Enhanced Toolpath Planning for Fused Filament Fabrication

2021 ◽  
Author(s):  
Hongrui Chen ◽  
Xingchen Liu
Keyword(s):  
Fused Filament Fabrication ◽  
Toolpath Planning

scholarly journals 3D Printing of Objects with Continuous Spatial Paths by A Multi-Axis Robotic FFF Platform

2021 ◽  
Vol 11 (11) ◽  
pp. 4825
Author(s):  
Yuan Yao ◽  
Yichi Zhang ◽  
Mohamed Aburaia ◽  
Maximilian Lackner
Keyword(s):  
3D Printing ◽  
Surface Quality ◽  
Degrees Of Freedom ◽  
Arm Movement ◽  
Print Quality ◽  
Robot Arm ◽  
Fused Filament Fabrication ◽  
Toolpath Planning ◽  
Staircase Effect ◽  
Generate Curve

Conventional Fused Filament Fabrication (FFF) equipment can only deposit materials in a single direction, limiting the strength of printed products. Robotic 3D printing provides more degrees of freedom (DOF) to control the material deposition and has become a trend in additive manufacturing. However, there is little discussion on the strength effect of multi-DOF printing. This paper presents an efficient process framework for multi-axis 3D printing based on the robot to improve the strength. A multi-DOF continuous toolpath planning method is designed to promote the printed part’s strength and surface quality. We generate curve layers along the model surfaces and fill Fermat spiral in the layers. The method makes it possible to take full advantage of the multi-axis robot arm to achieve smooth printing on surfaces with high curvature and avoid the staircase effect and collision in the process. To further improve print quality, a control strategy is provided to synchronize the material extrusion and robot arm movement. Experiments show that the tensile strength increases by 22–167% compared with the conventional flat slicing method for curved-surface parts. The surface quality is improved by eliminating the staircase effect. The continuous toolpath planning also supports continuous fiber-reinforced printing without a cutting device. Finally, we compared with other multi-DOF printing, the application scenarios, and limitations are given.

scholarly journals A Continuous Fiber-Reinforced Additive Manufacturing Processing Based on PET Fiber and PLA

Materials ◽  
2020 ◽  
Vol 13 (14) ◽  
pp. 3044
Author(s):  
Yuan Yao ◽  
Meng Li ◽  
Maximilian Lackner ◽  
Lammer Herfried
Keyword(s):  
Mechanical Tests ◽  
Thermoplastic Composites ◽  
Fiber Reinforced ◽  
Continuous Fiber ◽  
Polyamide 66 ◽  
Fused Filament Fabrication ◽  
Planning Strategy ◽  
Material Recycling ◽  
Material Recovery ◽  
Toolpath Planning

Continuous fiber-reinforced manufacturing has many advantages, but the fabrication cost is high and its process is difficult to control. This paper presents a method for printing fiber-reinforced composite on the common fused filament fabrication (FFF) platform. Polylactic Acid (PLA) and Polyethylene terephthalate (PET) fibers are used as printing materials. A spatial continuous toolpath planning strategy is employed to reduce the workload of post-processing without cutting the fiber. Experimental results show that this process not only enables the printing of models with complex geometric shapes but also supports material recycling and reuse. A material recovery rate of 100% for continuous PET fiber and 83% for PLA were achieved for a better environmental impact. Mechanical tests show that the maximum tensile strength of continuous PET fiber-reinforced thermoplastic composites (PFRTPCs) is increased by 117.8% when compared to polyamide-66 (PA66).

Enhanced Toolpath Planning for Fused Filament Fabrication

2020 ◽  
Author(s):  
Hongrui Chen ◽  
Xingchen Liu
Keyword(s):  
Volume Fraction ◽  
Software Process ◽  
Manufacturing Processes ◽  
Fused Filament Fabrication ◽  
Fermat Curve ◽  
Toolpath Planning ◽  
Material Deposition ◽  
3D Geometry ◽  
Fermat Curves ◽  
Printing Time

Abstract The slicing software process the 3D geometry into 2D slices and toolpaths for additive manufacturing processes. Most slicing software allows users to select from an array of infill patterns and to specify the overall infill volume fraction globally. However, the ability to locally control the volume fraction, and mechanical properties, is often limited. In this paper, we propose a novel toolpath enhancing algorithm to enable the local control on the volume fraction of various stock and custom infill patterns. In particular, the algorithm widens the infill pattern by directly modifying their toolpath with connected Fermat curves. By preserving the topology of the original toolpath, the connected Fermat curve not only produces predictable boosts in part performance but also minimized the printing time by eliminating extruder traversals without material deposition. The field that controls local volume fraction can be designed either manually or through optimization. The effectiveness of the proposed approach in toolpath generation is demonstrated through volume fraction fields designed by both approaches.

Evaluation of a Modified Fused Filament Fabrication Material for Use As Thermal Protection

2019 ◽  
Author(s):  
Steven Kim ◽  
Alexa Devega ◽  
Mallory Sico ◽  
Hao Wu ◽  
William Fahy ◽  
...  
Keyword(s):  
Thermal Protection ◽  
Fused Filament Fabrication

scholarly journals Surface modification of fused filament fabrication (FFF) 3D printed substrates by inkjet printing polyimide for printed electronics

2020 ◽  
Vol 36 ◽  
pp. 101544
Author(s):  
Devin J. Roach ◽  
Christopher Roberts ◽  
Janet Wong ◽  
Xiao Kuang ◽  
Joshua Kovitz ◽  
...  
Keyword(s):  
Surface Modification ◽  
Inkjet Printing ◽  
Printed Electronics ◽  
Fused Filament Fabrication ◽  
3D Printed

scholarly journals Evaluation of the Circularity of Recycled PLA Filaments for 3D Printers

2020 ◽  
Vol 10 (24) ◽  
pp. 8967
Author(s):  
Victor Gil Muñoz ◽  
Luisa M. Muneta ◽  
Ruth Carrasco-Gallego ◽  
Juan de Juanes Marquez ◽  
David Hidalgo-Carvajal
Keyword(s):  
Circular Economy ◽  
Protective Equipment ◽  
Fused Filament Fabrication ◽  
Circular Loop ◽  
Material Supply ◽  
3D Printers ◽  
3D Printed ◽  
Circular Economics ◽  
High Level ◽  
Additional Value

The circular economy model offers great opportunities to companies, as it not only allows them to capture additional value from their products and materials, but also reduce the fluctuations of price-related risks and material supply. These risks are present in all kind of businesses not based on the circular economy. The circular economy also enables economic growth without the need for more resources. This is because each unit has a higher value as a result of recycling and reuse of products and materials after use. Following this circular economics framework, the Polytechnic University of Madrid (Universidad Politécnica de Madrid, UPM) has adopted strategies aimed at improving the circularity of products. In particular, this article provides the result of obtaining recycled PLA filament from waste originating from university 3D FFF (fused filament fabrication) printers and waste generated by “Coronamakers” in the production of visors and parts for PPEs (Personal Protective Equipment) during the lockdown period of COVID-19 in Spain. This filament is used in the production of 3D printed parts that university students use in their classes, so the circular loop is closed. The obtained score of Material Circularity Indicator (MCI) of this material has been calculated, indicating its high level of circularity.

scholarly journals Design of Shape Memory Thermoplastic Material Systems for FDM-Type Additive Manufacturing

Materials ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4254
Author(s):  
Paulina A. Quiñonez ◽  
Leticia Ugarte-Sanchez ◽  
Diego Bermudez ◽  
Paulina Chinolla ◽  
Rhyan Dueck ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Shape Memory ◽  
Thermomechanical Processing ◽  
Material Selection ◽  
Shape Memory Polymer ◽  
Fused Filament Fabrication ◽  
Materials Development ◽  
Thermoplastic Material ◽  
Polymer Systems ◽  
Injection Molded

The work presented here describes a paradigm for the design of materials for additive manufacturing platforms based on taking advantage of unique physical properties imparted upon the material by the fabrication process. We sought to further investigate past work with binary shape memory polymer blends, which indicated that phase texturization caused by the fused filament fabrication (FFF) process enhanced shape memory properties. In this work, two multi-constituent shape memory polymer systems were developed where the miscibility parameter was the guide in material selection. A comparison with injection molded specimens was also carried out to further investigate the ability of the FFF process to enable enhanced shape memory characteristics as compared to other manufacturing methods. It was found that blend combinations with more closely matching miscibility parameters were more apt at yielding reliable shape memory polymer systems. However, when miscibility parameters differed, a pathway towards the creation of shape memory polymer systems capable of maintaining more than one temporary shape at a time was potentially realized. Additional aspects related to impact modifying of rigid thermoplastics as well as thermomechanical processing on induced crystallinity are also explored. Overall, this work serves as another example in the advancement of additive manufacturing via materials development.

scholarly journals Modeling Fused Filament Fabrication using Artificial Neural Networks

2021 ◽  
Author(s):  
Paul Oehlmann ◽  
Paul Osswald ◽  
Juan Camilo Blanco ◽  
Martin Friedrich ◽  
Dominik Rietzel ◽  
...  
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Cost Effective ◽  
Print Quality ◽  
Ann Model ◽  
Production Environment ◽  
Fused Filament Fabrication ◽  
Artificial Neural ◽  
Using Data ◽  
Artificial Neural Network Ann

AbstractWith industries pushing towards digitalized production, adaption to expectations and increasing requirements for modern applications, has brought additive manufacturing (AM) to the forefront of Industry 4.0. In fact, AM is a main accelerator for digital production with its possibilities in structural design, such as topology optimization, production flexibility, customization, product development, to name a few. Fused Filament Fabrication (FFF) is a widespread and practical tool for rapid prototyping that also demonstrates the importance of AM technologies through its accessibility to the general public by creating cost effective desktop solutions. An increasing integration of systems in an intelligent production environment also enables the generation of large-scale data to be used for process monitoring and process control. Deep learning as a form of artificial intelligence (AI) and more specifically, a method of machine learning (ML) is ideal for handling big data. This study uses a trained artificial neural network (ANN) model as a digital shadow to predict the force within the nozzle of an FFF printer using filament speed and nozzle temperatures as input data. After the ANN model was tested using data from a theoretical model it was implemented to predict the behavior using real-time printer data. For this purpose, an FFF printer was equipped with sensors that collect real time printer data during the printing process. The ANN model reflected the kinematics of melting and flow predicted by models currently available for various speeds of printing. The model allows for a deeper understanding of the influencing process parameters which ultimately results in the determination of the optimum combination of process speed and print quality.

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