Tailoring the shape memory properties of silica nanoparticle infused photopolymer composites for 4D printing applications: A Taguchi analysis

Dhanunjayarao, Borra N.; Naidu, N. V. Swamy; Sanivada, Usha Kiran; Fangueiro, Raúl

doi:10.1016/j.eurpolymj.2023.112174

Cited by 8 publications

(3 citation statements)

References 29 publications

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“…However, the traditional experimental approach to this determination is both time-consuming and cost-ineffective due to the numerous settings involved. Recognizing the need for a more efficient methodology, mathematical models, particularly the Taguchi method as elucidated by Taguchi [30], have emerged as effective alternatives, minimizing the number of experiments and optimizing results [39][40][41]. This study delves into an investigation of thirteen parameters to delineate their impacts on the properties of the 3D fabricated parts.…”

Section: Design Of Experimentsmentioning

confidence: 99%

Optimizing FDM process parameters: predictive insights through taguchi, regression, and neural networks

Khusheef,

Hashemi,

Shahbazi

2024

Phys. Scr.

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Fused deposition modelling (FDM) is a popular additive manufacturing process used for rapid prototyping and the production of complex geometries. Despite its popularity, FDM's susceptibility to variations in numerous process parameters can significantly impact the quality, design, functionality, and mechanical properties of 3D printed parts. This study explores thirteen FDM process parameters and their influence on the mechanical properties of polylactic acid (PLA) polymer, encompassing surface roughness, warpage, tensile and bending strength, elongation at break, deformation, and microhardness. The optimum parameters were identified alongside key contributors by applying the Taguchi method, signal-to-noise ratios, and analysis of variances (ANOVA). Notably, specific FDM parameters significantly affect the surface profile, with layer thickness contributing 32.65% and fan speed contributing 8.59% to the observed variations. Similarly, warping values show notable influence from nozzle temperature (29.53%), wall thickness (16.74%), layer thickness (16.56%), and retraction distance (12.80%). Tensile strength is primarily determined by wall thickness (31.83%), followed by infill percentage (26.73%) and infill pattern (16.18%). Elongation at break predominantly correlates with wall thickness (44.82%), with a supplementary contribution from nozzle temperature (10.90%). Microhardness lacks a dominant parameter. Bending strength variations primarily arise from layer thickness (38%), wall thickness (37.6%), and infill percentage (9.17%). Deformation tendencies are influenced by layer thickness (19.20%), print speed (11.37%), wall thickness, and fan speed (10.9% each). The optimized dataset of FDM process parameters was then employed in two prediction models: multiple-regression and artificial neural network (ANN). Evaluation based on the correlation coefficient (R2) and root mean squared error (RMSE) indicates that the ANN model outperforms the multiple-regression approach. The results indicate that precise control of FDM parameters, coupled with ANN predictions, facilitates the fabrication of 3D-printed parts with the desired mechanical characteristics.

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Section: Design Of Experimentsmentioning

confidence: 99%

Optimizing FDM process parameters: predictive insights through taguchi, regression, and neural networks

Khusheef,

Hashemi,

Shahbazi

2024

Phys. Scr.

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“…In another study, it was reported that the infusion of SiO 2 nanoparticles into a blend of soft and hard polymer provided remarkable R f and recovery values. The R f value increased from 93.03% to 100% and recovery value increased from 90% to 100% [ 102 ].…”

Section: Shape Memory Polymer (Smp) Nanocompositementioning

confidence: 99%

Recent Advances in Polymer Nanocomposites: Unveiling the Frontier of Shape Memory and Self-Healing Properties—A Comprehensive Review

Jamil,

Faizan,

Adeel

et al. 2024

Molecules

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Shape memory and self-healing polymer nanocomposites have attracted considerable attention due to their modifiable properties and promising applications. The incorporation of nanomaterials (polypyrrole, carboxyl methyl cellulose, carbon nanotubes, titania nanotubes, graphene, graphene oxide, mesoporous silica) into these polymers has significantly enhanced their performance, opening up new avenues for diverse applications. The self-healing capability in polymer nanocomposites depends on several factors, including heat, quadruple hydrogen bonding, π–π stacking, Diels–Alder reactions, and metal–ligand coordination, which collectively govern the interactions within the composite materials. Among possible interactions, only quadruple hydrogen bonding between composite constituents has been shown to be effective in facilitating self-healing at approximately room temperature. Conversely, thermo-responsive self-healing and shape memory polymer nanocomposites require elevated temperatures to initiate the healing and recovery processes. Thermo-responsive (TRSMPs), light-actuated, magnetically actuated, and Electrically actuated Shape Memory Polymer Nanocomposite are discussed. This paper provides a comprehensive overview of the different types of interactions involved in SMP and SHP nanocomposites and examines their behavior at both room temperature and elevated temperature conditions, along with their biomedical applications. Among many applications of SMPs, special attention has been given to biomedical (drug delivery, orthodontics, tissue engineering, orthopedics, endovascular surgery), aerospace (hinges, space deployable structures, morphing aircrafts), textile (breathable fabrics, reinforced fabrics, self-healing electromagnetic interference shielding fabrics), sensor, electrical (triboelectric nanogenerators, information energy storage devices), electronic, paint and self-healing coating, and construction material (polymer cement composites) applications.

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“…By incorporating stimuli-responsive materials, 4D printing enables the creation of freeform components that can change shape over a predetermined period of time [3], consequently increasing its possible applications in numerous areas, such as Aerospace [4], Automotive [5], Robotics [6], engineering, and Bio-medical [7]. To fully realize the potential of 4D printing in diverse industries, further research and development is necessary [8]. One of the areas of research that has gained significant interest is the development of shape memory behavior in carbon fibers reinforced with polylactic acid (CF/PLA) composites.…”

Section: Introductionmentioning

confidence: 99%

Design and Optimization of 4D Printed Carbon Fiber Reinforced Poly Lactic Acid Parts Using Fused Deposition Modeling for Shape Memory Applications: A Taguchi Approach

Venkatesh,

Krishna,

Kiran

et al. 2023

MSF

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The manufacturing industry has witnessed substantial interest in the advancement of 4D printing technology in recent years. This technology has enabled the production of complex structures with enhanced functionality and adaptability. Fused Deposition Modeling (FDM) has become a preferred technique for 4D printing due to its ease of use, affordability, and versatile nature. To achieve efficient and effective 4D printing, the process parameters must be optimised to ensure the desired shape recovery behaviour of the printed parts. The main objective of this study is to optimize the process parameters for the production of 4D printed components using FDM technology and Carbon Fiber reinforced Poly Lactic Acid (CF/PLA) Shape Memory Polymer Composites (SMPCs). This study examines the shape recovery properties of the printed components by modifying the process parameters, including Infill Density (ID), Geometrical Thickness (GT), and Bending Angle (BA), through the implementation of Design of Experiments (DOE) L9 Orthogonal Array (OA). Utilizing Analysis of Variance (ANOVA) to determine the significant factors and their optimum levels, the process parameters are statistically analysed. The results indicate that ID and GT are the statistically significant parameters, and the optimum levels for parameters includes 20% ID, 1.5mm GT, and 300 BA led to faster shape recovery. This study demonstrates the effectiveness of the Taguchi approach in the design and optimization of the process parameters for 4D printed parts using FDM.

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Tailoring the shape memory properties of silica nanoparticle infused photopolymer composites for 4D printing applications: A Taguchi analysis

Cited by 8 publications

References 29 publications

Optimizing FDM process parameters: predictive insights through taguchi, regression, and neural networks

Optimizing FDM process parameters: predictive insights through taguchi, regression, and neural networks

Recent Advances in Polymer Nanocomposites: Unveiling the Frontier of Shape Memory and Self-Healing Properties—A Comprehensive Review

Design and Optimization of 4D Printed Carbon Fiber Reinforced Poly Lactic Acid Parts Using Fused Deposition Modeling for Shape Memory Applications: A Taguchi Approach

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