A computational method for improving the data acquisition process in the Laser Metal Deposition

Abstract

Laser metal deposition (LMD) has developed rapidly in recent years. Although the technology is gaining attention, the data obtained from in-situ sensors are noisy due to the brief processing window and must be analyzed automatically to ensure the reliability of the data acquisition process. Traditionally, researchers used a simple Moving Average (MA) to diminish the peaks of the signals that may inflate the estimation for further data analysis. Spatter is one of the indicators that can describe the process stability of LMD. The generation of spatters is linked to peaks of the signals and has concept drift characteristics. Therefore, this study aims to detect and distinguish between point anomaly and concept drift in data streams in order to remove the extreme values that can mask the actual performance of the deposition process. The proposed method comprises two main components: (1) differencing method to flag the potential point outlier and (2) the density-based method to verify whether the flagged observations are outliers or not. We evaluated and compared our proposed method with the DSPOT method. The results show that our proposed method outperforms the DSPOT method based on the evaluation metrics (Recall, Precision, and F1-score) and the computation time.