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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.

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%.

Knowing the tolerance interval capabilities (TICs) of a manufacturing process is of prime interest, especially if specifications link the manufacturer to a customer. These TICs can be determined using the machine performance concept of ISO 22514. However, few works have applied this to Additive Manufacturing printers, while testing most of the printing area as recommended takes a very long time (nearly 1 month is common). This paper, by proposing a novel part design called COMPAQT (Component for Machine Performances Assessment in Quick Time), aims at giving the same level of printing area coverage, while keeping the manufacturing time below 24 h. The method was successfully tested on a material extrusion printer. It allowed the determination of potential and real machine tolerance interval capabilities. Independently of the feature size, those aligned with the X axis achieved lower TICs than those aligned with the Y axis, while the Z axis exhibited the best performance. The measurements specific to one part exhibited a systematic error centered around 0 mm ± 0.050 mm, while those involving two parts reached up to 0.314 mm of deviation. COMPAQT can be used in two applications: evaluating printer tolerance interval capabilities and tracking its long-term performance by incorporating it into batches of other parts.

The performance assessment of additive manufacturing (AM) printers is still a challenge since no dedicated standard exists. This paper proposes a systematic method for evaluating the dimensional and geometrical performance of such machines using the concept of machine performance. The method was applied to an Ultimaker 2+ printer producing parts with polylactic acid (PLA). The X and Y axes of the printer were the most performant and led to narrower potential and real tolerance intervals than the Z axis. The proposed systematic framework can be used to assess the performance of any material extrusion printer and its achievable tolerance intervals.