Intensive processing optimization for achieving strong and ductile Al-Mn-Mg-Sc-Zr alloy produced by selective laser melting

Bayoumy, Dina; Schliephake, Daniel; Dietrich, Stefan; Wu, Xinhua; Zhu, Yuman; Huang, Aijun

doi:10.1016/j.matdes.2020.109317

Cited by 86 publications

(34 citation statements)

References 59 publications

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“…Al, Mg, and Sc were melted and gas atomized to form alloy particles with 2.5 MPa gas pressure (Figure 1a), which is taken as the powder of laser selective melting process. In terms of powder quality, the ratios of deformity and adhesion are 8.4% and 15.8%, respectively, and the average particle size is 30 μm, which is similar to the report by Bayoumy et al [22]. The specimen possesses sandwich structure, for which its upper and lower end layer comprise a continuous thin plate with a thickness of 1 mm, and the middle layer is grid structure [23] with a thickness of 18mm (Figure 1b).…”

Section: Materials and Structural Dimensionssupporting

confidence: 85%

Force and Microstructure Variation of SLM Prepared AlMgSc Samples during Three-Point Bending

Nie

Zhang

et al. 2022

Materials

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Lightweight parts manufactured by metal selective laser melting (SLM) are widely applied in machinery industries because of their high specific strength, good energy absorption effect, and complex shape that are difficult to form by mechanical machining. These samples often serve in three-dimensional stress states. However, previous publications mainly focused on the unidirectional tensile/compressive properties of the samples. In this paper, AlMgSc samples with different geometric parameters were prepared by the SLM process, and the variation of force and microstructure during three-point bending were systematically investigated. The results demonstrate that the deformation resistance of these samples has good continuity without mutation in bending, even for brittle materials; the bending force-displacement curves exhibit representative variation stages during the entire bending process; the equivalent bending strength deduced from free bending formula is not applicable when compactability is less than 67%. The variations of grain orientation and size of the three representative bending layers also show regularity.

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Section: Materials and Structural Dimensionssupporting

confidence: 85%

Force and Microstructure Variation of SLM Prepared AlMgSc Samples during Three-Point Bending

Nie

Zhang

et al. 2022

Materials

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“…At this time, the bubbles released during the melting of the powder cannot be discharged, and pores will appear during SLM process. When input energy density reach the highest, the excessive energy will pass through the metal powder to melt the solidified metal again, increasing the thermal stress between metals and cracks being prone to appear [ 32 , 33 ]. Moreover, decreasing the scanning speed and appropriately increasing the laser power can increase the density of the alloy, and the influence is greater than the energy density.…”

Section: Resultsmentioning

confidence: 99%

Effect of process parameters on microstructures and properties of Al–42Si alloy fabricated by selective laser melting

Cai¹,

Liu

Xuan

et al. 2022

Heliyon

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“…The presence of porosity affects the mechanical integrity, especially the fatigue life of L-PBF parts [58]. Figure 4 illustrates the effect of process parameters choice on the type of porosity formed in an aluminum alloy manufactured by L-PBF [59]. Generally, porosity can be classified according to its geometry, spherical (gas-induced) and non-spherical (process-induced).…”

Section: Porositymentioning

confidence: 99%

Applications of Machine Learning in Process Monitoring and Controls of L-PBF Additive Manufacturing: A Review

et al. 2021

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One of the main issues hindering the adoption of parts produced using laser powder bed fusion (L-PBF) in safety-critical applications is the inconsistencies in quality levels. Furthermore, the complicated nature of the L-PBF process makes optimizing process parameters to reduce these defects experimentally challenging and computationally expensive. To address this issue, sensor-based monitoring of the L-PBF process has gained increasing attention in recent years. Moreover, integrating machine learning (ML) techniques to analyze the collected sensor data has significantly improved the defect detection process aiming to apply online control. This article provides a comprehensive review of the latest applications of ML for in situ monitoring and control of the L-PBF process. First, the main L-PBF process signatures are described, and the suitable sensor and specifications that can monitor each signature are reviewed. Next, the most common ML learning approaches and algorithms employed in L-PBFs are summarized. Then, an extensive comparison of the different ML algorithms used for defect detection in the L-PBF process is presented. The article then describes the ultimate goal of applying ML algorithms for in situ sensors, which is closing the loop and taking online corrective actions. Finally, some current challenges and ideas for future work are also described to provide a perspective on the future directions for research dealing with using ML applications for defect detection and control for the L-PBF processes.

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Intensive processing optimization for achieving strong and ductile Al-Mn-Mg-Sc-Zr alloy produced by selective laser melting

Cited by 86 publications

References 59 publications

Force and Microstructure Variation of SLM Prepared AlMgSc Samples during Three-Point Bending

Force and Microstructure Variation of SLM Prepared AlMgSc Samples during Three-Point Bending

Effect of process parameters on microstructures and properties of Al–42Si alloy fabricated by selective laser melting

Applications of Machine Learning in Process Monitoring and Controls of L-PBF Additive Manufacturing: A Review

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