Design as a Marked Point Process

John Quigley,David Purves,Jonathan Corney,Andrew Sherlock,Gokula Vasantha

doi:10.1115/1.4052844

Abstract

Abstract 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.

Highlights

It has been argued that only 20% of design information is reused despite 90% of all design activities being based on the variants of existing designs [1], and that on average only 28% of design information is reused within manufacturing applications [2]
The aim of this research was to “Define a computational framework that can support an interactive design process with suggestions of features based on three inputs: a knowledge of existing designs; the state of an emerging design and a location on the surface of the emerging design.”
The authors believe that system described meets this goal and has established how the feature content of mechanical designs can be amalgamated and transformed into a likelihood function that defines the probability of particular design features occurring at specific locations on a model

Summary

Introduction

It has been argued that only 20% of design information is reused despite 90% of all design activities being based on the variants of existing designs [1], and that on average only 28% of design information is reused within manufacturing applications [2]. A system is required where design features may be suggested to the designer for effective reuse, and these design reuse procedures can be learned from historical data [3, 4]. This paper introduces the underpinning mathematics required for implementation of a new generation of user interfaces that automatically identifies appropriate characteristics of previous designs for reuse based on a designer’s real time activity. As a design evolves the system generates predictions of the features which might be incorporated, and are informed by both previous work and the new, ongoing design. In order to identify the most relevant features, and avoid presenting the user with an overwhelming number of suggestions, the work reported exploits the location of information (i.e. features and mouse pointer) on a 3D Computer Aided Design (CAD) model so that predictions can be appropriate to specific positions on an engineering component. The system does not dictate any order of operations and allows the engineer’s focus to move around the component

Objectives

Methods

Results

Discussion

Conclusion