Laser Powder Bed Fusion Process Parameters’ Optimization for Fabrication of Dense IN 625

Paraschiv, Alexandru; Matache, Gheorghe; Condruz, Mihaela Raluca; Frigioescu, T.; Pambaguian, Laurent

doi:10.3390/ma15165777

Cited by 17 publications

(3 citation statements)

References 55 publications

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“…Therefore, the relationship between 𝑃 𝐿 and part density in PBF-LB/M is only limited and applicable for producing Nd-based feedstocks via PBF-LB/M and may depend on various factors, such as the 𝐷 L , 𝑣 𝑠 , powder bed properties, and process parameters. [48] It is noteworthy that the thermal-microstructural simulations were performed with unity absorption. In other words, a complete power absorption within the laser spot was assumed, whereas such absorption has been shown to depend on 𝑃 𝐿 and 𝐷 L [49,50] .…”

Section: Resultsmentioning

confidence: 99%

Influence of silver nanoparticle additivation on Nd-Fe-B permanent magnets produced by Laser Powder Bed Fusion

Gabriel

Liu

Staab

et al. 2023

Preprint

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Powder bed fusion of metals using a laser beam (PBF-LB/M) is an established additive manufacturing (AM) method that can be used to fabricate geometrically complex NdFe-B magnets. However, the magnetic properties of Nd-Fe-B magnets manufactured by PBF-LB/M are typically inferior to conventionally produced magnets. To overcome this drawback, we modified the surface of the permanent magnet feedstock powder with 1 wt.% surfactant-free Ag nanoparticles (NPs) supporting the formation of relevant phases required for permanent magnetic performance to achieve a suitable micro- and nanostructure after AM. Our study is accompanied by finite element simulations, revealing the impact and dependency of process parameters during PBF-LB/M: a wide temperature field with a high-gradient profile in the front and on the bottom of an overheated region, implying a vast local heating/cooling rate and in-process high thermal stress. We found experimentally that the as-built part density can be affected by both the laser power and scan speed, causing a reduction in density as both parameters increase. The functionality and microstructural properties are also investigated via VSM, HR-SEM, EDX, EBSD, and exemplarily with HR-TEM-EDX and APT. Our study found that modifying MQP-S with Ag NPs increases the coercivity by approximately 20%, which we correlate to a decreased grain size. Additionally, we identified three distinct phases in the modified and unmodified samples, where Ag is primarily found in the intergranular and Nd-rich phases of the as-built parts. Overall, the study's findings contribute to the understanding of the factors that affect the quality and magnetic properties of Nd-Fe-B magnets fabricated through PBF-LB/M and provide valuable insights for further research in this area.

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Section: Resultsmentioning

confidence: 99%

Influence of silver nanoparticle additivation on Nd-Fe-B permanent magnets produced by Laser Powder Bed Fusion

Gabriel

Liu

Staab

et al. 2023

Preprint

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“…In compiling our dataset, we favored data measured via image analysis but also included some data measured via the Archimedes method. This approach was adopted for two main reasons: firstly, the discrepancy between the two methods is typically minor within our low-porosity region of interest 32 , 33 , and the errors of relative densities far from 98% are disregarded as the relative density is normalized by the sigmoid function. Secondly, as supported by Halevy et al 34 , larger datasets with some measurement errors are generally more beneficial for training and generalization than smaller, flawless datasets.…”

Section: Methodsmentioning

confidence: 99%

Material-agnostic machine learning approach enables high relative density in powder bed fusion products

Wang,

Jeong,

Kim

et al. 2023

Nat Commun

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This study introduces a method that is applicable across various powder materials to predict process conditions that yield a product with a relative density greater than 98% by laser powder bed fusion. We develop an XGBoost model using a dataset comprising material properties of powder and process conditions, and its output, relative density, undergoes a transformation using a sigmoid function to increase accuracy. We deeply examine the relationships between input features and the target value using Shapley additive explanations. Experimental validation with stainless steel 316 L, AlSi10Mg, and Fe60Co15Ni15Cr10 medium entropy alloy powders verifies the method’s reproducibility and transferability. This research contributes to laser powder bed fusion additive manufacturing by offering a universally applicable strategy to optimize process conditions.

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“…Powder Bed Fusion (PBF) [ 54 , 55 ] and Directed Energy Deposition (DED) [ 56 ] are also characterized by their unique process parameters that have direct implications on surface quality. While the powder bed fusion techniques such as SLS, SLM, DMLS, and L-PBF ( Table 1 ) are fundamentally identical processes that use a laser to selectively melt or sinter a bed of powder material to create a 3D object, there exist some differences in the way these processes work, such as the type of laser used, the power of the laser, and the scanning strategy used to melt or sinter the powder.…”

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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Laser Powder Bed Fusion Process Parameters’ Optimization for Fabrication of Dense IN 625

Cited by 17 publications

References 55 publications

Influence of silver nanoparticle additivation on Nd-Fe-B permanent magnets produced by Laser Powder Bed Fusion

Influence of silver nanoparticle additivation on Nd-Fe-B permanent magnets produced by Laser Powder Bed Fusion

Material-agnostic machine learning approach enables high relative density in powder bed fusion products

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

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