Hybrid Tower, Designing Soft Structures

Thomsen, Mette Ramsgaard; Tamke, Martin; Deleuran, Anders Holden; Tinning, Ida Katrine Friis; Evers, Henrik Leander; Gengnagel, Christoph; Schmeck, Michel

doi:10.1007/978-3-319-24208-8_8

Cited by 15 publications

(12 citation statements)

References 3 publications

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“…As a general stiffness value cannot be determined, current state of art practice uses multiple iterations of 1:1 prototypes in order to iteratively calibrate the digital simulation until it predicts the resulting physical shape relatively precisely. 15,49 Due to the intricacy of the multi scalar relationship on all levels between stitch and fabrication the simulation parameters of one project can still not be transferred to a subsequent one, which makes extensive prototyping unavoidable.…”

Section: For Fab Data: Knitconementioning

confidence: 99%

Towards machine learning for architectural fabrication in the age of industry 4.0

Thomsen

Nicholas

Tamke

et al. 2020

International Journal of Architectural Computing

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Machine Learning (ML) is opening new perspectives for architectural fabrication, as it holds the potential for the profession to shortcut the currently tedious and costly setup of digital integrated design to fabrication workflows and make these more adaptable. To establish and alter these workflows rapidly becomes a main concern with the advent of Industry 4.0 in building industry. In this article we present two projects, which presents how ML can lead to radical changes in generation of fabrication data and linking these directly to design intent. We investigate two different moments of implementation: linking performance to the generation of fabrication data (KnitCone) and integrating the ability to adapt fabrication data in realtime as response to fabrication processes (Neural-Network Steered Robotic Fabrication). Together they examine how models can employ design information as training data and be trained to by step processes within the digital chain. We detail the advantages and limitations of each experiment, we reflect on core questions and perspectives of ML for architectural fabrication: the nature of data to be used, the capacity of these algorithms to encode complexity and generalize results, their task-specificness versus their adaptability and the tradeoffs of using them with respect to conventional explicit analytical modelling.

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Section: For Fab Data: Knitconementioning

confidence: 99%

Towards machine learning for architectural fabrication in the age of industry 4.0

Thomsen

Nicholas

Tamke

et al. 2020

International Journal of Architectural Computing

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“…In contrast, exploratory investigations in non-metric spaces are mainly carried by means of physical ex-perimentation. For example, an increased flexibility in topological modelling has been reported with the use of analog models in projects like the CITA tower (Thomsen et al 2015) and the ICD/ITKE M1 La Tour (Lienhard et al 2013a). In both cases, a multistep modelling workflow has been proposed to solve the inherent geometrical, topological and material complexity of each system.…”

Section: Elastically Deformable Structuresmentioning

confidence: 99%

Topology-driven Form-finding - Implementation of an Evolving Network Model for Extending Design Spaces in Dynamic Relaxation

Suzuki¹,

Knippers²

2017

Proceedings of the 22nd Conference on Computer Aided Architectural Design Research in Asia (CAADRIA)

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“…Since the stitch effect is different in Transformational Area, as it is shown in Figure 2 over the Physical Knit Model, we can use it as a qualitative criterion to determine the performance of the optimization algorithms. (Thomsen et al, 2015). Right: Fabricating Networks, Flower Antenna Sculpture (Davis, 2021) CALIBRATING A FORMFINDING ALGORITHM FOR SIMULATION OF TENSIONED KNITTED TEXTILE ARCHITECTURAL MODELS…”

Section: Introducing Workflow Case Study Challenges and Research Goalsmentioning

confidence: 99%

“…Figure1: Case studies of projects made of conical knitted textiles. Left: Hybrid Tower(Thomsen et al, 2015). Right: Fabricating Networks, Flower Antenna Sculpture(Davis, 2021) …”

mentioning

confidence: 99%

Calibrating a Formfinding Algorithm for Simulation of Tensioned Knitted Textile Architectural Models

Oghazian¹,

Brown²,

Davis³

2022

CAADRIA Proceedings

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This paper presents an optimization-based calibration process for tuning a digital formfinding algorithm used with knitted textile materials in architectural tension structures. 3D scanning and computational optimization are employed to accurately approximate a physical model in a digital workflow that can be used to establish model settings for future exploration within a knit geometric typology. Several aspects of the process are investigated, including different optimization algorithms and various approaches to data extraction. The goal is to determine the appropriate optimization method and data extraction, as well as automate the process of adjusting formfinding settings related to the length of the meshes associated with the knitted textile behavior. The calibration process comprises three steps: extract data from a 3D scanned model; determine the bounds of formfinding settings; and define optimization variables, constraints, and objectives to run the optimization process. Knitted textiles made of natural yarns are organic materials and when used at the industrial level can satisfy DSG 9 factors to promote sustainable industrialization and foster innovation in building construction through developing sustainable architectural systems. The main contributions of this paper are calibrated digital models of knitted materials and a comparison of the most effective algorithms and model settings, which are a starting point to apply this process to a wider range of knit geometries. These models enhance the implementation and further development of novel architectural knitted systems.

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Hybrid Tower, Designing Soft Structures

Cited by 15 publications

References 3 publications

Towards machine learning for architectural fabrication in the age of industry 4.0

Towards machine learning for architectural fabrication in the age of industry 4.0

Topology-driven Form-finding - Implementation of an Evolving Network Model for Extending Design Spaces in Dynamic Relaxation

Calibrating a Formfinding Algorithm for Simulation of Tensioned Knitted Textile Architectural Models

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