Investigating the effect of fused deposition modeling processing parameters using Taguchi design of experiment method

This paper aims to present a first step toward developing a comprehensive methodology for fully resolved numerical simulations of fusion deposition modeling (FDM).,A front-tracking/finite volume method previously developed for simulations of multiphase flows is extended to model the injection of hot polymer and its cooling down.,The accuracy and convergence properties of the new method are tested by grid refinement, and the method is shown to produce convergent solutions for the shape of the filament, the temperature distribution, contact area and reheat region when new filaments are deposited on top of previously laid down filaments.,The present paper focuses on modeling the fluid flow and the cooling. The modeling of solidification, volume changes and residual stresses will be described in Part II.,The ability to carry out fully resolved numerical simulations of the fusion deposition process is expected to help explore new deposition strategies and provide the “ground truth” for the development of reduced-order models.,The present paper is the first fully resolved simulation of the deposition in fusion filament modeling.

Fully resolved numerical simulations of fused deposition modeling. Part I: fluid flow

Additive manufacturing (or three-dimensional (3D) printing) is constantly growing as an innovative process for the production of complex-shape components. Among the seven recognized 3D printing technologies, fused deposition modeling (FDM) covers a very important role, not only for producing representative 3D models, but, mainly due to the development of innovative material like Peek and Ultem, also for realizing structurally functional components. However, being FDM a production process involving high thermal gradients, non-negligible deformations and residual stresses may affect the 3D printed component. In this work we focus on meso/macroscopic simulations of the FDM process using abaqus software. After describing in detail the methodological process, we investigate the impact of several parameters and modeling choices (e.g., mesh size, material model, time-step size) on simulation outcomes and we validate the obtained results with experimental measurements.

Finite Element Analysis of Additive Manufacturing Based on Fused Deposition Modeling: Distortions Prediction and Comparison With Experimental Data

Additive manufacturing (AM) has first emerged in 1987 with the invention of stereolithography. The AM is an important, rapidly emerging, manufacturing technology that takes the information from a computer-aided design (CAD) and builds parts in a layer-by-layer style. As this technology offers many advantages such as manufacturing of complex geometries, reducing manufacturing cost and energy consumption, it has transformed manufacturing from the mass production to the mass customization. Also, it has found wide applications in several fields although some drawbacks. This paper presents the state of the art of the different AM processes, the material processing issues, and the post-processing operations. A comparison between AM and conventional processes is presented as well. We finish by presenting some prospects of this technology such as hybrid manufacturing and 4D printing.

https://hal.archives-ouvertes.fr/hal-02309255/document

Additive manufacturing technology: the status, applications, and prospects

In this study, significant process parameters (layer thickness, build orientation, infill density and number of contours) are optimized for enhancing the magnitude/dimensional preciseness of fused deposition modeling (FDM) devise units. Hybrid statistical tools such as response surface methodology–genetic algorithm (RSM–GA), artificial neural network (ANN) and artificial neural network-genetic algorithm (ANN-GA) in MAT LAB 16.0 are utilized for training and optimization. An attempt has been made to build up a mathematical model in order to set up an indirect correlation between various FDM process parameters and magnitude preciseness. Sequentially to verify the different developed models and the optimum process parameters setting validation tests were also performed. The results showed that various hybrid statistical tools such as RSM-GA, ANN and ANN-GA are very adequate tools for FDM process parameter optimization. The minimum percentage variation in length = 0.06409%, width = 0.03961% and thickness = 0.85689% can be obtained by using ANN-GA.

Modeling and parametric optimization of FDM 3D printing process using hybrid techniques for enhancing dimensional preciseness

Dimensional Tolerances for Additive Manufacturing: Experimental Investigation for Fused Deposition Modeling

Zusammenfassung Am Beispiel des chinesisch-deutschen Kooperationsstudiengangs Maschinenbau an der Chinesisch-Deutschen Technischen Fakultät in Qingdao (China) werden unter Rückgriff auf unterschiedliche im Rahmen formativer Evaluation erhobener Datensätze Möglichkeiten und Grenzen der Integration von Fach- und Sprachlernen sowie Optimierungsmöglichkeiten der Unterstützung des studienvorbereitenden und -begleitenden (Fach-)Spracherwerbs diskutiert.

/pdf/integration-von-sprach-und-fachlernen-im-kontext-chinesisch-4a2e8fw0hi.pdf

Integration von Sprach- und Fachlernen im Kontext chinesisch-deutscher Kooperationsstudiengänge am Beispiel des Maschinenbaustudiums an der Chinesisch-Deutschen Technischen Fakultät (CDTF, Qingdao/Paderborn)

/pdf/p-ost-migrantische-perspektiven-3o91igiv.pdf

Vera Denzer

Papers

Dimensional Tolerances for Additive Manufacturing: Experimental Investigation for Fused Deposition Modeling

Integration von Sprach- und Fachlernen im Kontext chinesisch-deutscher Kooperationsstudiengänge am Beispiel des Maschinenbaustudiums an der Chinesisch-Deutschen Technischen Fakultät (CDTF, Qingdao/Paderborn)

P-ost-migrantische Perspektiven?