Data Treatment of In Situ Monitoring Systems in Selective Laser Melting Machines

Abstract

Recently, additive manufacturing (AM) has observed tremendous growth in various industries such as aerospace, biomedical, automotive, energy, and tooling due to its capability to manufacture complex parts with ease. According to Wohler's report of 2019, the metal AM industry has shown record growth with an increase of 41.9%. [1] The reported growth indicates that the AM industry has significant potential in patient-specific medical implants, lightweight components for the aerospace and automotive sector, which eventually reduced fuel consumption. However, the process repeatability and reliability remain a significant concern in AM. [2] Therefore, the need for implementing in situ or realtime monitoring sensors is inevitable.Laser-powder bed fusion (L-PBF) also, commonly known as selective laser melting (SLM) or direct laser melt sintering (DMLS), is one of the many AM techniques. L-PBF uses the metal powder material deposited in a layer-by-layer fashion to create a 3D model from a digital computer-aided design (CAD). After each powder recoating layer, the laser fuses the metal powder material selectively for that specific layer; afterward, the next new layer of powder is uniformly distributed by the recoater. The process repeats until the whole 3D model is manufactured. The total manufacturing time can vary from hours to days, based on the part's geometrical features. Despite tremendous technological advancements in developing the latest commercial L-PBF systems, the process reliability and repeatability still need to materialize. Thus, the L-PBF process has many input parameters that can significantly influence the part's overall quality. [3][4][5] For instance, Van Elsen has listed at least 50 parameters that can affect the final part quality. [6] The expensive and time-consuming nondestructive techniques (NDTs), such as computed tomography (CT), are used to check the overall part health and quantify the defects. [6] Therefore, to improve the comprehensive quality assurance and monitor the process in real time, active research is directed on using in situ sensors based on real-time monitoring, data analytics, and process control. The overall thrust has been developing the models with hardware and data analytics techniques to isolate the type, location, and severity of the defects in real time. [7] The final aim can be achieved by developing a closed feedback control loop to mitigate these defects inline. In the literature, different means of measurement, such as infrared thermography, pyrometry, optical spectroscopy, ultrasonic sensors, and so on, are reported to monitor the L-PBF process. [8,9] Considering the subject's vast scope, we focus mainly on reviewing the recent development in fault detection in the L-PBF process using machine learning (ML) based on sensors' responses.A large scale of data being generated with an increase in the number of sensors used to monitor the process. An extensive set of data also poses a significant challenge related to storage and postprocessing. ML is an alternative way to c...