Using in-situ process monitoring data to identify defective layers in Ti-6Al-4V additively manufactured porous biomaterials

Abstract

Additive manufacturing processes, such as Laser Powder Bed Fusion (L-PBF), facilitates the manufacture of porous biomaterials structures, which can be used for example to enhance bone tissue regeneration. In-situ process monitoring techniques such as meltpool emission monitoring are increasingly being applied for the monitoring of the l-PBF processes. This paper investigates the use of statistical anomaly detection to analyse in-situ process monitoring data obtained during l-PBF. In this study a Renishaw 500M was used to produce porous structures, using Ti-6Al-4 V feedstock powder. During the l-PBF process, a co-axial photodiode-based process monitoring system was utilised to generate data relating to both the meltpool and the operational behaviour of the laser. Porous structures were created with intentionally defective layers, whereby the laser power was selectively reduced at specific layers. Control samples were also created where no intentionally defective layers were created. In addition, an un-intentionally defective sample was also analysed. The Generalized Extreme Studentized Deviate (GESD) test was employed to identify any defective layers within the structures. When this approach was applied to data generated during the processing of the structures with reduced input energy layers, the number of defective layers identified corresponded exactly with the known amount. When the test was run on the meltpool data, corresponding to the un-intentional defective structure, 30 layers were identified as defective. When examined, the identified layers corresponded to the physical location of the defect within the sample. The results obtained in this study indicate that the GESD test is an effective and computationally inexpensive method of identifying defective layers created during the l-PBF process.