Performance of High-Layer-Thickness Ti6Al4V Fabricated by Electron Beam Powder Bed Fusion under Different Accelerating Voltage Values

Li, Hongxin; Liang, Xiaoyu; Li, Yang; Feng, Lin

doi:10.3390/ma15051878

Cited by 14 publications

(6 citation statements)

References 29 publications

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“…In this context, e.g., the work of Barari et al and Li et al who considered the influence of L h on surface roughness, should be mentioned. [26,27] They determined that the build speed increases with increasing L h . However, the surface roughness was affected negatively.…”

Section: Determination Of Suitable Process Parametersmentioning

confidence: 99%

Laser Powder Bed Fusion of Hard‐Phase Containing Co‐Base Alloy

Vehrs,

Röttger,

Roj

et al. 2024

Adv Eng Mater

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This work considers the processing of the hard‐phase containing CoCrW‐alloy Celsit F using powder bed fusion‐laser beam/metal (PBF‐LB/M), whereby a suitable process window for producing dense parts having a low‐defect density is determined. Several cuboid specimens are manufactured by varying the exposure parameters (laser power, hatch distance, scanning speed). With the help of quantitative image analysis, the samples are evaluated regarding crack density and porosity, and optimal exposure parameters are derived. Dense samples with a low defect density can be produced if the energy density is 60–70 J mm−3 and the base‐plate is preheated to 800 °C. Choosing lower energy densities leads to increased pore formation, whereas higher energy densities result in increased ablation and cracking. With a view to a later application in which additively manufactured hot work tools are made of Celsit F, the possibility of introducing internal cavities as potential cooling channels is considered. It can be shown that the hardness sideways is lower than above and below the cavity due to a changed microstructure and locally different heat dissipation. The results of this work give a first guideline for the PBF‐LB/M‐processing of wear‐resistant CoCrW alloys with a high hard phase volume content to tools for hot working.

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Section: Determination Of Suitable Process Parametersmentioning

confidence: 99%

Laser Powder Bed Fusion of Hard‐Phase Containing Co‐Base Alloy

Vehrs,

Röttger,

Roj

et al. 2024

Adv Eng Mater

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“…Additionally, the recoil pressure is utilized to describe the fluid flow phenomena of molten metal at the surface of the molten region when the temperature at the molten pool surface exceeds the boiling point of material. The recoil pressure is defined by Equation ( 10) [27,28].…”

Section: Thermo-fluid Modelmentioning

confidence: 99%

Effect of hatch spacing in selective laser melting process of Ti-6Al-4V alloy on finished surface roughness: A computational study

THONGPRON,

NINPETCH,

CHALERMKARNNON

et al. 2024

J Met Mater Miner

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The Selective Laser Melting (SLM) process is a promising additive manufacturing technology for producing complex metal parts. However, this process has certain limitations, including lack of fusion and defects, which can hinder its industrial applications. These challenges can be addressed by determining the appropriate overlap percentage parameters. This study used numerical simulation to explore the effect of overlap percentage on multi-track melting in the SLM process for Ti-6Al-4V. The simulation results categorize the multi-track melting morphologies into two distinct groups. The first group, (0% to 40% overlap), displays consistent surface roughness with an average Ra value of 7 µm. In contrast, the second group (50% to 60% overlap), shows non-uniform shapes. Specifically, the three central tracks in this group present an Ra value of approximately 4 µm. However, when all five tracks of this group are taken into account, the average Ra rises to 14 µm. These differences in surface roughness can be attributed to factors like energy density and the Marangoni effect. Both melt pool depth and shrinkage depth in the overlap area are significant as they might contribute to the risk of the lack of fusion defects. The insights from this research hold potential in shaping scanning pattern strategy design.

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“…In Table 1, the surface roughness of most additively manufactured techniques is presented. 0.02 [20] 5-25 [21] Fused Deposition Modeling (FDM) 0.05 [22] 0.5-20 [23] Laminated Object Manufacturing (LOM) 0.10 [24] 0.8-2.5 [25] Electron Beam Melting (EBM) 0.05 [26] 1-20 [27,28] Direct Metal Laser Sintering (DMLS) 0.02 [29] 3-12 [30] Binder Jetting 0.035 [31] 3-13 [32] Direct Energy Deposition (DED) 0.25 [33] 5.08-227 [34,35] Laser powder bed fusion (L-PBF) 0.02 [36] 3.5-13.45 [37] Selective Laser Melting (SLM) 0.02 [38] 30-60 [39] The surface quality of additively manufactured components is influenced by a multitude of factors that interact in intricate ways. These factors include the choice of AM technique, powder characteristics, layer thickness, scanning strategy, and energy parameters specific to each technique.…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Intelligence for Surface Roughness Prediction of Additively Manufactured Components

Batu,

Lemu,

Shimels

2023

Materials

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Additive manufacturing has gained significant popularity from a manufacturing perspective due to its potential for improving production efficiency. However, ensuring consistent product quality within predetermined equipment, cost, and time constraints remains a persistent challenge. Surface roughness, a crucial quality parameter, presents difficulties in meeting the required standards, posing significant challenges in industries such as automotive, aerospace, medical devices, energy, optics, and electronics manufacturing, where surface quality directly impacts performance and functionality. As a result, researchers have given great attention to improving the quality of manufactured parts, particularly by predicting surface roughness using different parameters related to the manufactured parts. Artificial intelligence (AI) is one of the methods used by researchers to predict the surface quality of additively fabricated parts. Numerous research studies have developed models utilizing AI methods, including recent deep learning and machine learning approaches, which are effective in cost reduction and saving time, and are emerging as a promising technique. This paper presents the recent advancements in machine learning and AI deep learning techniques employed by researchers. Additionally, the paper discusses the limitations, challenges, and future directions for applying AI in surface roughness prediction for additively manufactured components. Through this review paper, it becomes evident that integrating AI methodologies holds great potential to improve the productivity and competitiveness of the additive manufacturing process. This integration minimizes the need for re-processing machined components and ensures compliance with technical specifications. By leveraging AI, the industry can enhance efficiency and overcome the challenges associated with achieving consistent product quality in additive manufacturing.

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Performance of High-Layer-Thickness Ti6Al4V Fabricated by Electron Beam Powder Bed Fusion under Different Accelerating Voltage Values

Cited by 14 publications

References 29 publications

Laser Powder Bed Fusion of Hard‐Phase Containing Co‐Base Alloy

Laser Powder Bed Fusion of Hard‐Phase Containing Co‐Base Alloy

Effect of hatch spacing in selective laser melting process of Ti-6Al-4V alloy on finished surface roughness: A computational study

Application of Artificial Intelligence for Surface Roughness Prediction of Additively Manufactured Components

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