<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In order to further enhance the application of additive manufacturing (AM) processes, such as the laser powder bed fusion (L-PBF) process, reliable material data are required. However, the resulting specimen properties are significantly influenced by the process parameters and may also vary depending on the material used. Therefore, the prediction of the final properties is difficult. In the following, the effect of residual stresses on the fatigue strength of 316L steel, a commonly used steel in AM, is investigated using a Weibull distribution. The underlying residual stress distributions as a result of the building process are approximated for two building directions using finite element (FE) models. These imply significantly different distributions of tensile and compressive residual stresses within the component. Apart from the residual stresses, the impact of the mean stress sensitivity is discussed as this also influences the predicted fatigue strength values.</p>
</abstract>

In order to further enhance the application of additive manufacturing (AM) processes, such as the laser powder bed fusion (L-PBF) process, reliable material data are required. However, the resulting specimen properties are significantly influenced by the process parameters and may also vary depending on the material used. Therefore, the prediction of the final properties is difficult. In the following, the effect of residual stresses on the fatigue strength of 316L steel, a commonly used steel in AM, is investigated using a Weibull distribution. The underlying residual stress distributions as a result of the building process are approximated for two building directions using finite element (FE) models. These imply significantly different distributions of tensile and compressive residual stresses within the component. Apart from the residual stresses, the impact of the mean stress sensitivity is discussed as this also influences the predicted fatigue strength values.

Numerical Investigation of Production-Related Characteristics Regarding their Influence on the Fatigue Strength of Additively Manufactured Components

Bremen Institute for Mechanical Engineering (bime), University of Bremen

Faculty 5, City University of Applied Sciences Bremen

Acta Mechanica et Automatica

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

In order to further enhance the application of additive manufacturing (AM) processes, such as the laser powder bed fusion (L-PBF) process, reliable material...

Numerical Investigation of Production-Related Characteristics Regarding their Influence on the Fatigue Strength of Additively Manufactured Components

Published Online: May 10, 2023

Page range: 288 - 291

Received: Oct 10, 2022

Accepted: Jan 05, 2023

DOI: https://doi.org/10.2478/ama-2023-0033

Keywords
additive manufacturing, fatigue strength, Weibull distribution, FEM

© 2023 Michaela Zeißig et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Numerical Investigation of Production-Related Characteristics Regarding their Influence on the Fatigue Strength of Additively Manufactured Components

Published Online: May 10, 2023

Page range: 288 - 291

Received: Oct 10, 2022

Accepted: Jan 05, 2023

DOI: https://doi.org/10.2478/ama-2023-0033

Keywordsadditive manufacturing, fatigue strength, Weibull distribution, FEM

© 2023 Michaela Zeißig et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
additive manufacturing, fatigue strength, Weibull distribution, FEM