GENE_ARCH: An Evolution-Based Generative Design System for Sustainable Architecture

2006 ◽  
pp. 109-118 ◽  
Author(s):  
Luisa Caldas
Keyword(s):  
Design System ◽  
Generative Design ◽  
Sustainable Architecture

scholarly journals Architectural Design Exploration Using Generative Design: Framework Development and Case Study of a Residential Block

Buildings ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 201
Author(s):  
Jani Mukkavaara ◽  
Marcus Sandberg
Keyword(s):  
Architectural Design ◽  
Solution Space ◽  
Energy Performance ◽  
Design System ◽  
Future Research ◽  
Generative Design ◽  
Design Exploration ◽  
Novel Approach ◽  
Generative Algorithms

The use of generative design has been suggested to be a novel approach that allows designers to take advantage of computers’ computational capabilities in the exploration of design alternatives. However, the field is still sparsely explored. Therefore, this study aimed to investigate the potential use of generative design in an architectural design context. A framework was iteratively developed alongside a prototype, which was eventually demonstrated in a case study to evaluate its applicability. The development of a residential block in the northern parts of Sweden served as the case. The findings of this study further highlight the potential of generative design and its promise in an architectural context. Compared to previous studies, the presented framework is open to other generative algorithms than mainly genetic algorithms and other evaluation models than, for instance, energy performance models. The paper also presents a general technical view on the functionality of the generative design system, as well as elaborating on how to explore the solution space in a top-down fashion. This paper moves the field of generative design further by presenting a generic framework for architectural design exploration. Future research needs to focus on detailing how generative design should be applied and when in the design process.

scholarly journals Progress and recent trends in generative design

2020 ◽  
Vol 318 ◽  
pp. 01006
Author(s):  
Ioannis Ntintakis ◽  
George E. Stavroulakis
Keyword(s):  
Additive Manufacturing ◽  
Design System ◽  
Generative Design ◽  
Collaborative Tools ◽  
Design And Manufacturing ◽  
Recent Developments ◽  
Recent Trends ◽  
Design Systems ◽  
Intelligent Technology ◽  
Current Systems

Due to recent developments in the field of additive manufacturing enormous advantages have become in product design and manufacturing process. Before the appearance of additive manufacturing, developing very complex or light weight structures was difficult to manufacture. The development of artificial intelligent technology helps to develop new collaborative tools and algorithms. Generative design approach is one of them. The outcome model from a generative design study is not depending only from designer/engineer experience or his knowledge. Designers can react with sophisticated algorithms through CAD programs to specify the shape and the topology of the model. A significant tool on a generative design system is topology optimization which is able to generate different solutions. The changes in design process are significant. A rough conceptual design (sketch) or a 3d model is first prepared. Then, boundary conditions, safety factor, manufacturing limitations and materials properties are defined. The generative design system generates potential solutions. It’s up to the designer to find the design that best fits to his need. In this paper the review covers the limitations of current systems through the study of specific design cases using commercial generative design systems.

Comparing Designs: A Graphical Diff for Digital Mockups

1999 ◽  
Author(s):  
Jeff Heisserman
Keyword(s):  
Efficient Algorithms ◽  
Design System ◽  
Generative Design ◽  
Complex Assembly ◽  
Functional Systems ◽  
Geometric Data ◽  
Assembly Sequences ◽  
Operational Information ◽  
Design And Manufacture ◽  
Competitive Product

Abstract The design and manufacture of aircraft require management of a product that is both large and complex, with as many as live million parts, tens of gigabytes of geometric data, multiple functional systems, manufacturing plans, assembly sequences, and maintenance and operational information. The access and manipulation of the data by the designers is essential to developing an efficient and competitive product. In developing and manufacturing these products, there are a number of situations where there is a need to compare designs. In this paper, we describe the problem of comparing and merging different versions of a design, discuss criteria for determining differences, and present novel and efficient algorithms for their computation. We have developed facilities for computing the differences between complex assembly structures with large amounts of geometric data, and for interactively visualizing these differences. These facilities have been implemented in our Genesis generative design system.

scholarly journals Strategies for Consumer Control of Complex Product Forms in Generative Design Systems

2013 ◽  
Author(s):  
Axel Nordin ◽  
Damien Motte ◽  
Robert Bjärnemo
Keyword(s):  
Control Strategy ◽  
Control Strategies ◽  
The Other ◽  
Design System ◽  
Generative Design ◽  
Full Control ◽  
Total Control ◽  
Consumer Control ◽  
Design Systems ◽  
Product Forms

In recent years, the number of products that can be tailored to consumers’ needs and desires has increased dramatically; there are many opportunities to individualize the colors, materials or options of products. However, current trends indicate that the future consumer will not be satisfied with mere material and color choices, but will desire control over form as well. While it is technically feasible to allow consumers to partially mass-customize the form of products subject to functional and production constraints through the use of a generative design system, the question of how the control of form should be presented to the user arises. The issue becomes especially important when the product form is based on complex morphologies, which require in-depth knowledge of their parameters to be able to control them fully. In this paper, we discuss this issue and present and test two strategies for controlling complex forms in consumer-oriented generative design systems, one offering the user full control over the design (“total control” strategy), while the other automatically generates designs for the user (“no control” strategy). The implementation of those two control strategies in a generative design system for two categories of products (bookshelf and table) and five types of morphologies are described and tested with a number of design interested participants to estimate their level of satisfaction with the two control strategies. The empirical study shows that the participants enjoyed both the total control and no control strategies. The development of the full control modes for the five morphologies was on the other hand not straightforward, and in general, making the controls meaningful to the consumer can be difficult with complex morphologies. It seems that a consumer-oriented generative design system with two different control strategies, as the ones presented in this article, would offer the most satisfaction.

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