Journal: Artificial intelligence for engineering design, analysis and manufacturing

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Cambridge University Press

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ISSN

0890-0604
1469-1760

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1997 - 199922010 - 20206
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Publications 1 - 8 of 8
  • Languages and Semantics of Grammatical Discrete Structures
    Item type: Journal Article
    Shea, Kristina; Cagan, Jonathan (1999)
    Artificial intelligence for engineering design, analysis and manufacturing
  • An Interactive, Visual Approach to Developing and Applying Parametric Three-Dimensional Spatial Grammars
    Item type: Journal Article
    Hoisl, Frank; Shea, Kristina (2011)
    Artificial intelligence for engineering design, analysis and manufacturing
  • A 3D, performance-driven generative design framework: automating the link from a 3D spatial grammar interpreter to structural finite element analysis and stochastic optimization
    Item type: Journal Article
    Zimmermann, Luca; Chen, Tian; Shea, Kristina (2018)
    Artificial intelligence for engineering design, analysis and manufacturing
    Since the introduction of spatial grammars 45 years ago, numerous grammars have been developed in a variety of fields from architecture to engineering design. Their benefits for solution space exploration when computationally implemented and combined with optimization have been demonstrated. However, there has been limited adoption of spatial grammars in engineering applications for various reasons. One main reason is the missing, automated, generalized link between the designs generated by the spatial grammar and their evaluation through finite-element analysis (FEA). However, the combination of spatial grammars with optimization and simulation has the advantage over continuous structural topology optimization in that explicit constraints, for example, modeling style and fabrication processes, can be included in the spatial grammar. This paper discusses the challenges in providing a generalized approach by demonstrating the implementation of a framework that combines a three-dimensional spatial grammar interpreter with automated FEA and stochastic optimization using simulated annealing (SA). Guidelines are provided for users to design spatial grammars in conjunction with FEA and integrate automatic application of boundary conditions. A simulated annealing method for use with spatial grammars is also presented including a new method to select rules through a neighborhood definition. To demonstrate the benefits of the framework, it is applied to the automated design and optimization of spokes for inline skate wheels. This example highlights the advantage of spatial grammars for modeling style and additive manufacturing (AM) constraints within the generative system combined with FEA and optimization to carry out topology and shape optimization. The results verify that the framework can generate structurally optimized designs within the style and AM constraints defined in the spatial grammar, and produce a set of topologically diverse, yet valid design solutions.
  • Innovative Dome Design
    Item type: Journal Article
    Shea, Kristina; Cagan, J. (1997)
    Artificial intelligence for engineering design, analysis and manufacturing
  • Spatial grammar implementation
    Item type: Review Article
    McKay, Alison; Chase, Scott; Shea, Kristina; et al. (2012)
    Artificial intelligence for engineering design, analysis and manufacturing
  • Automated areas of interest analysis for usability studies of tangible screen-based user interfaces using mobile eye tracking
    Item type: Journal Article
    Batliner, Martin; Hess, Stephan; Ehrlich-Adám, C.; et al. (2020)
    Artificial intelligence for engineering design, analysis and manufacturing
    The user's gaze can provide important information for human–machine interaction, but the analysis of manual gaze data is extremely time-consuming, inhibiting wide adoption in usability studies. Existing methods for automated areas of interest (AOI) analysis cannot be applied to tangible products with a screen-based user interface (UI), which have become ubiquitous in everyday life. The objective of this paper is to present and evaluate a method to automatically map the user's gaze to dynamic AOIs on tangible screen-based UIs based on computer vision and deep learning. This paper presents an algorithm for automated Dynamic AOI Mapping (aDAM), which allows the automated mapping of gaze data recorded with mobile eye tracking to the predefined AOIs on tangible screen-based UIs. The evaluation of the algorithm is performed using two medical devices, which represent two extreme examples of tangible screen-based UIs. The different elements of aDAM are examined for accuracy and robustness, as well as the time saved compared to manual mapping. The break-even point for an analyst's effort for aDAM compared to manual analysis is found to be 8.9 min gaze data time. The accuracy and robustness of both the automated gaze mapping and the screen matching indicate that aDAM can be applied to a wide range of products. aDAM allows, for the first time, automated AOI analysis of tangible screen-based UIs with AOIs that dynamically change over time. The algorithm requires some additional initial input for the setup and training, but analyzed gaze data duration and effort is only determined by computation time and does not require any additional manual work thereafter. The efficiency of the approach has the potential for a broader adoption of mobile eye tracking in usability testing for the development of new products and may contribute to a more data-driven usability engineering process in the future.
  • Opening our worlds
    Item type: Other Journal Item
    Shea, Kristina (2012)
    Artificial intelligence for engineering design, analysis and manufacturing
  • Three-dimensional labels
    Item type: Journal Article
    Hoisl, Frank; Shea, Kristina (2013)
    Artificial intelligence for engineering design, analysis and manufacturing
Publications 1 - 8 of 8