ccm/publications huggingface dataset:
Emergence of Engineering Design Self-efficacy in Engineering Identity Development
Authors: Jongho John Park and Elizabeth Starkey and Nathan Choe and Christopher McComb
Published in: Ninth International Conference on Design Computing and Cognition, 2020

In engineering education, the goal is not only to teach the technical engineering skills but to positively influence the formation of students’ engineering identity to enhance future success. Often, first-year engineering classes that have a large formative role in students’ academic careers are taught using a project-based learning approach. However, the effect of such courses on the growth and development of students’ engineering identity, and in turn their design cognition, has not been studied in detail. This work studied the growth of engineering identity of 15 weeks in one such course. Engineering identity was assessed through several engineering identity instruments as well as the use of natural language processing.
Comparison of Exams and Design Practica for Assessment in First Year Engineering Design Courses
Authors: Hannah Nolte and Catherine GP Berdanier and Jessica Menold and Christopher McComb
Published in: ASME 2020 International Design Engineering Technical Conferences and …, 2020

In response to calls for engineering programs to better prepare students for future careers, many institutions offer courses with a design component to first-year engineering students. This work proposes that traditional exam-based assessments of design concepts are inadequate, and alternative forms of assessment are needed to assess student learning in design courses. This paper investigates the self-efficacy differences between a traditional exam and a two-part practicum as a mid-semester assessment for introductory engineering students enrolled in a first-year design course. Increased self-efficacy has been linked to various positive student outcomes and increased retention of underrepresented students. The practicum consisted of an in-class team design task and an out-of-class individual reflection, while the exam was a traditional, individual written exam. All students completed a pre-assessment …
A Differential Game Approach to Dynamic Competitive Decisions Toward Human-Computer Collaboration
Authors: Alparslan Emrah Bayrak and Christopher McComb and Jonathan Cagan and Kenneth Kotovsky
Published in: ASME 2019 International Design Engineering Technical Conferences and …, 2019

Partnership between humans and computers has a significant potential to extend the ability of humans to address complex design problems. This paper presents a decision-making process for computers to effectively collaborate with humans in the solution of complex problems under dynamic competition. In the proposed process, the computers learn strategies and objectives from prior experimental data and provide strategy suggestions to human collaborators. The study integrates clustering and sequential learning methods from machine learning with a differential game formulation based on model predictive control to find dynamic Nash equilibrium solutions to zero-sum games. The application of the proposed approach is demonstrated on the real-time strategy game Starcraft II that offers a dynamic competitive problem comparable in complexity to real-world applications. The results show that the proposed …
Complexity Reduction in Mass Customization To Facilitate Better Decision Support
Authors: Jai Banerjee and Madura Mundale and Adnan Sachche and Christopher McComb
Published in: ASME 2019 International Design Engineering Technical Conferences and …, 2019

Mass customization provides a way to satisfy an increasing number of customers by tailoring product details to their preferences. Moreover, there is a considerable amount of pressure on industries that embrace mass customization in their product portfolios to consistently produce innovative and market defining technology. To ease this pressure, this study offers organizations a perspective on what combination of attributes is most influential on choice when approaching decision support for the purchase of a Configure-to-Order product. Laptops are used here as a case study, but the approach is generalizable to any family of products. This approach first uses metrics gained from a modified conjoint analysis applied to data gathered from a student consumer base. Customer satisfaction is measured as a function of how many product offerings are provided, identifying and quantifying a trade-off between …
Creation and Implementation of a Project Framework to Improve Cornerstone Engineering Design
Authors: Nicholas A Meisel and Christopher McComb and Jessica Menold and Sarah Ritter
Published in: ASEE First-Year Engineering Education Conference, 2019

Introduction to Engineering Design (EDSGN 100) acts as a gateway engineering course for over 3800 students across 20 Penn State campuses each year. Recently, the course has incorporated six educational modules, which cover topics from creativity to professional communication to making. However, these modules require a unifying experience so that the students are able to perceive how the content from the individual modules coalesces to form the unique identity of an engineer. To address this need, a new framework is proposed to guide the creation and implementation of an 8-week long design challenge within EDSGN 100. This framework identifies a series of 8 project characteristics necessary to create a clear connection between the content from each of the individual modules and the successful execution of a world-class engineering design project. This ensures that faculty-developed design projects are of an appropriate scope and context in order to provide proper scaffolding to support the six individual educational modules. This paper demonstrates how such a framework can be applied to both create new projects for the course, as well as redesign existing projects to better meet course goals and incorporate module content.
Application of Project-based Learning within the Context of Sustainable Development in Education of International Engineer Citizens
Authors: Fariborz M Tehrani and Christopher McComb and Nell Papavasiliou
Published in: The 5th Iran International Conference on Engineering Education, 2017

This paper outlines a project-based learning approach to engineering education. This approach directly employs sustainable development as a vehicle to prepare students for global engineering challenges. The theoretical concept of the presented work utilizes the extended situational teaching model. This model explains the role of the teacher-leader in engineering classroom to help students with gaining essential elements of engineering education, including superior knowledge, reliable skills, and professional attitude. Case studies and examples of experiential learning projects demonstrate the development of links between classroom teaching, research advising, labo-
Evaluating Large Language Models for Material Selection
Authors: Daniele Grandi and Yash Patawari Jain and Allin Groom and Brandon Cramer and Christopher McComb
Published in: Journal of Computing and Information Science in Engineering, 2024

Material selection is a crucial step in conceptual design due to its significant impact on the functionality, aesthetics, manufacturability, and sustainability impact of the final product. This study investigates the use of Large Language Models (LLMs) for material selection in the product design process and compares the performance of LLMs against expert choices for various design scenarios. By collecting a dataset of expert material preferences, the study provides a basis for evaluating how well LLMs can align with expert recommendations through prompt engineering and hyperparameter tuning. The divergence between LLM and expert recommendations is measured across different model configurations, prompt strategies, and temperature settings. This approach allows for a detailed analysis of factors influencing the LLMs' effectiveness in recommending materials. The results from this study highlight two failure …
Hybrid Geometry/Property Autoencoders for Multi-Lattice Transitions
Authors: Martha Baldwin and Nicholas A Meisel and Christopher McComb
Published in: 34th Annual International Solid Freeform Fabrication Symposium, 2023

Additive manufacturing has revolutionized structural optimization by enhancing component strength and reducing material requirements. One approach used to achieve these improvements is the application of multi-lattice structures. The performance of these structures heavily relies on the detailed design of mesostructural elements. Many current approaches use data-driven design to generate multi-lattice transition regions, making use of models that jointly address the geometry and properties of the mesostructures. However, it remains unclear whether the integration of mechanical properties into the data set for generating multi-lattice interpolations is beneficial beyond geometry alone. To address this issue, this work implements and evaluates a hybrid geometry/property machine learning model for generating multi-lattice transition regions. We compare the results of this hybrid model to results obtained using a geometry-only model. Our research determined that incorporating physical properties decreased the number of variables to address in the latent space, and therefore improves the ability of generative models for developing transition regions of multi-lattice structures.
Adaptation and Challenges in Human-AI Partnership for the Design of Complex Engineering Systems
Authors: Zeda Xu and Chloe Hong and Nicolas F Soria Zurita and Joshua T. Gyory and Gary Stump and Hannah Nolte and Jonathan Cagan and Christopher McComb
Published in: International Design Engineering Technical Conferences and Computers and …, 2023

Exploring the opportunities for incorporating Artificial Intelligence (AI) to support team problem solving has been the focus of intensive ongoing research. However, while the incorporation of such AI tools into human team problem solving can improve team performance, it is still unclear what modality of AI integration will lead to a genuine human-AI partnership capable of mimicking the dynamic adaptability of humans. This work unites human designers with AI Partners as fellow team members who can both reactively and proactively collaborate in real-time towards solving a complex and evolving engineering problem. Team performance and problem-solving behaviors are examined using the HyForm collaborative research platform. The problem constraints are unexpectedly changed midway through problem solving to simulate the nature of dynamically evolving engineering problems. This work shows that …
Manufacturing Fixation in Design: Exploring the Effects of Manufacturing Fixation during Idea Generation
Authors: Jennifer Brennan and William Miney and Timothy W Simpson and Kathryn Jablokow and Christopher Mccomb
Published in: Journal of Mechanical Design 145 (1), 1-8, 2023

Capitalizing on any new or unfamiliar manufacturing technology requires an ability to look beyond the manufacturing limitations that have constrained one's design ideas in the past. For advanced manufacturing technology with unique capabilities such as additive manufacturing, this becomes critical for designing effective geometric features and parts. However, cognitive bias or fixation on familiar manufacturing processes may make it challenging for designers to determine how to take advantage of new manufacturing technology. For example, a person that is used to generating design ideas for subtractive manufacturing may struggle when asked to use additive manufacturing. This research is the first to provide evidence that fixation on certain manufacturing types can impact designers’ ability to utilize new, and less familiar, technologies during idea generation. We call this new form of design fixation …
Deriving Recommendations for the Use of Agent-Based Models in Engineering Design
Authors: Malena Agyemang and Noriana Radwan and Sierra Hicks and Fariha Azhar and Ambrosio Valencia-Romero and Christopher McComb
Published in: International Design Engineering Technical Conferences and Computers and …, 2022

Agent-based modeling (ABM) is a widely used simulation methodology. The use of ABM in engineering design continues to grow due to increasing interest in modeling and simulating human systems. However, there are few resources specific to engineering design that guide how to develop an ABM, execute an ABM experiment, and assess the subsequent results. This inhibits the effective use of ABM by design researchers who are new to the methodology. The goal of this paper is to provide a resource for both novice and experienced design researchers that details a systematic approach to ABM. First, this paper reviews current ABM research in engineering design, with a specific focus on models used to simulate human systems. Specifically, we review this work with respect to a step-by-step approach to modeling developed in the systems simulation literature. Second, each of the steps is contextualized …
A Computational Study of Design Team Robustness Through the Lens of Cognitive Style
Authors: Noriana Radwan and Christopher McComb
Published in: Journal of Mechanical Design, 2022

High-performing design teams are characterized by their ability to maintain performance across a variety of problem types. This is often referred to as robustness, and is usually achieved through careful management of team processes. However, there exists an opportunity to design teams that are likely to be inherently robust by addressing and embracing the individual variability of team members. Cognitive style provides an avenue by which we can compose robust teams based on the problem-solving approach of the individual. In this work, we used the KAI agent-based organizational optimization model (KABOOM) to evaluate the effects of team composition and team structure on the robustness of overall team performance. Teams of homogeneous and heterogeneous KAI styles were tasked to solve a variety of different abstract design problems and evaluated based on their performance with and without …
Pitch Imperfect: Designing 3D Printed Claves to Mimic the Sound of Their Wooden Counterparts
Authors: Emmeline Evans and Christopher McComb
Published in: Solid Freeform Fabrication Symposium, 2021

Despite the benefits afforded students by music education, public schools operating on insufficient budgets often cut music programs to reduce expenses. Students deserve access to high quality instruments, regardless of socioeconomic status or district funding. Therefore, the goal of this research is to develop 3D printed, PLA claves that reproduce the sound of wooden claves. This study examined clave vibration by approximating claves as damped, simply supported, thin beams. The frequency predictions obtained from that model are compared to experimental results obtained by recording clave prototypes and analyzing the resulting frequency spectra. Results indicate that while it is technically feasible to 3D print a correctly pitched PLA clave, the design would not be suitable for an education instrument.
Automated Heuristic Induction From Human Design Data
Authors: Lucas Puentes and Jonathan Cagan and Christopher McComb
Published in: ASME 2020 International Design Engineering Technical Conferences and …, 2020

Through experience, designers develop guiding principles, or heuristics, to aid decision-making in familiar design domains. Generalized versions of common design heuristics have been identified across multiple domains and applied by novices to design problems. Previous work leveraged a sample of these common heuristics to assist in an agent-based design process, which typically lacks heuristics. These predefined heuristics were translated into sequences of specifically applied design changes that followed the theme of the heuristic. To overcome the upfront burden, need for human interpretation, and lack of generality of this manual process, this paper presents a methodology that induces frequent heuristic sequences from an existing timeseries design change dataset. Individual induced sequences are then algorithmically grouped based on similarity to form groups that each represent a shared …
Towards an Understanding of Semantic Memory During Idea Generation
Authors: Attakias Mertens and Christopher McComb and Christine Toh
Published in: ASME 2020 International Design Engineering Technical Conferences and …, 2020

Research in new product design still lacks an understanding of how the types of information used by designers can lead to more successful designs and what cognitive components are involved in the process of generating new ideas. Some theories have arisen that focus on memory usage that could have an impact in idea generation early on in the design process. This framework forms the basis of the current study, focused on identifying the underlying cognitive processes that are active during the design process. To accomplish this, undergraduate students were recruited from the University of Nebraska-Omaha. During the study, participants were presented a design problem, given information pieces that corresponded to the Information Archetypes Framework, and asked to generate ideas for a solution. Students were then asked to recall the information pieces from memory. Participants’ data were analyzed …
A Standardized Framework for Communicating and Modelling Parametrically Defined Mesostructure Patterns
Authors: Glen Williams and J O'Brien and A Mezghani and M Eachus and Nicholas A Meisel and Christopher McComb
Published in: 30th Annual International Solid Freeform Fabrication Symposium, 2019

Intricate mesostructures in additive manufacturing (AM) designs can offer enhanced strength-to-weight performance. However, complex mesostructures can also hinder designers, often resulting in unpalatably large digital files that are difficult to modify. Similarly, existing methods for defining and representing complex mesostructures are highly variable, which further increases the challenge in realizing such structures for AM. To address these gaps, we propose a standardized framework for designing and representing mesostructured components tailored to AM. Our method uses a parametric language to describe complex patterns, defined by a combination of macrostructural, mesostructural, and vector field information. We show how various mesostructures, ranging from simple rectilinear patterns to complex, vector field-driven cellular cutouts can be represented using few parameters (unit cell dimensions, orientation, and spacing). Our proposed framework has the potential to significantly reduce file size, while its extensible nature enables it to be expanded in the future.
Evaluating Idea Quantity And Variety When Using Digital Collaboration Tools To Support Brainstorming In Non-Collocated Teams
Authors: Dorcas Kaweesa and Christopher McComb and Jessica Menold and Sarah Ritter and Nicholas A Meisel
Published in: ASME 2019 International Design Engineering Technical Conferences and …, 2019

The advent of modern digital communication technology has enabled engineers to effectively collaborate regardless of team members’ geographic locations. As such, introducing engineering students to virtual environments and collaborative tools is particularly important to prepare them for careers in increasingly digital environments. This study investigates how integrating online collaboration tools in students’ idea generation activities impacts the i) quantity and ii) variety of ideas generated after a peer-feedback session. Students from five sections of a first-year engineering design course were assigned to either a collocated design team or a non-collocated design team to participate in a collaborative design feedback activity. Students individually generated an idea set using an online brainstorming tool (Stormboard), received peer-feedback via one of two delivery conditions (in-person or virtual through …
Data-Driven Inpainting for Full-Part Temperature Monitoring in Additive Manufacturing
Authors: Jiangce Chen and Mikhail Khrenov and Jiayi Jin and Sneha Prabha Narra and Christopher McComb
Published in: Journal of Manufacturing Systems, 2024

Understanding the temperature history over a part during additive manufacturing (AM) is important for optimizing the process and ensuring product quality, as temperature impacts melt pool geometry, defect formation, and microstructure evolution. While in-process temperature monitoring holds promise for evaluating the part quality, existing thermal sensors used in AM provide only partial measurements of the temperature distribution over the part. In this work, we introduce an innovative approach for reconstructing the complete temperature profile using partial data. We formulate this challenge as an inpainting problem, a canonical task in machine learning which entails recovering missing information across a spatial domain. We present a data-driven model based on graph convolutional neural networks. To train the inpainting model, we employ a finite element simulation to generate a diverse dataset of …
GARCH-Informed Neural Networks for Volatility Prediction in Financial Markets
Authors: Zeda Xu and John Liechty and Sebastian Benthall and Nicholas Skar-Gislinge and Christopher McComb
Published in: ICAIF '24: 5th ACM International Conference on AI in Finance, 2024

Volatility, which indicates the dispersion of returns, is a crucial measure of risk and is hence used extensively for pricing and discriminating between different financial investments. As a result, accurate volatility prediction receives extensive attention. The Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model and its succeeding variants are well established models for stock volatility forecasting. More recently, deep learning models have gained popularity in volatility prediction as they demonstrated promising accuracy in certain time series prediction tasks. Inspired by Physics-Informed Neural Networks (PINN), we constructed a new, hybrid Deep Learning model that combines the strengths of GARCH with the flexibility of a Long Short-Term Memory (LSTM) Deep Neural Network (DNN), thus capturing and forecasting market volatility more accurately than either class of models are capable of on their own. We refer to this novel model as a GARCH-Informed Neural Network (GINN). When compared to other time series models, GINN showed superior out-of-sample prediction performance in terms of the Coefficient of Determination (), Mean Squared Error (MSE), and Mean Absolute Error (MAE).
Putting The Ghost In The Machine: Emulating Cognitive Style in Large Language Models
Authors: Vasvi Agarwal and Kathryn Jablokow and Christopher McComb
Published in: Journal of Computing and Information Science in Engineering, 2024

Large Language Models (LLMs) have emerged as pivotal technology in the evolving world. Their significance in design lies in their transformative potential to support engineers and collaborate with design teams throughout the design process. However, it is not known whether LLMs can emulate the cognitive and social attributes which are known to be important during design, such as cognitive style. This research evaluates the efficacy of LLMs to emulate aspects of Kirton's Adaption-Innovation theory, which characterizes individual preferences in problem-solving. Specifically, we use LLMs to generate solutions for three design problems using two different cognitive style prompts (adaptively-framed and innovatively-framed). Solutions are evaluated with respect to feasibility and paradigm relatedness, which are known to have discriminative value in other studies of cognitive style. We found that solutions generated …