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The Selective Laser Melting (SLM) is one of the additive manufacturing (AM) process that allows the manufacturing of three-dimensional complex functional structures of metals or alloys using high power laser. Thus, the machine learning approach is robust and cost-effective for techniques like selective laser melting, where to obtain labeled data is time-consuming and expensive. Here, drift classification is based on overheating and lack of fusion defects. Then the trained classifier successfully classifies the layers of the unlabeled case study samples into “drift” and “no-drift” labels. A balanced labeled dataset for training the support vector machine algorithm by inducing drifts in the parts artificially (overheating and lack of fusion) was prepared. Also, a supervised machine learning approach for in-line detection of the drift in the final part is used. In this work, a sensitivity analysis of the melt pool monitoring system installed in the commercial SLM 280 HL machine is presented. Thus, the sensitivity analysis of sensors installed on the machine to various defects and easy detectability of the drift during the process is the need of the time. However, understanding the correlation between the acquired data from sensors and build quality is a challenge and time-consuming. In-situ monitoring systems comprising high precision sensors such as photodiodes, High-speed IR cameras both in co-axial and off-axis positions have been installed in commercially available machines to improve the overall performance of the process. Besides, all the advantages over traditional manufacturing techniques, reliability, and repeatability is still a challenge. Selective laser melting has seen a growing demand in niche industry applications for its complexity-free manufacturing capabilities.