Development of a machine learning model to predict the friction stir welding tool condition

Abstract

Tool damage results in surface polishing and dimensional accuracy losses for machined parts, as well as potential damage to a workpiece or machine. In the industrial business, tool condition monitoring (TCM) is significant. As a result, it is essential for production efficiency and profitability. In order to prevent damages from occurring, also known as predictive maintenance, we may use the TCMS to identify tool wear and damage that eventually impacts the workpiece. Machine learning models play a significant part in TCMS by way of a GUI that identifies the tool's failure state. This method can be applied in any kind of system which is used in the industry to predict the damage. This TCMS is applied in a friction stir welding machine (FSW) to predict its tool wear. Friction stir welding, an eco-friendly solid state welding method, is used to join two metals that are difficult to fuse together using conventional fusion welding. FSW is largely recognized as the most important innovation in metal joining technology in the previous decade. This FSW has been widely employed in numerous sectors such as shipbuilding, computer covers, vehicles, and so on. Developing a GUI for the prediction of tool condition in FSW with AZ31B(Mg) as a workpiece is a novel approach. In this condition monitoring technique, the real time vibrational data is collected with the help of an accelerometer. These data’s are being processed and features are extracted and a proper classifier is used to predict the faults. This paper aims to develop a part of TCMS through vibrational data. GUI and ML models were developed to predict the condition of FSW tools. The ML model includes Decision Tree, Random Forest, Light Gradient Boosted Machine Classifiers. Among all these Light Gradient Boosted Machine Classifiers perform better. Using this Classifier a GUI is built to predict the condition.