A case study: Mechanical modeling optimization of cellular structure fabricated using wood flour-filled polylactic acid composites with fused deposition modeling

2019 ◽  
Vol 216 ◽  
pp. 360-365 ◽  
Author(s):  
Yubo Tao ◽  
Ling Pan ◽  
Dexi Liu ◽  
Peng Li
Keyword(s):  
Polylactic Acid ◽  
Cellular Structure ◽  
Fused Deposition Modeling ◽  
Wood Flour ◽  
Mechanical Modeling ◽  
Fused Deposition ◽  
Deposition Modeling

scholarly journals Electrical and Thermal Conductivity of Polylactic Acid (PLA)-Based Biocomposites by Incorporation of Nano-Graphite Fabricated with Fused Deposition Modeling

Polymers ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 549 ◽  
Author(s):  
Rui Guo ◽  
Zechun Ren ◽  
Hongjie Bi ◽  
Min Xu ◽  
Liping Cai
Keyword(s):  
Thermal Conductivity ◽  
Polylactic Acid ◽  
Fused Deposition Modeling ◽  
Wood Flour ◽  
Thermoplastic Polyurethane ◽  
Volume Resistivity ◽  
Fused Deposition ◽  
Multi Walled Carbon Nanotubes ◽  
Deposition Modeling ◽  
Walled Carbon Nanotubes

The aim of the study was to improve the electrical and thermal conductivity of the polylactic acid/wood flour/thermoplastic polyurethane composites by Fused Deposition Modeling (FDM). The results showed that, when the addition amount of nano-graphite reached 25 pbw, the volume resistivity of the composites decreased to 108 Ω·m, which was a significant reduction, indicating that the conductive network was already formed. It also had good thermal conductivity, mechanical properties, and thermal stability. The adding of the redox graphene (rGO) combined with graphite into the composites, compared to the tannic acid-functionalized graphite or the multi-walled carbon nanotubes, can be an effective method to improve the performance of the biocomposites, because the resistivity reduced by one order magnitude and the thermal conductivity increased by 25.71%. Models printed by FDM illustrated that the composite filaments have a certain flexibility and can be printed onto paper or flexible baseplates.

scholarly journals Energy and Eco-Impact Evaluation of Fused Deposition Modeling and Injection Molding of Polylactic Acid

2021 ◽  
Vol 13 (4) ◽  
pp. 1875
Author(s):  
Emmanuel Ugo Enemuoh ◽  
Venkata Gireesh Menta ◽  
Abdulaziz Abutunis ◽  
Sean O’Brien ◽  
Labiba Imtiaz Kaya ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Injection Molding ◽  
Polylactic Acid ◽  
Fused Deposition Modeling ◽  
Plastic Injection Molding ◽  
Measurement Models ◽  
Fused Deposition ◽  
Dog Bone ◽  
Deposition Modeling ◽  
Plastic Injection

There is limited knowledge about energy and carbon emission performance comparison between additive fused deposition modeling (FDM) and consolidation plastic injection molding (PIM) forming techniques, despite their recent high industrial applications such as tools and fixtures. In this study, developed empirical models focus on the production phase of the polylactic acid (PLA) thermoplastic polyester life cycle while using FDM and PIM processes to produce American Society for Testing and Materials (ASTM) D638 Type IV dog bone samples to compare their energy consumption and eco-impact. It was established that energy consumption by the FDM layer creation phase dominated the filament extrusion and PLA pellet production phases, with, overwhelmingly, 99% of the total energy consumption in the three production phases combined. During FDM PLA production, about 95.5% of energy consumption was seen during actual FDM part building. This means that the FDM process parameters such as infill percentage, layer thickness, and printing speed can be optimized to significantly improve the energy consumption of the FDM process. Furthermore, plastic injection molding consumed about 38.2% less energy and produced less carbon emissions per one kilogram of PLA formed parts compared to the FDM process. The developed functional unit measurement models can be employed in setting sustainable manufacturing goals for PLA production.

Knowledge-Based Design of Artificial Neural Network Topology for Additive Manufacturing Process Modeling: A New Approach and Case Study for Fused Deposition Modeling

2018 ◽  
Vol 141 (2) ◽  
Author(s):  
Hari P. N. Nagarajan ◽  
Hossein Mokhtarian ◽  
Hesam Jafarian ◽  
Saoussen Dimassi ◽  
Shahriar Bakrani-Balani ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Additive Manufacturing ◽  
Fused Deposition Modeling ◽  
Expert Knowledge ◽  
Fused Deposition ◽  
Knowledge Based ◽  
Deposition Modeling ◽  
Artificial Neural

Additive manufacturing (AM) continues to rise in popularity due to its various advantages over traditional manufacturing processes. AM interests industry, but achieving repeatable production quality remains problematic for many AM technologies. Thus, modeling different process variables in AM using machine learning can be highly beneficial in creating useful knowledge of the process. Such developed artificial neural network (ANN) models would aid designers and manufacturers to make informed decisions about their products and processes. However, it is challenging to define an appropriate ANN topology that captures the AM system behavior. Toward that goal, an approach combining dimensional analysis conceptual modeling (DACM) and classical ANNs is proposed to create a new type of knowledge-based ANN (KB-ANN). This approach integrates existing literature and expert knowledge of the AM process to define a topology for the KB-ANN model. The proposed KB-ANN is a hybrid learning network that encompasses topological zones derived from knowledge of the process and other zones where missing knowledge is modeled using classical ANNs. The usefulness of the method is demonstrated using a case study to model wall thickness, part height, and total part mass in a fused deposition modeling (FDM) process. The KB-ANN-based model for FDM has the same performance with better generalization capabilities using fewer weights trained, when compared to a classical ANN.

scholarly journals ANALYZING THE EFFECT OF NOZZLE DIAMETER IN FUSED DEPOSITION MODELING FOR EXTRUDING POLYLACTIC ACID USING OPEN SOURCE 3D PRINTING

2016 ◽  
Vol 78 (10) ◽  
Author(s):  
Nor Aiman Sukindar ◽  
M. K. A. Ariffin ◽  
B. T. Hang Tuah Baharudin ◽  
Che Nor Aiza Jaafar ◽  
Mohd Idris Shah Ismail
Keyword(s):  
Pressure Drop ◽  
3D Printing ◽  
Polylactic Acid ◽  
Fused Deposition Modeling ◽  
Nozzle Diameter ◽  
Optimum Range ◽  
Fused Deposition ◽  
Geometrical Error ◽  
Deposition Modeling ◽  
Printing Time

Fused deposition modeling (FDM) is one of the Rapid Prototyping (RP) technologies. The 3D Printer has been widely used in the fabrication of 3D products. One of the main issues has been to obtain a high quality for the finished parts. The present study focuses on the effect of nozzle diameter in terms of pressure drop, geometrical error as well as extrusion time. While using polylactic acid (PLA) as a material, the research was conducted using Finite Element Analysis (FEA) by manipulating the nozzle diameter, and the pressure drop along the liquefier was observed. The geometrical error and printing time were also calculated by using different nozzle diameters. Analysis shows that the diameter of the nozzle significantly affects the pressure drop along the liquefier which influences the consistency of the road width thus affecting the quality of the product’s finish. The vital aspect is minimizing the pressure drop to be as low as possible, which will lead to a good quality final product. The results from the analysis demonstrate that a 0.2 mm nozzle diameter contributes the highest pressure drop, which is not within the optimum range. In this study, by considering several factors including pressure drop, geometrical error and printing time, a 0.3 mm nozzle diameter has been suggested as being in the optimum range for extruding PLA material using open-source 3D printing. The implication of this result is valuable for a better understanding of the melt flow behavior of the PLA material and for choosing the optimum nozzle diameter for 3D printing.

Temperature-dependent electrical resistance of conductive polylactic acid filament for fused deposition modeling

2018 ◽  
Vol 99 (5-8) ◽  
pp. 1215-1224 ◽  
Author(s):  
Fraser Daniel ◽  
Naim Hossain Patoary ◽  
Arden L. Moore ◽  
Leland Weiss ◽  
Adarsh D. Radadia
Keyword(s):  
Polylactic Acid ◽  
Electrical Resistance ◽  
Fused Deposition Modeling ◽  
Temperature Dependent ◽  
Fused Deposition ◽  
Deposition Modeling

scholarly journals Rice straw powder/polylactic acid biocomposites for three-dimensional printing

2020 ◽  
Vol 29 ◽  
pp. 2633366X2096736
Author(s):  
Wangwang Yu ◽  
Lili Dong ◽  
Wen Lei ◽  
Jianan Shi
Keyword(s):  
Polylactic Acid ◽  
Rice Straw ◽  
Fused Deposition Modeling ◽  
Three Dimensional ◽  
Three Dimensional Printing ◽  
Soil Burial ◽  
Fused Deposition ◽  
Before And After ◽  
Deposition Modeling ◽  
Burial Duration

The research aim of this work was to understand the effects of the soil burial of rice straw on the morphology and properties of 3D-printed rice straw powder (RSP)/polylactic acid (PLA) biocomposites. The rice straw buried in the soil for various days was grounded and sieved into powder at 120 mesh. The RSP was then mixed with PLA at a mass ratio of 15/100 and the mixture was extruded into filament, followed by a fused deposition modeling 3D printing process. The as-prepared products were characterized in terms of morphological, mechanical, thermal, and nonisothermal crystallization properties. The results show that cavities with large holes induced by fused deposition modeling exhibit on the cross section of RSP/PLA biocomposite. The longer the burial duration of rice straw, the more the cavities with large holes could be observed on the surface. Therefore, soil burial of rice straw improved the thermal stability of the biocomposites while depressing their mechanical properties due to the amplification of the cavities. The crystallinity of the biocomposites was maintained at a low level (<9%) before and after the soil burial process.

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