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Additively manufactured textiles and parametric modelling by generative algorithms in orthopaedic applications

RAPID PROTOTYPING JOURNAL 2020年第5期26卷 827-834页

作者： Ricotta, Vito Campbell, R. Ian Ingrassia, Tommaso Nigrelli, Vincenzo Univ Palermo Dipartimento Ingn Palermo Italy Loughborough Univ Dept Design Loughborough Leics England

Purpose The purpose of this paper is to implement a new process aimed at the design and production of orthopaedic devices fully manufacturable by additive manufacturing (AM). In this context, the use of generative algorithms for parametric modelling of additively manufactured textiles (AMTs) also has been investigated, and new modelling solutions have been proposed. Design/methodology/approach A new method for the design of customised elbow orthoses has been implemented. In particular, to better customise the elbow orthosis, a generative algorithm for parametric modelling and creation of a flexible structure, typical of an AMT, has been developed. Findings To test the developed modelling algorithm, a case study based on the design and production of an elbow orthosis made by selective laser sintering was investigated. The obtained results have demonstrated that the implemented algorithm overcomes many drawbacks typical of the traditional CAD modelling approaches. The parametric CAD model of the orthosis obtained through the new approach is characterised by a flexible structure with no deformations or mismatches and has been effectively used to produce the prototype through AM technologies. Originality/value The obtained results present innovative elements of originality in the CAD modelling sector, which can contribute to solving problems related to modelling for AM in different application fields.

关键词： Additive manufacturing Additively manufactured textiles generative algorithms Elbow orthosis CAD modeling AM technologies

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Managing variable-dimension structural optimization problems using generative algorithms

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STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION 2015年第4期52卷 695-715页

作者： Khetan, Ashish Lohan, Danny J. Allison, James T. Univ Illinois Dept Ind & Enterprise Syst Engn 117 Transportat Bldg104 S Mathews Ave Urbana IL 61801 USA

This article introduces a novel design abstraction concept for efficient truss topology and geometry optimization. The core advancement introduced here is to represent truss topology and geometry using rules of generative algorithms, and to operate on generative algorithm rules using a genetic algorithm rather than directly on the design description. This indirect design representation supports efficient exploration of variable and high-dimension design topologies. generative design strategies are also independent of any kind of ground structure, thus avoiding the inherent limitations of ground structure approaches that may hinder innovative design solutions by defining a priori what topologies may be considered. We present new generative algorithm strategies that automatically satisfy structural stability constraints, and that can produce truss topologies with a diversity of patterns within an individual truss design. Truss topology and geometry is optimized in an outer-loop by a genetic algorithm that operates on generative algorithm rules, and size optimization is performed in an inner-loop for each candidate topology using sequential linear programming. The proposed methodology supports concurrent optimization of truss topology, geometry, and size. The generative algorithm abstraction layer also supports the design of variable-dimension structures, which can be generated from the same fixed-dimension rule set. Finally, we demonstrate the effectiveness of the new methodology by examining archetypal two- and three-dimensional truss design optimization problems.

关键词： Truss topology optimization generative algorithms Structural optimization

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Rethinking Computer-Aided Architectural Design (CAAD) - From generative algorithms and Architectural Intelligence to Environmental Design and Ambient Intelligence 19th

Rethinking Computer-Aided Architectural Design (CAAD) - From...

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19th International Conference on Computer-Aided Architectural Design (CAAD) - Design Imperatives - The Future is Now

作者： Stojanovski, Todor Zhang, Hui Frid, Emma Chhatre, Kiran Peters, Christopher Samuels, Ivor Sanders, Paul Partanen, Jenni Lefosse, Deborah KTH Royal Inst Technol Stockholm Sweden Univ Birmingham Urban Morphol Res Grp Birmingham W Midlands England Deakin Univ Melbourne Vic Australia Tallinn Univ Technol Tallinn Estonia Sapienza Rome Italy

ISBN: (纸本)9789811912801;9789811912795

Computer-Aided Architectural Design (CAAD) finds its historical precedents in technological enthusiasm for generative algorithms and architectural intelligence. Current developments in Artificial Intelligence (AI) and paradigms in Machine Learning (ML) bring new opportunities for creating innovative digital architectural tools, but in practice this is not happening. CAAD enthusiasts revisit generative algorithms, while professional architects and urban designers remain reluctant to use software that automatically generates architecture and cities. This paper looks at the history of CAAD and digital tools for Computer Aided Design (CAD), Building Information Modeling (BIM) and Geographic Information Systems (GIS) in order to reflect on the role of AI in future digital tools and professional practices. Architects and urban designers have diagrammatic knowledge and work with design problems on symbolic level. The digital tools gradually evolved from CAD to BIM software with symbolical architectural elements. The BIM software works like CAAD (CAD systems for Architects) or digital board for drawing and delivers plans, sections and elevations, but without AI. AI has the capability to process data and interact with designers. The AI in future digital tools for CAAD and Computer-Aided Urban Design (CAUD) can link to big data and develop ambient intelligence. Architects and urban designers can harness the benefits of analytical ambient intelligent AIs in creating environmental designs, not only for shaping buildings in isolated virtual cubicles. However there is a need to prepare frameworks for communication between AIs and professional designers. If the cities of the future integrate spatially analytical AI, are to be made smart or even ambient intelligent, AI should be applied to improving the lives of inhabitants and help with their daily living and sustainability.

关键词： Artificial Intelligence (AI) Computer-Aided Architectural Design (CAAD) Architectural intelligence generative algorithms Environmental design Ambient intelligence

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Topology optimization for heat conduction using generative design algorithms

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STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION 2017年第3期55卷 1063-1077页

作者： Lohan, Danny J. Dede, Ercan M. Allison, James T. Univ Illinois Dept Ind & Enterprise Syst Engn 117 Transportat Bldg104 S Mathews Ave Urbana IL 61801 USA Toyota Res Inst North Amer Elect Res Dept 1555 Woodridge Ave Ann Arbor MI 48105 USA

In this article we present a new approach to topological design for steady-state heat conduction. The method capitalizes on the use of a generative algorithm to represent topology, resulting in a decrease in the number of variables in the design description. Using a generative algorithm as a design abstraction, the optimization technique is targeted to dendritic topologies that are known to perform well for heat conduction. Specifically, a traditional topology optimization technique (SIMP) is confirmed to produce branching characteristics in optimal designs. The Space Colonization Algorithm, which can generate similar topological patterns, is selected for in-depth investigation. A genetic algorithm drives generation of design candidates, providing a highly diversified search of the target design space. Finally, several synthesized optimal designs for steady-state heat conduction, derived using the described algorithms, are compared using commercial finite element software.

关键词： Topology optimization generative algorithms Conductive heat transfer

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Reverse Engineering Workflows for the Structural Assessment of Historical Buildings

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INTERNATIONAL JOURNAL OF ARCHITECTURAL HERITAGE 2025年第5期19卷 702-723页

作者： Massafra, Angelo Prati, Davide Gulli, Riccardo Alma Mater Studiorum Univ Bologna Dept Architecture DA Viale Risorgimento 2 I- 40136 Bologna Italy Univ Bergamo Dept Engn & Appl Sci DISA Bergamo Italy

Supported by advancements in 3D scanning and parametric modeling tools within the architecture, engineering, and construction sectors, reverse engineering processes for cultural heritage (CH) have recently gained popularity. While many studies have focused on simple 3D reconstructions to create virtual environments, a specialized subarea of this research field has targeted more specific digitization objectives, including, among many, structural analysis of building components. This emerging field has not yet been systematically developed due to the intrinsic challenges associated with CH. Within this context, this paper proposes and describes a reverse engineering-based method that utilizes terrestrial laser scanning and visual programming (VP) to analyze displacements and deformations that occurred over time in historical masonry buildings and applied it to the timber trusses, masonry facades, and columns of two selected Italian case study buildings. This method allows for comparing the surveyed condition of these components, considered the "deformed state", with their ideal configuration, reconstructed using VP algorithms, considered the "original state". The outcomes of this comparison facilitate the investigation of the components' structural behavior and support joint considerations to assess the overall condition of the investigated building, providing helpful knowledge for guiding structural improvement interventions.

关键词： Built cultural heritage generative algorithms parametric modeling reverse engineering structural analysis terrestrial laser scanning

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Using Hybrid Discriminative-generative Models for Binary Classification

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AUTOMATIC CONTROL AND COMPUTER SCIENCES 2019年第4期53卷 320-327页

作者： Abroyan, N. Natl Polytech Univ Armenia Inst Informat & Telecommun Technol & Elect Yerevan 375009 Armenia

Discriminative and generative machine learning algorithms have been successfully used in different classification tasks during the last several decades. They both have some advantages and disadvantages and depending on a problem, one type of algorithm performs better than the other one. In this paper we contribute to the research of combination of both approaches and propose literature based a hybrid discriminative-generative generic model. Also, we propose hybrid model structure finding and building a new algorithm. We present theoretical and practical advantages of the hybrid model over its consisting algorithms, efficiency of the model structure finding algorithm, then perform experiments and compare results.

关键词： machine learning classification discriminative algorithms generative algorithms hybrid model

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generative Design for Additively Manufactured Textiles in Orthopaedic Applications 10th

Generative Design for Additively Manufactured Textiles in Or...

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10th International Joint Conference on Mechanics, Design Engineering and Advanced Manufacturing, JCM 2020

作者： Ricotta, V. Campbell, R. Ian Ingrassia, T. Nigrelli, V. Dipartimento di Ingegneria Università degli Studi di Palermo Viale delle Scienze Palermo90128 Italy Loughborough Design School Loughborough University LoughboroughLE11 3TU United Kingdom

ISBN: (纸本)9783030705657

The aim of this work is to implement a new process for the design and production of orthopaedic devices to realize entirely by Additive Manufacturing (AM). In particular, a generative algorithm for parametric modelling of flexible structures to use in orthopaedic devices has been developed. The developed modelling algorithm has been applied to a case study based on the design and production of a customized elbow orthosis made by Selective Laser Sintering. The results obtained have demonstrated that the developed algorithm overcomes many drawbacks typical of traditional CAD modelling approaches. FEM simulations have been also performed to validate the design of the orthosis. The new modelling algorithm allows designers to model flexible structures with no deformations or mismatches and to create parametric CAD models to use for the production of orthopaedic devices through AM technologies. © 2021, The Author(s).

关键词： Additive Manufacturing Additively manufactured textiles CAD modelling Elbow orthosis generative algorithms

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An integrated framework for multi-criteria optimization of thin concrete shells at early design stages

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ADVANCED ENGINEERING INFORMATICS 2018年 38卷 330-342页

作者： Gomes, Carlos Parente, Manuel Azenha, Miguel Lino, Jose Carlos Shay Murtagh Precast Ltd Mullingar N91 WY91 Westmeath Ireland INESC TEC Inst Syst & Comp Engn Technol & Sci Rua Dr Roberto Frias P-4200465 Porto Portugal Univ Minho ISISE Sch Engn Dept Civil Engn Campus Azurem P-4800058 Guimaraes Portugal

Thin shells are crucially dependent on their shape in order to obtain proper structural performance. In this context, the optimal shape will guarantee performance and safety requirements, while minimizing the use of materials, as well as construction/maintenance costs. Thin shell design is a team-based, multidisciplinary, and iterative process, which requires a high level of interaction between the various parties involved, especially between the Architecture and Engineering teams. As a result of technological development, novel concepts and tools become available to support this process. On the one hand, concepts like Integrated Project Delivery (IPD) show the potential to have a high impact on multidisciplinary environments such as the one in question, supporting the early decision-making process with the availability of as much information as possible. On the other hand, optimization techniques and tools should be highlighted, as they fit the needs and requirements of both the shell shape definition process and the IPD concept. These can be used not only to support advanced design stages, but also to facilitate the initial formulation of shape during the early interactions between architect and structural engineer from an IPD point of view. This paper proposes a methodology aimed at enhancing the interactive and iterative process associated with the early stages of thin shell design, supported by an integrated framework. The latter is based on several tools, namely Rhinoceros 3D, Grasshopper, and Robot Structural Analysis. In order to achieve full integration of the support tools, a custom devised module was developed, so as to allow interoperability between Grasshopper and Robot Structural Analysis. The system resorts to various technologies targeted at improving the shell shape definition process, such as formfinding techniques, parametric and generative models, as well as shape optimization techniques that leverage on multi criteria evolutionary algorithms. Th

关键词： Thin concrete shells Interoperability Optimization generative algorithms

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Anamorphic Projection: Analogical/Digital algorithms

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NEXUS NETWORK JOURNAL 2015年第1期17卷 253-285页

作者： Di Paola, Francesco Pedone, Pietro Inzerillo, Laura Santagati, Cettina Univ Palermo Dept Architecture Darch I-90128 Palermo Italy Ist Euro Mediterraneo Sci & Tecnol IEMEST Dept Commun Interact Graph & Augmented Real I-90139 Palermo Italy Politecn Milan Bulding Architectural Engn EDA I-23900 Lecce Italy Univ Catania Dept Architecture I-95125 Catania Italy

The study presents the first results of a wider research project dealing with the theme of "anamorphosis", a specific technique of geometric projection of a shape on a surface. Here we investigate how new digital techniques make it possible to simplify the anamorphic applications even in cases of projections on complex surfaces. After a short excursus of the most famous historical and contemporary applications, we propose several possible approaches for managing the geometry of anamorphic curves both in the field of descriptive geometry (by using interactive tools such as CabrAnot sign and GeoGebra) and during the complex surfaces realization process, from concept design to manufacture, through CNC systems (by adopting generative procedural algorithms elaborated in Grasshopper).

关键词： Anamorphosis Anamorphic technique Descriptive geometry Architectural geometry generative algorithms Free form surfaces

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Augmenting small biomedical datasets using generative AI methods based on self-organizing neural networks

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BRIEFINGS IN BIOINFORMATICS 2024年第1期26卷 bbae640页

作者： Ultsch, Alfred Loetsch, Joern Univ Marburg DataBion Res Grp Hans Meerwein Str 6 D-35032 Marburg Germany Goethe Univ Inst Clin Pharmacol Theodor Stern Kai 7 D-60590 Frankfurt Germany Univ Helsinki Fac Med Haartmaninkatu 8 Helsinki 00014 Finland Fraunhofer Inst Translat Med & Pharmacol ITMP Theodor Stern Kai 7 D-60596 Frankfurt Germany

Small sample sizes in biomedical research often led to poor reproducibility and challenges in translating findings into clinical applications. This problem stems from limited study resources, rare diseases, ethical considerations in animal studies, costly expert diagnosis, and others. As a contribution to the problem, we propose a novel generative algorithm based on self-organizing maps (SOMs) to computationally increase sample sizes. The proposed unsupervised generative algorithm uses neural networks to detect inherent structure even in small multivariate datasets, distinguishing between sparse "void" and dense "cloud" regions. Using emergent SOMs (ESOMs), the algorithm adapts to high-dimensional data structures and generates for each original data point k new points by randomly selecting positions within an adapted hypersphere with distances based on valid neighborhood probabilities. Experiments on artificial and biomedical (omics) datasets show that the generated data preserve the original structure without introducing artifacts. Random forests and support vector machines cannot distinguish between generated and original data, and the variables of original and generated data sets are not statistically different. The method successfully augments small group sizes, such as transcriptomics data from a rare form of leukemia and lipidomics data from arthritis research. The novel ESOM-based generative algorithm presents a promising solution for enhancing sample sizes in small or rare case datasets, even when limited training data are available. This approach can address challenges associated with small sample sizes in biomedical research, offering a tool for improving the reliability and robustness of scientific findings in this field. Availability: R library "Umatrix" (https://***/package=Umatrix).

关键词： machine learning generative algorithms data science biomedical data self-organizing maps artificial neurons data generation

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