ccm/publications huggingface dataset:
Conceptual Design Generation Using Large Language Models
Authors: Kevin Ma and Daniele Grandi and Christopher McComb and Kosa Goucher-Lambert
Published in: International Design Engineering Technical Conferences and Computers and …, 2023

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 …
When faced with increasing complexity: The effectiveness of AI assistance for drone design
Authors: Binyang Song and Nicolas F Soria Zurita and Hannah Nolte and Harshika Singh and Jonathan Cagan and Christopher McComb
Published in: Journal of Mechanical Design, 2022

As artificial intelligence (AI) assistance tools become more ubiquitous in engineering design, it becomes increasingly necessary to understand the influence of AI assistance on the design process and design effectiveness. Previous work has shown the advantages of incorporating AI design agents to assist human designers. However, the influence of AI assistance on the behavior of designers during the design process is still unknown. This study examines the differences in participants’ design process and effectiveness with and without AI assistance during a complex drone design task using the HyForm design research platform. Data collected from this study are analyzed to assess the design process and effectiveness using quantitative methods, such as hidden Markov models and network analysis. The results indicate that AI assistance is most beneficial when addressing moderately complex objectives but …
Using Hidden Markov Models to Uncover Underlying States in Neuroimaging Data for a Design Ideation Task
Authors: Kosa Goucher-Lambert and Christopher McComb
Published in: International Conference on Engineering Design, 2019

Recently, design researchers have begun to use neuroimaging methods (e.g., functional magnetic resonance imaging, fMRI) to understand a variety of cognitive processes relevant to design. However, common neuroimaging analysis techniques require significant assumptions relating temporal and spatial information during model formulation. In this work, we apply hidden Markov Models (HMM) in order to uncover patterns of brain activation in a design-relevant fMRI dataset. The underlying fMRI data comes from a prior research study in which participants generated solutions for twelve open-ended design problems from the literature. HMMs are generative models that are able to automatically infer the internal state characteristics of a process by observing state emissions. In this work, we demonstrate that distinct states can be extracted from the design ideation fMRI dataset, and that designers are likely to …
A Conceptual Framework for Multidisciplinary Design Research with Example Application to Agent-Based Modeling
Authors: Christopher McComb and Kathryn Jablokow
Published in: Design Studies, 2022

The design community traditionally excels at multidisciplinary research efforts, integrating work from social science, engineering, computer science, and other disciplines. Simultaneously, design is also developing as a discipline in its own right. In order to balance a multidisciplinary 1 tradition against consolidation around a core set of disciplinary foundations, the design community must make intentional decisions about engaging in multidisciplinary research. Towards this end, we propose the Degrees of Disciplinarity Framework, a lingua franca for describing varying relationships between disciplines in a given research effort. We also introduce research archetypes in relation to this conceptual …
Using Workplace Thriving Theory to Investigate First-Year Engineering Students’ Abilities to Thrive during the Transition to Online Learning due to COVID-19
Authors: Sandeep Krishnakumar and Torsten Maier and Catherine GP Berdanier and Sarah Ritter and Christopher McComb and Jessica Menold
Published in: Journal of Engineering Education, 2022

During the onset of the COVID‐19 crisis, universities rapidly pivoted to online formats and were often unable to adhere to the best practices of online learning highlighted in prior literature. It is well documented that a variety of barriers impeded “normal” educational practices.The purpose of this paper is to investigate the perceptions of first‐year engineering students enrolled in an introductory engineering design course during the rapid transition to online working environments. We view students' perceptions through the theoretical lens of workplace thriving theory, a framework that allowed us to capture aspects of education required for students to thrive in non‐optimum learning settings.This research employed semi‐structured interview methods with 13 students enrolled in an introductory engineering design course that relies on project‐based team learning. We …
Using Physics-Informed Generative Adversarial Networks to Perform Super-Resolution for Multiphase Fluid Simulations
Authors: Matthew Li and Christopher McComb
Published in: Journal of Computing and Information Science in Engineering, 2022

Computational fluid dynamics (CFD) simulations are useful in the field of engineering design as they provide deep insights on product or system performance without the need to construct and test physical prototypes. However, they can be very computationally intensive to run. Machine learning methods have been shown to reconstruct high-resolution single-phase turbulent fluid flow simulations from low-resolution inputs. This offers a potential avenue towards alleviating computational cost in iterative engineering design applications. However, little work thus far has explored the application of machine learning image super-resolution methods to multiphase fluid flow (which is important for emerging fields such as marine hydrokinetic energy conversion). In this work, we apply a modified version of the super-resolution generative adversarial network (SRGAN) model to a multiphase turbulent fluid flow problem …
Mining Design Heuristics for Additive Manufacturing Via Eye-Tracking Methods and Hidden Markov Modeling
Authors: Priyesh Mehta and Manoj Malviya and Christopher McComb and Guha Manogharan and Catherine GP Berdanier
Published in: Journal of Mechanical Design 142 (12), 2020

In this research, we collected eye-tracking data from nine engineering graduate students as they redesigned a traditionally manufactured part for additive manufacturing (AM). Final artifacts were assessed for manufacturability and quality of final design, and design behaviors were captured via the eye-tracking data. Statistical analysis of design behavior duration shows that participants with more than 3 years of industry experience spend significantly less time removing material and revising than those with less experience. Hidden Markov modeling (HMM) analysis of the design behaviors gives insight to the transitions between behaviors through which designers proceed. Findings show that high-performing designers proceeded through four behavioral states, smoothly transitioning between states. In contrast, low-performing designers roughly transitioned between states, with moderate transition probabilities …
A Strategic Decision-making Architecture Toward Hybrid Teams for Dynamic Competitive Problems
Authors: Alparslan Emrah Bayrak and Christopher McComb and Kenneth Kotovsky and Jonathan Cagan
Published in: Decision Support Systems, 2021

Advances in artificial intelligence create new opportunities for computers to support humans as peers in hybrid teams in several complex problem-solving situations. This paper proposes a decision-making architecture for adaptively informing decisions in human-computer collaboration for large-scale competitive problems under dynamic environments. The proposed architecture integrates methods from sequence learning, model predictive control, and game theory. Computers in this architecture learn objectives and strategies from experimental data to support humans with strategic decisions while operational decisions are made by humans. The paper also presents data-driven methods for partitioning tasks among a team of computers in this architecture. The generalized methodology is illustrated on the real-time strategy game Starcraft II. The results from this application show that low-performing players can …
Can Design Teams Be Empathically Creative? A Simulation-Based Investigation on the Role of Team Empathy on Concept Generation and Selection
Authors: Mohammad Alsager Alzayed and Scarlett R Miller and Jessica Menold and Jacquelyn Huff and Christopher McComb
Published in: ASME 2020 International Design Engineering Technical Conferences and …, 2020

Research on empathy has been surging in popularity in the engineering design community since empathy is known to help designers develop a deeper understanding of the users’ needs. Because of this, the design community has been invested in devising and assessing empathic design activities. However, research on empathy has been primarily limited to individuals, meaning we do not know how it impacts team performance, particularly in the concept generation and selection stages of the design process. Specifically, it is unknown how the empathic composition of teams, average (elevation) and standard deviation (diversity) of team members’ empathy, would impact design outcomes in the concept generation and selection stages of the design process. Therefore, the goal of the current study was to investigate the impact of team trait empathy on concept generation and selection in an engineering …
Drawing Inspiration From Human Design Teams For Better Search And Optimization: The Heterogeneous Simulated Annealing Teams Algorithm
Authors: Christopher McComb and Jonathan Cagan and Kenneth Kotovsky
Published in: Journal of Mechanical Design, 2016

Insights uncovered by research in design cognition are often utilized to develop methods used by human designers; in this work, such insights are used to inform and improve computational methodologies. This paper introduces the heterogeneous simulated annealing team (HSAT) algorithm, a multiagent simulated annealing (MSA) algorithm. HSAT is based on a validated computational model of human-based engineering design and retains characteristics of the model that structure interaction between team members and allow for heterogeneous search strategies to be employed within a team. The performance of this new algorithm is compared to several other simulated annealing (SA) based algorithms on three carefully selected benchmarking functions. The HSAT algorithm provides terminal solutions that are better on average than other algorithms explored in this work.
Artificial intelligence and engineering design
Authors: James T Allison and Michel-Alexandre Cardin and Chris McComb and Max Yi Ren and Daniel Selva and Conrad Tucker and Paul Witherell and Yaoyao Fiona Zhao
Published in: Journal of Mechanical Design 144 (2), 020301, 2022

Artificial intelligence (AI) has had a strong presence in engineering design for decades, and while theory, methods, and tools for engineering design have advanced significantly during this time, many grand challenges remain. Modern advancements in AI, including new strategies for capturing, storing, and analyzing data, have the potential to revolutionize engineering design processes in a variety of ways. The purpose of this special issue is to consolidate recent research activities that utilize existing or new AI methods to advance engineering design knowledge and capabilities.During the conception of this special issue, we identified three core interfaces between the research domains of engineering design and AI:(1) leveraging AI methods directly in engineering design methods,(2) creating new AI capabilities that are inspired by unique challenges that arise in engineering design, and (3) creating and analyzing …
Design strategy network: a deep hierarchical framework to represent generative design strategies in complex action spaces
Authors: Ayush Raina and Jonathan Cagan and Christopher McComb
Published in: Journal of Mechanical Design 144 (2), 021404, 2022

Generative design problems often encompass complex action spaces that may be divergent over time, contain state-dependent constraints, or involve hybrid (discrete and continuous) domains. To address those challenges, this work introduces Design Strategy network (DSN), a data-driven deep hierarchical framework that can learn strategies over these arbitrary complex action spaces. The hierarchical architecture decomposes every action decision into first predicting a preferred spatial region in the design space and then outputting a probability distribution over a set of possible actions from that region. This framework comprises a convolutional encoder to work with image-based design state representations, a multi-layer perceptron to predict a spatial region, and a weight-sharing network to generate a probability distribution over unordered set-based inputs of feasible actions. Applied to a truss design study …
Expanding the Solution Space in Engineering Design Education: A Simulation-Based Investigation of Product Dissection
Authors: Mohammad Alsager Alzayed and Christopher McComb and Samuel T Hunter and Scarlett R Miller
Published in: Journal of Mechanical Design 141 (3), 032001, 2019

Product dissection has been highlighted as an effective means of interacting with example products in order to produce creative outcomes. While product dissection is often conducted as a team in engineering design education, the research on the effectiveness of product dissection activities has been primarily limited to individuals. Thus, the purpose of this study was to investigate the impact of the type(s) of product dissected in a team environment on encouraging creative design outcomes (variety, novelty, and quantity) and the underlying influence of educational level and dissection modality on these effects. This was accomplished through a computational simulation of 14,000 teams of noninteracting brainstorming individuals generated by a statistical bootstrapping technique using a design repository of 931 ideas generated by first-year and senior engineering students. The results of the study highlight the …
A Multi-Agent Reinforcement Learning Framework for Intelligent Manufacturing with Autonomous Mobile Robots
Authors: Akash Agrawal and Sung Jun Won and Tushar Sharma and Mayuri Deshpande and Christopher McComb
Published in: International Conference on Engineering Design, 2021

Intelligent manufacturing (IM) embraces Industry 4.0 design principles to advance autonomy and increase manufacturing efficiency. However, many IM systems are created ad hoc, which limits the potential for generalizable design principles and operational guidelines. This work offers a standardizing framework for integrated job scheduling and navigation control in an autonomous mobile robot driven shop floor, an increasingly common IM paradigm. We specifically propose a multi-agent framework involving mobile robots, machines, humans. Like any cyberphysical system, the performance of IM systems is influenced by the construction of the underlying software platforms and the choice of the constituent algorithms. In this work, we demonstrate the use of reinforcement learning on a sub-system of the proposed framework and test its effectiveness in a dynamic scenario. The case study demonstrates collaboration …
"Thus, I had to go with what I had": A Multiple Methods Exploration of Novice Designers Articulation of Prototyping Decisions
Authors: Jessica Menold and Catherine GP Berdanier and Christopher McComb and Emma Hocker and Lisa Gardner
Published in: ASME 2018 International Design Engineering Technical Conferences and …, 2018

Effectively communicating designs to stakeholders or end users is a critical step in the design process yet can be a difficult challenge for engineers. Prototypes are unique tools that can enhance communication between these two groups, as prototypes are physical manifestations of the designer’s mental model. Previous work has demonstrated that novice designers often struggle to use prototypes as communication tools. We argue that it is critical that engineering students learn to fully leverage prototypes, and thus the current work sought to understand the relationship between argumentation, prototyping, and design decisions. In order to understand the communication patterns of novice designers during a prototyping task, a controlled study was conducted with a total of 46 undergraduate engineering students. The analysis of quantitative and qualitative data point to the intricate linkages between how students …
Goal-Directed Design Agents: Integrating Visual Imitation with One-Step Lookahead Optimization for Generative Design
Authors: Ayush Raina and Lucas Puentes and Jonathan Cagan and Christopher McComb
Published in: Journal of Mechanical Design, 2021

Engineering design problems often involve large state and action spaces along with highly sparse rewards. Since an exhaustive search of those spaces is not feasible, humans utilize relevant domain knowledge to condense the search space. Deep learning agents (DLAgents) were previously introduced to use visual imitation learning to model design domain knowledge. This note builds on DLAgents and integrates them with one-step lookahead search to develop goal-directed agents capable of enhancing learned strategies for sequentially generating designs. Goal-directed DLAgents can employ human strategies learned from data along with optimizing an objective function. The visual imitation network from DLAgents is composed of a convolutional encoder–decoder network, acting as a rough planning step that is agnostic to feedback. Meanwhile, the lookahead search identifies the fine-tuned design …
The Cognitive Experience of Engineering Design: An Examination of First-Year Student Stress Across Principal Activities of the Engineering Design Process
Authors: Hannah Nolte and Christopher McComb
Published in: Design Science, 2021

The engineering design process can produce stress that endures even after it has been completed. This may be particularly true for students who engage with the process as novices. However, it is not known how individual components of the design process induce stress in designers. This study explored the cognitive experience of introductory engineering design students during concept generation, concept selection and physical modelling to identify stress signatures for these three design activities. Data were collected for the design activities using pre- and post-task surveys. Each design activity produced distinct markers of cognitive experience and a unique stress signature that was stable across design activity themes. Rankings of perceived sources of stress also differed for each design activity. Students, however, did not perceive any physiological changes due to the stress of design for any of the design …
Impossible by design? Fairness, strategy, and Arrow’s impossibility theorem
Authors: Christopher McComb and Kosa Goucher-Lambert and Jonathan Cagan
Published in: Design Science, 2017

The design process often requires work by teams, rather than individuals. During team based design it is likely that situations will arise in which individual members of the team have different opinions, yet a group decision must still be made. Unfortunately, Arrow’s impossibility theorem indicates that there is no method for aggregating group preferences that will always satisfy a small number of ‘fair’ conditions. This work seeks to identify methods of combining individual preferences that can come close to satisfying Arrow’s conditions, enabling decisions that are fairer in practice. First, experiential conjoint analysis was used to obtain individual empirical utility functions for drinking mug designs. Each empirical utility function represented individual members who were part of a design team. Then, a number of functions for constructing group preference were analysed using both randomly generated preferences and …
Detection Of System Compromise In Additive Manufacturing Using Video Motion Magnification
Authors: Sakthi Kumar Arul Prakash and Tobias Mahan and Glen Williams and Christopher McComb and Jessica Menold and Conrad Tucker
Published in: Journal of Mechanical Design, 2020

Three-dimensional printing systems have expanded the access to low cost, rapid methods for attaining physical prototypes or products. However, a cyber attack, system error, or operator error on a 3D-printing system may result in catastrophic situations, ranging from complete product failure, to small types of defects which weaken the structural integrity of the product. Such defects can be introduced early-on via solid models or through G-codes for printer movements at a later stage. Previous works have studied the use of image classifiers to predict defects in real-time and offline. However, a major restriction in the functionality of these methods is the availability of a dataset capturing diverse attacks on printed entities or the printing process. This paper introduces an image processing technique that analyzes the amplitude and phase variations of the print head platform arising through induced system …
Design Strategy Transfer in Cognitively-Inspired Agents
Authors: Ayush Raina and Christopher McComb and Jonathan Cagan
Published in: ASME 2018 International Design Engineering Technical Conferences and …, 2018

Planning and strategizing are essential parts of the design process and are based on the designer’s skill. Further, planning is an abstract skill that can be transferred between similar problems. However, planning and strategy transfer within design have not been effectively modeled within computational agents. This paper presents an approach to represent this strategizing behavior using a probabilistic model. This model is employed to select the operations that computational agents should perform while solving configuration design tasks. This work also demonstrates that this probabilistic model can be used to transfer strategies from human data to computational agents in a way that is general and useful. This study shows a successful transfer of design strategy from human-to-computer agents, opening up the possibility of deriving high-performing behavior from designers and using it to guide computational …
Synthesis of physical processes of permafrost degradation and geophysical and geomechanical properties of permafrost
Authors: Min Liew and Xiaohang Ji and Ming Xiao and Louise Farquharson and Dmitry Nicolsky and Vladimir Romanovsky and Matthew Bray and Xiong Zhang and Christopher McComb
Published in: Cold Regions Science and Technology 198, 103522, 2022

Recent permafrost degradation across the high northern latitude regions has impacted the performance of the civil infrastructure. This study summarizes the current state of physical processes of permafrost degradation in a geotechnical context and the properties of permafrost-affected soils critical for evaluating the performance of infrastructures commonly built in the high northern latitude regions. We collected a total of 96 datasets with 3162 data points from 38 journal and conference publications and analyzed the variations of geomechanical and geophysical properties under the effects of permafrost degradation. The datasets represent a range of geomechanical and geophysical properties of permafrost-affected soils with different compositions under different testing conditions. While the data collected are highly scattered, regression analysis shows that most geomechanical and geophysical properties have …