Aiming to help researchers capture early-stage Product Development (PD) activity , this article presents a new method for digitally capturing  prototypes. The motivation for this work is to understand prototyping in the early stages of PD projects, and this article investigates if and how digital capture of physical prototypes can be used for this purpose. In PD case studies, such early-stage prototypes are usually rough and of low-fidelity and are thus often discarded or substantially modified through the projects. Hence, retrospective access to prototypes is a challenge when trying to gather accurate empirical data. To capture the prototypes developed through the early stages of a project, a new method has been developed for digitally capturing physical prototypes through multi-view images, along with metadata describing by who, when and where the prototypes were captured.  In this article, one project is shown in detail to demonstrate how this capturing system can gather empirical data for enriching PD case studies on early-stage projects that focus on prototyping for concept generation. The first approach is to use the multi-view images for a qualitative assessment of the projects, which can provide new insights and understanding on various aspects like design decisions, trade-offs and specifications. The second approach is to analyse the metadata provided by the system to give understanding into prototyping patterns in the projects. The analysis of metadata provides insight into prototyping progression, including the frequency of prototyping, which days the project participants are most active, and how the prototyping changes over time .
Aiming to help researchers capture output from the early stages of engineering design projects , this article presents a new research tool for digitally capturing physical prototypes. The motivation for this work is to collect observations that can aid in understanding prototyping in the early stages of engineering design projects, and this article investigates if and how digital capture of physical prototypes can be used for this purpose. Early-stage prototypes are usually rough and of low-fidelity and are thus often discarded or substantially modified through the projects. Hence, retrospective access to prototypes is a challenge when trying to gather accurate empirical data. To capture the prototypes developed through the early stages of a project, a new research tool has been developed for capturing prototypes through multi-view images, along with metadata describing by whom, why, when and where the prototypes were captured. Over the course of 17 months, this research tool has been used to capture more than 800 physical prototypes from 76 individual users across many projects. In this article, one project is shown in detail to demonstrate how this capturing system can gather empirical data for enriching engineering design project cases that focus on prototyping for concept generation. The authors also analyse the metadata provided by the system to give understanding into prototyping patterns in the projects. Lastly, through enabling digital capture of large quantities of data, the research tool presents the foundations for training artificial intelligence-based predictors and classifiers that can be used for analysis in engineering design research .
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early-stage Product Development (PD) activity
output from the early stages of engineering design projects
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method
research tool
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physical
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understand
collect observations that can aid in understanding
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PD
engineering design
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In PD case studies, such early-stage
Early-stage
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method
research tool
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digitally capturing physical
capturing
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who,
whom, why,
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Over the course of 17 months, this research tool has been used to capture more than 800 physical prototypes from 76 individual users across many projects.
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PD case studies on early-stage projects
engineering design project cases
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first approach is to use the multi-view images for a qualitative assessment of the projects, which can provide new insights and understanding on various aspects like design decisions, trade-offs and specifications. The second approach is to
authors also
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The analysis of metadata provides insight into prototyping progression, including the frequency of prototyping, which days the project participants are most active, and how the prototyping changes over time
Lastly, through enabling digital capture of large quantities of data, the research tool presents the foundations for training artificial intelligence-based predictors and classifiers that can be used for analysis in engineering design research