Network theory and CAD collections

Abstract

Graph and network theory have become commonplace in modern life. So widespread in fact that most people not only understand the basics of what a network is, but are adept at using them and do so daily. This has not long been the case however and the relatively quick growth and uptake of network technology has sparked the interest of many scientists and researchers. The Science of Networks has sprung up, showing how networks are useful in connecting molecules and particles, computers and web pages, as well as people. Despite being shown to be effective in many areas, network theory has yet to be applied to mechanical engineering design. This work makes use of network science advances and explores how they can impact Computer Aided Design (CAD) data. CAD data is considered the most valuable design data within mechanical engineering and two places large collections are found are educational institutes and industry. This work begins by exploring 5 novel networks of different sized CAD collections, where metrics and network developments are assessed. From there collections from educational and industrial settings are explored in depth, with novel methods and visualisations being presented. The results of this investigation show that network science provides interesting analysis of CAD collections and two key discoveries are presented: network metrics and visualisations are shown to be effective at highlighting plagiarism in collections of students' CAD submissions. Also when used to assess collections of real world company data, network theory is shown to provide unique metrics for analysis and characterising collections of CAD and associated data.