A Meta-Generation framework for Industrial System Generation

Generative design is an increasingly important tool in the industrial world. It allows the designers and engineers to easily explore vast ranges of design options, providing a cheaper and faster alternative to the trial and failure approaches. Thanks to the flexibility they offer, Deep Generative Models are gaining popularity amongst Generative Design technologies. However, developing and evaluating these models can be challenging. The field lacks accessible benchmarks, in order to evaluate and compare objectively different Deep Generative Models architectures. Moreover, vanilla Deep Generative Models appear to be unable to accurately generate multi-components industrial systems that are controlled by latent design constraints. To address these challenges, we propose an industry-inspired use case that incorporates actual industrial system characteristics. This use case can be quickly generated and used as a benchmark. We propose a Meta-VAE capable of producing multi-component industrial systems and showcase its application on the proposed use case

Generative Representations for Computer-Automated Evolutionary Design

With the increasing computational power of computers, software design systems are progressing from being tools for architects and designers to express their ideas to tools capable of creating designs under human guidance. One of the main limitations for these computer-automated design systems is the representation with which they encode designs. If the representation cannot encode a certain design, then the design system cannot produce it. To be able to produce new types of designs, and not just optimize pre-defined parameterizations, evolutionary design systems must use generative representations. Generative representations are assembly procedures, or algorithms, for constructing a design thereby allowing for truly novel design solutions to be encoded. In addition, by enabling modularity, regularity and hierarchy, the level of sophistication that can be evolved is increased. We demonstrate the advantages of generative representations on two different design domains: the evolution of spacecraft antennas and the evolution of 3D objects

Making customized tree-like structures: integrating algorithmic design with digital fabrication

The ultimate goal of this paper is to contribute for the discussion on the role of digital technologies in architecture, focusing on the convergence of generative design systems with digital fabrication processes for expanding design capabilities. It presents a generative design system of customized tree-like structures for supporting irregular roof surfaces, as an alternative to conventional architectural design processes. It discusses the introduction of an algorithmic and parametric approach to design problems as a methodology for promoting design experimentation and enabling the fabrication of complex design configurations

Applying Generative Systems to Product Design

Generative Design provides multiple benefits to the development of new products. First is the creation of intricate patterns that resemble natural systems, moving away from geometric shapes typical of mechanical design. Second is the automation of processes where computers perform complex and repetitive tasks that would be too hard or tedious for humans to do. The opportunities that automation provides is frequently considered the main benefit of generative design in the creation of new products, buildings and systems. In both of these approaches, the output that computers generate is driven primarily by a designer’s vision that already has a general idea of how the result might look like. A new approach for generative design by software company Autodesk allows designers to define goals and criteria for functional CAD designs, and then having a program generate iterations of potential solutions. This process presents a radical shift where the computer is not just facilitating the ideas of the designer but rather designing itself. While designers still are in charge of the process, deciding which solutions are suitable for further refinement and implementation, the relationship between human and machine becomes collaborative. This paper explores the concepts described above and it shares the Author’s design explorations where both approaches for generative design are used in product design. Examples include products using Voronoi patterns and procedural networks where the physical appearance of the product is strikingly intricate and appealing, while the physical attributes of the product are not necessarily improved. Other examples illustrate the application of generative design structures created freely by the computer, following only set goals for supporting weight loads at given points. This process results in unique structures that are lightweight and strong but might also have a polarizing appearance for specific product applications. These examples will enable discussion on how designers will continue to integrate automation and generative systems into their process as technology continues to develop

Restart Strategies for Constraint-Handling in Generative Design Systems

Product alternatives suggested by a generative design system often need to be evaluated on qualitative criteria. This evaluation necessitates that several feasible solutions which fulfill all technical constraints can be proposed to the user of the system. Also, as concept development is an iterative process, it is important that these solutions are generated quickly; i.e., the system must have a low convergence time. A problem, however, is that stochastic constraint-handling techniques can have highly unpredictable convergence times, spanning several orders of magnitude, and might sometimes not converge at all. A possible solution to avoid the lengthy runs is to restart the search after a certain time, with the hope that a new starting point will lead to a lower overall convergence time, but selecting an optimal restart-time is not trivial. In this paper, two strategies are investigated for such selection, and their performance is evaluated on two constraint-handling techniques for a product design problem. The results show that both restart strategies can greatly reduce the overall convergence time. Moreover, it is shown that one of the restart strategies can be applied to a wide range of constraint-handling techniques and problems, without requiring any fine-tuning of problem-specific parameters