Print parameter optimization and mechanical deformation analysis of alumina-nanoparticle doped photocurable nanocomposites fabricated using vat-photopolymerization based additive technology

Raj, Ratnesh; Dixit, Amit Rai; Singh, Sarthak S.; Paul, Sudeepto

doi:10.1016/j.addma.2022.103201

Cited by 17 publications

(7 citation statements)

References 53 publications

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“…Instead, the fabric thickness was less than the real cure depth, with increased fabric content due to platform compaction. Even a single-fabric blended resin sample obeys the Beer-Lambert law of the linear relationship between absorbance and concentration (Raj et al , 2022).…”

Section: Resultsmentioning

confidence: 99%

Fabrication of continuous woven E-glass fiber composite using vat photopolymerization additive manufacturing process

Moharana,

Raj,

Dixit

2023

RPJ

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Purpose The industrial application of continuous glass fabric-reinforced polymer composites (GFRPCs) is growing; however, the manufacturing boundedness of complex structures and the high cost of molds restrict their use. This research proposes a three-dimensional (3 D) printing process for GFRPCs that allows low-cost and rapid fabrication of complex composite parts. Design/methodology/approach The composite is manufactured using a digital light processing (DLP) based Vat-photopolymerization (VPP) process. For the composites, suitable resin material and glass fabrics are chosen based on their strength, stiffness, and printability. Jacob's working curve characterizes the curing parameters for adequate adhesion between the matrix and fabrics. The tensile and flexural properties were examined using UTM. The fabric distribution and compactness of the cured resin were analyzed in scanning electron microscopy. Findings The result showed that the object could print at a glass fabric content of 40 volume%. In DLP-based VPP printing technology, the adequate exposure time was found to be 30 seconds for making a GFRPC. The tensile strength and Young's modulus values were increased by 5.54 and 8.81 times, respectively than non-reinforced cured specimens. The flexural strength and modulus were also effectively increased to 2.8 and 3 times more than the neat specimens. In addition, the process is found to help fabricate the functional component. Originality/value The experimental procedure to fabricate GFRPC specimens through DLP-based AM is a spectacular experimental approach.

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Section: Resultsmentioning

confidence: 99%

Fabrication of continuous woven E-glass fiber composite using vat photopolymerization additive manufacturing process

Moharana,

Raj,

Dixit

2023

RPJ

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“…Some literature suggested that the Jacob's working curve for plain photopolymer and nanoparticle dispersed photopolymer suspension determined the exact cure depth to achieve high‐geometric accuracy of printed parts 25 . The alumina nanoparticle‐filled composite typically showed 30–35 mJ/cm 2 of exposure energy to cure the suspension at 50‐micron layer thickness 26 . Similarly, short carbon and glass fibers have also been examined on SL photopolymer curing.…”

Section: Introductionmentioning

confidence: 99%

“…25 The alumina nanoparticle-filled composite typically showed 30-35 mJ/cm 2 of exposure energy to cure the suspension at 50-micron layer thickness. 26 Similarly, short carbon and glass fibers have also been examined on SL photopolymer curing. Glass fibers are cured better in pure photopolymer as fiber weight fraction increases.…”

Section: Introductionmentioning

confidence: 99%

Effect of glass fiber reinforcement on compressive strength of photopolymer composite fabricated using vat‐photopolymerization additive technique: An experimental and modeling approach

Moharana,

Kumar,

Raj

et al. 2023

Polymer Composites

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Short or nanofillers‐reinforced composites increase the thermo‐mechanical characteristics of additive manufacturing (AM) base materials. Effective outputs were regulated via parametric optimisation and custom production. One of the most used AM method, Vat photopolymerization (VPP), creates complex and geometrically precise photopolymer parts. This study prepares photopolymer composite (PPC) samples with 2.0, 4.0, 6.0, and 8.0 volume percentages of short glass fiber (SGF) using an inter‐stage stirring method in DLP‐based VPP process. An adequate curing time and formable layer thickness were evaluated for the printability performance of SGF‐photopolymer composite. The 4.0% PPC‐sample showed a 20% higher compressive yield strength than pure photopolymer and other volume fractions. The consequences of increased strength values were assessed by the effect of stirring method and the fractography analysis of PPC specimen. The investigation additionally encompassed numerical simulations that employed an innovative technique for constructing and simulating three‐dimensional representative volume elements (RVEs). The RVEs were designed to replicate the experimental outcomes by randomly situating and orienting fillers within the matrix. The study employs the calibrated Three Network (TN) viscoplastic constitutive material model as the matrix property, and the simulation results demonstrate a margin of error of 5%–10% when compared to the experimental findings.Highlights Layer‐wise availability of SGFs through inter‐stage stirring method The exposure time and layer thickness were determined by Jacob's working curve Concept of RVE is used to combine individual SGF layers into a single layer SEM showed that increasing SGF layer resists cracking better than neat matrix Experimental data and numerical simulations are in good agreement

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“…Current research in AM quality control predominantly focuses on the regulation and surveillance of these 3D-printed layers. 9 The in-plane shapes of the printed layers have been predicted, increasing the geometrical accuracy of extrusion-based additively manufactured parts. A framework for 3D Printing quality monitoring can be suggested using layer-by-layer inspection.…”

Section: Introductionmentioning

confidence: 99%

“…In the realm of 3D Printing, the precision of extruded layers plays a crucial role in determining the ultimate product quality. Current research in AM quality control predominantly focuses on the regulation and surveillance of these 3D‐printed layers 9 . The in‐plane shapes of the printed layers have been predicted, increasing the geometrical accuracy of extrusion‐based additively manufactured parts.…”

Section: Introductionmentioning

confidence: 99%

Predicting dimensional accuracy in 3D printed polydimethylsiloxane‐carbon nanotubes composites via machine learning

Raj,

Mahato,

Moharana

et al. 2023

Polymer Composites

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Direct ink writing (DIW) is a rapidly expanding additive manufacturing (AM) technique valued for its cost‐efficiency and material adaptability. While DIW offers substantial process flexibility, achieving precise manufacturing demands a thorough understanding of its printability parameters. Key process variables such as printing speed, nozzle‐bed gap, pulses for extrusion, and extrusion multiplier wield significant influence over the shape and dimensions of printed components. In this study, a machine learning model based on k‐nearest neighbors is employed to predict dimensional accuracy. The model is trained using measured width and thickness data from printed rings, with flow and printing‐speed tests defining parameter boundaries for printing. The final model demonstrates an accuracy of 81% and 74% for two PDMS‐CNT composite ink compositions. The principal components analysis underscores the pivotal roles of the extrusion factor and printing speed in achieving superior geometric fidelity. Additionally, retraction‐residual tests were conducted, revealing that the initial 10 seconds of extrusion hold critical importance as they can lead to distortions when traversing printed regions or perimeters, necessitating consideration in part design. To evaluate layer adhesion strength, a crucial aspect of AM, T‐peel tests were performed. Furthermore, rheological tests provided insights into extrudability, printable reinforcing percentages, shape retention, and yield stress.Highlights Rheological tests yield insights into minimum reinforcing% and shape retention. Developed k‐NN model predicts accuracy using measured printed rings' data. PC analysis shows key roles of extrusion factor and print speed. Retraction‐residual tests reveal initial 10 seconds are critical. Model validation results show 83.45% and 77.95% accuracy, close to prediction.

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Print parameter optimization and mechanical deformation analysis of alumina-nanoparticle doped photocurable nanocomposites fabricated using vat-photopolymerization based additive technology

Cited by 17 publications

References 53 publications

Fabrication of continuous woven E-glass fiber composite using vat photopolymerization additive manufacturing process

Fabrication of continuous woven E-glass fiber composite using vat photopolymerization additive manufacturing process

Effect of glass fiber reinforcement on compressive strength of photopolymer composite fabricated using vat‐photopolymerization additive technique: An experimental and modeling approach

Predicting dimensional accuracy in 3D printed polydimethylsiloxane‐carbon nanotubes composites via machine learning

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