Metal 3D printing is one of the fastest growing additive manufacturing (AM) technologies in recent years. Despite the improvements and capabilities, reliable metal printing is still not well understood. One of the barriers of industrialization of metal AM is process monitoring and quality assurance of the printed product. These barriers are especially much highlighted in aerospace and medical device manufacturing industries where the high reliability and quality is needed. Selective Laser Melting (SLM) is one of the main metal 3D printing methods where it is known that more than 50 parameters are affecting the quality of the print. However, the current SLM printing process barely utilize a fraction of the collected data during production. Up to this point, no study to the best of our knowledge examines the correlation of factors affecting the quality of the print. After reviewing the current state of the art of process monitoring for metal AM involving SLM, we propose a method to control the process of the print in each layer and prevent the defects using data-driven techniques. A numerical study using simulated numbers is provided to demonstrate how the proposed method can be implemented.

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