Effect of Post-Processing Treatment on Fatigue Performance of Ti6Al4V Alloy Manufactured by Laser Powder Bed Fusion

Fatigue properties of parts are of particular concern for safety-critical structures. It is well-known that discontinuities in shape or non-uniformities in materials are frequently a potential nucleus of fatigue failure. This is especially crucial for the Ti6Al4V alloy, which presents high susceptibility to the notch effect. This study investigates how post-processing treatments affect the mechanical performance of Ti6Al4V samples manufactured by laser powder bed fusion technology. All the fatigue samples were subjected to a HIP cycle and post-processed by machining and using combinations of alternative mechanical and electrochemical surface treatments. The relationship between surface properties such as roughness, topography and residual stresses with fatigue performance was assessed. Compressive residual stresses were introduced in all surface-treated samples, and after tribofinishing, roughness was reduced to 0.31 ± 0.10 µm, which was found to be the most critical factor. Fractures occurred on the surface as HIP removed critical internal defects. The irregularities found in the form of cavities or pits were stress concentrators that initiated cracks. It was concluded that machined surfaces presented a fatigue behavior comparable to wrought material, offering a fatigue limit superior to 450 MPa. Additionally, alternative surface treatments showed a fatigue behavior equivalent to the casting material.This research was funded by the Departamento de Desarrollo Económico, Sostenibilidad y Medio Ambiente of the Basque Government (ELKARTEK 2022 KK-2022/00070), by the Departamento de Desarrollo Económico y Competitividad of the Basque Government (ELKARTEK 2019 KK-2019/00077) and by the European Union (project TIFAN, JTI-CS-2013-1-ECO-01-066)

Prediction of Surface Roughness of SLM Built Parts after Finishing Processes Using an Artificial Neural Network

A known problem of additive manufactured parts is their poor surface quality, which influences product performance. There are different surface treatments to improve surface quality: blasting is commonly employed to improve mechanical properties and reduce surface roughness, and electropolishing to clean shot peened surfaces and improve the surface roughness. However, the final surface roughness is conditioned by multiple parameters related to these techniques. This paper presents a prediction model of surface roughness (Ra) using an Artificial Neural Network considering two parameters of the SLM manufacturing process and seven blasting and electropolishing processes. This model is proven to be in agreement with 429 experimental results. Moreover, this model is then used to find the optimal conditions to be applied during the blasting and the electropolishing in order to improve the surface roughness by roughly 60%

Prediction of Surface Roughness of SLM Built Parts after Finishing Processes Using an Artificial Neural Network

A known problem of additive manufactured parts is their poor surface quality, which influences product performance. There are different surface treatments to improve surface quality: blasting is commonly employed to improve mechanical properties and reduce surface roughness, and electropolishing to clean shot peened surfaces and improve the surface roughness. However, the final surface roughness is conditioned by multiple parameters related to these techniques. This paper presents a prediction model of surface roughness (Ra) using an Artificial Neural Network considering two parameters of the SLM manufacturing process and seven blasting and electropolishing processes. This model is proven to be in agreement with 429 experimental results. Moreover, this model is then used to find the optimal conditions to be applied during the blasting and the electropolishing in order to improve the surface roughness by roughly 60%

Red de docentes y repositorio digital de recursos educativos: Una historia del capitalismo contemporáneo II. La crisis del fordismo a través fuentes fílmicas, literarias y estéticas

Depto. de Filosofía y SociedadFac. de FilosofíaFALSEsubmitte