scholarly journals Optimization of the fused deposition modeling-based fabrication process for polylactic acid microneedles

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Libo Wu ◽  
Jongho Park ◽  
Yuto Kamaki ◽  
Beomjoon Kim
Keyword(s):  
Additive Manufacturing ◽  
Polylactic Acid ◽  
Chemical Etching ◽  
Fused Deposition Modeling ◽  
Sensory Nerve ◽  
Nerve Fibers ◽  
Fabrication Process ◽  
Medical Applications ◽  
Fused Deposition ◽  
Deposition Modeling

AbstractA microneedle (MN) array is a novel biomedical device adopted in medical applications to pierce through the stratum corneum while targeting the viable epidermis and dermis layers of the skin. Owing to their micron-scale dimensions, MNs can minimize stimulations of the sensory nerve fibers in the dermis layer. For medical applications, such as wound healing, biosensing, and drug delivery, the structure of MNs significantly influences their mechanical properties. Among the various microfabrication methods for MNs, fused deposition modeling (FDM), a commercial 3D printing method, shows potential in terms of the biocompatibility of the printed material (polylactic acid (PLA)) and preprogrammable arbitrary shapes. Owing to the current limitations of FDM printer resolution, conventional micron-scale MN structures cannot be fabricated without a post-fabrication process. Hydrolysis in an alkaline solution is a feasible approach for reducing the size of PLA needles printed via FDM. Moreover, weak bonding between PLA layers during additive manufacturing triggers the detachment of PLA needles before etching to the expected sizes. Furthermore, various parameters for the fabrication of PLA MNs with FDM have yet to be sufficiently optimized. In this study, the thermal parameters of the FDM printing process, including the nozzle and printing stage temperatures, were investigated to bolster the interfacial bonding between PLA layers. Reinforced bonding was demonstrated to address the detachment challenges faced by PLA MNs during the chemical etching process. Furthermore, chemical etching parameters, including the etchant concentration, environmental temperature, and stirring speed of the etchant, were studied to determine the optimal etching ratio. To develop a universal methodology for the batch fabrication of biodegradable MNs, this study is expected to optimize the conditions of the FDM-based fabrication process. Additive manufacturing was employed to produce MNs with preprogrammed structures. Inclined MNs were successfully fabricated by FDM printing with chemical etching. This geometrical structure can be adopted to enhance adhesion to the skin layer. Our study provides a useful method for fabricating MN structures for various biomedical applications.

Property Enhancement of Carbon Fiber-Reinforced Polylactic Acid Composites Prepared by Fused-Deposition Modeling

2020 ◽  
pp. 455-478
Author(s):  
Pravin R. Kubade ◽  
Hrushikesh B. Kulkarni ◽  
Vinayak C. Gavali
Keyword(s):  
Mechanical Properties ◽  
Additive Manufacturing ◽  
Carbon Fiber ◽  
Polylactic Acid ◽  
Fused Deposition Modeling ◽  
Three Dimensional ◽  
Fiber Reinforced ◽  
Fused Deposition ◽  
Deposition Modeling ◽  
Properties Of Composites

Additive Manufacturing or three-dimensional printing refers to a process of building lighter, stronger three-dimensional parts, manufactured layer by layer. Additive manufacturing uses a computer and CAD software which passes the program to the printer to build the desired shape. Metals, thermoplastic polymers, and ceramics are the preferred materials used for additive manufacturing. Fused deposition modeling is one additive manufacturing technique involving the use of thermoplastic polymer for creating desired shape. Carbon fibers can be added into polymer to strengthen the composite without adding additional weight. Present work deals with the manufacturing of Carbon fiber-reinforced Polylactic Acid composites prepared using fused deposition modeling. Mechanical and thermo-mechanical properties of composites are studied as per ASTM standards and using sophisticated instruments. It is observed that there is enhancement in thermo-mechanical properties of composites due to addition reinforcement which is discussed in detail.

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 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.

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.

The influence of load direction, microstructure, raster orientation on the quasi-static response of fused deposition modeling ABS

2019 ◽  
Vol 25 (3) ◽  
pp. 462-472 ◽  
Author(s):  
Oluwakayode Bamiduro ◽  
Gbadebo Owolabi ◽  
Mulugeta A. Haile ◽  
Jaret C. Riddick
Keyword(s):  
Additive Manufacturing ◽  
Fused Deposition Modeling ◽  
Mechanical Response ◽  
Mechanical Performance ◽  
Bead Geometry ◽  
Static Response ◽  
Load Direction ◽  
Content Type ◽  
Fused Deposition ◽  
Deposition Modeling

Purpose The continual growth of additive manufacturing has increased tremendously because of its versatility, flexibility and high customization of geometric structures. However, design hurdles are presented in understanding the relationship between the fabrication process and materials microstructure as it relates to the mechanical performance. The purpose of this paper is to investigate the role of build architecture and microstructure and the effects of load direction on the static response and mechanical properties of acrylonitrile butadiene styrene (ABS) specimens obtained via the fused deposition modeling (FDM) processing technique. Design/methodology/approach Among additive manufacturing processes, FDM is a prolific technology for manufacturing ABS. The blend of ABS combines strength, rigidity and toughness, all of which are desirable for the production of structural materials in rapid manufacturing applications. However, reported literature has varied widely on the mechanical performance due to the proprietary nature of the ABS material ratio, ultimately creating a design hurdle. While prior experimental studies have studied the mechanical response via uniaxial tension testing, this study has aimed to understand the mechanical response of ABS from the materials’ microstructural point of view. First, ABS specimen was fabricated via FDM using a defined build architecture. Next, the specimens were mechanically tested until failure. Then finally, the failure structures were microstructurally investigated. In this paper, the effects of microstructural evolution on the static mechanical response of various build architecture of ABS aimed at FDM manufacturing technique was analyzed. Findings The results show that the rastering orientation of 0/90 exhibited the highest tensile strength followed by fracture at its maximum load. However, the “45” bead direction of the ABS fibers displayed a cold-drawing behavior before rupture. The morphology analyses before and after tensile failure were characterized by a scanning electron microscopy (SEM) which highlighted the effects of bead geometry (layers) and areas of stress concentration such as interstitial voids in the material during build, ultimately compromising the structural integrity of the specimens. Research limitations/implications The ability to control the constituents and microstructure of a material during fabrication is significant to improving and predicting the mechanical performance of structural additive manufacturing components. In this report, the effects of microstructure on the mechanical performance of FDM-fabricated ABS materials was discussed. Further investigations are planned in understanding the effects of ambient environmental conditions (such as moisture) on the ABS material pre- and post-fabrication. Originality/value The study provides valuable experimental data for the purpose of understanding the inter-dependency between build parameters and microstructure as it relates to the specimens exemplified strength. The results highlighted in this study are fundamental to the development of optimal design of strength and complex ultra-lightweight structure efficiency.

Part Monitoring and Quality Assessment of Conformal Additive Manufacturing Using Image Reconstruction

2018 ◽  
Author(s):  
Michael D. Kutzer ◽  
Levi D. DeVries ◽  
Cooper D. Blas
Keyword(s):  
Additive Manufacturing ◽  
Fused Deposition Modeling ◽  
Digital Camera ◽  
Image Data ◽  
Digital Cameras ◽  
Public And Private ◽  
The Public ◽  
Fused Deposition ◽  
Complex Part ◽  
Deposition Modeling

Additive manufacturing (AM) technologies have become almost universal in concept development, prototyping, and education. Advances in materials and methods continue to extend this technology to small batch and complex part manufacturing for the public and private sectors. Despite the growing popularity of digital cameras in AM systems, use of image data for part monitoring is largely unexplored. This paper presents a new method for estimating the 3D internal structure of fused deposition modeling (FDM) processes using image data from a single digital camera. Relative transformations are established using motion capture, and the 3D model is created using knowledge of the deposition path coupled with assumptions about the deposition cross-section. Results show that part geometry can be estimated and visualized using the methods presented in this work.

scholarly journals EFFECT OF EXTRUSION PROCESS PARAMETERS ON MECHANICAL PROPERTIES OF 3D PRINTED PLA PRODUCT

2021 ◽  
Vol 6 (2) ◽  
pp. 119
Author(s):  
Nanang Ali Sutisna ◽  
Rakha Amrillah Fattah
Keyword(s):  
Mechanical Properties ◽  
Additive Manufacturing ◽  
Fused Deposition Modeling ◽  
Extrusion Process ◽  
Experimental Procedure ◽  
Fused Deposition ◽  
Novel Approach ◽  
Deposition Modeling ◽  
3D Printed ◽  
3D Digital Models

The method of producing items through synchronously depositing material level by level, based on 3D digital models, is named Additive Manufacturing (AM) or 3D-printing. Amongs many AM methods, the Fused Deposition Modeling (FDM) technique along with PLA (Polylactic acid) material is commonly used in additive manufacturing. Until now, the mechanical properties of the AM components could not be calculated or estimated until they've been assembled and checked. In this work, a novel approach is suggested as to how the extrusion process affects the mechanical properties of the printed component to obtain how the parts can be manufactured or printed to achieve improved mechanical properties. This methodology is based on an experimental procedure in which the combination of parameters to achieve an optimal from a manufacturing experiment and its value can be determined, the results obtained show the effect of the extrusion process affects the mechanical properties.

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