Carbon Nanotube Reinforced Fused Deposition Modeling Using Microwave Irradiation

2016 ◽  
Author(s):  
Meng Zhang ◽  
Xiaoxu Song ◽  
Weston Grove ◽  
Emmett Hull ◽  
Z. J. Pei ◽  
...  
Keyword(s):  
Carbon Nanotubes ◽  
Carbon Nanotube ◽  
Additive Manufacturing ◽  
Microwave Irradiation ◽  
Computer Aided Design ◽  
Fused Deposition Modeling ◽  
Layer By Layer ◽  
Sem Images ◽  
Fused Deposition ◽  
Deposition Modeling

Additive manufacturing (AM) is a class of manufacturing processes where material is deposited in a layer-by-layer fashion to fabricate a three-dimensional part directly from a computer-aided design model. With a current market share of 44%, thermoplastic-based additive manufacturing such as fused deposition modeling (FDM) is a prevailing technology. A key challenge for AM parts (especially for parts made by FDM) in engineering applications is the weak inter-layer adhesion. The lack of bonding between filaments usually results in delamination and mechanical failure. To address this challenge, this study embedded carbon nanotubes into acrylonitrile butadiene styrene (ABS) thermoplastics via a filament extrusion process. The vigorous response of carbon nanotubes to microwave irradiation, leading to the release of a large amount of heat, is used to melt the ABS thermoplastic matrix adjacent to carbon nanotubes within a very short time period. This treatment is found to enhance the inter-layer adhesion without bulk heating to deform the 3D printed parts. Tensile and flexural tests were performed to evaluation the effects of microwave irradiation on mechanical properties of the specimens made by FDM. Scanning electron microscopic (SEM) images were taken to characterize the fracture surfaces of tensile test specimens. The actual carbon nanotube contents in the filaments were measured by conducting thermogravimetric analysis (TGA). The effects of microwave irradiation on the electrical resistivity of the filament were also reported.

scholarly journals Desain dan Pembuatan Prototype Piston Honda MEGAPRO FI Menggunakan 3D Printing

2020 ◽  
Vol 1 (2) ◽  
pp. 81-91
Author(s):  
Frince Marbun ◽  
Richard A.M. Napitupulu
Keyword(s):  
3D Printing ◽  
Computer Aided Design ◽  
Fused Deposition Modeling ◽  
Layer By Layer ◽  
Printing Technology ◽  
3D Printing Technology ◽  
Fused Deposition ◽  
Deposition Modeling ◽  
Manufacturing Technologies ◽  
Aided Design

3D printing technology has great potential in today's manufacturing world, one of its uses is in making miniatures or prototypes of a product such as a piston. One of the most famous and inexpensive 3D printing (additive manufacturing) technologies is Fused Deposition Modeling (FDM), the principle FDM works by thermoplastic extrusion through a hot nozzle at melting temperature then the product is made layer by layer. The two most commonly used materials are ABS and PLA so it is very important to know the accuracy of product dimensions. FDM 3D Printing Technology is able to make duplicate products accurately using PLA material. FDM machines work by printing parts that have been designed by computer-aided design (CAD) and then exported in the form of STL or .stl files and uploaded to the slicer program to govern the printing press according to the design. Using Anet A8 brand 3D printing tools that are available to the public, Slicing of general CAD geometry files such as autocad and solidwork is the basis for making this object. This software is very important to facilitate the design process to be printed. Some examples of software that can be downloaded and used free of charge such as Repetier-Host and Cura. by changing the parameters in the slicer software is very influential in the 3D printing manufacturing process.

CubeSat Fabrication through Additive Manufacturing and Micro-Dispensing

2011 ◽  
Vol 2011 (1) ◽  
pp. 001021-001027 ◽  
Author(s):  
Cassie Gutierrez ◽  
Rudy Salas ◽  
Gustavo Hernandez ◽  
Dan Muse ◽  
Richard Olivas ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Computer Aided Design ◽  
Printed Circuit Boards ◽  
Fused Deposition Modeling ◽  
Integrated Approach ◽  
Layer By Layer ◽  
New Paradigm ◽  
Spatial Gradients ◽  
Fused Deposition ◽  
On Demand

Fabricating entire systems with both electrical and mechanical content through on-demand 3D printing is the future for high value manufacturing. In this new paradigm, conformal and complex shapes with a diversity of materials in spatial gradients can be built layer-by-layer using hybrid Additive Manufacturing (AM). A design can be conceived in Computer Aided Design (CAD) and printed on-demand. This new integrated approach enables the fabrication of sophisticated electronics in mechanical structures by avoiding the restrictions of traditional fabrication techniques, which result in stiff, two dimensional printed circuit boards (PCB) fabricated using many disparate and wasteful processes. The integration of Additive Manufacturing (AM) combined with Direct Print (DP) micro-dispensing and robotic pick-and-place for component placement can 1) provide the capability to print-on-demand fabrication, 2) enable the use of micron-resolution cavities for press fitting electronic components and 3) integrate conductive traces for electrical interconnect between components. The fabrication freedom introduced by AM techniques such as stereolithography (SL), ultrasonic consolidation (UC), and fused deposition modeling (FDM) have only recently been explored in the context of electronics integration and 3D packaging. This paper describes a process that provides a novel approach for the fabrication of stiff conformal structures with integrated electronics and describes a prototype demonstration: a volumetrically-efficient sensor and microcontroller subsystem scheduled to launch in a CubeSat designed with the CubeFlow methodology.

Effects of Process Variables on Extrusion of Carbon Fiber Reinforced ABS Filament for Additive Manufacturing

2015 ◽  
Author(s):  
Emmett Hull ◽  
Weston Grove ◽  
Meng Zhang ◽  
Xiaoxu Song ◽  
Z. J. Pei ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Carbon Fiber ◽  
Computer Aided Design ◽  
Fused Deposition Modeling ◽  
Layer By Layer ◽  
Fiber Reinforced ◽  
Process Variables ◽  
Fused Deposition ◽  
3D Printers ◽  
Carbon Fiber Reinforced

Additive manufacturing (3D printing) is a class of manufacturing processes where material is deposited in a layer-by-layer fashion to fabricate a three-dimensional part directly from a computer-aided design (CAD) model. With a current market share of 44%, thermoplastic-based additive manufacturing such as fused deposition modeling (FDM) is a prevailing technology. A preliminary extrusion process is required to produce thermoplastic filaments for use in FDM 3D printers. It is crucial that extruded filament must have constant dimensional accuracy for FDM 3D printers to produce the desired object with precision. In this study, carbon fibers were blended with acrylonitrile butadiene styrene (ABS) thermoplastics to produce carbon fiber reinforced ABS filaments in order to improve the mechanical properties of FDM-printed objects. During filament extrusion, three process variables showed significant effects on filament diameter, expansion percentage, and extrusion rate. These process variables included carbon fiber content, extrusion temperature, and nozzle size. The objective of this study is to test the feasible ranges of these process variables and to investigate their effects on filament extrusion. Results of this study will provide knowledge on quality improvement of carbon fiber reinforced ABS filament extrusion for additive manufacturing.

scholarly journals A New Specimen Geometry for Evaluation of the Mechanical Orthotropic Properties Presented in Parts Printed by FDM

2021 ◽  
Vol 9 (12) ◽  
pp. 1741-1749
Author(s):  
Alexandre A. Cavalcante
Keyword(s):  
Additive Manufacturing ◽  
Fused Deposition Modeling ◽  
Tensile Load ◽  
Specimen Geometry ◽  
Layer By Layer ◽  
Testing Specimen ◽  
Fused Deposition ◽  
Dog Bone ◽  
Anisotropic Mechanical Properties ◽  
Deposition Modeling

Abstract: Additive manufacturing (AM) by FDM (Fused Deposition Modeling) has been increasingly adopted due to the low cost of 3D printers as an option capable of producing parts with complex geometries. Since the FDM process is a layer-by-layer manufacturing method, the characterization of the behavior of parts manufactured by this technology, especially with regard to anisotropic mechanical properties, has led to many works relating printing parameters with tensile strength. However, the use of specimens with the conventional flat "dog bone" and cylindrical geometries specified in the ASTM-638 standards do not perfectly suit the special characteristics of parts produced by FDM, since these standards were created for solid and isotropic materials. A new geometry for specimens printed in FDM to study anisotropy transverse to layer deposition is suggested in this work. Problems such as slippage and crushing in the grips of the test machines due to the fragility of the bound between the beds, as well as the appearance of lateral forces that distort the results due to misalignment of the tensile load, twists and curvature of the specimens, normally observed in the Strain measurements by extensometers, are suppressed with the adoption of the new geometry presented in this work. Keywords: Fused Deposition Modeling, Additive Manufacturing, Mechanical Strength, Tensile Testing, Specimen Geometry

Quantitative strength analysis for 3D-printed specimens in a Tri-Hexagon pattern

2021 ◽  
pp. 095440622110211
Author(s):  
Sourabh Tandon ◽  
Ruchin Kacker ◽  
KG Sudhakar
Keyword(s):  
Tensile Strength ◽  
Fused Deposition Modeling ◽  
Acrylonitrile Butadiene Styrene ◽  
Strength Properties ◽  
Strength Analysis ◽  
Layer By Layer ◽  
Sem Images ◽  
Fused Deposition ◽  
Deposition Modeling ◽  
Suitable Combination

ABS (Acrylonitrile Butadiene Styrene) and PLA (Polylactic acid) are the most commonly used thermoplastic materials in the AM (Additive Manufacturing) to build objects adding layer by layer. Addition of reinforcement such as carbon fibres to these materials is common to enhance strength properties. This work aims the fabrication of ABS, PLA, and PLA+CF specimens using the FDM (Fused Deposition Modeling). Subsequently, the tensile strength of printed specimens at Tri-Hexagon pattern for various % infills with different orientations is investigated. For a desired strength, the analysis facilitates the designer to choose suitable combination of specimen orientation at a specific infill and pattern. PLA+CF and ABS specimens with Tri-hexagon pattern at 0° orientation retain considerable strength when infill % gets lowered, whereas, virgin PLA observes higher strength stability at 90° orientation. The magnitudes of Ultimate tensile strength increased with infill %. The peak strength for PLA+CF specimen (with 100% infill without any pattern) at 0° and 90° orientation was 22% and 5% more, respectively, compared to 45° orientation. SEM images reveal a decrease in strength with ABS and PLA specimens due to the presence of voids between the layers, whereas showed strong bonding between CF and PLA matrix. Tri-Hexagon pattern showed better strength than the honeycomb, line, and rectilinear, especially at lower infills. Specimens both with and without Tri-hexagonal pattern observed superior ductile characteristics at 0° and 90° orientation, whereas most inferior at 45°.

An Update on the Use of Alginate in Additive Biofabrication Techniques

2019 ◽  
Vol 25 (11) ◽  
pp. 1249-1264 ◽  
Author(s):  
Amoljit Singh Gill ◽  
Parneet Kaur Deol ◽  
Indu Pal Kaur
Keyword(s):  
Fused Deposition Modeling ◽  
Low Cost ◽  
Selective Laser Sintering ◽  
Layer By Layer ◽  
Three Dimension ◽  
Anionic Polymer ◽  
Fused Deposition ◽  
The Status ◽  
Deposition Modeling ◽  
Extrusion Printing

Background: Solid free forming (SFF) technique also called additive manufacturing process is immensely popular for biofabrication owing to its high accuracy, precision and reproducibility. Method: SFF techniques like stereolithography, selective laser sintering, fused deposition modeling, extrusion printing, and inkjet printing create three dimension (3D) structures by layer by layer processing of the material. To achieve desirable results, selection of the appropriate technique is an important aspect and it is based on the nature of biomaterial or bioink to be processed. Result & Conclusion: Alginate is a commonly employed bioink in biofabrication process, attributable to its nontoxic, biodegradable and biocompatible nature; low cost; and tendency to form hydrogel under mild conditions. Furthermore, control on its rheological properties like viscosity and shear thinning, makes this natural anionic polymer an appropriate candidate for many of the SFF techniques. It is endeavoured in the present review to highlight the status of alginate as bioink in various SFF techniques.

scholarly journals Carbon Nanotube-Based Composite Filaments for 3D Printing of Structural and Conductive Elements

2021 ◽  
Vol 11 (3) ◽  
pp. 1272
Author(s):  
Bartłomiej Podsiadły ◽  
Piotr Matuszewski ◽  
Andrzej Skalski ◽  
Marcin Słoma
Keyword(s):  
Carbon Nanotubes ◽  
Fused Deposition Modeling ◽  
Supply Voltage ◽  
Acrylonitrile Butadiene Styrene ◽  
Structural Elements ◽  
Filling Fraction ◽  
Fused Deposition ◽  
Structural Electronics ◽  
Measured Sample ◽  
Deposition Modeling

In this publication, we describe the process of fabrication and the analysis of the properties of nanocomposite filaments based on carbon nanotubes and acrylonitrile butadiene styrene (ABS) polymer for fused deposition modeling (FDM) additive manufacturing. Polymer granulate was mixed and extruded with a filling fraction of 0.99, 1.96, 4.76, 9.09 wt.% of CNTs (carbon nanotubes) to fabricate composite filaments with a diameter of 1.75 mm. Detailed mechanical and electrical investigations of printed test samples were performed. The results demonstrate that CNT content has a significant influence on mechanical properties and electrical conductivity of printed samples. Printed samples obtained from high CNT content composites exhibited an improvement in the tensile strength by 12.6%. Measurements of nanocomposites’ electrical properties exhibited non-linear relation between the supply voltage and measured sample resistivity. This effect can be attributed to the semiconductor nature of the CNT functional phase and the occurrence of a tunnelling effect in percolation network. Detailed I–V characteristics related to the amount of CNTs in the composite and the supply voltage influence are also presented. At a constant voltage value, the average resistivity of the printed elements is 2.5 Ωm for 4.76 wt.% CNT and 0.15 Ωm for 9.09 wt.% CNT, respectively. These results demonstrate that ABS/CNT composites are a promising functional material for FDM additive fabrication of structural elements, but also structural electronics and sensors.

scholarly journals Automated Testing and Characterization of Additive Manufacturing (ATCAM)

2021 ◽  
Author(s):  
Arash Alex Mazhari ◽  
Randall Ticknor ◽  
Sean Swei ◽  
Stanley Krzesniak ◽  
Mircea Teodorescu
Keyword(s):  
Additive Manufacturing ◽  
Fused Deposition Modeling ◽  
Cell System ◽  
Automated Testing ◽  
Fused Deposition ◽  
Deposition Modeling ◽  
Machine Calibration

AbstractThe sensitivity of additive manufacturing (AM) to the variability of feedstock quality, machine calibration, and accuracy drives the need for frequent characterization of fabricated objects for a robust material process. The constant testing is fiscally and logistically intensive, often requiring coupons that are manufactured and tested in independent facilities. As a step toward integrating testing and characterization into the AM process while reducing cost, we propose the automated testing and characterization of AM (ATCAM). ATCAM is configured for fused deposition modeling (FDM) and introduces the concept of dynamic coupons to generate large quantities of basic AM samples. An in situ actuator is printed on the build surface to deploy coupons through impact, which is sensed by a load cell system utilizing machine learning (ML) to correlate AM data. We test ATCAM’s ability to distinguish the quality of three PLA feedstock at differing price points by generating and comparing 3000 dynamic coupons in 10 repetitions of 100 coupon cycles per material. ATCAM correlated the quality of each feedstock and visualized fatigue of in situ actuators over each testing cycle. Three ML algorithms were then compared, with Gradient Boost regression demonstrating a 71% correlation of dynamic coupons to their parent feedstock and provided confidence for the quality of AM data ATCAM generates.

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.

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