ccm/publications huggingface dataset:
Self Learning Design Agent (SLDA): Enabling Deep Learning and Tree Search in Complex Action Spaces
Authors: Ayush Raina and Jonathan Cagan and Christopher McComb
Published in: ASME 2022 International Design Engineering Technical Conferences and …, 2022

Building an AI agent that can design on its own has been a goal since the 1980s. Recently, deep learning has shown the ability to learn from large-scale data, enabling significant advances in data-driven design. However, learning over prior data limits us only to solve problems that have been solved before and biases us towards existing solutions. The ultimate goal for a design agent is the ability to learn generalizable design behavior in a problem space without having seen it before. We introduce a self-learning agent framework in this work that achieves this goal. This framework integrates a deep policy network with a novel tree search algorithm, where the tree search explores the problem space, and the deep policy network leverages self-generated experience to guide the search further. This framework first demonstrates an ability to discover high-performing generative strategies without any prior data …
When the Going Gets Tough: Exploring Changes in the Startup Landscape Due to the Challenges of 2020
Authors: Tobias Mahan and Pratima Saravanan and Sandeep Krishnakumar and Hannah Nolte and Christopher McComb and Jessica Menold
Published in: ASME 2021 International Design Engineering Technical Conferences and …, 2021

Crowdfunding platforms like Kickstarter provide new product developers with a novel and readily accessible platform to interact with potential customers, seek funding, and gain valuable feedback from target users. Successful startup design processes are often praised due to startups’ agility and ability to adapt to changing markets under extreme resource constraints. The COVID-19 pandemic changed the market in a number of significant ways, and publicly available data on Kickstarter provides valuable insight into the fundamental ways that product developers adapt to volatiles markets created by a global crisis. Leveraging linguistic analysis, topic modelling, k-means clustering, and principal component analysis, startup data was analyzed to identify overarching trends in startup launch campaigns across years. Specifically, this work identifies emerging trends in the Kickstarter market in 2020 to understand how …
Engineering Futures: Updating a Successful Professional Development Program to Address New Challenges
Authors: Katy Luchini-Colbry and Christopher McComb and Julie Rojewski and Astri Briliyanti and Dirk Colbry
Published in: Proceedings of the 2019 ASEE Annual Conference & Exposition, 2019

Engineering Futures (EF) is a professional development program developed by Tau Beta Pi, the Engineering Honor Society, in the 1980s to provide undergraduate engineering students with the “soft skills” necessary for professional success. Originally, the EF program included a series of day-long, interactive workshops led by volunteer facilitators and hosted on-site at college campuses. The original sessions included People Skills (interpersonal problem communication and resolution); Team Chartering (understanding team dynamics); Group Process (tools for effective meetings); and Analytical Problem Solving (brainstorming, list reduction and evaluation criteria). Over the decades, the EF program adapted to meet the changing needs of undergraduate students, with options for shorter sessions and the addition of a module on Effective Presentation Skills in the early 2000s. In the 2010s, the EF program directors began to explore opportunities to expand the curriculum to address new challenges. A new partnership in 2015 led to the addition of two new modules: Equity, Inclusion & Engineering Ethics; and Research Mentoring. In 2017, Tau Beta Pi partnered with several other organizations in a successful proposal to the National Science Foundation to develop updated training materials focusing on communications, teamwork and leadership skills. These materials are being designed in a modular fashion that allows them to be adjusted for different audiences (undergraduates, graduate students, professionals) and the project includes funding for a “train the trainers” program that will enable the EF materials to be deployed nationally at little or …
On The Analysis Of A Compromised Additive Manufacturing System Using Spatio-Temporal Decomposition
Authors: Sakthi Kumar Arul Prakash and Tobias Mahan and Glen Williams and Christopher McComb and Jessica Menold and Conrad Tucker
Published in: ASME 2019 International Design Engineering Technical Conferences and …, 2019

3D 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, making it unreliable for its intended use. 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 as a print is in progress and also by studying the printed entity once the print is complete. 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 a visual inspection technique that analyzes the …
Capturing Local Temperature Evolution during Additive Manufacturing through Fourier Neural Operators
Authors: Jiangce Chen and Martha Xu and Wenzhuo and Baldwin and Bjorn Nijhuis and Ton van den Boogaard and Noelia Grande Gutierrez and Sneha Prabha Narra and Christopher McComb
Published in: Journal of Manufacturing Science and Engineering, 2024

High-fidelity, data-driven models that can quickly simulate thermal behavior during additive manufacturing (AM) are crucial for improving the performance of AM technologies in multiple areas, such as part design, process planning, monitoring, and control. However, complexities of part geometries make it challenging for current models to maintain high accuracy across a wide range of geometries. In addition, many models report a low mean-square error (MSE) across the entire domain of a part. However, in each time-step, most areas of the domain do not experience significant changes in temperature, except for the regions near recent depositions. Therefore, the MSE-based fidelity measurement of the models may be overestimated. This article presents a data-driven model that uses the Fourier neural operator to capture the local temperature evolution during the AM process. Besides MSE, the model is also …
Skeleton-based Human Action Recognition in a Thermal Comfort Context
Authors: John Martins and Katherine Flanigan and Christopher McComb
Published in: 10th ACM International Conference on Systems for Energy-Efficient Buildings …, 2023

 Thermal comfort optimization is key to ensuring the well-being of building occupants and promoting intelligent energy use in the built environment. In the current space of thermal comfort analysis, many techniques are used ranging from more intrusive wearable sensors and qualitative occupant surveys to less intrusive infrared thermal monitoring and human action recognition (HAR). However, as these methods increase surveillance of building occupants in often complex environments, privacy preservation and accurate analysis are essential for optimal thermal control. This paper focuses on uplifting the ability of skeleton-based HAR for use in thermal comfort-related action recognition as this method has been shown to have promising action recognition accuracy while maintaining user privacy. While we focus here on thermal comfort, our analysis and emphasis on the use and advancement of privacy-preserving …
Investigating the Relationship between Mindfulness, Stress, and Creativity in Introductory Engineering Design
Authors: Hannah Nolte and Nicolas F Soria Zurita and Elizabeth Starkey and Christopher McComb
Published in: Design Science, 2023

Mindfulness-based interventions (MBIs) for engineering design are promising for their stress management, cognition and well-being benefits. Prior work concluded that engineering design is stressful and that each engineering design stage has unique stressors. This non-randomized study investigated the effect of an MBI on students’ cognitive stress and final design creativity during a multistage, hands-on design assessment. Data were collected using surveys, project deliverables and follow-up interviews. While no significant increase was found in students’ measured state mindfulness due to the intervention, students in the MBI condition were more likely to perceive the intervention positively compared to students in the control condition (alternative use tasks). Students in both conditions were found to have similar levels of state stress, which indicates that the MBI had no observable effect on students’ measured …
Independence or Interaction? Understanding the Benefits and Limitations of Nominally Inspired and Interacting Sub-Structured Teams in a Virtual and Interdisciplinary Engineering Design Task
Authors: Joshua T Gyory and Nicolas F Soria Zurita and Jonathan Cagan and Christopher McComb
Published in: Journal of Mechanical Design, 2023

Teams are common throughout engineering practice and industry when solving complex, interdisciplinary problems. Previous works in engineering problem solving have studied the effectiveness of teams and individuals, showing that in some circumstances, individuals can outperform collaborative teams working on the same task. The current work extends these insights to novel team configurations in virtual, interdisciplinary teams. In these team configurations, the whole meta-team can interact, but the sub-teams within them may or may not. Here, team performance and process are studied within the context of a complex drone design and path-planning problem. Via a collaborative research platform called HyForm, communication and behavioral patterns can be tracked and analyzed throughout problem solving. This work shows that nominally inspired sub-structured teams, where members work …
Design for Advanced Manufacturing
Authors: Yaoyao Fiona Zhao and Stefanie Elgeti and James K. Guest and Christopher McComb and Julián Norato and Deepesh Toshniwal
Published in: Journal of Mechanical Design, 2023

Design and manufacturing are intimately coupled in the conceptualization and realization of products. Engineering design is regularly a key component of innovation and successful product development, yet promising designs that are very costly or challenging to manufacture may never be realized in practice. Advancement of manufacturing technologies tends to lower barriers to realization and expands design freedom but may also introduce unique restrictions on the design space. A clear example of this connection is the maturation of additive manufacturing technologies, which has created immense design freedom for both researchers and practitioners to explore. New capabilities, such as the ability to fabricate complex shapes, tailor materials and properties, and provide complex functionalities, present a whole new paradigm and a wider range of opportunities for intricately design, highly functional, cost …
Idea Selection in Design Teams: A Computational Framework and Insights in the Presence of Influencers
Authors: Harshika Singh and Gaetano Cascini and Christopher McComb
Published in: Design Science, 2022

Idea selection is crucial in design as it impacts the outcome of a project. A collaborative design activity could be considered as a social process where the interactions and individual states (such as the importance in the team and self-efficacy level) could affect decision-making. It is often seen in design teams that some individuals, referred to as ‘influencers’ in the article have more capacity to influence than others, hence they govern the team process for better or worse. Due to the limited work done in the past to study the effect of these influencers on design outcomes, the work aims at increasing the understanding by presenting some insights from its agent-based simulation. The simulation results show how different influencer team compositions affect design outcomes in terms of quality and exploration of the solutions. The idea selection starts with the agents who are ready with their solution in their ‘mind’. The work …
Towards Characterizing Cognitive Style Coping Behavior in Engineering Design
Authors: Noriana Radwan and Jessica Menold and Jesse McTernan and Kathryn Jablokow and Christopher McComb
Published in: ASME 2022 International Design Engineering Technical Conferences and …, 2022

Problem solving is a crucial aspect of engineering design, and cognitive style, or preferred problem-solving style, is an important way to understand designers’ individual behavior. When there is significant misalignment between the problem frame and one’s cognitive style, it results in cognitive gaps. Large cognitive gaps may hinder effective problem solving. According to Kirton’s adaption-innovation theory, such gaps are managed through coping behavior. However, coping behavior is understudied in the literature, especially when assessing person-problem gaps. We argue that a deeper understanding of these gaps and its resulting coping strategies in problem solving can aid in facilitating effective design. Therefore, the current study was developed to understand how cognitive style and the problem frame impacts solution outcomes; and identify the design strategies and behaviors that are elicited by …
Analyzing Expert Design Cost Estimation for Additive Manufacturing
Authors: Glen Williams and Nicholas A Meisel and Timothy W Simpson and Christopher McComb
Published in: ASME 2022 International Design Engineering Technical Conferences and …, 2022

Compared to conventional fabrication, additive manufacturing enables production of far more complex geometries with less tooling and increased automation. However, despite the common perception of AM’s “free” geometric complexity, this freedom comes with a literal cost: more complex geometries may be challenging to design, potentially manifesting as increased engineering labor cost. Being able to accurately predict design cost is essential to reliably forecasting large-scale design for additive manufacturing projects, especially for those using expensive processes like laser powder bed fusion of metals. However, no studies have quantitatively explored designers’ ability to complete this forecasting. In this study, we address this gap by analyzing the uncertainty of expert design cost estimation. First, we establish a methodology to translate computer-aided design data into descriptive vectors capturing …
Identification of the Barriers to Data-Centric Approach in the Construction Industry
Authors: Ali Karji and John Messner and Robert Leicht and Christopher McComb
Published in: 2022 Construction Research Congress, 2022

With the advancement of technology, many industries are improving their workflows and productivity by embracing digital transformation, and specifically data-centric approaches. Construction, however, is often slower to adopt new technology and innovations. A data-centric approach can enrich project delivery value and streamline processes through applications of data science and emerging innovations, such as artificial intelligence. This study aims to identify the barriers to adopting data-centric approaches in the construction industry. Initially, a literature review on Building Information Modeling (BIM) implementation identified 35 barriers. Next, the barriers were organized and refined to be more specific to a data-centric approach with the help of expert focus group meetings. Finally, the barriers were categorized based on experts’ opinions into five categories: Process, Information, Financial, People, and Risk. The …
Agents and the Arctic: The Case for Increased Use of Agent-Based Modeling to Study Permafrost
Authors: Sierra Hicks and Lilian Alessa and Christopher McComb
Published in: https://www.thearcticinstitute.org/agents-arctic-case-increased-use-agent …, 2021

BackgroundPermafrost refers to land (soil, sand, rock, and ice) which has remained below 0 degrees Celsius for two or more consecutive years. Mostly found within polar regions and high mountain ranges, permafrost underlies nearly a quarter of the northern hemisphere, extending up to 1.7 kilometers into the Earth. Permafrost can be up to 70 percent ice, the thawing of which results in damage to the built environment and, ultimately, the lives of people who are part of Arctic communities. On a global scale, the thawing of permafrost complicates our ability to forecast climate change dynamics as organic materials preserved within it are broken down by bacteria, a process which releases carbon dioxide and methane into the atmosphere and accelerates global warming. The impacts range from affecting local livelihoods and lifestyles to the ability to maintain and deploy national defense assets. Such a complex system necessitates a more strategic approach if we are to make evidence-based decisions with respect to economies, infrastructure, food, energy and water security (FEWS), national response assets (eg, search and rescue, law enforcement, border protection, humanitarian response and community health and vitality), taxpayer burdens of re-locating remote communities whose sustainability is threatened by climate change and other variables, all of which are interdependent on social and behavioral actions at multiple scales.
Using Deep Learning to Simulate Multi-Disciplinary Design Teams
Authors: Gary Stump and Michael Yukish and Jonathan Cagan and Christopher McComb
Published in: ASME 2021 International Design Engineering Technical Conferences and …, 2021

Human subject experiments are often used in research efforts to understand human behavior in design. However, such research is often time-consuming, expensive, and limited in scope due to the need to experimentally control specific variables. This work develops an initial digital simulation of team-based multidisciplinary design, where the actions of individual team members are simulated using deep learning models trained on historical human design trends. The main benefit of this work is to simulate design session events and interactions without human participants, developing a complimentary method to rapidly perform digital team-based experiments. This research merges the benefits of purely data-driven modeling with minimal assumptions about process, along with the strengths of agent-based modeling in which it is possible to tailor agent behavior. Initial results show that the simulated design team …
An Adversarial Agent-Based Design Method Using Stochastic Stackelberg Game Conditions
Authors: Sean Rismiller and Jonathan Cagan and Christopher McComb
Published in: Journal of Mechanical Design, 2021

Products must often endure challenging conditions while fulfilling their intended functions. Game-theoretic methods can readily create a wide variety of these conditions to consider when creating designs. This work introduces Cognitively Inspired Adversarial Agents (CIAAs) that use a Stackelberg game format to generate designs resistant to these conditions. These agents are used to generate designs while considering a multidimensional attack. Designs are produced under these adversarial conditions and compared to others generated without considering adversaries to confirm the agents’ performance. The agents create designs able to withstand multiple combined conditions.
Image-based Data on Strain Fields of Microstructures with Porosity Defects
Authors: Pranav Milind Khanolkar and Saurabh Basu and Christopher McComb
Published in: Data in Brief, 2021

The present article provides a compilation of microstructures and respective strain fields expressed by them during elastic loading. These microstructures were synthesized in Abaqus Standard software and their strain fields were modelled using Abaqus based static implicit analysis. The Python Development Environment (PDE) in Abaqus was used. These microstructures were subjected to uniform displacement boundary condition to obtain strain fields in the plane-strain mode. The purpose of the generating this data was to test the efficacy of convolutional neural networks (CNNs) in predicting strain fields. This raw data consisting of microstructure and their strain fields was converted to images using MATLAB as two dimensional arrays with each pixel denoting value to be used as input for training the CNN. This processed data in the form of images can be potentially used in deep learning or data science …
Composing Diverse Design Teams: A Simulation-Based Investigation on the Role of Personality Traits and Risk-Taking Attitudes on Team Empathy
Authors: Mohammad Alsager Alzayed and Scarlett R Miller and Christopher McComb
Published in: Design Computing and Cognition’20, 509, 2020

Empathy is known to help engineering designers develop a deeper understanding of the users’ needs. However, prior research on individuals has identified that individual differences, such as personality traits and risk-taking attributes, could significantly impact designer’s empathy. While this prior research provides a context for why individual differences may impact a designer’s empathy, engineering design activities are typically deployed in teams, and thus a team-centered view of empathy is also needed. As such, the goal of the current study was to investigate the role of the diversity in personality traits and risk-taking attitudes in impacting team empathy in engineering design. This was accomplished through a computational simulation of 13,482 teams generated by a statistical bootstrapping technique drawing upon a data set from 103 first-year engineering students. When composing highly empathic teams, the …
Using Adaption-Innovation Theory to Simulate Robustness in Design Teams
Authors: Noriana Radwan and Christopher McComb
Published in: Proceedings of the 11th International Conference on Computational Creativity, 2020

Creative design is often accomplished through teams, and the specific composition of those teams can limit or enhance the sum creativity. However, it is not known how team composition is related to a team’s ability to achieve good solutions over various problems (ie, robustness). Here, we build that relationship between composition and robustness through a series of agent-based simulations. The factor that we specifically investigate is cognitive style, which describes the manner in which individuals solve problems and present solutions in social interactions based on cognitive processes. Under Kirton’s Adaption Innovation (KAI) Theory, cognitive style is related to creativity. Specifically, an individual’s KAI score, the defining measure of cognitive style, describes the degree to which they prefer high-utility solutions, or high-novelty solutions–the two necessary conditions for creativity. In many cases the long-term success of a team is closely tied to their ability to perform consistently well across multiple design problems, termed robustness. Leveraging computational agents, we use adaption-innovation theory as the primary factor to examine robustness among homogenous and heterogeneous agents. Different approaches to composing teams with homogenous and heterogeneous cognitive styles did not substantially impact robustness. However, the average robustness of the teams improved as team size increased.