Additive manufacturing (AM) is a special technology that offers several advantages compared to the conventional methodologies. For instance, it allows to build components with complex geometry, difficult to make in a different way, build integrated parts deleting joining issues, etc. However, its application in engineering fields is still limited because the dependency between the mechanical properties of the obtained components and the manufacturing parameters is not in depth understood. Improve the performances of AM components, under static and dynamic loadings, is crucial to ensure their durability and reliability and in recent years it became a challenging research field. In this work, the effect of the surface roughness on the multiaxial fatigue resistance of Ti6Al4V thin-walled tubular samples, made by the Selective Laser Melting (SLM) process, was investigated. In particular, experiments under combined axial–torsional loadings were carried out on two batches of samples, made by different surface roughness (machined and as built samples). Maximum valley depth Rv was used as a representative parameter of the surface roughness as it geometrically represents a stress concentration zone where crack initiates and propagates. An effective strain intensity factor range, based on the modified Smith Watson and Topper (MSWT) model, that includes the roughness parameter Rv, is proposed and used for a better correlation of the fatigue data. To prove the reliability of the proposed model, the Socie and the Reddy & Fatemi effective strain-based intensity factor range were also used but a bigger scattering of results was observed. The MSWT model was also applied to predict the failure plane and the obtained results were compared with the predictions of the Fatemi-Socie model. Results revealed that, for SLM Ti6Al4V alloy components, the MSWT model is more accurate. © 2022 Elsevier Ltd

Surface roughness effect on multiaxial fatigue behavior of additively manufactured Ti6Al4V alloy

Renzo D. A.;Filippo Berto
Ultimo
Conceptualization
2022

Abstract

Additive manufacturing (AM) is a special technology that offers several advantages compared to the conventional methodologies. For instance, it allows to build components with complex geometry, difficult to make in a different way, build integrated parts deleting joining issues, etc. However, its application in engineering fields is still limited because the dependency between the mechanical properties of the obtained components and the manufacturing parameters is not in depth understood. Improve the performances of AM components, under static and dynamic loadings, is crucial to ensure their durability and reliability and in recent years it became a challenging research field. In this work, the effect of the surface roughness on the multiaxial fatigue resistance of Ti6Al4V thin-walled tubular samples, made by the Selective Laser Melting (SLM) process, was investigated. In particular, experiments under combined axial–torsional loadings were carried out on two batches of samples, made by different surface roughness (machined and as built samples). Maximum valley depth Rv was used as a representative parameter of the surface roughness as it geometrically represents a stress concentration zone where crack initiates and propagates. An effective strain intensity factor range, based on the modified Smith Watson and Topper (MSWT) model, that includes the roughness parameter Rv, is proposed and used for a better correlation of the fatigue data. To prove the reliability of the proposed model, the Socie and the Reddy & Fatemi effective strain-based intensity factor range were also used but a bigger scattering of results was observed. The MSWT model was also applied to predict the failure plane and the obtained results were compared with the predictions of the Fatemi-Socie model. Results revealed that, for SLM Ti6Al4V alloy components, the MSWT model is more accurate. © 2022 Elsevier Ltd
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11573/1654338
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