Enhancing 3D Printing Producibility in Polylactic Acid Using Fused Filament Fabrication Fused Deposition Modelling and Machine Learning
Notice bibliographique
Résumé
<title>Abstract</title> Polylactic Polylactic acid (PLA) is one of the high applicable material which is used in the 3D printers due to some significant features like its deformation property and affordable costacid (PLA) is brittle in nature with extensive deformation property. For improvement of the end-use quality, it is of significant importance to enhance the quality of Fused Filament Fabrication (FFF)fused deposition modeling (FDM)-printed objects in PLA. The purpose of this investigation is to boost toughness and to reduce the production cost of the FDMFFF-printed tensile test samples with the desired part thickness. Due to prevent from many numerous and idle printing samples the response Surface Method (RSM) is used.To attain the research purpose number of experiments are designed and analyzed by the Response Surface Method (RSM). The statistical analysis is performed to deal with this concern considering extruder temperature (ET), infill percentage (IP), and layer thickness (LT) as controlled factors. The tensile test specimens are printed based on the designed experiments, and the tensile strength tests are conducted by SANTAM 150 universal testing machine based on ASTM D638. The pattern for filling is designed based on honeycomb which is applied to produce lightweight and high-strength specimens. The area under Force- Extension curve up to fracture is acquired as the toughness of the printed specimens. This study also developed a modeling process using artificial neural network (ANN) and artificial neural network- genetic algorithm (ANN-GA) techniques to develop an accurate estimation for toughness, part thickness, and production cost dependent variables. Results were evaluated by correlation coefficient and RMSE values. According to the modeling results, ANN-GA as a hybrid machine learning (ML) technique could could successfully improveenhances the accuracy of modeling about 7.5, 11.5 and 4.5 % for toughness, part thickness, and production cost, respectively, in comparison with those for the single ANN method. On the other side, the optimization results confirm that the optimized specimen is cost-effective and able to comparatively undergo deformation, which enables the usability of printed PLA objects. The research is accomplished under the constraints of PLA compatibility with existing Fused Filament Fabrication fused deposition modeling installation, in the absence of the functional assistant of the machine in the absence of the functional assistant of the machine. Although the mechanical properties and dimensional accuracy of PLA have already been studied, there is little literature on the toughness of the printed PLA with honeycomb internal fill pattern.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».