Statistics of extremes-based analytics of geometrical defects and the effect on fatigue scattering in laser powder bed fusion
Creators: name: Kousoulas, Panayiotis
Publisher: name: No Publisher Supplied
Year: 2024

description: Compared to traditional subtractive manufacturing processes, laser-based powder bed fusion (LPBF) shows promise for making complex metal parts with design freedom, short development time, and environmental sustainability. However, there is a consensus within the additive manufacturing (AM) community that the random geometrical defects (e.g., porosity, lack-of-fusion) produced in LPBF processes impose a great challenge to fabricating load-bearing parts, particularly under dynamic loading conditions.Fatigue performance is a critical design specification for any manufacturers of mechanical components under dynamic loading (e.g., aerospace, automotive, military). Many researchers have shown random scattering of fatigue life for materials produced with LPBF. This uncertainty is largely caused by the random geometrical defects with random shapes, sizes, and locations within parts that act as stress concentrations. Therefore, it is imperative to quantify defect sizes and distributions to predict the critical, life-limiting defect size that significantly reduces fatigue life.Furthermore, the bulk literature is limited to gross fatigue fracture while the fatigue initiation and development process is poorly understood due to the constraint of available online fatigue monitoring techniques. It is more practical to monitor the fatigue damage process in metal AM components rather than solely rely on fatigue model predictions because monitoring adds safety to a design by providing physical evidence of component damage before reaching critical levels. Fatigue monitoring has been researched for decades in a variety of applications. Only in this past decade has fatigue monitoring been applied to AM materials and only in the past several years has machine learning (ML) been applied to predicting the fatigue performance of AM materials. There is thus a great opportunity to explore physics-informed ML models for AM fatigue prediction, which can leverage the monitoring data to improve the fatigue learning process.Addressing these challenges is critical to qualify LPBF as a standard industrial process for fabricating load-bearing metal parts. This work addresses the challenges of characterization of geometrical defect size and distribution, fatigue monitoring, and fatigue scattering due to geometrical defects, all in the context of materials made by LPBF. The objective of this study is to obtain data from throughout the LPBF design cycle to lay the foundation for future work in modeling the process-quality-fatigue (PQF) causal relationship. First, the state-of-the-art fatigue monitoring and analytics in metal AM are reviewed. This is followed by two consecutive studies on geometrical defects in LPBFed SS-316L material. The first study presents a statistics-of-extreme methodology for predicting the maximum expected defect size in a part. The second study adopts a scientific point of view to quantify the effect of a wide range of porosity on fatigue performance.The key conclusions are the following. The quality of LPBF material can be described by its maximum expected defect size, which can be extrapolated from a sampled distribution of defect sizes. At a minimum, hundreds of defect sizes should be sampled to obtain an accurate representation of the actual defect size distribution. Several different distribution functions (well-correlated to the dataset) should also be compared to select a realistic extrapolated maximum defect size (i.e., larger than the maximum measured defect but not too large). In-situ fatigue monitoring techniques can provide data on the entire fatigue process (i.e., crack initiation, crack propagation, rapid fracture) that can be leveraged as a fatigue process signature for fatigue modeling. The scattering of fatigue life and fatigue limit mainly impacted by random porosity in LPBF materials are characterized by normal distributions. Resonant fatigue frequency and machine power consumption act as fatigue process signatures.