Effect of powder particle size distribution on the surface finish of components manufactured by laser powder bed fusion

Sendino, Sara; Martı́nez, Silvia; Lartategui, Fernando; Gardon, M.; Lamíkiz, Aitzol; González, J.

doi:10.1007/s00170-022-10423-9

Cited by 11 publications

(3 citation statements)

References 25 publications

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“…Addressing these challenges is crucial for optimising AM processes and ensuring consistent dimensional quality. Surface roughness plays a crucial role in the overall quality of L-PBF parts, influencing dimensional accuracy and critical performance aspects like fatigue strength and surface finish [48,51]. Leica software enabled roughness analysis, and Figure 13 presents an example of how it is given in the data collected, in this case for supplier 1.…”

Section: Surface Roughnessmentioning

confidence: 99%

“…Process parameters and powder characteristics likely underlie these observed differences. The solidification behaviour of the melt pool is strongly influenced by factors like laser power, scanning strategy, and layer thickness, all of which can impact surface roughness [51][52][53][54]. Additionally, variations in powder particle morphology (noted in the Section 3.1.1) could contribute.…”

Section: Surface Roughnessmentioning

confidence: 99%

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Benchmarking L-PBF Systems for Die Production: Powder, Dimensional, Surface, Microstructural and Mechanical Characterisation

Costa,

Sequeiros,

Santos

et al. 2024

Metals

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While conventional die manufacturing techniques often lead to limitations in production speed and design intricacy due to labour-intensive procedures like machining and casting, Additive Manufacturing (AM) emerges as a key player offering substantial potential for cost reduction and process improvement in mass production. This study benchmarks four leading Laser Powder Bed Fusion (L-PBF) systems for producing maraging steel (EN 1.2709) dies. Despite the shared material and technology, variations in dimensional accuracy, surface finish, and microstructure were observed among the maraging steel parts. SEM/EDS, EBSD, hardness testing, and dimensional analysis revealed system-specific performance differences. Additionally, select parts underwent heat treatment and tensile testing, demonstrating the impact of post-processing on mechanical properties. These results offer valuable guidance for industrial stakeholders considering AM, highlighting the importance of supplier selection and process optimisation for achieving consistent part quality and unlocking the full potential of AM technologies.

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Section: Surface Roughnessmentioning

confidence: 99%

Section: Surface Roughnessmentioning

confidence: 99%

Benchmarking L-PBF Systems for Die Production: Powder, Dimensional, Surface, Microstructural and Mechanical Characterisation

Costa,

Sequeiros,

Santos

et al. 2024

Metals

View full text Add to dashboard Cite

show abstract

“…Material properties: These include data related to the material used in the additive manufacturing process, such as powder size, particle shape, and chemical composition, especially in methods such as L-PBF for metal AM. Material properties can also have a significant impact on surface roughness [ 124 ].…”

Section: Ai-based Surface Roughness Prediction Overviewmentioning

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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Effect of powder particle size distribution and contouring on build quality in electron beam powder bed fusion of a medium-C hot-work tool steel

Sullivan,

Sharif Hedås,

Jerhamre Engström

et al. 2023

Int J Adv Manuf Technol

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An electron beam powder bed fusion (EPBF) printability study of a medium-C hot-work tool steel with focus on part density and surface roughness was performed using three different powder particle size distributions (PSDs) of 45–105 $$\mathrm{\mu mmm}$$ μ mmm (typical for EPBF), 20–60 $$\mathrm{\mu mmm}$$ μ mmm (typical for laser beam powder bed fusion), and a 50–50 wt.% mixture of these two powders. First, acceptable process parameter windows were generated based on as-printed density for each PSD. Full density parts (at least 99.5% dense according to NIST) were produced using the 20–60 $$\mathrm{\mu mmm}$$ μ mmm PSD and the mix PSD. Fifteen different contouring strategies were also tested for potential improvement of the as-printed side surface roughnesses, which ranged from 23.3 to 25.7 $$\mathrm{\mu mmm}$$ μ mmm among the three PSDs. Side surface roughness as low as 13.8 $$\mathrm{\mu mmm}$$ μ mmm was attained by using contouring strategies employing two contouring lines, which were typically observed to be more effective than one-line strategies. Overall, the 20–60 $$\mathrm{\mu mmm}$$ μ mmm PSD was determined to convey a better as-printed build quality over a wider range of parameters without sacrificing process productivity.

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Effect of powder particle size distribution on the surface finish of components manufactured by laser powder bed fusion

Cited by 11 publications

References 25 publications

Benchmarking L-PBF Systems for Die Production: Powder, Dimensional, Surface, Microstructural and Mechanical Characterisation

Benchmarking L-PBF Systems for Die Production: Powder, Dimensional, Surface, Microstructural and Mechanical Characterisation

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

Effect of powder particle size distribution and contouring on build quality in electron beam powder bed fusion of a medium-C hot-work tool steel

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