Additive Manufacturing of Kevlar Reinforced Epoxy Composites

2019 ◽  
Author(s):  
Nashat Nawafleh ◽  
Jordan Chabot ◽  
Mutabe Aljaghtham ◽  
Cagri Oztan ◽  
Edward Dauer ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Mechanical Performance ◽  
Failure Strain ◽  
Flexural Modulus ◽  
Layer By Layer ◽  
Structural Components ◽  
Thermoset Composites ◽  
3D Objects ◽  
Layer Deposition ◽  
Subtractive Manufacturing

Abstract Additive manufacturing is defined as layer-by-layer deposition of materials on a surface to fabricate 3D objects with reduction in waste, unlike subtractive manufacturing processes. Short, flexible Kevlar fibers have been used in numerous studies to alter mechanical performance of structural components but never investigated within printed thermoset composites. This study investigates the effects of adding short Kevlar fibers on mechanical performance of epoxy thermoset composites and demonstrates that the addition of Kevlar by 5% in weight significantly improves flexure strength, flexural modulus, and failure strain by approximately 49%, 19%, and 38%, respectively. Hierarchical microstructures were imaged using scanning electron microscopy to observe the artefacts such as porosity, infill and material interdiffusion, which are inherent drawbacks of the 3D printing process.

The Earlier the Better? Investigating the Importance of Timing on Effectiveness of Design for Additive Manufacturing Education

2018 ◽  
Author(s):  
Rohan Prabhu ◽  
Scarlett R. Miller ◽  
Timothy W. Simpson ◽  
Nicholas A. Meisel
Keyword(s):  
Additive Manufacturing ◽  
Self Efficacy ◽  
Educational Interventions ◽  
Layer By Layer ◽  
Design For Additive Manufacturing ◽  
Layer Deposition ◽  
Subtractive Manufacturing ◽  
The Impact ◽  
Design Ideas ◽  
Am Processes

Additive Manufacturing (AM) is a novel process that enables the manufacturing of complex geometries through layer-by-layer deposition of material. AM processes provide a stark contrast to traditional, subtractive manufacturing processes, which has resulted in the emergence of design for additive manufacturing (DfAM) to capitalize on AM’s capabilities. In order to support the increasing use of AM in engineering, it is important to shift from the traditional design for manufacturing and assembly mindset, towards integrating DfAM. To facilitate this, DfAM must be included in the engineering design curriculum in a manner that has the highest impact. While previous research has systematically organized DfAM concepts into process capability-based (opportunistic) and limitation-based (restrictive) considerations, limited research has been conducted on the impact of teaching DfAM on the student’s design process. This study investigates this interaction by comparing two DfAM educational interventions conducted at different points in the academic semester. The two versions are compared by evaluating the students’ perceived utility, change in self-efficacy, and the use of DfAM concepts in design. The results show that introducing DfAM early in the semester when students have little previous experience in AM resulted in the largest gains in students perceiving utility in learning about DfAM concepts and DfAM self-efficacy gains. Further, we see that this increase relates to greater application of opportunistic DfAM concepts in student design ideas in a DfAM challenge. However, no difference was seen in the application of restrictive DfAM concepts between the two interventions. These results can be used to guide the design and implementation of DfAM education.

scholarly journals ADDITIVE MANUFACTURING - APPLICATION OPPORTUNITIES FOR THE AVIATION INDUSTRY

2015 ◽  
Vol 6 (2) ◽  
pp. 63-86
Author(s):  
Dipesh Dhital ◽  
Yvonne Ziegler
Keyword(s):  
Additive Manufacturing ◽  
3D Printing ◽  
Computer Aided Design ◽  
Free Form ◽  
Aviation Industry ◽  
Layer By Layer ◽  
Printing Technology ◽  
3D Printing Technology ◽  
Subtractive Manufacturing ◽  
Aided Design

Additive Manufacturing also known as 3D Printing is a process whereby a real object of virtually any shape can be created layer by layer from a Computer Aided Design (CAD) model. As opposed to the conventional Subtractive Manufacturing that uses cutting, drilling, milling, welding etc., 3D printing is a free-form fabrication process and does not require any of these processes. The 3D printed parts are lighter, require short lead times, less material and reduce environmental footprint of the manufacturing process; and is thus beneficial to the aerospace industry that pursues improvement in aircraft efficiency, fuel saving and reduction in air pollution. Additionally, 3D printing technology allows for creating geometries that would be impossible to make using moulds and the Subtractive Manufacturing of drilling/milling. 3D printing technology also has the potential to re-localize manufacturing as it allows for the production of products at the particular location, as and when required; and eliminates the need for shipping and warehousing of final products.

scholarly journals Evaluation of Assembly Part Build Orientation in Additive Manufacturing Environment using Data Envelopment Analysis

2019 ◽  
Vol 293 ◽  
pp. 02002 ◽  
Author(s):  
Kasin Ransikarbum ◽  
Rapeepan Pitakaso ◽  
Namhun Kim
Keyword(s):  
Data Envelopment Analysis ◽  
Additive Manufacturing ◽  
Process Planning ◽  
Data Envelopment ◽  
3D Objects ◽  
Lathe Machine ◽  
Subtractive Manufacturing ◽  
Personal Use ◽  
Using Data

Whereas Subtractive Manufacturing (SM) is a process by which 3D objects are constructed by cutting material away from a solid block of material, such as milling and lathe machine; Additive Manufacturing (AM) is a synonym for 3D printing and other processes by which 3D objects are constructed by successively depositing material in layers. Recently, AM has become widespread for both industrial and personal use thanks to the freedom and benefits it provides in designing parts, reducing lead time, improving inventory, and supply chain. However, few studies examine process planning issues in AM. In addition, existing studies focus on production of an individual part alone. In this study, we examine the assembly orientation alternatives’ efficiency using Data Envelopment Analysis (DEA) technique for different AM technologies and their associated materials under conflicting criteria. A case study of hardware fasteners using bolt and nut fabrication is illustrated in the study. Our results show that different AM technologies and materials clearly impact efficiency of part production and thus suggest optimal orientation in AM process planning platform.

Investigation of Parameter Spaces for Topology Optimization With Three-Dimensional Orientation Fields for Multi-Axis Additive Manufacturing

2020 ◽  
Vol 143 (5) ◽  
Author(s):  
Joseph R. Kubalak ◽  
Alfred L. Wicks ◽  
Christopher B. Williams
Keyword(s):  
Topology Optimization ◽  
Additive Manufacturing ◽  
Design Space ◽  
Mechanical Performance ◽  
Unit Length ◽  
Three Dimensions ◽  
Layer By Layer ◽  
Material Distribution ◽  
Orientation Fields ◽  
Material Orientation

Abstract The layer-by-layer deposition process used in material extrusion (ME) additive manufacturing results in inter- and intra-layer bonds that reduce the mechanical performance of printed parts. Multi-axis (MA) ME techniques have shown potential for mitigating this issue by enabling tailored deposition directions based on loading conditions in three dimensions (3D). Planning deposition paths leveraging this capability remains a challenge, as an intelligent method for assigning these directions does not exist. Existing literature has introduced topology optimization (TO) methods that assign material orientations to discrete regions of a part by simultaneously optimizing material distribution and orientation. These methods are insufficient for MA–ME, as the process offers additional freedom in varying material orientation that is not accounted for in the orientation parameterizations used in those methods. Additionally, optimizing orientation design spaces is challenging due to their non-convexity, and this issue is amplified with increased flexibility; the chosen orientation parameterization heavily impacts the algorithm’s performance. Therefore, the authors (i) present a TO method to simultaneously optimize material distribution and orientation with considerations for 3D material orientation variation and (ii) establish a suitable parameterization of the orientation design space. Three parameterizations are explored in this work: Euler angles, explicit quaternions, and natural quaternions. The parameterizations are compared using two benchmark minimum compliance problems, a 2.5D Messerschmitt–Bölkow–Blohm beam and a 3D Wheel, and a multi-loaded structure undergoing (i) pure tension and (ii) three-point bending. For the Wheel, the presented algorithm demonstrated a 38% improvement in compliance over an algorithm that only allowed planar orientation variation. Additionally, natural quaternions maintain the well-shaped design space of explicit quaternions without the need for unit length constraints, which lowers computational costs. Finally, the authors present a path toward integrating optimized geometries and material orientation fields resulting from the presented algorithm with MA–ME processes.

scholarly journals Tension testing of additively manufactured specimens of 17-4 PH processed by Bound Metal Deposition

2021 ◽  
Vol 1201 (1) ◽  
pp. 012037
Author(s):  
F Bjørheim ◽  
I M La Torraca Lopez
Keyword(s):  
Additive Manufacturing ◽  
Computer Aided Design ◽  
Industrial Sector ◽  
Metal Deposition ◽  
Layer By Layer ◽  
High Momentum ◽  
Subtractive Manufacturing ◽  
Tension Testing ◽  
Thermal Histories ◽  
Aided Design

Abstract In contrast to the traditional ways of subtractive manufacturing, additive manufacturing (AM), also known as 3D printing, adapts computer-aided design to iteratively build the component or part layer by layer. The technology has recently gained a high momentum, both within academia, but also within the industrial sector. However, it is common that parts produced by AM will have more defects than parts produced by traditional methods. The objective of this paper is to investigate a new method of additive manufacturing, namely the bound metal deposition method (BMD). This method seemed promising from the perspective that the metal is not iteratively being melted, similar to such as welding. In fact, the part is first printed, then washed, for then to be sintered. Consequently, avoiding the complex thermal histories/cycles. It was found that the material will exhibit anisotropic behaviour, and have a mesh of crack like defects, related to the printing orientation.

scholarly journals Environment and Safety Impacts of Additive Manufacturing: A Review

2019 ◽  
Vol 27 (44) ◽  
pp. 9-20
Author(s):  
Eva Buranská ◽  
Ivan Buranský ◽  
Ladislav Morovič ◽  
Katarína Líška
Keyword(s):  
Additive Manufacturing ◽  
Energy Use ◽  
Three Dimensional ◽  
Cost Effective ◽  
Liquid Sheet ◽  
Raw Material ◽  
Layer By Layer ◽  
Environmental Implications ◽  
Subtractive Manufacturing ◽  
Complicated Geometries

Abstract The paper is focused on additive manufacturing (AM) which is the process of producing objects from a three-dimensional (3D) model by joining materials layer by layer, as opposed to the subtractive manufacturing methodologies [1], directly from raw material in powder, liquid, sheet, or a filament form without the need for moulds, tools, or dies. The article demonstrates potential environmental implications of additive manufacturing related to the key issues including energy use, occupational health, waste and lifecycle impact. AM provides a cost-effective and time-efficient way to fabricating products with complicated geometries, advanced material properties and functionality. Based on this review, we identified that additive manufacturing will have a significant societal impact in the near future. A critical technical review of the promises and potential issues of AM is beneficial for advancing its further development.

Cold Spray for Additive Manufacturing: Possibilities and Challenges

2019 ◽  
Vol 813 ◽  
pp. 423-428 ◽  
Author(s):  
Saeed Garmeh ◽  
Mehdi Jadidi ◽  
Ali Dolatabadi
Keyword(s):  
Additive Manufacturing ◽  
Cold Spray ◽  
Particle Deposition ◽  
Temperature Sensitive ◽  
Layer By Layer ◽  
3D Objects ◽  
Flat Substrate ◽  
Inclined Surfaces ◽  
Oxygen Sensitive ◽  
Sharp Edges

Cold spray (CS) is a deposition technique to form a coating from the particles with temperature lower than their melting point. In this technique, particles are accelerated by a supersonic flow of a carrier gas such as air or nitrogen. Upon impact, particles undergo significant plastic deformation that bonds them to the substrate. Since the particles are not molten, this deposition method does not apply a lot of heat to the substrate and this makes CS the best candidate for temperature sensitive and oxygen sensitive materials. CS can be adapted to form 3D objects following layer-by-layer approach. This is called cold gas dynamic manufacturing (CGDM) or cold spray as additive manufacturing. Developing complex shapes by CGDM may result in formation of inclined surfaces, corners and sharp edges. Deposition in those regions is often accompanied with challenges that affect the accuracy and efficiency of the manufacturing. In this study, CGDM for two typical shapes such as cylinder and frustum on a flat substrate has been simulated to represent the additively manufactured parts. Particle trajectories and impact conditions i.e. velocity and size distributions have been compared. The results of numerical modelling provided useful information for understanding the limitations and challenges associated with CGDM that can help us to improve the quality and precision of particle deposition.

scholarly journals Knowledge Management for Topological Optimization Integration in Additive Manufacturing

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Nicolas Gardan
Keyword(s):  
Knowledge Management ◽  
Additive Manufacturing ◽  
Mechanical Performance ◽  
Functional Performance ◽  
Topological Optimization ◽  
Layer By Layer ◽  
Support Material ◽  
Manufacturing Costs ◽  
Material Volume ◽  
Globalized World

Engineering design optimization of mechanical structures is nowadays essential in the mechanical industry (automotive, aeronautics, etc.). To remain competitive in the globalized world, it is necessary to create and design structures that, in addition to complying specific mechanical performance, should be less expensive. Engineers must then design parts or assemblies that are a better compromise between mechanical and functional performance, weight, manufacturing costs, and so forth. In this context Additive Manufacturing (AM) process offers the possibility to avoid tools and manufacture directly the part. There are numerous technologies which are using different kind of material. For each of these, there are at least two materials: the production material and the support one. Support material is, in most cases, cleaned and becomes a manufacturing residue. Improving the material volume and the global mass of the product is an essential aim surrounding the integration of simulation in additive manufacturing process. Moreover, the layer-by-layer technology of additive manufacturing allows the design of innovative objects, and the use of topological optimization in this context can create a very interesting combination. The purpose of our paper is to present the knowledge management of an AM trade oriented tool which integrated the topological optimization of parts and internal patterns.

scholarly journals Additive manufacturing of polymer-based structures by extrusion technologies

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Alianna Maguire ◽  
Neethu Pottackal ◽  
M A S R Saadi ◽  
Muhammad M Rahman ◽  
Pulickel M Ajayan
Keyword(s):  
Additive Manufacturing ◽  
Composite Materials ◽  
High Performance ◽  
Mechanical Performance ◽  
Three Dimensional ◽  
Future Research ◽  
Layer By Layer ◽  
Structural Applications ◽  
Materials Used ◽  
Extrusion Printing

Abstract Extrusion-based additive manufacturing (AM) enables the fabrication of three-dimensional structures with intricate cellular architectures where the material is selectively dispensed through a nozzle or orifice in a layer-by-layer fashion at the macro-, meso-, and micro-scale. Polymers and their composites are one of the most widely used materials and are of great interest in the field of AM due to their vast potential for various applications, especially for the medical, military, aerospace, and automotive industries. Because architected polymer-based structures impart remarkably improved material properties such as low density and high mechanical performance compared to their bulk counterparts, this review focuses particularly on the development of such objects by extrusion-based AM intended for structural applications. This review introduces the extrusion-based AM techniques followed by a discussion on the wide variety of materials used for extrusion printing, various architected structures, and their mechanical properties. Notable advances in newly developed polymer and composite materials and their potential applications are summarized. Finally, perspectives and insights into future research of extrusion-based AM on developing high-performance ultra-light materials using polymers and their composite materials are discussed.

scholarly journals Bead Geometry Prediction in Laser-Wire Additive Manufacturing Process Using Machine Learning: Case of Study

2021 ◽  
Vol 11 (24) ◽  
pp. 11949
Author(s):  
Natago Guilé Mbodj ◽  
Mohammad Abuabiah ◽  
Peter Plapper ◽  
Maxime El Kandaoui ◽  
Slah Yaacoubi
Keyword(s):  
Machine Learning ◽  
Additive Manufacturing ◽  
Prediction Model ◽  
Travel Speed ◽  
Bead Geometry ◽  
Layer By Layer ◽  
Evolutionary Trend ◽  
Deposition Parameters ◽  
Layer Deposition ◽  
Height Prediction

In Laser Wire Additive Manufacturing (LWAM), the final geometry is produced using the layer-by-layer deposition (beads principle). To achieve good geometrical accuracy in the final product, proper implementation of the bead geometry is essential. For this reason, the paper focuses on this process and proposes a layer geometry (width and height) prediction model to improve deposition accuracy. More specifically, a machine learning regression algorithm is applied on several experimental data to predict the bead geometry across layers. Furthermore, a neural network-based approach was used to study the influence of different deposition parameters, namely laser power, wire-feed rate and travel speed on bead geometry. To validate the effectiveness of the proposed approach, a test split validation strategy was applied to train and validate the machine learning models. The results show a particular evolutionary trend and confirm that the process parameters have a direct influence on the bead geometry, and so, too, on the final part. Several deposition parameters have been found to obtain an accurate prediction model with low errors and good layer deposition. Finally, this study indicates that the machine learning approach can efficiently be used to predict the bead geometry and could help later in designing a proper controller in the LWAM process.

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