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Additive Manufacturing: Topology Optimization and Cellular Microstructures

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".

Deadline for manuscript submissions: closed (20 October 2022) | Viewed by 40903

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Prof. Dr. Georgios E Stavroulakis

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Institute of Computational Mechanics and Optimization, School of Production Engineering and Management, Technical University of Crete, 73100 Chania, Greece
Interests: computational mechanics; smart structures; identification; structural control
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Dr. Loucas Papadakis

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Department of Mechanical Engineering, Frederick University, Nicosia 1036, Cyprus
Interests: manufacturing processes; additive manufacturing; thermomechanical modelling’ vehicle structures
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Dr. Ioannis Ntintakis

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Department of Mechanical Engineering, Hellenic Mediterranean University, 71410 Heraklion, Greece
Interests: design for additive manufacturing; product design; topology optimization; lightweight structures
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Special Issue Information

Dear Colleagues,

Topology optimization (TO) is a mathematical method that spatially optimizes the distribution of material within a defined domain, by fulfilling predefined constraints and, if required, the cost function. However, topology optimization results are characterized by high complexity. The proposed optimized structures are difficult to manufacture with subtractive technologies. Additive manufacturing (AM) is a well-established technology already applied for the fabrication of structural components with nearly no geometric constraints. The combination of TO and AM allows for the creation of optimized parts with reduced mass and increased stiffness. Additive manufacturing leads to new structural design constraints and manufacturing defects, such as porosity and unmelt regions, shape inaccuracy after support structures removal, degradation of material properties, etc. Additionally, defects induced during additive manufacturing processes including unmelt regions and pores are the main cause of fatigue failure. These defects may cause crack initiation under cyclic loading.

Developments in AM techniques enable the fabrication of materials with intricate cellular architectures. A rapidly growing research area of cellular structures is auxetic materials with negative Poisson’s ratio (NPR). These materials expand in the lateral direction when stretched longitudinally or contract laterally under uniaxial compression. These materials possess a combination of high stiffness and strength with significant weight savings and demonstrate a series of particular characteristics over conventional materials, such as excellent indentation resistance, high shear stiffness, remarkable fracture toughness, and unique acoustic energy and impact absorption abilities.

The purpose of this Special Issue is to encourage the two scientific communities of additive manufacturing and topology optimization to focus on this novel and rapidly growing research area. In addition to the above fields, example topics may include new auxetic materials applications, machine learning applications, and novel algorithms linking topology optimization with additive manufacturing. This issue will publish original research papers, short reports, and reviews related to cellular structures fabricated with 3D printing and topology optimization methods for additive manufacturing.

Prof. Dr. Georgios Ε Stavroulakis
Dr. Loucas Papadakis
Dr. Ioannis Ntintakis
Guest Editors

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Keywords

design for AM
lightweight, topological, lattice, and cellular structures
auxetic structures
materials for AM
surface finish and post processing operations
thermomechanical analysis for shape distortion prediction and compensation
reverse engineering methods
integrated computational materials engineering (ICME) in additive manufacturing
defects and stress inspection of 3D printed structures
support structures
artificial intelligence and machine learning for AM

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Published Papers (8 papers)

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Research

15 pages, 2844 KB

Open AccessArticle

Deep Learning in Design of Semi-Automated 3D Printed Chainmail with Pre-Programmed Directional Functions for Hand Exoskeleton

by Izabela Rojek, Jakub Kopowski, Piotr Kotlarz, Janusz Dorożyński, Ewa Dostatni and Dariusz Mikołajewski

Appl. Sci. 2022, 12(16), 8106; https://doi.org/10.3390/app12168106 - 12 Aug 2022

Cited by 7 | Viewed by 2527

Abstract

The aim of this paper is to refine a scientific solution to the problem of automated or semi-automated efficient and practical design of 3D printed chainmails of exoskeletons with pre-programmed properties (variable stiffness/flexibility depending on direction) reflecting individual user needs, including different types and degrees of deficit. We demonstrate this with the example of using chainmail in a hand exoskeleton, where 3D printed chainmail components can be arranged in a single-layer structure with adjustable one- or two-way bending modulus. The novelty of the proposed approach consists in combining the use of real data from research on the exoskeleton of the hand, new methods of their analysis using deep neural networks, with a clear and scalable design of a 3D printed fabric product that can be personalized (mechanical parameters such as stiffness and bend angles in various directions) to the needs and goals of therapy in a particular patient. So far, this approach is unique, having no equivalent in the literature. This paves the way for a wider implementation of adaptive chainmails based on machine learning, more efficient for more complex chainmail designs. Full article

(This article belongs to the Special Issue Additive Manufacturing: Topology Optimization and Cellular Microstructures)

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19 pages, 52138 KB

Open AccessArticle

Infill Microstructures for Additive Manufacturing

by Ioannis Ntintakis and Georgios E. Stavroulakis

Appl. Sci. 2022, 12(15), 7386; https://doi.org/10.3390/app12157386 - 22 Jul 2022

Cited by 16 | Viewed by 3741

Abstract

Additive Manufacturing (AM) is a well-known and rapidly advancing method, especially in the manufacturing of high-strength and lightweight microstructures. Utilizing AM, it is possible to fabricate any structure as complicated as it is. For an efficient and cost-effective printing, a critical parameter is the infill, which can be characterized from an easy 2D shape to high complexity. At the same time, Topology Optimization (TO) is an appropriate method to create high-strength and mass optimized microstructure lattices. In the current study, TO starts from a solid cubic volume of 15 × 15 mm, and different boundary conditions of two new cellular microstructures designed with 0.4 and 0.1 relative density are applied, respectively. The adopted TO method was Solid Isotropic Material with Penalization (SIMP), which predicts an optimal material distribution within a given design domain. TO methods do not check other characteristics of the structure, such as anisotropy. To evaluate and characterize the optimized microstructure, a general purpose homogenization method is utilized to calculate the Zener ratio and the elastic modulus. Using Fused Filament Fabrication (FFF), which is a material extrusion 3D printing method, lattice structure samples are fabricated and then tested in compression and tensile strength tests. The comparative results from the homogenization study showed that both microstructures have anisotropic behavior and an accepted response in the stress test similar to the homogenized material. The experimental results show that the mechanical behavior of the lattice structure changes significantly when the cell mapping angle differs. Full article

(This article belongs to the Special Issue Additive Manufacturing: Topology Optimization and Cellular Microstructures)

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20 pages, 7354 KB

Open AccessArticle

A Semi-Automated 3D-Printed Chainmail Design Algorithm with Preprogrammed Directional Functions for Hand Exoskeleton

by Jakub Kopowski, Dariusz Mikołajewski, Piotr Kotlarz, Ewa Dostatni and Izabela Rojek

Appl. Sci. 2022, 12(10), 5007; https://doi.org/10.3390/app12105007 - 16 May 2022

Cited by 6 | Viewed by 3286

Abstract

The problem of computerising the design and development of 3D-printed chainmail with programmed directional functions provides a basis for further research, including the automation of medical devices. The scope of the present research was focused on computational optimisation of the selection of materials and shapes for 3D printing, including the design of medical devices, which constitutes a significant scientific, technical, and clinical problem. The aim of this article was to solve the scientific problem of automated or semi-automated efficient and practical design of 3D-printed chainmail with programmed directional functions (variable stiffness/elasticity depending on the direction). We demonstrate for the first time that 3D-printed particles can be arranged into single-layer chainmail with a tunable one- or two-directional bending modulus for use in a medical hand exoskeleton. In the present work, we accomplished this in two ways: based on traditional programming and based on machine learning. This paper presents the novel results of our research, including 3D printouts, providing routes toward the wider implementation of adaptive chainmails. Our research resulted in an automated or semi-automated efficient and practical 3D printed chainmail design with programmed directional functions for a wrist exoskeleton with variable stiffness/flexibility, depending on the direction. We also compared two methodologies of planning and construction: the use of traditional software and machine-learning-based software, with the latter being more efficient for more complex chainmail designs. Full article

(This article belongs to the Special Issue Additive Manufacturing: Topology Optimization and Cellular Microstructures)

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17 pages, 10626 KB

Open AccessArticle

Numerical Modeling and Experimental Investigation of Effective Elastic Properties of the 3D Printed Gyroid Infill

by Philip Bean, Roberto A. Lopez-Anido and Senthil Vel

Appl. Sci. 2022, 12(4), 2180; https://doi.org/10.3390/app12042180 - 19 Feb 2022

Cited by 29 | Viewed by 7900

Abstract

A numerical homogenization approach is presented for the effective elastic moduli of 3D printed cellular infills. A representative volume element of the infill geometry is discretized using either shell or solid elements and analyzed using the finite element method. The elastic moduli of the bulk cellular material are obtained through longitudinal and shear deformations of a representative volume element under periodic boundary conditions. The method is used to analyze the elastic behavior of gyroid infills for varying infill densities. The approach is validated by comparing the Young’s modulus and Poisson’s ratio with those obtained from compression experiments. Results indicate that although the gyroid infill exhibits cubic symmetry, it is nearly isotropic with a low anisotropy index. The numerical predictions are used to develop semi-empirical equations of the effective elastic moduli of gyroid infills as a function of infill density in order to inform design and topology optimization workflows. Full article

(This article belongs to the Special Issue Additive Manufacturing: Topology Optimization and Cellular Microstructures)

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20 pages, 21236 KB

Open AccessArticle

Large-Scale Truss Topology and Sizing Optimization by an Improved Genetic Algorithm with Multipoint Approximation

by Tianshan Dong, Shenyan Chen, Hai Huang, Chao Han, Ziqi Dai and Zihan Yang

Appl. Sci. 2022, 12(1), 407; https://doi.org/10.3390/app12010407 - 31 Dec 2021

Cited by 6 | Viewed by 2932

Abstract

Truss size and topology optimization problems have recently been solved mainly by many different metaheuristic methods, and these methods usually require a large number of structural analyses due to their mechanism of population evolution. A branched multipoint approximation technique has been introduced to decrease the number of structural analyses by establishing approximate functions instead of the structural analyses in Genetic Algorithm (GA) when GA addresses continuous size variables and discrete topology variables. For large-scale trusses with a large number of design variables, an enormous change in topology variables in the GA causes a loss of approximation accuracy and then makes optimization convergence difficult. In this paper, a technique named the label–clip–splice method is proposed to improve the above hybrid method in regard to the above problem. It reduces the current search domain of GA gradually by clipping and splicing the labeled variables from chromosomes and optimizes the mixed-variables model efficiently with an approximation technique for large-scale trusses. Structural analysis of the proposed method is extremely reduced compared with these single metaheuristic methods. Numerical examples are presented to verify the efficacy and advantages of the proposed technique. Full article

(This article belongs to the Special Issue Additive Manufacturing: Topology Optimization and Cellular Microstructures)

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15 pages, 5573 KB

Open AccessArticle

Experimental and Numerical Analysis of 3D Printed Polymer Tetra-Petal Auxetic Structures under Compression

by Demetris Photiou, Stelios Avraam, Francesco Sillani, Fabrizio Verga, Olivier Jay and Loucas Papadakis

Appl. Sci. 2021, 11(21), 10362; https://doi.org/10.3390/app112110362 - 4 Nov 2021

Cited by 34 | Viewed by 8738

Abstract

Auxetic structures possess a negative Poisson ratio (ν < 0) as a result of their geometrical configuration, which exhibits enhanced indentation resistance, fracture toughness, and impact resistance, as well as exceptional mechanical response advantages for applications in defense, biomedical, automotive, aerospace, sports, consumer goods, and personal protective equipment sectors. With the advent of additive manufacturing, it has become possible to produce complex shapes with auxetic properties, which could not have been possible with traditional manufacturing. Three-dimensional printing enables easy and precise control of the geometry and material composition of the creation of desirable shapes, providing the opportunity to explore different geometric aspects of auxetic structures with a variety of different materials. This study investigated the geometrical and material combinations that can be jointly tailored to optimize the auxetic effects of 2D and 3D complex structures by integrating design, modelling approaches, 3D printing, and mechanical testing. The simulation-driven design methodology allowed for the identification and creation of optimum auxetic prototype samples manufactured by 3D printing with different polymer materials. Compression tests were performed to characterize the auxetic behavior of the different system configurations. The experimental investigation demonstrated a Poisson’s ration reaching a value of ν = −0.6 for certain shape and material combinations, thus providing support for preliminary finite element studies on unit cells. Finally, based on the experimental tests, 3D finite element models with elastic material formulations were generated to replicate the mechanical performance of the auxetic structures by means of simulations. The findings showed a coherent deformation behavior with experimental measurements and image analysis. Full article

(This article belongs to the Special Issue Additive Manufacturing: Topology Optimization and Cellular Microstructures)

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22 pages, 10219 KB

Open AccessFeature PaperArticle

Cost-Aware Design and Fabrication of New Support Structures in Laser Powder Bed Fusion: Microstructure and Metallurgical Properties

by Bharath Bhushan Ravichander, Sourabh Thakare, Aditya Ganesh-Ram, Behzad Farhang, Manjunath Hanumantha, Yiran Yang, Narges Shayesteh Moghaddam and Amirhesam Amerinatanzi

Appl. Sci. 2021, 11(21), 10127; https://doi.org/10.3390/app112110127 - 28 Oct 2021

Cited by 4 | Viewed by 4669

Abstract

This study investigates the effect of support structures on the properties of Inconel 718 (i.e., IN718) parts produced by the laser powder bed fusion (LPBF) additive manufacturing process. Specifically, the effects of support structure shape (i.e., pin-type, angled-type, cone-type) and geometry (i.e., support wall thickness, and gap) on their composition, hardness, microstructure, and material/time consumption are investigated and compared to the conventionally fabricated Inconel 718. From the microstructural analysis, the deepest melt pools appeared to be formed in the sample fabricated on top of the pin-type support structure having a relatively low wall thickness. The XRD results conveyed that a proper selection of geometrical variables for designing support structure results in elevated levels of the strengthening phases of IN718. The sample fabricated on top of the pin-type support structure showed the highest Vickers hardness value of 460.5 HV, which was even higher than what was reported for the heat-treated wrought Inconel 718 (355–385 HV). Moreover, for the thinner support wall thickness, an improvement in the hardness value of the fabricated samples was observed. This study urges a reconsideration of the common approach of selecting supports for additive manufacturing of samples when a higher quality of the as-fabricated parts is desired. Full article

(This article belongs to the Special Issue Additive Manufacturing: Topology Optimization and Cellular Microstructures)

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13 pages, 5445 KB

Open AccessArticle

Low Thermal Expansion Machine Frame Designs Using Lattice Structures

by Poom Juasiripukdee, Ian Maskery, Ian Ashcroft and Richard Leach

Appl. Sci. 2021, 11(19), 9135; https://doi.org/10.3390/app11199135 - 30 Sep 2021

Cited by 4 | Viewed by 3858

Abstract

In this work, we investigated tessellating cellular (or lattice) structures for use in a low thermal expansion machine frame. We proposed a concept for a lattice structure with tailorable effective coefficient of thermal expansion (CTE). The design is an assembly of two parts: a lattice outer part and a cylindrical inner part, which are made of homogenous materials with different positive CTEs. Several lattice design variations were investigated and their thermal and mechanical performance analysed using a finite element method. Our numerical models showed that a lattice design using Nylon 12 and ultra-high molecular weight polyethylene could yield an effective in-plane CTE of 1 × 10⁻⁹ K⁻¹ (cf. 109 × 10⁻⁶ K⁻¹ for solid Nylon 12). This paper showed that the combination of design optimisation and additive manufacturing can be used to achieve low CTE structures and, therefore, low thermal expansion machine frames of a few tens of centimetres in height. Full article

(This article belongs to the Special Issue Additive Manufacturing: Topology Optimization and Cellular Microstructures)

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