scholarly journals Design of Shoe Soles Using Lattice Structures Fabricated by Additive Manufacturing

2019 ◽  
Vol 1 (1) ◽  
pp. 719-728 ◽  
Author(s):  
Guoying Dong ◽  
Daniel Tessier ◽  
Yaoyao Fiona Zhao
Keyword(s):  
Additive Manufacturing ◽  
Fused Deposition Modeling ◽  
Lattice Structure ◽  
Lattice Structures ◽  
Compression Tests ◽  
Element Analysis ◽  
Fused Deposition ◽  
Footwear Industry ◽  
Deposition Modeling ◽  
Shoe Soles

AbstractAdditive manufacturing (AM) has enabled great application potential in several major industries. The footwear industry can customize shoe soles fabricated by AM. In this paper, lattice structures are discussed. They are used to design functional shoe soles that can have controllable stiffness. Different topologies such as Diamond, Grid, X shape, and Vintiles are used to generate conformal lattice structures that can fit the curved surface of the shoe sole. Finite element analysis is conducted to investigate stress distribution in different designs. The fused deposition modeling process is used to fabricate the designed shoe soles. Finally, compression tests compare the stiffness of shoe soles with different lattice topologies. It is found that the plantar stress is highly influenced by the lattice topology. From preliminary calculations, it has been found that the shoe sole designed with the Diamond topology can reduce the maximum stress on the foot. The Vintiles lattice structure and the X shape lattice structure are stiffer than the Diamond lattice. The Grid lattice structure buckles in the experiment and is not suitable for the design.

An Algorithm for Generating 3D Lattice Structures Suitable for Printing on a Multi-Plane FDM Printing Platform

2018 ◽  
Author(s):  
Ismayuzri B. Ishak ◽  
Mark B. Moffett ◽  
Pierre Larochelle
Keyword(s):  
Additive Manufacturing ◽  
Fused Deposition Modeling ◽  
Lattice Structure ◽  
Geometric Model ◽  
Three Dimensional ◽  
Manufacturing Processes ◽  
Lattice Structures ◽  
Fused Deposition ◽  
Structure Generator ◽  
Conventional Machining

Manufacturing processes for the fabrication of complex geometries involve multi-step processes when using conventional machining techniques with material removal processes. Additive manufacturing processes give leverage for fabricating complex geometric structures compared to conventional machining. The capability to fabricate 3D lattice structures is a key additive manufacturing characteristic. Most conventional additive manufacturing processes involve layer based curing or deposition to produce a three-dimensional model. In this paper, a three-dimensional lattice structure generator for multi-plane fused deposition modeling printing was explored. A toolpath for an input geometric model with an overhang structure was able to be generated. The input geometric model was able to be printed using a six degree of freedom robot arm platform. Experimental results show the achievable capabilities of the 3D lattice structure generator for use with the multi-plane platform.

scholarly journals Systematic Experimental Evaluation of Function Based Cellular Lattice Structure Manufactured by 3D Printing

2021 ◽  
Vol 11 (21) ◽  
pp. 10489
Author(s):  
Shaheen Perween ◽  
Muhammad Fahad ◽  
Maqsood A. Khan
Keyword(s):  
Experimental Evaluation ◽  
Fused Deposition Modeling ◽  
Lattice Structure ◽  
Cylindrical Specimen ◽  
Compressive Load ◽  
Lattice Structures ◽  
Mass Ratio ◽  
Compressive Properties ◽  
Fused Deposition ◽  
Deposition Modeling

Additive manufacturing (AM) has a greater potential to construct lighter parts, having complex geometries with no additional cost, by embedding cellular lattice structures within an object. The geometry of lattice structure can be engineered to achieve improved strength and extra level of performance with the advantage of consuming less material and energy. This paper provides a systematic experimental evaluation of a series of cellular lattice structures, embedded within a cylindrical specimen and constructed according to terms and requirements of ASTMD1621-16, which is standard for the compressive properties of rigid cellular plastics. The modeling of test specimens is based on function representation (FRep) and constructed by fused deposition modeling (FDM) technology. Two different test series, each having eleven test specimens of different parameters, are printed along with their replicates of 70% and 100% infill density. Test specimens are subjected to uniaxial compressive load to produce 13% deformation to the height of the specimen. Comparison of results reveals that specimens, having cellular lattice structure and printed with 70% infill density, exhibit greater strength and improvement in strength to mass ratio, as compared to the solid printed specimen without structure.

scholarly journals Analysis of the Influencing Factors of FDM-Supported Positions for the Compressive Strength of Printing Components

Materials ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4008
Author(s):  
Zhengkai Feng ◽  
Heng Wang ◽  
Chuanjiang Wang ◽  
Xiujuan Sun ◽  
Shuai Zhang
Keyword(s):  
Compressive Strength ◽  
Stress Intensity ◽  
Influencing Factors ◽  
Fused Deposition Modeling ◽  
Experimental Results ◽  
Compression Tests ◽  
Suspended State ◽  
Fused Deposition ◽  
Deposition Modeling ◽  
Reference Experiment

Fused deposition modeling (FDM) has the advantage of being able to process complex workpieces with relatively simple operations. However, when processing complex components in a suspended state, it is necessary to add support parts to be processed and formed, which indicates an excessive dependence on support. The stress intensity of the supported positions of the printing components can be modified by changing the supporting model of the parts, their density, and their distance in relation to the Z direction in the FDM printing settings. The focus of the present work was to study the influences of these three modified factors on the stress intensity of the supporting position of the printing components. In this study, 99 sets of compression tests were carried out using a position of an FDM-supported part, and the experimental results were observed and analyzed with a 3D topographic imager. A reference experiment on the anti-pressure abilities of the printing components without support was also conducted. The experimental results clarify how the above factors can affect the anti-pressure abilities of the supporting positions of the printing components. According to the results, when the supporting density is 30% and the supporting distance in the Z direction is Z = 0.14, the compressive strength of the printing component is lowest. When the supporting density of the printing component is ≤30% and the supporting distance in the Z direction is Z ≥ 0.10, the compressive strength of printing without support is greater than that of the linear support model. Under the same conditions, the grid-support method offers the highest compressive strength.

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 Comparison Of FEA Simulations And Experimental Results For As-Built Additively Manufactured Dogbone Specimens

2021 ◽  
Author(s):  
Prathamesh Baikerikar ◽  
Cameron J Turner
Keyword(s):  
Fused Deposition Modeling ◽  
Experimental Tests ◽  
Continuum Models ◽  
Element Analysis ◽  
Fused Deposition ◽  
Accuracy And Precision ◽  
Structure Strength ◽  
Deposition Modeling ◽  
End Use ◽  
Strength And Stiffness

Abstract Parts built using Fused Deposition Modeling (FDM – an additive manufacturing technology) differ from their design model in terms of their microstructure and material properties. These differences lead to a certain amount of ambiguity regarding the structure, strength and stiffness of the final FDM part. Increasing use of FDM parts as end use products, necessitates accurate simulations and analyses during part design. However, analysis methods such as Finite Element Analysis, are used for analysis of continuum models, and may not accurately represent the non-continuous non-linear FDM parts. Therefore, it is necessary to determine the accuracy and precision of FEA for FDM parts. The goal of this study is to compare FEA simulations of the as-built geometries with the experimental tests of actual FDM parts. Dogbone geometries that include different infill patterns are tested under tensile loading and later simulated using FEA. This study found that FEA results are not always an accurate or reliable means of predicting FDM part behaviors.

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