Influence of PLA Filament Conditions on Characteristics of FDM Parts

Puerta, Ana Pilar Valerga; Batista, M.; Salguero, Jorge; Girot, F.

doi:10.3390/ma11081322

Cited by 133 publications

(120 citation statements)

References 17 publications

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“…The results of this study agree with the findings of Galantucci et al [34] that printing speed is the most effective process parameter determining dimensional accuracy. Valerga et al [30] have affirmed the negative influence of high printing temperatures on dimensional accuracy. The results of Sood et al [35] agree only in part with this study, since they recommended greater layer thickness to reduce percentage deviation.…”

Section: Dimensional Accuracymentioning

confidence: 95%

“…Yang et al [32] and Pérez et al [28] confirmed the low impact of printing temperature, if it is considered independently from the other parameters. Valerga et al [30] validated only the negative influence of high printing temperature, but the interaction between temperature and layer thickness was not examined. However, this investigation has shown that high temperature, when combined with thin layers, decreases the surface roughness, but the influence of printing temperature on surface roughness in general is only marginal.…”

Section: Surface Roughnessmentioning

confidence: 99%

“…Roughness could be optimized by a part orientation of 90 • , and a reduction in model interior from solid to high density. Valerga et al [30] studied the influence of material pigmentation, environmental humidity, and extrusion temperature on surface quality, dimensional quality, and mechanical strength. Only the extrusion temperature significantly affected surface and dimensional qualities.…”

Section: Introductionmentioning

confidence: 99%

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Experiment-Based Process Modeling and Optimization for High-Quality and Resource-Efficient FFF 3D Printing

2020

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This article reports on the investigation of the effects of process parameters and their interactions on as-built part quality and resource-efficiency of the fused filament fabrication 3D printing process. In particular, the influence of five process parameters: infill percentage, layer thickness, printing speed, printing temperature, and surface inclination angle on dimensional accuracy, surface roughness of the built part, energy consumption, and productivity of the process was examined using Taguchi orthogonal array (L50) design of experiment. The experimental results were analyzed using ANOVA and statistical analysis, and the parameters for optimal responses were identified. Regression models were developed to predict different process responses in terms of the five process parameters experimentally examined in this study. It was found that dimensional accuracy is negatively influenced by high values of layer thickness and printing speed, since thick layers of printed material tend to spread out and high printing speeds hinder accurate deposition of the printed material. In addition, the printing temperature, which regulates the viscosity of the used material, plays a significant role and helps to minimize the dimensional error caused by thick layers and high printing speeds, whereas the surface roughness depends very much on surface inclination angle and layer thickness, which together determine the influence of the staircase effect. Energy consumption and productivity are primarily affected by printing speed and layer thickness, due to their high correlation with build time.

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Section: Dimensional Accuracymentioning

confidence: 95%

Section: Surface Roughnessmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Experiment-Based Process Modeling and Optimization for High-Quality and Resource-Efficient FFF 3D Printing

2020

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“…Raju et al [19] also showed that layer thickness and build orientation were two significant parameters for surface quality. Valerga et al [62] analyzed various PLA filament conditions to know their impact on surface quality, along with the dimensional accuracy and tensile strength of the FDM printed part. They considered extrusion temperature, humidity, and pigmentation color as variables.…”

Section: Surface Roughnessmentioning

confidence: 99%

A Systematic Survey of FDM Process Parameter Optimization and Their Influence on Part Characteristics

Dey

Yodo

2019

JMMP

335

257

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Fused deposition modeling (FDM) is an additive manufacturing (AM) process that is often used to fabricate geometrically complex shaped prototypes and parts. It is gaining popularity as it reduces cycle time for product development without the need for expensive tools. However, the commercialization of FDM technology in various industrial applications is currently limited due to several shortcomings, such as insufficient mechanical properties, poor surface quality, and low dimensional accuracy. The qualities of FDM-produced products are affected by various process parameters, for example, layer thickness, build orientation, raster width, or print speed. The setting of process parameters and their range depends on the section of FDM machines. Filament materials, nozzle dimensions, and the type of machine determine the range of various parameters. The optimum setting of parameters is deemed to improve the qualities of three-dimensional (3D) printed parts and may reduce post-production work. This paper intensively reviews state-of-the-art literature on the influence of parameters on part qualities and the existing work on process parameter optimization. Additionally, the shortcomings of existing works are identified, challenges and opportunities to work in this field are evaluated, and directions for future research in this field are suggested.

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“…In connection with [93] where increased humidity has been proven to enhance particle size growth, adjustment of humidity within the printing workspace may be an approach to promoting the growth of emitted particles to less hazardous larger agglomerates, although sustaining high humidity may present practical issues. 3D printing filament printability and print operation can be adversely affected by the presence of moisture in the air [127]. This could be a low priority measure, employed in cases where multiple printers need to be operated and further risk mitigation is required.…”

mentioning

confidence: 99%

3D-Printed Lab-on-a-Chip Diagnostic Systems-Developing a Safe-by-Design Manufacturing Approach

Karayannis¹,

Petrakli²,

Gkika³

et al. 2019

Micromachines

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The aim of this study is to provide a detailed strategy for Safe-by-Design (SbD) 3D-printed lab-on-a-chip (LOC) device manufacturing, using Fused Filament Fabrication (FFF) technology. First, the applicability of FFF in lab-on-a-chip device development is briefly discussed. Subsequently, a methodology to categorize, identify and implement SbD measures for FFF is suggested. Furthermore, the most crucial health risks involved in FFF processes are examined, placing the focus on the examination of ultrafine particle (UFP) and Volatile Organic Compound (VOC) emission hazards. Thus, a SbD scheme for lab-on-a-chip manufacturing is provided, while also taking into account process optimization for obtaining satisfactory printed LOC quality. This work can serve as a guideline for the effective application of FFF technology for lab-on-a-chip manufacturing through the safest applicable way, towards a continuous effort to support sustainable development of lab-on-a-chip devices through cost-effective means.

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Influence of PLA Filament Conditions on Characteristics of FDM Parts

Cited by 133 publications

References 17 publications

Experiment-Based Process Modeling and Optimization for High-Quality and Resource-Efficient FFF 3D Printing

Experiment-Based Process Modeling and Optimization for High-Quality and Resource-Efficient FFF 3D Printing

A Systematic Survey of FDM Process Parameter Optimization and Their Influence on Part Characteristics

3D-Printed Lab-on-a-Chip Diagnostic Systems-Developing a Safe-by-Design Manufacturing Approach

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