Engineering Design work typically consists of reusing, configuring, and assembling of existing components, solutions and knowledge. It has been suggested that more than 75% of design activity comprises reuse of previously existing knowledge. However in spite of the importance of design reuse activities researchers have estimated that 69% of companies have no systematic approaches to preventing the "reinvention of the wheel". The major issue for supporting design re-use is providing solutions that partially re-use previous designs to satisfy new requirements. Although 3D Search technologies that aim to create "a Google for 3D shapes" have been increasing in capability and speed for over a decade they have not found widespread application and have been referred to as "a solution looking for a problem"! This project is motivated by the belief that, with a new type of user interface, 3D search could be the solutions to the design reuse problem. The novel user interface proposed can be best understood in term of an analogy to the text message systems of mobile phones. On mobile phones 'Predictive text' systems complete words or phrases by matching fragments against dictionaries or phrases used in previous messages. Similarly a 'predictive CAD' system would complete 3D models using 'shape search' technology to interactively match partial CAD features against component databases. In this way the system would prompt the users with fragments of 3D components that complete, or extend, geometry added by the user. Such a system could potential increase design productivity by making the reuse of established designs an efficient part of engineering design. Although feature based retrieval of components from databases of 3D components has been demonstrated by many researchers so far the systems reported have been relatively slow and unable to be components of an interactive design system. However recent breakthroughs in sub-graph matching algorithms have enabled the emergence of a new generation of shape retrieval algorithms, which coupled with multi-core hardware, are now fast enough to support interactive, predictive design interfaces. This proposal aims to investigate the hypothesis that a "Predictive CAD" system would allow engineers to more effectively design new components that incorporate established, or standard, functional or manufacturing geometries. This would find commercial applications within large or distributed engineering organizations. This project can be regarded as an example of "big data" being employed to increase design productivity because even small engineering companies will have many hundreds of megabytes of CAD data that a "Predictive CAD" system would effectively pattern match against.

Recently the use of Crowdsourcing to deliver HITs has demonstrated a feasible way of providing cheap, robust, content based, Image analysis. This proposal seeks funding to investigate if a similar approach can be used to solve the geometric reasoning problems found in Mechanical CAD/CAM.Micro-outsourcing, or crowdsourcing, is a neologism for the act of taking a task traditionally performed by an employee or contractor, and outsourcing it to an undefined, generally large group of people, in the form of an open call. For example, the public may be invited to develop a new technology, carry out a design task, refine an algorithm or help capture, systematize or analyze large amounts of data. A Human Intelligence Task (HIT) is a problem that humans find simple, but computers find extremely difficult. For example a HIT related to a photograph could be: Is there a dog in this photograph? Many manufacturing operations require geometric reasoning to sequence, or recognize, various patterns, or constraints, in 2D and 3D shapes. Finding the best solutions to these problems would increase the productivity of numerous industries and impact directly on their profits. However frequently these types of problems are effectively incomputable (i.e. NP-complete) and so current practise is for CAD/CAM software to generate good , rather than optimum, solutions. If a Crowdsourcing approach to such difficult problems proves to be effective it would demonstrate how many similar pattern recognition and optimization problems manufacturing industry could be solved and provide a compelling demonstration of how a digital economy can distribute work, as well as, data. For much of its history CAD/CAM research has been motivated by the desire to increase the intelligence of systems by means of algorithms that could compute shape properties readily apparent to humans (eg. location of thin sections or holes). However this has proved to be difficult and where progress has been made it has generally solved special cases (eg. 2.5D geometry) rather than providing generic solutions. Examples of geometric reasoning problems still on the research agenda after decades of academic effort are numerous, for example: path planning, component packing, process planning, partial symmetry detection and shape feature recognition. Essential the difficulty is one of endowing computers with the appreciation of an object's overall form that humans gain so effortlessly. Interestingly similar difficulties have been encountered in image and speech recognition where automated systems still fail to reproduce human levels of performance.Because of this Geometric Reasoning represents a major technological bottleneck requiring many relatively trivial tasks to be done manually by engineers, a process that can be both time-consuming and sub-optimal (eg. frequently it will be infeasible to exhaustively explore all the alternatives paths, sequences or plans). Consequently removal of this geometric comprehension bottleneck would result in significant productivity gains across a wide range of industries. This proposal seeks to investigate the potential of a distributed approach (know colloquially as CrowdSourcing or Micro-outsourcing ) that has already proved its ability to provide practical solutions to many classic AI problems, such as image and speech interpretation. Research will use two exemplar applications to support a systematic investigation of the research issues. The first study will focus on a well defined task with easily quantifiable results (part nesting), while the second study will focus on a problem (shape similarity) easily stated but difficult to quantified.The project will create an experimental software platform, using the API of a commercial Crowdsourcing platform (i.e. Amazon's mechanical turk), to support the systematic investigation of the system's performance for these two different types of HIT.

Engineering Design work typically consists of reusing, configuring, and assembling of existing components, solutions and knowledge. It has been suggested that more than 75% of design activity comprises reuse of previously existing knowledge. However in spite of the importance of design reuse activities researchers have estimated that 69% of companies have no systematic approaches to preventing the "reinvention of the wheel". The major issue for supporting design re-use is providing solutions that partially re-use previous designs to satisfy new requirements. Although 3D Search technologies that aim to create "a Google for 3D shapes" have been increasing in capability and speed for over a decade they have not found widespread application and have been referred to as "a solution looking for a problem"! This project is motivated by the belief that, with a new type of user interface, 3D search could be the solutions to the design reuse problem. The system this research is aiming to produce is analogous to the text message systems of mobile phones. On mobile phones 'Predictive text' systems complete words or phrases by matching fragments against dictionaries or phrases used in previous messages. Similarly a 'predictive CAD' system would complete 3D models using 'shape search' technology to interactively match partial CAD features against component databases. In this way the system would prompt the users with fragments of 3D components that complete, or extend, geometry added by the user. Such a system could potential increase design productivity by making the reuse of established designs an efficient part of engineering design. Although feature based retrieval of components from databases of 3D components has been demonstrated by many researchers so far the systems reported have been relatively slow and unable to be components of an interactive design system. However recent breakthroughs in sub-graph matching algorithms have enabled the emergence of a new generation of shape retrieval algorithms, which coupled with multi-core hardware, are now fast enough to support interactive, predictive design interfaces. This proposal aims to investigate the hypothesis that a "Predictive CAD" system would allow engineers to more effectively design new components that incorporate established, or standard, functional or manufacturing geometries. This would find commercial applications within large or distributed engineering organizations. This project is an example of how data mining could potentially be employed to increase design productivity because even small engineering companies will have many hundreds of megabytes of CAD data that a "Predictive CAD" system would effectively pattern match against.