Fused deposition modeling based polymeric materials and their performance: A review

Vigneshwaran, S.; Pavan, M. Venkata; Babu, K. Ganesh; Balamurugan, K.

doi:10.1002/pc.26275

Cited by 94 publications

(65 citation statements)

References 143 publications

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“…Other polymeric materials used in FFF technology are polycaprolactone (PCL), polycarbonate (PC), polyamide (PA) or nylon, polypropylene (PP), polymethyl methylacrylate (PMMA), polystyrene (PS), polyetherimide (PEI), and various types of polyethylene (PE), including low density polyethylene (LDPE), linear low density polyethylene (LLDPE), high density polyethylene (HDPE), and polyethylene terephthalate (PET) [ 5 , 32 , 45 , 49 , 52 , 55 , 56 , 57 , 60 , 61 , 62 ]. A combination of the polymeric materials has been used in some scientific publications, such as ABS and PC, PLA and PC, PE and PP, and so on [ 32 , 60 , 62 ].…”

Section: Materials For Fff Technologymentioning

confidence: 99%

A Review of Polymer-Based Materials for Fused Filament Fabrication (FFF): Focus on Sustainability and Recycled Materials

et al. 2022

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Recently, Fused Filament Fabrication (FFF), one of the most encouraging additive manufacturing (AM) techniques, has fascinated great attention. Although FFF is growing into a manufacturing device with considerable technological and material innovations, there still is a challenge to convert FFF-printed prototypes into functional objects for industrial applications. Polymer components manufactured by FFF process possess, in fact, low and anisotropic mechanical properties, compared to the same parts, obtained by using traditional building methods. The poor mechanical properties of the FFF-printed objects could be attributed to the weak interlayer bond interface that develops during the layer deposition process and to the commercial thermoplastic materials used. In order to increase the final properties of the 3D printed models, several polymer-based composites and nanocomposites have been proposed for FFF process. However, even if the mechanical properties greatly increase, these materials are not all biodegradable. Consequently, their waste disposal represents an important issue that needs an urgent solution. Several scientific researchers have therefore moved towards the development of natural or recyclable materials for FFF techniques. This review details current progress on innovative green materials for FFF, referring to all kinds of possible industrial applications, and in particular to the field of Cultural Heritage.

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Section: Materials For Fff Technologymentioning

confidence: 99%

A Review of Polymer-Based Materials for Fused Filament Fabrication (FFF): Focus on Sustainability and Recycled Materials

et al. 2022

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“…Therefore, the fiber‐reinforced 3D printed composite materials with better mechanical properties are under deep exploration. [ 20 ]…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the fiberreinforced 3D printed composite materials with better mechanical properties are under deep exploration. [20] AE monitoring is a high-efficiency technology for researching the damage of composites during mechanical experiment and manufacturing process. Wu et al [21] put forward a real time monitoring technology for AM process failure diagnosis using AE technique because of its convenience to detect internal defects of the components, and was proved during the fused filament fabrication (FFF) process.…”

Section: Introductionmentioning

confidence: 99%

Flexural damage and failure behavior of 3D printed continuous fiber composites by complementary nondestructive testing technology

et al. 2022

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It is necessary to investigate the flexural damage and evolution behavior of 3D printed continuous fiber-reinforced composites. In this paper, acoustic emission technology (AE) is used to monitor the bending damage of 3D printed Kevlar fiber (KF) and glass fiber (GF)-reinforced composites in real time, and gray relational analysis (GRA) and k-means are utilized for clustering analysis of AE signals. Subsequently, micro-computed tomography (micro-CT) is employed to identify the internal damage degree. For 3D printed specimens, matrix buckling and interface failure are the main failure modes, and the number of matrix and interface damage is more than that of fiber damage, and the surface of compression side (specimen's top side) is more serious than that of the back side. KF-reinforced composites have poor delamination resistance and more serious damage than that of GF. The average maximum stress of GF and KF-reinforced specimens is 56.37 and 42.15 MPa, and the average bending modulus is 1576.42 and 1395.36 MPa, respectively. The combination of complementary detection methods is beneficial to the nondestructive evaluation and health monitoring of 3D printed composite materials.

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“…During recent years, exploration of the FDM technique makes additive manufacturing process cost effective as it reduces the cost of making mold for small batch production, therefore, FDM has become choice for the small batch production. [10] Review of previous literature studies on mechanical properties [2,[11][12][13][14] and surface quality of FDM parts pointed that nozzle diameter, build orientation, infill density, raster pattern, layer height and print speed are significant parameters which decide the tensile strength, build time, material consumption and surface quality of FDM fabricated parts. [6,[15][16][17][18][19][20][21][22] These critical process parameters require further exploration to study the impact of these parameters on tensile strength, surface quality, material consumption, and build time.…”

Section: Introductionmentioning

confidence: 99%

Development of artificial intelligence‐based neural network prediction model for responses of additive manufactured polylactic acid parts

et al. 2022

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Fused deposition modeling (FDM) is one of the most economical and popular technology amongst numerous additive manufacturing techniques. The quality of FDM fabricated parts is highly sensitive to the production parameters. Thus, in the present work, an investigation on the FDM printed polylactic acid parts has been performed considering six printing process parameters, that is, nozzle diameter, build orientation, raster pattern, layer height and print speed to develop the feedforward backpropagation (FFBP) artificial neural network prediction model for the prediction of responses, namely, tensile strength, material consumption, build time and surface quality. Tensile specimens as per L 27 orthogonal array are printed considering the various combination of parameters. The printed samples have been subjected to tensile strength testing, surface roughness measurement, build time recording, and material consumption evaluation. The highest tensile strength of 57.633 MPa, lowest surface roughness of 1.71 μm, lowest build time of 0.35 h and lowest material consumption of 7.8 g are observed. The experimental results have been used to develop the artificial intelligence-based prediction model through FFBP algorithm and sigmoid transfer function to predict the responses. The best performance of the developed neural network with R 2 for testing (0.99343), training (0.99366), and validation (0.99372) of data is recorded for prediction of responses with minimum percentage error. The study concluded that developed model is capable of predicting the responses of FDM process according to the input process parameters.

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Fused deposition modeling based polymeric materials and their performance: A review

Cited by 94 publications

References 143 publications

A Review of Polymer-Based Materials for Fused Filament Fabrication (FFF): Focus on Sustainability and Recycled Materials

A Review of Polymer-Based Materials for Fused Filament Fabrication (FFF): Focus on Sustainability and Recycled Materials

Flexural damage and failure behavior of 3D printed continuous fiber composites by complementary nondestructive testing technology

Development of artificial intelligence‐based neural network prediction model for responses of additive manufactured polylactic acid parts

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