Computational Design Generation and Evaluation of Beam-Based Tetragonal Bravais Lattice Structures for Tissue Engineering

2020 ◽  
Author(s):  
Amit M. E. Arefin ◽  
Paul F. Egan
Keyword(s):  
Medical Devices ◽  
Design Space ◽  
Specific Growth ◽  
Computational Design ◽  
Tissue Growth ◽  
Biological Behavior ◽  
Mapping Technique ◽  
Bravais Lattice ◽  
Unit Cells ◽  
Design Generation

Abstract The study and application of computational design is gaining importance in biomedical engineering as medical devices are becoming more complex, especially with the emergence of 3D printed scaffold structures. Scaffolds are medical devices that act as temporary mechanical support and facilitate biological interactions to regenerate damaged tissues. Past computational design studies have investigated the influence of geometric design in lattice structured scaffolds to investigate mechanical and biological behavior. However, these studies often focus on symmetric cubic structures leaving an opportunity for investigating a larger portion of the design space to find favorable scaffold configurations beyond these constraints. Here, tissue growth behavior is investigated for tetragonal Bravais lattice structured scaffolds by implementing a computational approach that combines a voxel-based design generation method, curvature-based tissue growth modeling, and a design mapping technique for selecting scaffold designs. Results show that tetragonal unit cells achieve higher specific tissue growth than cubic unit cells when investigated for a constant beam width, thus demonstrating the merits in investigating a larger portion of the design space. It is seen that cubic structures achieve around 50% specific growth, while tetragonal structures achieve more than 60% specific growth for the design space investigated. These findings demonstrate the need for continued adaption and use of computational design methodologies for biomedical applications, where the discovery of favorable solutions may significantly improve medical outcomes.

Heuristic-Guided Solution Search Through a Two-Tiered Design Grammar

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Lucas Puentes ◽  
Jonathan Cagan ◽  
Christopher McComb
Keyword(s):  
Design Space ◽  
Effective Means ◽  
Computational Design ◽  
Research Area ◽  
Domain Specific ◽  
Lower Tier ◽  
Design Changes ◽  
Incremental Design ◽  
Gradual Process ◽  
Design Generation

Abstract Grammar-based design is typically a gradual process; incremental design changes are performed until a problem statement has been satisfied. While they offer an effective means for searching a design space, standard grammars risk being computationally costly because of the iteration required, and the larger a given grammar the broader the search required. This paper proposes a two-tiered design grammar that enhances the computational design generation with generalized heuristics to provide a way to more efficiently search a design space. Specifically, this two-tiered grammar captures a combination of heuristic-based strategic actions (often observed in human designers) and smaller-scale modifications (common in traditional grammars). Rules in the higher tier are abstract and applicable across multiple design domains. Through associated guiding heuristics, these macrorules are translated down into a sequence of domain-specific, lower-tier microrules. This grammar is evaluated through an implementation within an agent-based simulated annealing team algorithm in which agents iteratively select actions from either the higher tier or the lower tier. This algorithm is used in two applications: truss generation, which is commonly used for testing engineering design methods, and wave energy converter design generation, which is currently a relevant research area in sustainable energy production. Comparisons are made between designs generated using only lower-tier rules and those generated using only higher-tier rules. Further tests demonstrate the efficacy of applying a combination of both lower-tier and higher-tier rules.

Design and Biological Simulation of 3D Printed Lattices for Biomedical Applications

2019 ◽  
Author(s):  
Paul F. Egan
Keyword(s):  
Sensitivity Analysis ◽  
Biomedical Applications ◽  
Volume Ratio ◽  
Computational Design ◽  
Tissue Growth ◽  
Analytical Relationship ◽  
Biological Growth ◽  
Trade Offs ◽  
Unit Cells ◽  
3D Printed

Abstract There is great potential for using 3D printed designs fabricated via additive manufacturing processes for diverse biomedical applications. 3D printing offers capabilities for customizing designs for each new fabrication that could leverage automated design processes for personalized patient care, but there are challenges in developing accurate and efficient assessment methods. Here, we conduct a sensitivity analysis for a biological growth simulation for evaluating 3D printed lattices for regenerating bone and then use these simulations to identify performance trends. Four design topologies were compared by generating varied unit cells. Biological growth was modeled in a voxel environment by simulating the advancement of a tissue front by calculating its local curvature. Designs were generated with properties suitable for bone tissue engineering, namely 50% porosity and microscale pores. The sensitivity analysis determined trade-offs between prediction consistency and computation time, suggesting calculating curvature within a radius of 7.5 voxels is sufficient for most cases. Topologies were compared in bulk with design variations. All topologies had similar tissue growth rates for a given surface-volume ratio, but with differing unit cell sizes. These findings inform future optimization for selecting unit cells based on volume requirements and other criteria, such as mechanical stiffness. A fitted analytical relationship predicted tissue growth rate based on a design’s surface-volume ratio, which enables design evaluation without computationally expensive simulations. Lattices were 3D printed with biocompatible materials as proof-of-concepts, demonstrating the feasibility of the approach for future computational design methods for personalized medicine.

scholarly journals Computational Design of Modular Robots Based on Genetic Algorithm and Reinforcement Learning

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 471
Author(s):  
Jai Hoon Park ◽  
Kang Hoon Lee
Keyword(s):  
Genetic Algorithm ◽  
Reinforcement Learning ◽  
Design Space ◽  
Learning Algorithm ◽  
Computational Design ◽  
Computational Method ◽  
Learning Ability ◽  
Modular Robots ◽  
Control Mechanisms ◽  
Candidate Structure

Designing novel robots that can cope with a specific task is a challenging problem because of the enormous design space that involves both morphological structures and control mechanisms. To this end, we present a computational method for automating the design of modular robots. Our method employs a genetic algorithm to evolve robotic structures as an outer optimization, and it applies a reinforcement learning algorithm to each candidate structure to train its behavior and evaluate its potential learning ability as an inner optimization. The size of the design space is reduced significantly by evolving only the robotic structure and by performing behavioral optimization using a separate training algorithm compared to that when both the structure and behavior are evolved simultaneously. Mutual dependence between evolution and learning is achieved by regarding the mean cumulative rewards of a candidate structure in the reinforcement learning as its fitness in the genetic algorithm. Therefore, our method searches for prospective robotic structures that can potentially lead to near-optimal behaviors if trained sufficiently. We demonstrate the usefulness of our method through several effective design results that were automatically generated in the process of experimenting with actual modular robotics kit.

Negotiating human engagement and the fixity of computational design: Toward a performative design space for the differently-abled bodymind

2020 ◽  
Vol 18 (2) ◽  
pp. 174-193
Author(s):  
Sean Ahlquist
Keyword(s):  
Environmental Factors ◽  
Structural Optimization ◽  
Design Process ◽  
Design Space ◽  
Computational Design ◽  
Social Nature ◽  
And Performance ◽  
The Moment ◽  
Cultural Entity

Computational design affords agency: the ability to orchestrate the material, spatial, and technical architectural system. In this specific case, it occurs through enhanced, authored means to facilitate making and performance—typically driven by concerns of structural optimization, material use, and responsivity to environmental factors—of an atmospheric rather than social nature. At issue is the positioning of this particular manner of agency solely with the architect auteur. This abruptly halts—at the moment in which fabrication commences—the ability to amend, redefine, or newly introduce fundamentally transformational constituents and their interrelationships and, most importantly, to explore the possibility for extraordinary outcomes. When the architecture becomes a functional, social, and cultural entity, in the hands of the idealized abled-bodied user, agency—especially for one of an otherly body or mind—is long gone. Even an empathetic auteur may not be able to access the motivations of the differently-abled body and neuro-divergent mind, effectively locking the constraints of the design process, which creates an exclusionary system to those beyond the purview of said auteur. It can therefore be deduced that the mechanisms or authors of a conventional computational design process cannot eradicate the exclusionary reality of an architectural system. Agency is critical, yet a more expansive terminology for agent and agency is needed. The burden to conceive of capacities that will always be highly temporal, social, unpredictable, and purposefully unknown must be shifted far from the scope of the traditional directors of the architectural system. Agency, and who it is conferred upon, must function in a manner that dissolves the distinctions between the design, the action of designing, the author of design, and those subjected to it.

scholarly journals Device design and optimization of CNTFETs for high-frequency applications

2021 ◽  
Author(s):  
Boli Peng ◽  
Manojkumar Annamalai ◽  
Sven Mothes ◽  
Michael Schröter
Keyword(s):  
High Frequency ◽  
Design Space ◽  
Field Effect Transistors ◽  
Device Design ◽  
Design And Optimization ◽  
Figures Of Merit ◽  
Device Architecture ◽  
Unit Cells ◽  
Minimal Contact ◽  
Tube Density

AbstractCarbon nanotube (CNT) field-effect transistors (FETs) have recently reached high-frequency (HF) performance similar to that of silicon RF-CMOS at the same gate length despite a tube density and current per tube that are far from the physical limits and suboptimal device architecture. This work reports on an investigation of the optimal device design for practical HF applications in terms of cut-off frequencies, power gain, and linearity. Different fundamental designs in the gate contact arrangement are considered based on a 3D device simulation of both CNTs and contacts. First, unit cells with a single CNT and minimal contact sizes are compared. The resulting simulation data are then extended toward a structure with two gate fingers and realistic contact sizes. Corresponding parasitic capacitances, as well as series and contact resistances, have been included for obtaining realistic characteristics and figures of merit that can be used for comparison with corresponding silicon RF MOSFETs. Finally, a sensitivity analysis of the device architecture with the highest performance is performed in order to find the optimal device design space.

scholarly journals Mechanical Metamaterials on the Way from Laboratory Scale to Industrial Applications: Challenges for Characterization and Scalability

Materials ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 3605 ◽  
Author(s):  
Sarah C. L. Fischer ◽  
Leonie Hillen ◽  
Chris Eberl
Keyword(s):  
Design Space ◽  
Materials Design ◽  
Industrial Applications ◽  
Research Field ◽  
Simulation Tools ◽  
Mechanical Metamaterials ◽  
Unit Cells ◽  
Key Aspects ◽  
Industrial Standards ◽  
Functional Components

Mechanical metamaterials promise a paradigm shift in materials design, as the classical processing-microstructure-property relationship is no longer exhaustively describing the material properties. The present review article provides an application-centered view on the research field and aims to highlight challenges and pitfalls for the introduction of mechanical metamaterials into technical applications. The main difference compared to classical materials is the addition of the mesoscopic scale into the materials design space. Geometrically designed unit cells, small enough that the metamaterial acts like a mechanical continuum, enabling the integration of a variety of properties and functionalities. This presents new challenges for the design of functional components, their manufacturing and characterization. This article provides an overview of the design space for metamaterials, with focus on critical factors for scaling of manufacturing in order to fulfill industrial standards. The role of experimental and simulation tools for characterization and scaling of metamaterial concepts are summarized and herewith limitations highlighted. Finally, the authors discuss key aspects in order to enable metamaterials for industrial applications and how the design approach has to change to include reliability and resilience.

Computational Investigation of Tissue and Blood Vessel Growth Trade-Offs in Hierarchical Lattices

2021 ◽  
Author(s):  
Amit M. E. Arefin ◽  
Paul F. Egan
Keyword(s):  
Blood Vessel ◽  
Computational Design ◽  
Tissue Growth ◽  
Beam Diameter ◽  
Design Decisions ◽  
Large Void ◽  
Biomechanical Behavior ◽  
Trade Offs ◽  
Vessel Growth ◽  
3D Printed

Abstract Computational design is growing in necessity for advancing biomedical technologies, particularly when considering complex systems with numerous trade-offs among design decisions and resulting biomechanical behavior. In tissue engineering applications, porous bone scaffold structures enabled by 3D printing can have intricate lattice structures and hierarchical features that mimic the biological hierarchy of natural bone. However, these hierarchies create challenges in predicting the tissue regeneration process and how different scales of the hierarchy drive varied biological behaviors. Smaller pores facilitate tissue growth while larger pores are necessary for blood vessel growth, however, identifying favorable trade-offs to maximize growth of both tissue and blood vessels remains a challenge, especially for complex 3D printed structures. Here, we adapt tissue and blood vessel growth models for predicting biological growth in scaffolds with varied combinations of beam diameter size, unit cell topology, and hierarchical pore size/distribution. Findings demonstrate that on a normalized scale lattices with no large voids provide greater tissue growth but less blood vessel growth in comparison to lattice layouts with large void areas. A lattice with large void channels provided the greatest blood vessel growth but poorer tissue growth, while a lattice with evenly distributed large voids provided a better compromise between the two types of growth. Overall, these findings demonstrate the merit in computational investigations for design trade-off comparisons in tissue scaffolds, and provide a foundation for future explorations of biological design decisions for regenerative medicine and 3D printed systems.

Whither design space?

2006 ◽  
Vol 20 (2) ◽  
pp. 63-82 ◽  
Author(s):  
ROBERT F. WOODBURY ◽  
ANDREW L. BURROW
Keyword(s):  
Network Structure ◽  
Design Space Exploration ◽  
Design Research ◽  
Design Space ◽  
Space Exploration ◽  
Computational Design ◽  
Exploration Process ◽  
Computational Structures

Design space exploration is a long-standing focus in computational design research. Its three main threads are accounts of designer action, development of strategies for amplification of designer action in exploration, and discovery of computational structures to support exploration. Chief among such structures is the design space, which is the network structure of related designs that are visited in an exploration process. There is relatively little research on design spaces to date. This paper sketches a partial account of the structure of both design spaces and research to develop them. It focuses largely on the implications of designers acting as explorers.

A Method for Visualizing the Relations Between Grammar Rules, Performance Objectives and Search Space Exploration in Grammar-Based Computational Design Synthesis

2015 ◽  
Author(s):  
Corinna Königseder ◽  
Kristina Shea
Keyword(s):  
Design Space Exploration ◽  
Design Space ◽  
Space Exploration ◽  
Computational Design ◽  
Search Space ◽  
Synthesis Process ◽  
Design Synthesis ◽  
Novel Approach ◽  
Grammar Rules ◽  
Computational Design Synthesis

Design grammars have been successfully applied in numerous engineering disciplines, e.g. in electrical engineering, architecture and mechanical engineering. A successful application of design grammars in Computational Design Synthesis (CDS) requires a) a meaningful representation of designs and the design task at hand, b) a careful formulation of grammar rules to synthesize new designs, c) problem specific design evaluations, and d) the selection of an appropriate algorithm to guide the synthesis process. Managing these different aspects of CDS requires not only a detailed understanding of each individual part, but also of the interdependencies between them. In this paper, a new method is presented to analyze the exploration of design spaces in CDS. The method analyzes the designs generated during the synthesis process and visualizes how the design space is explored with respect to a) design characteristics, and b) objectives. The selected algorithm as well as the grammar rules can be analyzed with this approach to support the human designer in successfully understanding and applying a CDS method. The case study demonstrates how the method is used to analyze the synthesis of bicycle frames. Two algorithms are compared for this task. Results demonstrate how the method increases the understanding of the different components in CDS. The presented research can be useful for both novices to CDS to help them gain a deeper understanding of the interplay between grammar rules and guidance of the synthesis process, as well as for experts aiming to further improve their CDS application by improving parameter settings of their search algorithms, or by further refining their design grammar. Additionally, the presented method constitutes a novel approach to interactively visualize design space exploration considering not only designs objectives, but also the characteristics and interdependencies of different designs.

Sign in / Sign up

Export Citation Format

Share Document