Critical parameters influencing the quality of prototypes in fused deposition modelling

Anitha, R.; Arunachalam, S.; Radhakrishnan, P.

doi:10.1016/s0924-0136(01)00980-3

Cited by 616 publications

(300 citation statements)

References 1 publication

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“…The Taguchi's design matrix, signal to noise ratio (S/N), and analysis of variance (ANOVA) were used by Anitha et al [95] to figure out that road width, deposition speed, and layer thickness are the important process parameters on ABS part surface roughness. The similar conclusion was also obtained by Nancharaiah et al [96] through Taguchi and ANOVA methods.…”

Section: Methodsmentioning

confidence: 99%

Integration of physically-based and data-driven approaches for thermal field prediction in additive manufacturing

Jin

2018

Materials & Design

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A quantitative understanding of thermal field evolution is vital for quality control in additive manufacturing (AM). Because of the unknown material parameters, high computational costs, and imperfect understanding of the underlying science, physically-based approaches alone are insufficient for component-scale thermal field prediction. Here, I present a new framework that integrates physically-based and data-driven approaches with quasi in situ thermal imaging to address this problem. The framework consists of (i) thermal modeling using 3D finite element analysis (FEA), (ii) surrogate modeling using functional Gaussian process, and (iii) Bayesian calibration using the thermal imaging data. Based on heat transfer laws, I first investigate the transient thermal behavior during AM using 3D FEA. A functional Gaussian process-based surrogate model is then constructed to reduce the computational costs from the high-fidelity, physically-based model. I finally employ a Bayesian calibration method, which incorporates the surrogate model and thermal measurements, to enable layer-to-layer thermal field prediction across the whole component. A case study on fused deposition modeling is conducted for components with 7 to 16 layers. The cross-validation results show that the proposed framework allows for accurate and fast thermal field prediction for components with different process settings and geometric designs. Integration of Physically-based and Data-driven Approaches for Thermal Field Prediction in Additive ManufacturingJingran Li GENERAL AUDIENCE ABSTRACT This paper aims to achieve the layer to layer temperature monitoring and consequently predict the temperature distribution for any new freeform geometry. An engineering statistical synergistic model is proposed to integrate the pure statistical methods and finite element modeling (FEM), which is physically meaningful as well as accurate for temperature prediction. Besides, this proposed synergistic model contains geometry information, which can be applied to any freeform geometry. This paper serves to enable a holistic cyber physical systems-based approach for the additive manufacturing (AM) not only restricted in fused deposition modeling (FDM) process but also can be extended to powder-based process like laser engineered net shaping (LENS) and selective laser sintering (SLS). This paper as well as the scheduled future works will make it affordable for customized AM including customized geometries and materials, which will greatly accelerate the transition from rapid prototyping to rapid manufacturing. This article demonstrates a first evaluation of engineering statistical synergistic model in AM technology, which gives a perspective on future researches about online quality monitoring and control of AM based data fusion principles. iv ACKNOWLEDGEMENTS

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Section: Methodsmentioning

confidence: 99%

Integration of physically-based and data-driven approaches for thermal field prediction in additive manufacturing

Jin

2018

Materials & Design

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“…Nozzle-based printing can be categorized into two groups: techniques with melting process and techniques without melting process. Nozzle-based printing with melting process include fused deposition modelling (FDM), precise extrusion manufacturing (PEM), multiphase jet solidification (MJS), precision extrusion deposition (PED), and 3D fiber deposition [31][32][33][34] . FDM technique is the most commonly used nozzle based 3D printing method in which thermoplastic filaments such as poly(lactice acid) (PLA) and acrylonitrile-butadienestyrene (ABS) are melted by heating and extruded on the build plate [35] .…”

Section: Nozzle-based Hydrogel 3d Printing Systemsmentioning

confidence: 99%

3D printing of hydrogel composite systems: Recent advances in technology for tissue engineering

Jang¹,

Jung²,

Pan³

et al. 2024

IJB

189

117

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Three-dimensional (3D) printing of hydrogels is now an attractive area of research due to its capability to fabricate intricate, complex and highly customizable scaffold structures that can support cell adhesion and promote cell infiltration for tissue engineering. However, pure hydrogels alone lack the necessary mechanical stability and are too easily degraded to be used as printing ink. To overcome this problem, significant progress has been made in the 3D printing of hydrogel composites with improved mechanical performance and biofunctionality. Herein, we provide a brief overview of existing hydrogel composite 3D printing techniques including laser based-3D printing, nozzle based-3D printing, and inkjet printer based-3D printing systems. Based on the type of additives, we will discuss four main hydrogel composite systems in this review: polymer-or hydrogel-hydrogel composites, particle-reinforced hydrogel composites, fiber-reinforced hydrogel composites, and anisotropic filler-reinforced hydrogel composites. Additionally, several emerging potential applications of hydrogel composites in the field of tissue engineering and their accompanying challenges are discussed in parallel.

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“…From the results it was found that layer thickness, raster angle, raster width and air gap significantly affect the elastic performance of the compliant ABS prototype. R. Anitha et al [10] Have studied the effect of various process parameters, i.e. layer thickness, road width and speed deposition of fused deposition modeling part.…”

Section: Bh Lee Et Al [9]mentioning

confidence: 99%

Optimization of Abs P430 Material Made of Fused Deposition Modeling (Fdm) Using Taguchi Method

2015

IJAERD

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Critical parameters influencing the quality of prototypes in fused deposition modelling

Cited by 616 publications

References 1 publication

Integration of physically-based and data-driven approaches for thermal field prediction in additive manufacturing

Integration of physically-based and data-driven approaches for thermal field prediction in additive manufacturing

3D printing of hydrogel composite systems: Recent advances in technology for tissue engineering

Optimization of Abs P430 Material Made of Fused Deposition Modeling (Fdm) Using Taguchi Method

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