Aktiviteter pr. år
Abstract
This PhD thesis rethinks form-matter relationships in architectural materials by introducing gradedCNC-knitted textiles as an innovative, lightweight solution for tensile membrane architecture.Addressing the challenges of material ‘s environmental impact in architecture, this PhD research shiftsthe paradigm of how materials are designed and utilized, proposing form-matter integration as asustainable design strategy.
| Originalsprog | Engelsk |
|---|
| Udgivelsessted | København |
|---|---|
| Forlag | Det Kongelige Akademi |
| Antal sider | 345 |
| Status | Udgivet - 2025 |
Kunstnerisk udviklingsvirksomhed (KUV)
- Nej
Aktiviteter
-
Differentiated Knit Materiality - Simulation for discovery of novel material gradings for CNC-knitted architectural membranes
Ayres, P. (Eksaminator), Gengnagel, C. (Eksaminator) & Popescu, M. (Eksaminator)
19 maj 2025Aktivitet: Undersøgelse › Eksamination
Citationsformater
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- Author
- BIBTEX
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Sinke, Y. (2025). Differentiated Knit Materiality: Simulation for discovery of novel material gradings of CNC-knitted architectural membranes. Det Kongelige Akademi.
Sinke, Yuliya. / Differentiated Knit Materiality : Simulation for discovery of novel material gradings of CNC-knitted architectural membranes. København : Det Kongelige Akademi, 2025. 345 s.
@phdthesis{f7303f7b4908438db217ee344aef91b0,
title = "Differentiated Knit Materiality: Simulation for discovery of novel material gradings of CNC-knitted architectural membranes",
abstract = "This PhD thesis rethinks form-matter relationships in architectural materials by introducing graded CNC-knitted textiles as an innovative, lightweight solution for tensile membrane architecture. Addressing the challenges of material{\textquoteleft}s environmental impact in architecture, this PhD research shifts the paradigm of how materials are designed and utilized, proposing form-matter integration as a sustainable design strategy. Knitting, an additive manufacturing technique traditionally used in garment production, offers significant potential for architectural applications due to its inherent ability to support local differentiation and its compatibility with digital fabrication, offering precise programmability and industrial scalability. However, current practices for simulating and specifying knitted materials in architectural contexts often fail to address the challenges of predicting material behavior at architectural scales and translating performance targets into physical textiles. Situated at the intersection of architecture, computational design, engineering, and digital fabrication, this research tackles critical issues in sustainable material production, material behaviour prediction, and interdisciplinary design workflows. By leveraging the capabilities of knit for functional material grading and harnessing simulation-driven design, the study transforms simulation from a post-validation tool into a proactive instrument for design exploration. By integrating simulation and analytical tools early in the design process, this research enables novel material gradings for textile membranes, forging a closer bond between form and matter and advancing sustainable design paradigms for architectural structures. This thesis investigates the use of simulation and computational tools to program the material properties of CNC-knitted textiles. These properties are precisely executed using industrial knitting machines, resulting in controlled strain behaviour for tensile knitted membranes in architectural applications. A hybrid methodology combining Research-by-Simulation (RbS) and Research-by-Design (RbD) underpins this work. The RbS component visualises and predicts material behaviour, while the RbD component focuses on creating physical prototypes and demonstrators to validate and refine simulation outputs. Together, these methods provide a comprehensive toolkit for iteratively refining design outcomes based on material behaviour, structural requirements, and fabrication constraints. At the core of this thesis methodology is the development of an expanded Digital Design Chain (DDC) for knitted architectural membranes, integrating three interconnected digital models: Design, Fabrication, and Evaluation Models. These models work collaboratively to ensure a seamless transition from digital design to physical fabrication, maintaining smooth data flow across scales and professional domains. The Design Model employs simulation and form-finding methodologies to align local material properties with global performance objectives, offering strategies for novel material gradings. The Fabrication Model addresses challenges of data flow and domain shifts from 3d design to 2d production, translating digital simulations into machine-readable instructions for industrial CNC-knitting machines. The Evaluation Model ensures alignment between digital designs and physical outcomes by calibrating simulations and addressing geometric deviations through optimization processes such as genetic algorithms. Together the network of models integrates digital form-finding, analysis-driven material specification, and machine-ready production workflows, validated through alignment between digital and physical artefacts. The findings demonstrate that CNC-knitted membranes are high-performance, waste-efficient, and scalable solutions for architectural applications. Full-scale prototypes designed and produced during this research — ranging from scaled probes to full-scale installations such as Zoirotia and GraCe — demonstrate the feasibility and scalability of these methods. The results showcase how localized material differentiation enhances performance criteria such as membrane curvature, flexibility, and resource efficiency, making CNC-knitted membranes a viable alternative to conventional woven composites. These prototypes illustrate the potential of CNC-knitting to address diverse architectural challenges, including tensile structures, adaptive facades, and lightweight canopies. This thesis contributes to architectural material innovation by providing a structured design framework for CNC-knitted membranes in tensile architecture through redefining the relationship between form and matter of architectural textiles, advocating for material-responsive design and fabrication processes. The findings highlight CNC-knitting as a transformative approach to sustainable architecture, promoting resource-efficient, high-performance, and visually expressive membrane structures. By advancing both the epistemology and methodology of simulations for material specification and behavior prediction, this research provides a roadmap for future interdisciplinary applications of graded knitted textile systems in architectural design.",
keywords = "architectural textiles, CNC-knitting, simulation, digital fabrication, Functionally graded materials, structural analysis",
author = "Yuliya Sinke",
year = "2025",
language = "English",
publisher = "Det Kongelige Akademi",
}
Sinke, Y 2025, Differentiated Knit Materiality: Simulation for discovery of novel material gradings of CNC-knitted architectural membranes. Det Kongelige Akademi, København.
Differentiated Knit Materiality: Simulation for discovery of novel material gradings of CNC-knitted architectural membranes. / Sinke, Yuliya.
København: Det Kongelige Akademi, 2025. 345 s.
København: Det Kongelige Akademi, 2025. 345 s.
Publikation: Bog / Antologi / Afhandling / Rapport › Ph.d.-afhandling
TY - BOOK
T1 - Differentiated Knit Materiality
T2 - Simulation for discovery of novel material gradings of CNC-knitted architectural membranes
AU - Sinke, Yuliya
PY - 2025
Y1 - 2025
N2 - This PhD thesis rethinks form-matter relationships in architectural materials by introducing graded CNC-knitted textiles as an innovative, lightweight solution for tensile membrane architecture. Addressing the challenges of material‘s environmental impact in architecture, this PhD research shifts the paradigm of how materials are designed and utilized, proposing form-matter integration as a sustainable design strategy. Knitting, an additive manufacturing technique traditionally used in garment production, offers significant potential for architectural applications due to its inherent ability to support local differentiation and its compatibility with digital fabrication, offering precise programmability and industrial scalability. However, current practices for simulating and specifying knitted materials in architectural contexts often fail to address the challenges of predicting material behavior at architectural scales and translating performance targets into physical textiles. Situated at the intersection of architecture, computational design, engineering, and digital fabrication, this research tackles critical issues in sustainable material production, material behaviour prediction, and interdisciplinary design workflows. By leveraging the capabilities of knit for functional material grading and harnessing simulation-driven design, the study transforms simulation from a post-validation tool into a proactive instrument for design exploration. By integrating simulation and analytical tools early in the design process, this research enables novel material gradings for textile membranes, forging a closer bond between form and matter and advancing sustainable design paradigms for architectural structures. This thesis investigates the use of simulation and computational tools to program the material properties of CNC-knitted textiles. These properties are precisely executed using industrial knitting machines, resulting in controlled strain behaviour for tensile knitted membranes in architectural applications. A hybrid methodology combining Research-by-Simulation (RbS) and Research-by-Design (RbD) underpins this work. The RbS component visualises and predicts material behaviour, while the RbD component focuses on creating physical prototypes and demonstrators to validate and refine simulation outputs. Together, these methods provide a comprehensive toolkit for iteratively refining design outcomes based on material behaviour, structural requirements, and fabrication constraints. At the core of this thesis methodology is the development of an expanded Digital Design Chain (DDC) for knitted architectural membranes, integrating three interconnected digital models: Design, Fabrication, and Evaluation Models. These models work collaboratively to ensure a seamless transition from digital design to physical fabrication, maintaining smooth data flow across scales and professional domains. The Design Model employs simulation and form-finding methodologies to align local material properties with global performance objectives, offering strategies for novel material gradings. The Fabrication Model addresses challenges of data flow and domain shifts from 3d design to 2d production, translating digital simulations into machine-readable instructions for industrial CNC-knitting machines. The Evaluation Model ensures alignment between digital designs and physical outcomes by calibrating simulations and addressing geometric deviations through optimization processes such as genetic algorithms. Together the network of models integrates digital form-finding, analysis-driven material specification, and machine-ready production workflows, validated through alignment between digital and physical artefacts. The findings demonstrate that CNC-knitted membranes are high-performance, waste-efficient, and scalable solutions for architectural applications. Full-scale prototypes designed and produced during this research — ranging from scaled probes to full-scale installations such as Zoirotia and GraCe — demonstrate the feasibility and scalability of these methods. The results showcase how localized material differentiation enhances performance criteria such as membrane curvature, flexibility, and resource efficiency, making CNC-knitted membranes a viable alternative to conventional woven composites. These prototypes illustrate the potential of CNC-knitting to address diverse architectural challenges, including tensile structures, adaptive facades, and lightweight canopies. This thesis contributes to architectural material innovation by providing a structured design framework for CNC-knitted membranes in tensile architecture through redefining the relationship between form and matter of architectural textiles, advocating for material-responsive design and fabrication processes. The findings highlight CNC-knitting as a transformative approach to sustainable architecture, promoting resource-efficient, high-performance, and visually expressive membrane structures. By advancing both the epistemology and methodology of simulations for material specification and behavior prediction, this research provides a roadmap for future interdisciplinary applications of graded knitted textile systems in architectural design.
AB - This PhD thesis rethinks form-matter relationships in architectural materials by introducing graded CNC-knitted textiles as an innovative, lightweight solution for tensile membrane architecture. Addressing the challenges of material‘s environmental impact in architecture, this PhD research shifts the paradigm of how materials are designed and utilized, proposing form-matter integration as a sustainable design strategy. Knitting, an additive manufacturing technique traditionally used in garment production, offers significant potential for architectural applications due to its inherent ability to support local differentiation and its compatibility with digital fabrication, offering precise programmability and industrial scalability. However, current practices for simulating and specifying knitted materials in architectural contexts often fail to address the challenges of predicting material behavior at architectural scales and translating performance targets into physical textiles. Situated at the intersection of architecture, computational design, engineering, and digital fabrication, this research tackles critical issues in sustainable material production, material behaviour prediction, and interdisciplinary design workflows. By leveraging the capabilities of knit for functional material grading and harnessing simulation-driven design, the study transforms simulation from a post-validation tool into a proactive instrument for design exploration. By integrating simulation and analytical tools early in the design process, this research enables novel material gradings for textile membranes, forging a closer bond between form and matter and advancing sustainable design paradigms for architectural structures. This thesis investigates the use of simulation and computational tools to program the material properties of CNC-knitted textiles. These properties are precisely executed using industrial knitting machines, resulting in controlled strain behaviour for tensile knitted membranes in architectural applications. A hybrid methodology combining Research-by-Simulation (RbS) and Research-by-Design (RbD) underpins this work. The RbS component visualises and predicts material behaviour, while the RbD component focuses on creating physical prototypes and demonstrators to validate and refine simulation outputs. Together, these methods provide a comprehensive toolkit for iteratively refining design outcomes based on material behaviour, structural requirements, and fabrication constraints. At the core of this thesis methodology is the development of an expanded Digital Design Chain (DDC) for knitted architectural membranes, integrating three interconnected digital models: Design, Fabrication, and Evaluation Models. These models work collaboratively to ensure a seamless transition from digital design to physical fabrication, maintaining smooth data flow across scales and professional domains. The Design Model employs simulation and form-finding methodologies to align local material properties with global performance objectives, offering strategies for novel material gradings. The Fabrication Model addresses challenges of data flow and domain shifts from 3d design to 2d production, translating digital simulations into machine-readable instructions for industrial CNC-knitting machines. The Evaluation Model ensures alignment between digital designs and physical outcomes by calibrating simulations and addressing geometric deviations through optimization processes such as genetic algorithms. Together the network of models integrates digital form-finding, analysis-driven material specification, and machine-ready production workflows, validated through alignment between digital and physical artefacts. The findings demonstrate that CNC-knitted membranes are high-performance, waste-efficient, and scalable solutions for architectural applications. Full-scale prototypes designed and produced during this research — ranging from scaled probes to full-scale installations such as Zoirotia and GraCe — demonstrate the feasibility and scalability of these methods. The results showcase how localized material differentiation enhances performance criteria such as membrane curvature, flexibility, and resource efficiency, making CNC-knitted membranes a viable alternative to conventional woven composites. These prototypes illustrate the potential of CNC-knitting to address diverse architectural challenges, including tensile structures, adaptive facades, and lightweight canopies. This thesis contributes to architectural material innovation by providing a structured design framework for CNC-knitted membranes in tensile architecture through redefining the relationship between form and matter of architectural textiles, advocating for material-responsive design and fabrication processes. The findings highlight CNC-knitting as a transformative approach to sustainable architecture, promoting resource-efficient, high-performance, and visually expressive membrane structures. By advancing both the epistemology and methodology of simulations for material specification and behavior prediction, this research provides a roadmap for future interdisciplinary applications of graded knitted textile systems in architectural design.
KW - architectural textiles
KW - CNC-knitting
KW - simulation
KW - digital fabrication
KW - Functionally graded materials
KW - structural analysis
M3 - Ph.D. thesis
BT - Differentiated Knit Materiality
PB - Det Kongelige Akademi
CY - København
ER -
Sinke Y. Differentiated Knit Materiality: Simulation for discovery of novel material gradings of CNC-knitted architectural membranes. København: Det Kongelige Akademi, 2025. 345 s.