Conceptual Design Generation Using Large Language Models

Concept generation is a creative step in the conceptual design phase, where designers often turn to brainstorming, mindmapping, or crowdsourcing design ideas to complement their own knowledge of the domain. Recent advances in natural language processing (NLP) and machine learning (ML) have led to the rise of Large Language Models (LLMs) capable of generating seemingly creative outputs from textual prompts. The success of these models has led to their integration and application across a variety of domains, including art, entertainment, and other creative work. In this paper, we leverage LLMs to generate solutions for a set of 12 design problems and compare them to a baseline of crowdsourced solutions. We evaluate the differences between generated and crowdsourced design solutions through multiple perspectives, including human expert evaluations and computational metrics. Expert evaluations indicate that the LLM-generated solutions have higher average feasibility and usefulness while the crowdsourced solutions have more novelty. We experiment with prompt engineering and find that leveraging few-shot learning can lead to the generation of solutions that are more similar to the crowdsourced solutions. These findings provide insight into the quality of design solutions generated with LLMs and begins to evaluate prompt engineering techniques that could be leveraged by practitioners to generate higher-quality design solutions synergistically with LLMs.Comment: Proceedings of the ASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference

ShipHullGAN : a generic parametric modeller for ship hull design using deep convolutional generative model

In this work, we introduce ShipHullGAN, a generic parametric modeller built using deep convolutional generative adversarial networks (GANs) for the versatile representation and generation of ship hulls. At a high level, the new model intends to address the current conservatism in the parametric ship design paradigm, where parametric modellers can only handle a particular ship type. We trained ShipHullGAN on a large dataset of 52,591 physically validated designs from a wide range of existing ship types, including container ships, tankers, bulk carriers, tugboats, and crew supply vessels. We developed a new shape extraction and representation strategy to convert all training designs into a common geometric representation of the same resolution, as typically GANs can only accept vectors of fixed dimension as input. A space-filling layer is placed right after the generator component to ensure that the trained generator can cover all design classes. During training, designs are provided in the form of a shape-signature tensor (SST) which harnesses the compact geometric representation using geometric moments that further enable the inexpensive incorporation of physics-informed elements in ship design. We have shown through extensive comparative studies and optimisation cases that ShipHullGAN can generate designs with augmented features resulting in versatile design spaces that produce traditional and novel designs with geometrically valid and practically feasible shapes

Investigating Decision Making in Engineering Design Through Complementary Behavioral and Cognitive Neuroimaging Experiments

<p>Decision-making is a fundamental process of human thinking and behavior. In engineering design, decision-making is studied from two different points of view: users and designers. User focused design studies tend to investigate ways to better inform the design process through the elicitation of preferences or information. Designer studies are broad in nature, but usually attempt to illustrate and understand some aspect of designer behavior, such as ideation, fixation, or collaboration. Despite their power, both qualitative and quantitative research methods are ultimately limited by the fact that they rely on direct input from the research participants themselves. This can be problematic, as individuals may not be able to accurately represent what they are truly thinking, feeling, or desiring at the time of the decision. A fundamental goal in both user- and designer-focused studies is to understand how the mind works while individuals are making decisions. This dissertation addresses these issues through the use of complementary behavioral and neuroimaging experiments, uncovering insights into how the mind processes design-related decision-making and the implications of those processes. To examine user decision-making, a visual conjoint analysis (preference modeling approach) was utilized for sustainable preference judgments. Here, a novel preference-modeling framework was employed, allowing for the real time calculation of dependent environmental impact metrics during individual choice decisions. However, in difficult moral and emotional decision-making scenarios, such as those involving sustainability, traditional methods of uncovering user preferences have proven to be inconclusive. To overcome these shortcomings, a neuroimaging approach was used. Specifically, study participants completed preference judgments for sustainable products inside of a functional magnetic resonance imaging (fMRI) scanner. Results indicated that theory of mind and moral reasoning processes occur during product evaluations involving sustainability. Designer decision-making was explored using an analogical reasoning and concept development experiment. First, a crowdsourcing method was used to obtain meaningful analogical stimuli, which were validated using a behavioral experiment. Following this, fMRI was used to uncover the neural mechanisms associated with analogical reasoning in design. Results demonstrated that analogies generally benefit designers; particularly after significant time on idea generation has taken place. Neuroimaging data helped to show two distinct brain activation networks based upon reasoning with and without analogies. We term these fixation driven external search and analogically driven internal search.. Fixation driven external search shows designers during impasse, as increased activation in brain regions associated with visual processing causes them to direct attention outward in search of inspiration. Conversely, during analogically driven internal search, significant areas of activation are observed in bilateral temporal and left parietal regions of the brain. These brain regions are significant, as prior research has linked them to semantic word-processing, directing attention to memory retrieval, and insight during problem solving. It is during analogically driven internal search that brain activity shows the most effective periods of ideation by participants.</p

Uncovering hidden patterns of design ideation using hidden Markov modeling and neuroimaging

Abstract: The study presented in this paper applies hidden Markov modeling (HMM) to uncover the recurring patterns within a neural activation dataset collected while designers engaged in a design concept generation task. HMM uses a probabilistic approach that describes data (here, fMRI neuroimaging data) as a dynamic sequence of discrete states. Without prior assumptions on the fMRI data's temporal and spatial properties, HMM enables an automatic inference on states in neurocognitive activation data that are highly likely to occur in concept generation. The states with a higher likelihood of occupancy show more activation in the brain regions from the executive control network, the default mode network, and the middle temporal cortex. Different activation patterns and transfers are associated with these states, linking to varying cognitive functions, for example, semantic processing, memory retrieval, executive control, and visual processing, that characterize possible transitions in cognition related to concept generation. HMM offers new insights into cognitive dynamics in design by uncovering the temporal and spatial patterns in neurocognition related to concept generation. Future research can explore new avenues of data analysis methods to investigate design neurocognition and provide a more detailed description of cognitive dynamics in design

Attention Affordances: Applying Attention Theory to the Design of Complex Visual Interfaces

The design of visual interfaces plays a crucial role in ensuring swift and accurate information search for operators, who use procedures and information tables to cope with problems arising during emergencies. The primary cognitive mechanism involved in information search is visual attention. However, design of interfaces is seldom done through applying predictions of theories of attention. Conversely, theories of attention are seldom tested in applied contexts. Combining application and attention research thus stands to benefit both fields. Therefore, this study tested three theories of visual attention that are especially relevant for information processing in emergencies—Load Theory, Feature Integration Theory, and Dilution Theory—as well as predictions about attentional guidance and capture of color in a complex visual interface. Evidence was found for several predictions from theory, especially from Feature Integration Theory. Implications for design practice and attention research are discusse

Data on human decision, feedback, and confidence during an artificial intelligence-assisted decision-making task

The data are collected from a human subjects study in which 100 participants solve chess puzzle problems with artificial intelligence (AI) assistance. The participants are assigned to one of the two experimental conditions determined by the direction of the change in AI performance at problem 20: 1) high- to low-performing and 2) low- to high-performing. The dataset contains information about the participants’ move before an AI suggestion, the goodness evaluation score of these moves, AI suggestion, feedback, and the participants’ confidence in AI and self-confidence during three initial practice problems and 30 experimental problems. The dataset contains 100 CSV files, one per participant. There is opportunity for this dataset to be utilized in various domains that research human-AI collaboration scenarios such as human-computer interaction, psychology, computer science, and team management in engineering/business. Not only can the dataset enable further cognitive and behavioral analysis in human-AI collaboration contexts but also provide an experimental platform to develop and test future confidence calibration methods

Framing and tracing human-centered design teams' method selection: an examination of decision-making strategies

Designers’ choices of methods are well known to shape project outcomes. However, questions remain about why design teams select particular methods and how teams’ decision-making strategies are influenced by project- and process-based factors. In this mixed-methods study, we analyze novice design teams’ decision-making strategies underlying 297 selections of human-centered design methods over the course of three semester-long project-based engineering design courses. We propose a framework grounded in 100+ factors sourced from new product development literature that classifies design teams’ method selection strategy as either Agent- (A), Outcome- (O), or Process- (P) driven, with eight further subclassifications. Coding method selections with this framework, we uncover three insights about design team method selection. First, we identify fewer outcomes-based selection strategies across all phases and innovation types. Second, we observe a shift in decision-making strategy from user-focused outcomes in earlier phases to product-based outcomes in later phases. Third, we observe that decision-making strategy produces a greater heterogeneity of method selections as compared to the class average as a whole, or project type alone. These findings provide a deeper understanding of designers’ method selection behavior and have implications for effective management of design teams, development of automated design support tools to aid design teams, and curation of design method repositoriesMarketing and Consumer Researc

Supporting human-centered design in psychologically distant problem domains: The design for cybersecurity cards

Increasingly digital products and services make cybersecurity a crucial issue for designers. However, human-centered designers struggle to consider it in their work, partially a consequence of the high psychological distance between designers and cybersecurity. In this work, we build on the Design for Cybersecurity (DfC) Cards, an intervention to help designers consider cybersecurity, and examine a project-based design course to understand how and why specific DfC cards were used. Three findings result. First, designers found the intervention useful across all design phases and activities. Second, the cards helped design teams refocus their attention on the problem domain and project outcome. Third, we identify a need for support in framing and converging during user research, opportunity identification, and prototyping. We argue that the psychological distance between designers and the problem space of cybersecurity partially explains these findings, and ultimately exacerbates existing challenges in the design process. These findings suggest that design interventions must consider the psychological distance between designer and problem space, and have application in design practice across many complex problem domains.Marketing and Consumer Researc