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

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_id	ecaade2018_108
id	ecaade2018_108
authors	Luo, Dan, Wang, Jingsong and Xu, Weiguo
year	2018
title	Applied Automatic Machine Learning Process for Material Computation
source	Kepczynska-Walczak, A, Bialkowski, S (eds.), Computing for a better tomorrow - Proceedings of the 36th eCAADe Conference - Volume 1, Lodz University of Technology, Lodz, Poland, 19-21 September 2018, pp. 109-118
doi	https://doi.org/10.52842/conf.ecaade.2018.1.109
summary	Machine learning enables computers to learn without being explicitly programmed. This paper outlines state-of-the-art implementations of machine learning approaches to the study of physical material properties based on Elastomer we developed, which combines with robotic automation and image recognition to generate a computable material model for non-uniform linear Elastomer material. The development of the neural network includes a few preliminary experiments to confirm the feasibility and the influential parameters used to define the final RNN neural network, the study of the inputs and the quality of the testing samples influencing the accuracy of the output model, and the evaluation of the generated material model as well as the method itself. To conclude, this paper expands such methods to the possible architectural implications on other non-uniform materials, such as the performance of wood sheets with different grains and tensile material made from composite materials.
keywords	neural network; robotic; material computation; automation
series	eCAADe
email
last changed	2022/06/07 07:59

_id	caadria2018_083
id	caadria2018_083
authors	Luo, Dan, Wang, Jinsong and Xu, Weiguo
year	2018
title	Robotic Automatic Generation of Performance Model for Non-Uniform Linear Material via Deep Learning
source	T. Fukuda, W. Huang, P. Janssen, K. Crolla, S. Alhadidi (eds.), Learning, Adapting and Prototyping - Proceedings of the 23rd CAADRIA Conference - Volume 1, Tsinghua University, Beijing, China, 17-19 May 2018, pp. 39-48
doi	https://doi.org/10.52842/conf.caadria.2018.1.039
summary	In the following research, a systematic approach is developed to generate an experiment-based performance model that computes and customizes properties of non-uniform linear materials to accommodate the form of designated curve under bending and natural force. In this case, the test subject is an elastomer strip of non-uniform sections. A novel solution is provided to obtain sufficient training data required for deep learning with an automatic material testing mechanism combining robotic arm automation and image recognition. The collected training data are fed into a deep combination of neural networks to generate a material performance model. Unlike most traditional performance models that are only able to simulate the final form from the properties and initial conditions of the given materials, the trained neural network offers a two-way performance model that is also able to compute appropriate material properties of non-uniform materials from target curves. This network achieves complex forms with minimal and effective programmed materials with complicated nonlinear properties and behaving under natural forces.
keywords	Material performance model; Deep Learning; Robotic automation; Material computation; Neural network
series	CAADRIA
email
last changed	2022/06/07 07:59

_id	caadria2018_306
id	caadria2018_306
authors	Liu, Jie, Ma, Hongtao, Tang, Ning, Xu, Weiguo and Luo, Dan
year	2018
title	Kinetair: Interactive Stairs with Multiple Functions
source	T. Fukuda, W. Huang, P. Janssen, K. Crolla, S. Alhadidi (eds.), Learning, Adapting and Prototyping - Proceedings of the 23rd CAADRIA Conference - Volume 2, Tsinghua University, Beijing, China, 17-19 May 2018, pp. 369-378
doi	https://doi.org/10.52842/conf.caadria.2018.2.369
summary	Kinetair is an interactive stairs prototype which could change its appearance according to the surrounding conditions, providing a diversity of functions, such as stairs, exhibition walls, furniture and so on. This research is based on the Interactive Architecture theory, integrating with digital fabrication technology. This paper will illustrate the origin of the concept, the concept development process, the fabrication process and the various possible application of Kinetair. This experiment evokes us to rethink the fundamental meanings of the architecture components in a brand new perspective, and stimulates designers to explore the new features of conventional constructions with cutting-edge technologies.
keywords	interactive stairs; stair design; kinetic structure; dynamic design; adaptive form
series	CAADRIA
email
last changed	2022/06/07 07:59

_id	caadria2023_395
id	caadria2023_395
authors	Luo, Jiaxiang, Mastrokalou, Efthymia, Aldaboos, Sarah and Aldabous, Rahaf
year	2023
title	Research on the Exploration of Sprayed Clay Material and Modeling System
source	Immanuel Koh, Dagmar Reinhardt, Mohammed Makki, Mona Khakhar, Nic Bao (eds.), HUMAN-CENTRIC - Proceedings of the 28th CAADRIA Conference, Ahmedabad, 18-24 March 2023, pp. 231–240
doi	https://doi.org/10.52842/conf.caadria.2023.2.231
summary	As a traditional building material, clay has been used by humans for a long time. From early civilisations, to the modern dependence on new technologies, the craft of clay making is commonly linked with the use of moulds, handmade creations, ceramic extruders, etc. (Schmandt and Besserat, 1977). Clay in the form of bricks is one of the oldest building materials known (Fernandes et al, 2010). This research expands the possibilities offered by standardised bricks by testing types of clay, forms, shapes, porosity, and structural methods. The traditional way of working with clay relies on human craftsmanship and is based on the use of semi-solid clay (Fernandes et al., 2010). However, there is little research on the use of clay slurry. With the rise of 3D printing systems in recent years, research and development has been emerging on using clay as a 3D printing filament (Gürsoy, 2018). Researchers have discovered that in order for 3D-printed clay slurry to solidify quickly to support the weight of the added layers during printing, curing agents such as lime, coal ash, cement, etc. have to be added to the clay slurry. After adding these substances, clay is difficult to be reused and can have a negative effect on the environment (Chen et al., 2021). In this study, a unique method for manufacturing clay elements of intricate geometries is proposed with the help of an internal skeleton that can be continuously reused. The study introduces the process of applying clay on a special structure through spraying and showcases how this method creates various opportunities for customisation of production.
keywords	Spray clay, Substructure, 3D printing, Modelling system, Reusable
series	CAADRIA
email
last changed	2023/06/15 23:14

_id	caadria2023_362
id	caadria2023_362
authors	Luo, Jiaxiang, Mastrokalou, Efthymia, Aldabous, Rahaf, Aldaboos, Sarah and Lopez Rodriguez, Alvaro
year	2023
title	Fabrication of Complex Clay Structures Through an Augmented Reality Assisted Platform
source	Immanuel Koh, Dagmar Reinhardt, Mohammed Makki, Mona Khakhar, Nic Bao (eds.), HUMAN-CENTRIC - Proceedings of the 28th CAADRIA Conference, Ahmedabad, 18-24 March 2023, pp. 413–422
doi	https://doi.org/10.52842/conf.caadria.2023.1.413
summary	The relationship between clay manufacturing and architectural design has a long trajectory that has been explored since the early 2000s. From a 3D printing or assembly perspective, using clay in combination with automated processes in architecture to achieve computational design solutions is well established. (Yuan, Leach & Menges, 2018). Craft-based clay art, however, still lacks effective computational design integration. With the improvement of Augmented Reality (AR) technologies (Driscoll et al., 2017) and the appearance of digital platforms, new opportunities to integrate clay manufacturing and computational design have emerged. The concept of digitally transferring crafting skills, using holographic guidance and machine learning, could make clay crafting accessible to more workers while creating the potential to share and exchange digital designs via an open-source manufacturing platform. In this context, this research project explores the potential of integrating computational design and clay crafting using AR. Moreover, it introduces a platform that enables AR guidance and the digital transfer of fabrication skills, allowing even amateur users with no prior making experience to produce complex clay components.
keywords	Computer vision, Distributed manufacturing, Augmented craftsmanship, Augmented reality, Real-time modification, Hololens
series	CAADRIA
email
last changed	2023/06/15 23:14

_id	caadria2022_278
id	caadria2022_278
authors	Ortner, F. Peter and Tay, Jing Zhi
year	2022
title	Optimizing Design Circularity: Managing Complexity in Design for Circular Economy Through Single and Multi-Objective Optimisation
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 191-200
doi	https://doi.org/10.52842/conf.caadria.2022.1.191
summary	This paper advances the application of computational optimization to design for circular economy (CE) by comparing results of scalarized single-objective optimization (SOO) and multi-objective optimization (MOO) to a furniture design case study. A framework integrating both methods is put forward based on results of the case study. Existing design frameworks for CE emphasize optimization through an iterative process of manual assessment and redesign (Ellen MacArthur Foundation, 2015). Identifying good design solutions for CE, however, is a complex and time-consuming process. Most prominent CE design frameworks list at least nine objectives, several of which may conflict (Reike et al., 2018). Computational optimization responds to these challenges by automating search for best solutions and assisting the designer to identify and manage conflicting objectives. Given the many objectives outlined in circular design frameworks, computational optimisation would appear a priori to be an appropriate method. While results presented in this paper show that scalarized SOO is ultimately more time-efficient for evaluating CE design problems, we suggest that given the presence of conflicting circular design objectives, pareto-set visualization via MOO can initially better support designers to identify preferences.
keywords	Design for Circular Economy, Computational Optimisation, Sustainability, Design Optimisation, SDG 11, SDG 12
series	CAADRIA
email
last changed	2022/07/22 07:34

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