Multiscale Modelling and Experimental Characterization of Additively Manufactured Polymer and Polymer Nanocomposites
Bibliographic record
Abstract
Fused deposition modelling (FDM) is a widely utilized additive manufacturing process that is used in several industrial sectors. The advantages of additive manufacturing over conventional methods are the ability to rapidly produce intricate forms and sizes with precision, without the need for additional machining to get a finished product or the assembly of many pieces to create a single component. The deposition process based on the layer-by-layer principle results in the formation of intrinsic voids inside the bulk of printed parts. The presence of voids, in conjunction with the various printing parameters employed throughout the process, has a significant impact on the performance in relation to their electrical, mechanical, or other properties. Researchers have employed experimental characterisation methods to comprehend the impact of these parameters and the concentration of voids on the properties of printed parts. Because of these voids and parameters effect, additively manufactured parts exhibit anisotropic behavior due to their inherent microstructure characteristics, which cannot be accurately captured by macroscale computational models. When employing nanofillers to enhance the characteristics of printed components, it is imperative for the computational tool to accurately account for the dispersion and agglomeration phenomena occurring at a smaller scale. The current models are unable to adequately address this. In terms of the experimental characterization, the manufacturing method of feedstock input filament necessitates over 24 hours for the solvent employed in the procedure to undergo evaporation. Furthermore, the dispersion observed in the literature at lower concentrations of nanofillers is reported poor in experimental studies. Therefore, to answer these challenges, this thesis presents a hierarchical multiscale model for studying the effect of fused deposition modelling process parameters on mechanical properties. This study examines four parameters: raster orientation, layer height, build orientation, and porosity. The generation of representative volume elements (RVEs) involves the mimicking of the microstructure of the printed parts. Once the understanding of effect of parameters concluded, the thesis introduced a novel geometrical void model integrated with asymptotic homogenization to study first time the impact of inter-raster distance, bead width, and layer height on the concentration of voids (in terms of volume) at constant infill density. The next study focussed on improvement of additively manufactured parts properties by incorporating multiwalled carbon nanotubes (MWCNTs) as nanofillers in polymer matrix. A novel computational model with three scales to determine the mechanical characteristics of nanocomposites fabricated using 3D printing is proposed. The proposed computational framework incorporates the Halpin Tsai and Eshelby Mori Tanaka models at the microscale integrated with the asymptotic homogenization model at the meso and macro scales. The work further presents a unique experimental approach for water-quenching, which is used to prepare insitu nanocomposite sheets while producing nanocomposite filaments. In addition to tensile testing, a morphological analysis was undertaken to examine the dispersion and agglomeration impact and efficacy of the newly proposed methodology. The present research work contributes to the scientific community as new effective modelling methods and novel experimental characterization method to study and characterize the polymer and polymer nanocomposites 3D printed parts. The polymer and nanocomposite computational models effectively provide tools for designing the additively manufactured parts with improved properties. The experimental method enables research community with rapidity in nanocomposite filament manufacturing having good dispersion and increased properties. The present study will allow new development in additively manufacturing industries such as cranioplasty biomedical industry, with reduced experimental testing, improved properties by controlling the parameters of FDM method.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".