Design of thermally deformable laminates using machine learning

Recent advances in material science and manufacturing have enabled designers to create objects which respond to changing environmental conditions by controlled deformation, without external mechanical or electrical actuation. The design of such smart materials has mostly been done through trial and error due to their complex nonlinear behavior. This paper will present how this problem is addressed by introducing a novel inverse design workflow. In this, a desired structural deformation is used as an input to a machine learned model which subsequently outputs the required geometric and material properties that will produce said deformation when exposed to an external stimulus. This workflow uses a Generative Adversarial Neural Network (GANN) trained on pairs of input cut-out patterns of laminate layers and their nonlinear Finite Element Analysis (FEA) simulation results. The method offers a significant performance speed-up while maintaining acceptable levels of accuracy, especially at the early design stage. This methodology could be further extended to the design of any nonlinear mechanical deformation

Computational Design Consultancy

The pervasiveness of the digital media has set the ground for tighter collaboration between the discipline involved in the architecture practice and potential for reconfiguring the well-established communication patterns in the industry to occur. Considering the context thereof, Computation Design Consultancy aims to connect different considerations and priorities raised by different parties involved in the architecture production system by means of digital computation. Here, we discusses the inefficiency of the existing system in engaging with the contemporary context influenced by the digital media as well as our approaches and findings thus far though the consultancy work

Simplexity

In this paper we discuss the design process that enables the integration of multiple concerns at an early stage of design by combining them into a single field that preconditions the space within which a specific design solution is developed. Our method is closely related to the development of interactive software tools that help the designer form a new intuition about the problem at hand. Two guiding concepts in this endeavour are simplexity [the desire to fine tune and build a system that yields a solution to a specific design problem by collapsing its inherent complexity] and defamiliarization [a side effect of having to represent things as numbers]. To demonstrate our strategy we will present the development of a computational design solution for small scale objects

Simplexity, the programming craft and architecture production

In resent years, digital design tools have become prevalent in the design community and their capabilities to manipulate geometry have grown into a trend among architects to generate complex forms. Working as computational design consultant in an engineering firm, between architecture and engineering we often come across the problems generated by a superficial use of digital tools in both disciplines and the incapacity of the current system to cope with their byproducts. Here we will discuss the problems we see with the current system and the opportunities opened by digital design tools. Two guiding concepts are simplexity [the desire to fine tune and build a system that yields a solution to a specific design problem by collapsing its inherent complexity] and defamiliarization [a side effect of having to represent things as numbers]. They can both affect the designer as an individual who chooses to engage with digital media as well as the production system in which he/she is embedded since he/she will have to find new channels of communication with other parties. To demonstrate our strategy and the obstacles faced we will examine our involvement in the development of a computational design solution for a small house designed by Future Systems architects

Structural Information as Material for Design

We present our investigations focusing on finding ways to design structural  solutions that respects criteria of efficiency, architectural intentions as well as intrinsic properties of the geometry. These are attempts to embed structural analysis results into the design space so that its form and structure will be affected by this information. The three examples show different approaches we have taken depending on the stage of design in which our processes intervened. The three approaches are Densification, Alignment, and Extraction.

Discretization of Continuous Surfaces as a Design Concern

The increasing trend in architecture to create unconventional forms opened up a new area of investigations in the employment of computational methods in design and construction.  Our investigation is undertaken within a structural engineering firm, Adams Kara Taylor and focuses on finding ways to design structural solutions that respect criteria of efficiency, architectural intentions as well as intrinsic properties of the geometry. In this paper, we present various approaches on discretization where a project is presented as a continuous form, envelope or skin that must be subsequently subdivided in order to yield a framing or cladding solution compatible with different manufacturing, design and engineering considerations. The first part of this paper illustrates such a project where we applied and developed one of our discretization approaches. The second part of the paper focuses on generalization where we present a series of methodologies and corresponding software tools developed for the purpose

The impact of genotyping error on haplotype reconstruction and frequency estimation

The choice of genotyping families vs unrelated individuals is a critical factor in any large-scale linkage disequilibrium (LD) study. The use of unrelated individuals for such studies is promising, but in contrast to family designs, unrelated samples do not facilitate detection of genotyping errors, which have been shown to be of great importance for LD and linkage studies and may be even more important in genotyping collaborations across laboratories. Here we employ some of the most commonly-used analysis methods to examine the relative accuracy of haplotype estimation using families vs unrelateds in the presence of genotyping error. The results suggest that even slight amounts of genotyping error can significantly decrease haplotype frequency and reconstruction accuracy, that the ability to detect such errors in large families is essential when the number/complexity of haplotypes is high (low LD/common alleles). In contrast, in situations of low haplotype complexity (high LD and/or many rare alleles) unrelated individuals offer such a high degree of accuracy that there is little reason for less efficient family designs. Moreover, parent-child trios, which comprise the most popular family design and the most efficient in terms of the number of founder chromosomes per genotype but which contain little information for error detection, offer little or no gain over unrelated samples in nearly all cases, and thus do not seem a useful sampling compromise between unrelated individuals and large families. The implications of these results are discussed in the context of large-scale LD mapping projects such as the proposed genome-wide haplotype map