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Abstract Developments in artificial intelligence (AI) are opening the possibilities for the development of more advanced design tools. An example of these innovations are generative design tools, in which the generation of complex and high performing products is possible. This study investigates the use of generative design tools and how they may influence the design process and designer behaviour. Six interviews of interdisciplinary designers were conducted to understand the implications of using generative design tools. It was observed that generative design tools primarily allow for quantitative inputs to the tool while qualitative metrics, such as aesthetics, are considered indirectly by designers. The subjectivity of the designer and how they incorporate the quantitative and qualitative metrics in the generative design tool can lead to differing outcomes between designers. Notable differences in tool usage are also observed between expert and novice computational designers. Additional studies should be conducted to further understand the extent generative design tools impact the design process, designer behaviour, and design outcomes. 

Abstract Generative design tools empowered by recent advancements in artificial intelligence (AI) offer the opportunity for human designers and design tools to collaborate in new, more advanced modes throughout various stages of the product design process to facilitate the creation of higher performing and more complex products. This paper explores how the use of these generative design tools may impact the design process, designer behavior, and overall outcomes. Six in-depth interviews were conducted with practicing and student designers from different disciplines who use commercial generative design tools, detailing the design processes they followed. From a grounded theory-based analysis of the interviews, a provisional process diagram for generative design and its uses in the early-stage design process is proposed. The early stages of defining tool inputs bring about a constraint-driven process in which designers focus on the abstraction of the design problem. Designers will iterate through the inputs to improve both quantitative and qualitative metrics. The learning through iteration allows designers to gain a thorough understanding of the design problem and solution space. This can bring about creative applications of generative design tools in early-stage design to provide guidance for traditionally designed products. 

Abstract Nearly every artifact of the modern engineering design process is digitally recorded and stored, resulting in an overwhelming amount of raw data detailing past designs. Analyzing this design knowledge and extracting functional information from sets of digital documents is a difficult and time-consuming task for human designers. For the case of textual documentation, poorly written superfluous descriptions filled with jargon are especially challenging for junior designers with less domain expertise to read. If the task of reading documents to extract functional requirements could be automated, designers could actually benefit from the distillation of massive digital repositories of design documentation into valuable information that can inform engineering design. This paper presents a system for automating the extraction of structured functional requirements from textual design documents by applying state of the art Natural Language Processing (NLP) models. A recursive method utilizing Machine Learning-based question-answering is developed to process design texts by initially identifying the highest-level functional requirement, and subsequently extracting additional requirements contained in the text passage. The efficacy of this system is evaluated by comparing the Machine Learning-based results with a study of 75 human designers performing the same design document analysis task on technical texts from the field of Microelectromechanical Systems (MEMS). The prospect of deploying such a system on the sum of all digital engineering documents suggests a future where design failures are less likely to be repeated and past successes may be consistently used to forward innovation. 

Abstract Engineers design for an inherently uncertain world. In the early stages of design processes, they commonly account for such uncertainty either by manually choosing a specific worst-case and multiplying uncertain parameters with safety factors or by using Monte Carlo simulations to estimate the probabilistic boundaries in which their design is feasible. The safety factors of this first practice are determined by industry and organizational standards, providing a limited account of uncertainty; the second practice is time intensive, requiring the development of separate testing infrastructure. In theory, robust optimization provides an alternative, allowing set-based conceptualizations of uncertainty to be represented during model development as optimizable design parameters. How these theoretical benefits translate to design practice has not previously been studied. In this work, we analyzed the present use of geometric programs as design models in the aerospace industry to determine the current state-of-the-art, then conducted a human-subjects experiment to investigate how various mathematical representations of uncertainty affect design space exploration. We found that robust optimization led to far more efficient explorations of possible designs with only small differences in an experimental participant’s understanding of their model. Specifically, the Pareto frontier of a typical participant using robust optimization left less performance “on the table” across various levels of risk than the very best frontiers of participants using industry-standard practices.