Knitit

Karmon, Ayelet; Sterman, Yoav; Shaked, Tom; Sheffer, Eyal; Nir, Shoval

doi:10.1145/3213512.3213516

Cited by 17 publications

(7 citation statements)

References 16 publications

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“…Within the architectural domain, two approaches to knit abstraction can be observed in the reviewed research outputs. The first approach involves detailed modeling of knitted structures using a high-resolution loop representation with straight springs, resulting in complex mesh topology [1,11,19]. Another approach adopts a strategy of a larger abstraction and utilizes quad-based meshes with lower complexity at sparser resolutions [2,3,8,16,18,24].…”

Section: 2mentioning

confidence: 99%

“…Abstracted mesh properties -rest length vs. stiffness values. Before the availability of plug-ins specifically designed for the structural evaluation of membrane designs, the assignment of mesh properties in simulations was often based on arbitrary cable stiffness values with target rest lengths [2,8,11,19,24]. With the introduction of plug-ins for Rhino Grasshopper like K2Engineering [15], Karamba [10], and Kiwi3D [13], a more standardized approach became possible.…”

Section: 22mentioning

confidence: 99%

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Simulation calibration of differentiated knitted membranes with evolutionary optimisation tools

Sinke,

Tamke,

Thomsen

2024

ACSE

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Knitting offers a flexible and customizable approach in architectural textile applications, challenging traditional woven membranes [28]. CNC (computer numerical control)-knitting technology enables the creation of gradient expansion membranes through precise stitch control, facilitating large-scale production with minimal waste [25,27,34]. This departure from cut-patternbased strategies typical in woven membranes allows for integration of diverse material properties in a single process. Our research focused on guiding material expansion in knitted membranes to achieve complex, non-developable surfaces through digital form-finding and structural analysis. However, the irregular material density distribution posed challenges in reliable digital simulations due to complexity and knowledge gaps. Successful simulation models require a deep understanding of material properties and abstraction strategies to balance computational feasibility and accuracy. Our study investigated these challenges through prototyping and calibration of simulation models, aiming for more geometrically accurate results in designing with differentiated CNCknitted membranes. Here, we presented the extension of the method for simulation and calibration of graded textiles, published earlier by the authors [32,35]. We extended previous methods for simulating graded textiles, experimenting with CNC-knitted ceiling panels of varied geometries and material gradients. Calibration involves adjusting stiffness values and mesh representations using evolutionary optimization algorithms to reduce geometric deviations between digital simulations and their physical artifacts (Figure 1).

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Section: 2mentioning

confidence: 99%

Section: 22mentioning

confidence: 99%

Simulation calibration of differentiated knitted membranes with evolutionary optimisation tools

Sinke,

Tamke,

Thomsen

2024

ACSE

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“…These tend to fall within the categories of geometric [18][19][20][21][22][23][24][25][26][27][28][29] and mechanical models, [30][31][32][33][34] graphics models, [35][36][37][38][39][40][41][42][43] modeling to knitting translation [44][45][46][47] and parametrized models. [48][49][50] Recently, work has been published by researchers at Carnegie Mellon University describing systems that have been developed to ease the steep learning curve required for designing and programming textile machinery, such as weftknitting machines. 44,45,47 This work allows the use of three-dimensional meshes, typically used in other CAD systems, for the development of knitting machine instructions.…”

Section: Modeling Of Weft-knit Structuresmentioning

confidence: 99%

Characterizing and predicting the self-folding behavior of weft-knit fabrics

Amanatides

Ghita

Evans

et al. 2022

Textile Research Journal

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Self-folding behavior is an exciting property of weft-knit fabrics that can be created using just front and back stitches. This behavior is easy to create, but not easy to anticipate and currently cannot be predicted by the existing computer-aided design software that controls industrial knitting machines. This work identifies the edge deformation behaviors that lead to self-folding in weft knits, and methods to characterize the mechanical forces driving these behaviors with regard to chosen manufacturing parameters. With this data and analysis of the fabric deformations, the self-folding behavior was purposely controlled using calculated scaling factors. Furthermore, theoretical equations were developed to mathematically predict these scaling factors, minimizing the trial and error required to design with self-folding behavior and create textiles with novel engineered properties. By understanding the mechanisms responsible for creating these three-dimensional self-folding textiles, they can then be designed in a programmable manner for use in technical applications.

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“…The inquiry builds on CITA's extensive practice with knit as architectural material, [33][34][35][36] but addresses a general concern, when working with computational knit. [37][38][39][40][41][42][43][44][45][46] Knit is a highly versatile material system. The composition and resulting performance of knitted textiles can be minutely controlled through the arrangement of stitches and the introduction of multiple yarns with different behaviours.…”

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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Knitit

Cited by 17 publications

References 16 publications

Simulation calibration of differentiated knitted membranes with evolutionary optimisation tools

Simulation calibration of differentiated knitted membranes with evolutionary optimisation tools

Characterizing and predicting the self-folding behavior of weft-knit fabrics

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

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