scholarly journals Enhancing 3D Printing Producibility in Polylactic Acid Using Fused Deposition Modelling and Machine Learning

2020 ◽  
Author(s):  
Mahmoud Moradi ◽  
M. Saleh Meiabadi ◽  
Mojtaba Karami Moghadam ◽  
Sina Ardabili ◽  
Shahab S. Band ◽  
...  
Keyword(s):  
Tensile Test ◽  
Polylactic Acid ◽  
Production Cost ◽  
Fused Deposition Modeling ◽  
Testing Machine ◽  
Accurate Estimation ◽  
Fused Deposition ◽  
Deposition Modeling ◽  
Fill Pattern ◽  
Part Thickness

Abstract: Polylactic acid (PLA) is brittle in nature with extensive deformation property. For improvement of the end-use quality, it is of significant importance to enhance the producibility of 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 FDM-printed tensile test samples with the desired part thickness. 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 layer thickness, infill percentage, and extruder temperature 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 honeycomb internal fill pattern is applied for the production of light-weight 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 ANN and ANN-GA techniques for developing an accurate estimation for toughness, part thickness, and production cost as dependant variables. Results were evaluated by correlation coefficient and RMSE values. According to the modeling results, ANN-GA as a hybrid ML technique could successfully improve 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. In the other side, the optimization results confirm that the optimized specimen is cost-effective and able to comparatively undergo deformation, which develops the usability of printed PLA objects. The research is accomplished under the constraints of PLA compatibility with existing fused deposition modeling setup without changing the functional hardware/software 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.

scholarly journals Enhancing 3D Printing Producibility in Polylactic Acid Using Fused Filament Fabrication Fused Deposition Modelling and Machine Learning

2020 ◽  
Author(s):  
Mahmoud Moradi ◽  
M. Saleh Meiabadi ◽  
Mojtaba Karami Moghadam ◽  
Sina Ardabili ◽  
Shahab S. Band ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Production Cost ◽  
Fused Deposition Modeling ◽  
Deformation Property ◽  
Fused Filament Fabrication ◽  
Fused Deposition ◽  
Surface Method ◽  
Deposition Modeling ◽  
Part Thickness

Abstract 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.

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.

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.

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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