Room- and High-Temperature Fatigue Strength of the T5 and Rapid T6 Heat-Treated AlSi10Mg Alloy Produced by Laser-Based Powder Bed Fusion

Abstract

The AlSi10Mg alloy produced by laser-based powder bed fusion (L-PBF) is widely used to produce high-value-added structural parts subjected to cyclic mechanical loads at high temperatures. The paper aims to widen the knowledge of the room- and high-temperature (200 °C) fatigue behavior of the L-PBF AlSi10Mg alloy by analyzing the fully reversed rotating bending test results on mechanically polished specimens. Two heat-treated conditions are analyzed: T5 (direct artificial aging: 4 h at 160 °C) and novel T6R (rapid solution: 10 min at 510 °C, artificial aging: 6 h at 160 °C). The study highlights that (i) the T6R alloy is characterized by higher fatigue strength at room (108 MPa) and high temperatures (92 MPa) than the T5 alloy (92 and 78 MPa, respectively); (ii) thermal exposure at 200 °C up to 17 h does not introduce macroscopical microstructural variation; (iii) fracture surfaces of the room- and high-temperature-tested specimens show comparable crack initiation, mostly from sub-superficial gas and keyhole pores, and failure propagation mechanisms. In conclusion, the L-PBF AlSi10Mg alloy offers good cyclic mechanical performances under various operating conditions, especially for the T6R alloy, and could be considered for structural components operating at temperatures up to 200 °C.

Influence of Microstructure on Fracture Mechanisms of the Heat-Treated AlSi10Mg Alloy Produced by Laser-Based Powder Bed Fusion

“…Consequently, moving from T6R (SHT at 510 • C for 10 min) to T6B (SHT at 540 • C for 1 h), the Si particle average area increases from 0.14 µm 2 to 0.55 µm 2 , while their density decreases from 1.48 particles/µm 2 to 0.28 particles/µm 2 (Figure 8). The T5 microstructure does not undergo macroscopic alterations due to the relatively low thermal exposure, keeping the cellular substructure of the AB alloy (Figure 7a,b) unaltered and relieving residual stress [47] (Figure 7). However, the high supersaturation of Si and Mg atoms in the Al lattice leads to the formation and coarsening of β-Mg2Si precursor phases and nano-sized Si precipitates inside the α-Al cells [48,49].…”

“…(a) (b) The T5 microstructure does not undergo macroscopic alterations due to the relatively low thermal exposure, keeping the cellular substructure of the AB alloy (Figure 7a,b) unaltered and relieving residual stress [47] (Figure 7). However, the high supersaturation of Si and Mg atoms in the Al lattice leads to the formation and coarsening of β-Mg2Si precursor phases and nano-sized Si precipitates inside the α-Al cells [48,49].…”

Mechanical properties of additively manufactured AlSi10Mg under quasi‐static and cyclic loading

“…To influence the microstructure of the material, as well as residual stresses and ultimately fatigue resistance, various heat treatments are applied to AM AlSi10Mg. These treatments include T5 direct aging (DA), 17,18 stress relief treatment (SR), [19][20][21] hot isostatic pressing (HIP), 22,23 T6, 17,24,25 and others. It is important to note that, in certain studies, 16,26,27 stress-relieved samples typically exhibit lower fatigue resistance compared to as-built samples.…”

Improving Text Summarization Quality by Combining T5-Based Models and Convolutional Seq2Seq Models

“…The paraphrasing techniques used are T5 convolutional and Seq2Seq, each model has its own advantages in summarizing text because the T5 model has advantages in producing language so that it produces text that is easier to understand structurally. (Song et al, 2022) (Chouikhi & Alsuhaibani, 2022) while the convolutional Seq2Seq model can perform settlement of unstructured data and perform feature extraction (Di Egidio et al, 2023) based on document data so that combining the models will get better results and can see the potential feature features in the representation of a very large number of words from T5 and completion of text that is unstructured from the convolutional Seq2Seq model. The purpose of combining this summary model is to see the quality of the text produced and to improve the abbreviations and accuracy of words according to the Big Indonesian Dictionary.…”