Recent trends and challenges in computer-aided design of additive manufacturing-based biomimetic scaffolds and bioartificial organs

Yoo, Dong-Jin

doi:10.1007/s12541-014-0583-7

Cited by 45 publications

(30 citation statements)

References 139 publications

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“…The fill density of 3D printed scaffolds/constructs strongly influences nutrient and waste transport [23]. Fill density is also strongly correlated with compressive strength [24][25][26]. The compressive strength of constructs was found to rise from 40 to 140 MPa as the fill density rose from 20 to 40% in an investigation by Fu et al [27].…”

Section: Introductionmentioning

confidence: 99%

Effects of slicing parameters on measured fill density for 3D printing of precision cylindrical constructs using Slic3r

Ravi

Shiakolas

2021

SN Appl. Sci.

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The goal of this research is to develop and verify an algorithm to predict the fill density of 3D printed cylindrical constructs as a function of critical slicing parameters. Open-source 3D printing is being applied to the pharmaceutical and biomedical domains where characteristics including drug release rate and compressive strength depend on fill density. Understanding how slicing parameters affect fill density in the printed construct is important to appropriately tailor these characteristics. In this study, we evaluated the relationship between slicing fill density (SFD), extrusion width (EW), layer height (LH), construct diameter and measured fill density (MFD). The developed algorithm provides novel insight into the effects of interconnects and rasters on the distribution of intra-matrix material. We analyze 27 combinations involving 3 levels of EW (0.40, 0.44, 0.48 mm), SFD (15, 25, 35%) and LH (0.15, 0.20, 0.25 mm). The SFD is smaller than and deviates from MFD with a maximum error of 18.62% and from predicted fill density (PFD) with a maximum error of 19.50% compared to the maximum error of 4.30% between PFD and MFD. The predicted interconnect contribution and error reduce with increasing SFD and cylinder diameter but are more prominent at lower values. Our work highlights the perils of employing open-source 3D printing without a sound understanding of the underlying parametric relationships. The proposed predictive model could be used in conjunction with Slic3r, an open-source slicing software, to predict fill density to a reasonable degree of accuracy (less than 5% error) for relatively smaller cylindrical constructs.

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Section: Introductionmentioning

confidence: 99%

Effects of slicing parameters on measured fill density for 3D printing of precision cylindrical constructs using Slic3r

Ravi

Shiakolas

2021

SN Appl. Sci.

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“…In the biomedical domain the porosity (1fill density ratio) of 3D printed constructs is often a critical parameter that determines ease of nutrient diffusion into and waste disposal away from the scaffold [21]. Porosity has also been shown to be strongly correlated with the compressive strength [22][23][24][25]. Fu et al found the compressive strength of scaffolds to increase from 40 MPa to 140 MPa with an increase in fill density from 20% to 40% [25].…”

Section: Introductionmentioning

confidence: 99%

Understanding the relationship between slicing and measured fill density in material extrusion 3D printing towards precision porosity constructs for biomedical and pharmaceutical applications

Ravi¹

2020

3D Print Med

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Background: Fill density is a critical parameter affecting the functional performance of 3D printed porous constructs in the biomedical and pharmaceutical domain. Numerous studies have reported the impact of fill density on the mechanical properties, diffusion characteristics and content release rates of constructs. However, due to the way in which slicing toolpath calculations are performed, there is substantial deviation between the measured and slicing fill density for relatively small sized constructs printed at low fill densities (high porosities). The purpose of the current study was to investigate this discrepancy using a combination of mathematical modeling and experimental validation. Methods: The open source slicer Slic3r was used to 3D print 20 mm × 20 mm × 5 mm constructs at three identified slicing fill density values, 9.58%, 20.36% and 32.33% (exact values entered into software), in triplicates. A mathematical model was proposed to accurately predict fill density, and the measured fill density was compared to both the predicted as well as the slicing fill density. The model was further validated at two additional slicing fill densities of 15% and 40%. The total material within the construct was analyzed from the perspective of material extruded within the beads as well as the bead to bead interconnects using the predictive model. Results: The slicing fill density deviated substantially from measured fill density at low fill densities with absolute errors larger than 26% in certain instances. The proposed model was able to predict fill density to within 5% of the measured fill density in all cases. The average absolute error between predicted vs. measured fill density was 3.5%, whereas that between slicing vs. measured fill density was 13%. The material extruded in the beads varied from 86.5% to 95.9%, whereas that extruded in the interconnects varied from 13.5% to 4.1%. Conclusions: The proposed model and approach was able to predict fill density to a reasonable degree of accuracy. Findings from the study could prove useful in applications where controlling construct fill density in relatively small sized constructs is important for achieving targeted levels of functional criteria such as mechanical strength, weight loss and content release rate.

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“…As a most promising field, additive manufacturing, especially its application in medical science (also known as three-dimensional bioprinting 1,2 ), has aroused widespread attention [3][4][5] . For instance, 3D printing technique has been one of the most focused research approaches for regeneration or repair of bone defects 6,7 .…”

Section: Introductionmentioning

confidence: 99%

3D multi-nozzle system with dual drives highly potential for 3D complex scaffolds with multi-biomaterials

Chen

Zhang

Chen

et al. 2017

Int. J. Precis. Eng. Manuf.

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Recent trends and challenges in computer-aided design of additive manufacturing-based biomimetic scaffolds and bioartificial organs

Cited by 45 publications

References 139 publications

Effects of slicing parameters on measured fill density for 3D printing of precision cylindrical constructs using Slic3r

Effects of slicing parameters on measured fill density for 3D printing of precision cylindrical constructs using Slic3r

Understanding the relationship between slicing and measured fill density in material extrusion 3D printing towards precision porosity constructs for biomedical and pharmaceutical applications

3D multi-nozzle system with dual drives highly potential for 3D complex scaffolds with multi-biomaterials

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