can make it easier for operators and the maintenance crew to collaborate. Vibration analysis is the first step in tool condition monitoring. Al5083 and AZ31B are used as the workpiece and H13 as the tool in this investigation. The signals are obtained from the experimental setup via DAQ, and LabView processes them. A Python script converts the raw signals into statistical data. Following that, the data was loaded into ML models and optimized using Optuna. TKinter has been used to create the GUI. For prediction, the best models were included in the GUI. By the deployed models, LGBM generates 96% for 1000 rpm, 96.55% for 1200 rpm, and 95.90% for 1400 rpm for Al5083 93.22% for 1000 rpm, 99.29% for 1200 rpm, and 91.50% for 1000 rpm for AZ31B. For real-time prediction, these models are thus connected to a graphical user interface. In each case, the LGBM classifier topped the others. This work served as an initial basis for the creation of a semi-onboard diagnostic approach that requires minimal human input.