From in-situ monitoring toward high-throughput process control: cost-driven decision-making framework for laser-based additive manufacturing

•The paper presents an ANN-based cost-driven classification method for intelligent porosity detection.•The method accounts for spatial distribution of defects and recommends cost-wise smart corrective actions.•The method excels at porosity detection in regard to both accuracy and cost when compared with the recent state of the arts.•Significant cost savings are shown by using the presented data- and cost-driven decision making framework.