Porosity Prediction in AM Using PBF-LB Employing Time-Series Classification

Abstract

Additive Manufacturing (AM) using Powder-Bed Fusion Laser-Beam (PBF-LB) has great potential; however, it has challenges due to its sensitivity to the process parameters [1]. The availability of big data generated in AM facilitates the employment of Machine Learning (ML) tools to understand the process and have a predictive control over the production. An intelligent system like this can thus reduce material wastage and energy cost while increasing a plant’s product quality and throughput. Time-series summary statistics (like mean and variance) can discard valuable discriminatory signatures embedded in raw sensor data. Therefore, special ML time-series classification (TSC) tools that can extract and utilise these signatures from the raw data are much more effective for a task like porosity prediction [1]. However, the data employed in [1] pertains to products with artificially designed pores or gaps. This study focuses on naturally occurring pores, rarer, and evaluates k-Nearest Neighbour (k-NN) with Dynamic Time Warping (DTW) over real-world manufacturing data to classify the porosity of individual raster scans. We believe that natural pores have more diverse signatures than artificial pores, as each pore varies in characteristics (like size and morphology).