Cumincad : CUMINCAD Papers : Search Results

CumInCAD is a Cumulative Index about publications in Computer Aided Architectural Design
supported by the sibling associations ACADIA, CAADRIA, eCAADe, SIGraDi, ASCAAD and CAAD futures

PDF papers
References

Hits 1 to 3 of 3

Reformat results as: short short into frame detailed detailed into frame

_id	acadia20_198
id	acadia20_198
authors	Sinke Baranovskaya, Yuliya; Tamke, Martin; Ramsgaard Thomsen, Mette
year	2020
title	Simulation and Calibration of Graded Knitted Membranes
doi	https://doi.org/10.52842/conf.acadia.2020.2.198
source	ACADIA 2020: Distributed Proximities / Volume I: Technical Papers [Proceedings of the 40th Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-95213-0]. Online and Global. 24-30 October 2020. edited by B. Slocum, V. Ago, S. Doyle, A. Marcus, M. Yablonina, and M. del Campo. 198-207.
summary	The grading of knit changes its geometrical performance and steers membrane expansion. However, knit possesses challenges of material predictability and digital simulation, due to its multiscalar complexity and anisotropic properties. Taking as a challenge the lack of digital solutions incorporating CNC-knit performance into the design model, this paper presents a novel approach for the design-integrated simulation of graded knit, informed by an empirical dataset analysis in combination with genetic optimization algorithms. Here the simulation design tool reflects the differences of industrially knitted textile panel behavior through digital mesh grading. Diversified fabric stiffness is achieved by intertwining the yarn into variegated stitch types that steer the textile expansion under load. These are represented digitally as zoned quad meshes with each segment assigned a stiffness value. Mesh stiffness values are optimized by minimizing the distance between the point clouds and the digital mesh, which are documented through deviation colored maps. This work concludes that design properties—pattern topology, stitch ratio, pattern density—play an important role in textile panel performance under load. Stiffness values derived from the optimization are higher for shallower designs and lower for the deeper cones.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	ijac202018402
id	ijac202018402
authors	Mette Ramsgaard Thomsen, Paul Nicholas, Martin Tamke, Sebastian Gatz, Yuliya Sinke and Gabriella Rossi
year	2020
title	Towards machine learning for architectural fabrication in the age of industry 4.0
source	International Journal of Architectural Computing vol. 18 - no. 4, 335–352
summary	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.
keywords	Machine learning, architectural design, industry 4.0, digital fabrication, robotic fabrication, CNC knit, neural networks
series	journal
email
last changed	2021/06/03 23:29

_id	cdrf2019_309
id	cdrf2019_309
authors	Yuliya Sinke, Sebastian Gatz, Martin Tamke, and Mette Ramsgaard Thomsen
year	2020
title	Machine Learning for Fabrication of Graded Knitted Membranes
doi	https://doi.org/https://doi.org/10.1007/978-981-33-4400-6_29
source	Proceedings of the 2020 DigitalFUTURES The 2nd International Conference on Computational Design and Robotic Fabrication (CDRF 2020)
summary	This paper examines the use of machine learning in creating digitally integrated design-to-fabrication workflows. As computational design allows for new methods of material specification and fabrication, it enables direct functional grading of material at high detail thereby tuning the design performance in response to performance criteria. However, the generation of fabrication data is often cumbersome and relies on in-depth knowledge of the fabrication processes. Parametric models that set up for automatic detailing of incremental changes, unfortunately, do not accommodate the larger topological changes to the material set up. The paper presents the speculative case study KnitVault. Based on earlier research projects Isoropia and Ombre, the study examines the use of machine learning to train models for fabrication data generation in response to desired performance criteria. KnitVault demonstrates and validates methods for shortcutting parametric interfacing and explores how the trained model can be employed in design cases that exceed the topology of the training examples.
series	cdrf
email
last changed	2022/09/29 07:51

No more hits.