Session: DFMLC-03-03: Design for Additive Manufacturing

Paper Number: 148276

148276 - Additive Manufacturing Fatigue Qualification of Ss 316l Processed by Laser Fusion 

Laser powder bed fusion (LPBF) is one of the major metal additive manufacturing (AM) processes. Compared to traditional subtractive manufacturing processes (e.g., machining, forging, etc.), LPBF offers an opportunity for making complex metal components with design freedom, short development time, and environmental sustainability. However, the LPBF fabricated components often suffer from severe fatigue scattering problems, i.e., the fatigue life exhibits a very large variation. Fatigue scattering of LPBF parts is induced by geometrical defects, such as gas pores and lack-of-fusion distributed throughout a printed part, which make it challenging to merit applications in high-reliability situations. There is a consensus within the AM community that fatigue scattering imposes a significant challenge to LPBF part qualification.
Murakami proposed a mechanics-based model for the fatigue limit of metallic materials based on defect size, measured by the square root of the projected defect area on the plane normal to the applied stress. This semi-empirical model can be applied to metal AM materials by using statistics of extremes of defect sizes measured from a specimen to predict a maximum defect size. This is then used to calculate the lower bound fatigue limit of LPBF materials based on the predicted worst-case scenario of having a very large defect. Although useful, this approach cannot be used on its own for quantifying the reliability of a material since the distribution of fatigue life is unknown.
While it is relatively easy to curate defect datasets, fatigue testing datasets are limited by the lengthy and expensive sample preparation and experimentation process. Many fatigue samples are needed to confidently measure the variation in S-N curves. Furthermore, LPBF fatigue studies in the literature are not consistent in terms of the number of data points and the runout criteria used to obtain an S-N curve. This raises questions about the statistical significance of the results, especially when quantifying the distribution of the fatigue life. This inconsistency may persist while LPBF standards are still in their infancy. Thus, there is clearly a need for standard methods of quantifying scatter in part quality, especially for fatigue performance, with limited data.
The objectives of this work are as follows. The fatigue life scattering of LPBFed SS 316L is quantified by 3 different methods for limited S-N curve data. First, 95% prediction intervals for the lower and upper bound finite fatigue life are used to capture the statistical scatter in the fatigue life at different stress amplitudes. The second and third methods involve other statistical distribution approaches. The three methods are then compared to investigate their potential strengths and weaknesses in estimating LPBF fatigue life reliability with limited data.

Presenting Author: Panayiotis Kousoulas Rutgers University

Presenting Author Biography: Panayiotis Kousoulas is a PhD student in the Mechanical and Aerospace Engineering Department of Rutgers University. He is part of the New Jersey Advanced Manufacturing Initiative at Rutgers, where he researches fatigue of additively manufactured metal materials. He and his research advisor, Dr. Yuebin Guo, have 3 publications on the quantification and impact of additive manufacturing defects on fatigue. In April of 2024, Mr. Kousoulas was selected as ASME’s 2024-2025 Donald O. Thompson Graduate Fellow and will incorporate nondestructive evaluation techniques in his metal additive manufacturing fatigue research.

Authors:

Panayiotis Kousoulas Rutgers University
Yuebin Guo Rutgers University
Zhimin Xi Rutgers University