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.

This proposal seeks to investigate the thesis that large numbers of industrial optimisation tasks can be outsourced to rural workers to provide a sustainable source of skilled employment. The project consortium brings together a team of collaborators with the interdisciplinary expertise required to investigate the social, business and technological issues inherent in the vision of "human intelligence" being subcontracted to rural workers from urban manufacturing industry. The goal of the three year program will be to deliver a 'business model' (supported by the performance and economic evidence from a series of trials) that can deliver long term benefits to rural populations. The challenges of delivering this objective range from the social and training issues associated with low literacy rates to the establishment of the performance metrics and pricing models required by the industrial customers. The project consortium of academics, government agency and commercial enterprises brings together a unique combination of skills that range from Crowdsouring and Interactive computer graphics to service innovation and Knowledge Process Outsourcing.

Today's machines and products are so advanced in terms of their materials, form, construction, control and drive systems that they require expertise and resource that extends beyond the reach of even the world's largest organisations. As a consequence, the design, development and manufacture of, for example, a modern aircraft is undertaken by a large globally distributed network of organisations. While defining this network poses a design problem by itself, it is the challenge of managing such large, highly distributed, high value projects that is of upmost concern to industry presently. This is not only because of the recent spate of high profile cost overruns, delivery setbacks and collapsed projects, but also because of aspects of leakage of intellectual property, exposure to risk, and difficulties capturing design records, lessons learned, decisions and rationale. Engineering projects of the sort previously described are critically dependent upon two key toolsets. These are electronic communication tools (e.g. email) and digital objects (reports, CAD models and simulations). These communication tools and digital objects are fundamentally related. Engineers around the globe communicate electronically in order to create and evolve digital objects which are the basis for the design, manufacture, assembly, delivery and maintenance of products and machines. It is this relationship and co-evolution of communication and digital objects that lies at the heart of every engineering project, embodying not only the engineering work itself but also control of intellectual property, decision making, rationale and problem solving. For these reasons, it is proposed that, through an understanding of the relationship and co-evolution of communication and digital objects, it is possible to improve the management, control and performance of engineering projects. The vision of this research will be realised through a suite of ICT tools that embody new methods and approaches for capturing and analysing the content and evolution of engineering communication and digital objects, and new methods and approaches for generating, representing, interacting with, and interpreting what are defined as signatures of in communications and digital objects. The term signature is used to represent a meaningful relationship between one or more dimensions of communication and/or digital objects at a point in time or over a period of time. The research programme firstly considers the two dimensions of communication and digital objects. The aim here is to characterise what are referred to as the "language of collaborative manufacturing" (content of communication) and "patterns of evolution of digital objects" (construction and changes to digital objects) and to explore means of classifying content and structure, and means of generating signatures. The programme then explores the relationship between the co-evolution of these two dimensions. Here the aim is to establish sets of signatures, relationships between signatures and patterns of signatures that embody meaning for improving aspects of collaborative engineering such as those previously stated. This phase then investigates means of representing and visualising the signatures/patterns. The third phase of the programme researches new methods and approaches for project stakeholders to interact and meaningfully interpret signature sets, relationships and patterns with the aim of providing continuous real-time feedback. Such capability will enable advance warning of issues, improved management, increased productivity and ultimately improved design and manufacture of the product. In addition to the three major phases, the programme has a research strand focussed on testing and validation of the new methods and approaches, and characterising best practice, as well as new ways of setting up and managing collaborative work which will be used as part of outreach and knowledge transfer activities.