In August of this year, the DMF group attended ICED25 (The International Conference in Engineering Design) on the UT Dallas Campus. Isabelle Ormerod, Jonathan Raines, Owen Peckham, Nathan Morris, Chris Snider and Aman Kukrega all presented papers across the course of the conference.
A mixed-method approach in ergonomic analysis utilising personalised data dashboards
Isabelle Ormerod, Mike Fraser, Natalie Shortt, Chris Snider
How we gather individual data to inform product design is changing. In ergonomics, methodologies are rooted in qualitative approaches, providing holistic yet less objective insights. In this work, we explore novel quantitative techniques—including machine vision and muscle sensing—to create personalized data dashboards that enrich qualitative practices in a mixed-method design. We conducted a pilot study (n=10), evaluating participants’ motion in a simple ergonomic task, followed by interviews discussing the dashboards. A thematic analysis showed that all participants agreed the dashboards affirmed their experience. Furthermore, the order of data presentation influenced their language, affecting subjectivity and specificity. This study highlights participants’ roles as stakeholders, underscoring the need for their engagement to achieve meaningful design outcomes.
Scaling Generative Design for Production Through the Use of Standard Parts
Jonathan Raines, David Barton, and Ben Hicks
Generative Design (GD) tools can produce high-performing components with complex geometries that are challenging to conceive via traditional methods. While potentially disruptive, GD tools have yet to achieve widespread use in industry. One reason is that current GD tools are limited to manufacturing methods capable of producing intricate geometries that GD often creates such as 3D printing. To overcome this barrier, this paper quantifies the benefit of altering generatively designed parts to use standardized elements like wire stock and sheet metal via processes such as CNC bending and water jet cutting. Using a parametric cost model, we show that parts incorporating standard components can halve the unit price for production volumes of only 4 parts. Finite Element Analysis (FEA) reveals that replacing up to 60 % of part volume has minimal impact on performance. Our findings highlight a gap and opportunity in existing GD research.
Spatial computing in design: opportunities and challenges of a new technological paradigm
Chris Snider, Aman Kukreja, Chris Cox
Spatial Computing (SC), the use of technology to blur the boundaries between physical and digital into an efficient, intuitive, high performance set of tools, holds huge promise for engineering design. With dramatic and accelerating industry prominence but little research in the design field, there is a need to generalize and frame SC for design. This paper contributes an operational framework for Spatial Engineering (SE) systems highlighting the roles of physical and digital users, objects, environments, and data, and five capabilities required for implementation. It then identifies value propositions for SE evidenced from review of the design field, including design activities in which value is generated. Finally, it presents research opportunities centered on good practice, system interaction and technology, and balancing overhead with the value that these systems provide.
Lightweighting using shells: An exploration of generatively designed hollow structures
Owen Peckham, Harrison Mogg-Walls, Al Azhar Al Amri, Ben Hicks, Mark Goudswaard
Despite the lightweighting benefts that hollow structures afford, current Generative Design (GD) tools are not capable of synthesising them by default. This paper proposes an approach to generate hollow structures using an off-the-shelf GD tool and an innovative shelling method. The approach is used to create solid and hollow variants of a load bearing component. These are modelled using Finite Element Analysis (FEA) then Additively Manufactured (AM) and characterised via destructive load testing. FEA results show that the shelled structures are up to 2.5 x stiffer than their solid counterparts however destructive testing revealed small stiffness losses attributable to the AM process. Despite the physical testing results the method offers the potential to apply GD tools to industries where hollow tubes are accepted practice, enabling part consolidation capabilities to be leveraged.
A knowledge framework of environment reconstruction methods for mixed reality prototype applications
Aman Kukreja, Mattia Tromboni, Chris Cox, Chris Snider
Mixed reality prototypes are used for applications like design, analysis, and training. They combine high-fidelity overlays on low-fidelity tangible prototypes, giving users physical interactions in virtual environments. Suitable virtual environments are crucial in taking full advantage of these prototypes. However, there is a lack of guidance in the literature on choosing environment reconstruction methods for various applications. The rapid advancements in this area necessitate the characterisation of the reconstruction methods. This paper thus presents a novel knowledge framework for mapping the reconstruction methods with the requirements of MR prototype applications. The aim of the proposed framework is to help designers and engineers make informed decisions. The effectiveness of the framework has been illustrated using five reconstruction methods and testing via four case studies.
Enabling data-driven design by deriving consumer appliance use from household energy data
Nathan Morris, James Gopsill, Sindre Wold Eikevåg, Maria Valero, Ben Hicks
Achieving Net Zero requires designers to have a better understanding of the product use with studies showing user behaviour, cultural norms, seasonality and product interactions concomitantly dictate energy consumption. Data on product use can support data-driven design processes that have been shown to improve the efficiency of existing products. The paper reports a method that generates data for data-driven design processes from non-intrusive load monitoring (NILM) of household energy consumption data. The method produced appliance classification accuracies of 0.9984 while reducing sample size, sampling frequency and machine learning model complexity showing potential for it to be deployed at scale across communities.
Team Reflections
Isabelle Ormerod – “I had a great time at my first ICED, it was a great chance to network with academics across the world, and felt our work sparked lots of interesting conversations and potential for future collaborations that came from presenting. I especially appreciated the lingering remarks that our group are always keen to ask questions and engage in the conference as fully as possible. One of my highlights was the ‘Mind the Bias: Understanding, Identifying and Mitigating Cognitive Biases in Design’ workshop run on the first day of the conference, which was very well facilitated.
We got the joy of experiencing every Texan activity one could think to enjoy at the conference, from line-dancing at the conference dinner to eating as much brisket as you could fathom!”
Jonathan Raines – “The Young Member’s was well structured. They got everyone to wear a badge with a category representing their work on it, allowing us to mingle and find people working on similar projects. Likewise, the PhD Forum was a great opportunity to get feedback on my work from academics at the forefront.”
Nathan Morris – ICED25 at the University of Texas at Dallas was an incredible opportunity to explore state-of-the-art design research. I especially enjoyed learning about advancements in automated disassembly, digital product passports and machine learning in design. The conference has inspired new ideas for integrating sustainability and circular economy principles into my future design projects.”
