Corney, Jonathan

Purves, David

Quigley, John

Sherlock, Andrew

Vasantha, Gokula

English

Although artificial intelligence (AI) systems which support composition using predictive text are well established, there are no analogous technologies for mechanical design. Motivated by the vision of a predictive system that learns from previous designs and can interactively provide a list of established feature alternatives to the designer as design progresses, this paper describes the theory, implementation, and assessment of an intelligent system that learns from a family of previous designs and generates inferences using a form of spatial statistics. The formalism presented models 3D design activity as a “marked point process” that enables the probability of specific features being added at particular locations to be calculated. Because the resulting probabilities are updated every time a new feature is added, the predictions will become more accurate as a design develops. This approach allows the cursor position on a CAD model to implicitly define a spatial focus for every query made to the statistical model. The authors describe the mathematics underlying a statistical model that amalgamates the frequency of occurrence of the features in the existing designs of a product family. Having established the theoretical foundations of the work, a generic six-step implementation process is described. This process is then illustrated for circular hole features using a statistical model generated from a dataset of hydraulic valves. The paper describes how the positions of each design’s extracted hole features can be homogenized through rotation and scaling. Results suggest that within generic part families (i.e., designs with common structure), a marked point process can be effective at predicting incremental steps in the development of new designs

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Design as a Marked Point Process

https://www.research.ed.ac.uk/files/278813840/md_144_2_021713.pdf

Design as a Marked Point Process

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