In this period we have been verifying and validating the various analytical methods and began research to understand user contexts in order that we can begin to research dashboard concepts. Work in the area of analysis of representations has led to models to predict time to completion and stability of CAD models. For example, we can accurately predict time to completion when the model is only about 50% complete:
Through this work we have partnered the National Composite Centre (NCC) to explore transfer-ability of the techniques to Finite Element Analysis (FEA). Work with the NCC is also exploring the automatic mapping of capability and competencies.
In this quarter we have demonstrated analytical approaches for revealing previously hidden product and process dependencies through analysis of User-CAD interaction and content of technical reports/communications and novel methods for monitoring and predicting likely project complexity for routine projects.
For example, we’ve been using co-occurrence analysis to reveal model product dependencies. However, unlike traditional methods, we can also include data from representations such as CAD models:
The 20th International Conference on Engineering Design took place 27-30th July 2015 in Milan. The LOCM team presented five papers across a range of topics:
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The 2015 Industry workshop an industry day was held on the 3rd July 2015 at the National Composites Centre in Bristol. The objective was to gather views on progress to date, to help us to shape the outputs to meet the challenges faced in managing engineering projects.
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Over the last quarter we have been consolidating our analysis, with the aim of grouping analyses into prototype dashboards for communications, records and representations (such as CAD). Further work has been completed to finalise the full range of project features associated with the concept of engineering project health monitoring. We have now identified 85 features (proxies) for engineering Project Health Monitoring (ePHM) and started work on validating these, and ranking their relative importance for engineering project health.
Due to the number of features, this is proving quite a time consuming task – here is a small sample of the matrix!
April 13th saw the University of Bristol host its Faculty of Engineering Industry Showcase Event which was attended by over 100 of the University’s industrial partners.
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The major focus of our research effort has been on the acquisition of further datasets, preparation of prototype dashboards (vision demonstrators) and preparation of conference papers.
For example, here is a new composite of various analyses:
We’re also pleased to report that a total of five conference papers were accepted for presentation at the prestigious International Conference on Engineering Design (http://iced2015.org/), to be held in Milan in July. Details of these papers may be found in our publications section. We are also pleased to announce that RWA have now joined the project.
Following the Project Advisory Group meeting of our industrial partners in the summer, and further feedback from industrial partners, the focus of research has been on the following areas: the configuration of prototype project dashboards and the development of the concept of engineering project health monitoring, and in particular, the proxies of performance of engineering projects – i.e. features of interest for project stakeholders. To address the latter a series of ethnographic studies are to be undertaken.
Below are some examples of composites of various analyses we are now able to undertake. Here we are mapping sentiment and type of email being sent onto a representation of a product – who is saying what about each part of the product? This could give project managers valuable early warning about potential issues:
Similarly, this example dashboard shows various information about aircraft repairs, using a visual representation of the aircraft and damage location:
Based on a review of extant research combined with scoping of datasets digital assets are to be split into three types (communications, records and representations) and four classes of attribute (physical, content, context, and semantic). A series of scoping studies are being undertaken around communication in a large systems engineering project, the digital assets associated with a Formula Student project and the workflow of an in-service repair and maintenance department.
We’ve also begun exploring visualisations of the outputs – here is a ‘theme river’ showing how various key topics from a project wax and wane over the lifetime of a project – all extracted automatically:
And here is an example of an automatic analysis of how terms used in a project are related to each other – this could be used to help uncover hidden dependencies, for example:
Since the project kick-off meeting in September the project team has focused on four interrelated areas. These are: understanding extant research, developing a data management plan, initial exploratory studies, and developing analytic capability including the use, modification and creation of tools (code) and associated methods, such as semantic analysis.
For example, this graph shows the evolution of various types of digital object over the life of a project. It looks pretty, but what we can tell from this? Does a ‘good’ project and a ‘bad’ project look the same?