Assuring the quality of design descriptions through the use of design configuration spaces

Lead Research Organisation: University of Leeds
Department Name: Mechanical Engineering

Abstract

The success of today's global supply networks depends on the efficient and effective communication of design descriptions (including design intent and shape definitions) that suit the requirements and capabilities of the wide range of engineering functions, processes and suppliers involved in the delivery of products to markets. Technical product data packages are used to provide these design descriptions. At a recent industry summit, a representative of Boeing noted that some 40% of the technical data needed to create a product resides outside the shape definitions in the technical product data package. The focus of this project is on the Bills of Materials (BoMs) that are integral parts of both shape definitions and the 40% of non-shape related product data. BoMs are fundamental because they act as integrators: adapting detailed design descriptions to suit the needs of particular engineering processes. The ability to reconfigure BoMs while maintaining internal consistency of the technical data package (where all BoM configurations are complete and compatible with each other) is a major challenge.

This proposal builds on a feasibility study that explored the use of embedding* to associate multiple BoMs with a single design description. From an engineering design perspective, based on discussions with four local SMEs and work on a case study related to a Rolls-Royce combustion system, we uncovered an urgent industry need to be able to associate multiple BoMs with one or more design descriptions. This need has remained hidden because current design technologies tend to subsume BoMs in proprietary data representations. However, engineers use BoMs and other design structures to adapt design descriptions for specific purposes. For this reason, new design technologies are needed that make BoMs and other design structures available for engineers to work with directly. From a design technology perspective, we have demonstrated that hypercube lattices can act as computational spaces within which BoMs can be reconfigured. However, the generated lattices are vast and, although we made in excess of hundred-fold improvements in the speed of lattice generation after consultation with the Leeds Advanced Research Computing team, the problem remains exponential in nature. For this project, the lattices will remain in the background, as a part of the technical apparatus. From an organisational psychology perspective, the ability to reconfigure BoMs creates opportunities for new ways of managing engineering knowledge in product development systems that take account of human and organisational behaviours, and individual preferences.

The goal of this project is to establish theoretical foundations, validated through a series of sharable software prototypes, to enable the reconfiguration of BoMs. The software prototypes will be designed for use by academic and industrial users to experiment with their own data and build understanding of the kinds of functionality required in such design tools. This will allow companies to better specify their long term information technology requirements for their IT system providers. A staged software engineering process will be used and a series of open source prototypes published at roughly six month intervals. This will create opportunities for meaningful interactions within the research team, and give industry partners early access to the research and opportunities to influence the research direction. In parallel, through the development of case studies in collaboration with industry partners and colleagues in other disciplines, we will build understanding of other types of design structure that occur in engineering design processes and develop cross-disciplinary learning opportunities.

* Embedding is a mathematical mechanism that allows one instance of a construct to be superimposed on another.

Planned Impact

The proposed research has high potential impact for a number of problem domains and solution providers. For engineering design, the research could improve key decision making activities by changing the ways in which engineers think about and work with BoMs and engineering information. In addition, there is potential for wider impact in other engineering sectors. For example, in the construction industry, the research could enhance the efficiency and effectiveness with which Building Information Models (BIM) are created and used through the whole life of a building. The research will also impact the maths, computing and system vendor communities by providing requirements for general purpose implementations of BoM configuration methods and tools. Our challenge lies in realising this impact for such a wide range of audiences and communities.

With respect to the RCUK typology of research impacts, the research will create both academic, and economic and societal impact.

Academic impact: Scientific advancement will be in the form of new implementation mechanisms that provide step improvements in the functionality of software used to configure BoMs. By making the benefits of embedding available to a wider community, who will not need to understand embedding or hypercube lattices to experience their benefits, we will improve relationships between industrial users and academic researchers which will lead to improved understanding of requirements for engineering, mathematics and computing. The work will improve the health of the academic discipline of engineering design by providing theoretical foundations for future generations of digital design system and improving our ability to work with BoMs. In addition, the open source software prototypes and learning resources will allow academics not involved with the project to develop future research from the software prototypes we develop and use findings in their teaching. The project will also develop three highly skilled PDRAs and enhance their CVs through publications and engagement with industry and other academic disciplines.

Economic and societal impact: If it became possible to easily and reliably configure multiple BoMs, as and when needed, then the efficiency and effectiveness of product development processes would be dramatically improved. This, in turn, could improve the innovation capacity of the organisations that operate these processes so contributing to wealth creation and economic prosperity. One of the real challenges in maximising environmental sustainability and protection through the life of large complex products lies in the poor quality of the product data that is available to engineers and other professionals once a product is in use. This is especially so when there is a need for information on how the product has been used after it was commissioned. The project will contribute to the development of engineering students (undergraduate and postgraduate): at Leeds and the OU through student projects linked to the research and in other institutions through digital learning assets published on Jorum (or equivalent) and downloadable software prototypes whose use will be integrated with projects associated with the digital assets.
 
Title End of project resource (web-based) 
Description A web site that reports findings in a form for industry users. The resource shows how our research can be used to address the following problems that occur in many industrial product development projects. - Configuring and reconciling part structures - Evaluating supply chain risks from part structures - Finding and classifying parts by shape - Judging the similarity of part shapes In addition, the resource outlines requirements for intelligent design systems that emerged from the project. 
Type Of Art Artefact (including digital) 
Year Produced 2022 
Impact The resource was used to inform a workshop contribution at the DCC 2022 conference in Glasgow. 
URL https://sites.google.com/view/designconfigurationspaces/home
 
Title Video to introduce the project to a lay audience 
Description An overview of the configuration management problem in product development processes and introduces the research being carried out in this project. 
Type Of Art Film/Video/Animation 
Year Produced 2020 
Impact The video was published on You Tube on 5th March 2020 so has not yet had any views. 
URL https://youtu.be/gbyPhurHjWg
 
Description Through the project we explored the application of two core technologies to the problem of design configuration:
- given a collection of parts, lattice theory was used to generate a representation of all possible configurations which, in turn, was used to enable the definition of new configurations (BoMs) that, as a collection, were self-consistent with each other and the initial collection of parts; and
- given a collection of part shapes, machine learning was used to cluster these shapes, so enabling users to select shapes that were similar to each other.

Together these informed work in the area of organisational psychology and human factors on requirements for design configuration tools and perspectives on shape similarity.

The project resulted in deeper understanding of the problem of design configuration, the potential of machine learning as a technology in the development of engineering design tools, and how such technologies might be used in engineering product development processes to improve the efficiency and effectiveness of such processes. New knowledge and capabilities in the following areas was generated:
- Configuring and reconciling part structures
- Evaluating supply chain risks from part structures
- Finding and classifying parts by shape
- Judging the similarity of part shapes
- Requirements for intelligent design systems
Exploitation Route The software tools developed are available (both .exes and source code) for anyone to download.
The ideas on supply chain risks have been published (in Journal of Systems Engineering, 2022) and we are using them in research proposals that are currently under development.
The ideas on design configuration and the use of lattices are published in a 2022 CAD Journal paper and have the potential to inform future generations of CAD package.
Sectors Aerospace, Defence and Marine,Construction,Energy,Manufacturing, including Industrial Biotechology,Transport

URL https://sites.google.com/view/designconfigurationspaces/home
 
Title PartFind 
Description A system that applies machine learning to cluster parts that are topologically similar. 
Type Of Technology Webtool/Application 
Year Produced 2022 
Open Source License? Yes  
Impact PartFind has been demonstrated in a motor manufacturing company and has been integrated with StrEmbed to support design configuration 
 
Title StrEmbed-6-1 
Description StrEmbed-6 is a graphical user interface for visualisation and manipulation of part-whole relationships in BoMs (Bills of Materials). It includes a STEP file reader, and a graphical user interface that displays geometric models and BoMs embedded in a lattice. It is interfaced with PartFind. 
Type Of Technology Software 
Year Produced 2022 
Open Source License? Yes  
Impact Includes creation of lattice object containing multiple bills of materials in one data structure that has been applied to at least four case studies and used in UG and PG student projects. Integrated with PartFind software. 
URL http://54.190.171.241:8501/
 
Title thazlehurst/PartMatch: prerelease_v0.1 
Description This is a prerelease placeholder of the PartMatch code for DCS. 
Type Of Technology Software 
Year Produced 2021 
Impact This is a first demonstration of the use of machine learning to determine the similarity of parts based on their topology. The software has been applied to a design case study and is showing promising results. 
URL https://zenodo.org/record/4597040
 
Description Poster presentation at DCC 2020 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Postgraduate students
Results and Impact McKay, A, Chau, HH, Rice HP, de Pennington A, Baker RB (2020) Assuring consistency of bills of materials using a visual computational approach. Poster presented Ninth International Conference on Design, Computing and Cognition (DCC20), Atlanta, USA, 14th-17th December 2020
Year(s) Of Engagement Activity 2020
 
Description Simpler, quicker, better: Improving the engineering design process, IMechE Webinar | 12:00 GMT, 16 November 2021 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Professional Practitioners
Results and Impact Profs Alison McKay and David Hogg introduced potential benefits of improved design configuration capabilities, and demonstrated progress on two prototype design tools: one that supports the definition of multiple product configurations while also ensuring consistency across these configurations and a second that is being developed to explore potential applications of machine learning in engineering design.

They highlighted future opportunities for advanced design tools and implications for both engineering design and machine learning communities.

The webinar concluded with a discussion, led by Dr Mark Robinson, on potential applications of machine learning to simplify and accelerate engineering design processes.

This led to a discussion at the end of the webinar and email communications with four or five participants after the event.
Year(s) Of Engagement Activity 2021
URL https://events.imeche.org/ViewEvent?code=TLE7450#msdynttrid=gesPa3dAVxzOT75EqZuJmb4xJfRe55_NzItpmZ64...