Sensor fusion model for weld quality monitoring in friction stir welding process using machine learning technique

Weld quality monitoring in welding processes is not new and many research works have been carried in this area. Sensor information based methodologies have been developed to an appreciable extent and many found its recognition in many industrial applications. The current study will present an approach of monitoring weld quality in friction stir welding process with sensor fusion technique. Real-time signals acquired using force and tool rotational speed sensors are fused at two levels. First level of fusion is performed at signal level and next level of fusion is performed at feature level. The results obtained from sensor fusion approach are compared with the results obtained with monitoring using individual sensors. Support vector machine based regression models are developed for the joint quality prediction. It is observed that the machine learning model developed with fused signal features results in prediction accuracy of 99.16% and the same obtained with only process parameters results in prediction accuracy of 95.34%. The proposed research work can be used as the first line of model for estimation of joint qualities in an industrial environment.