ccm/publications huggingface dataset:
Smooth Like Butter: Evaluating Multi-Lattice Transitions in Property-Augmented Latent Spaces
Authors: Martha Baldwin and Nicholas A Meisel and Christopher McComb
Published in: 3D Printing and Additive Manufacturing, 2024

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, where the macroscale performance relies on the detailed design of mesostructural lattice elements. Many current approaches to designing such structures use data-driven design to generate multi-lattice transition regions, making use of machine learning models that are informed solely by the geometry of the mesostructures. However, it remains unclear if the integration of mechanical properties into the dataset used to train such machine learning models would be beneficial beyond using geometric data alone. To address this issue, this work implements and evaluates a hybrid geometry/property variational autoencoder (VAE) for generating multi-lattice transition regions. In our study …
Symmetry Heuristics for Stable Reinforcement Learning Design Agents
Authors: Akash Agrawal and Christopher McComb
Published in: Design Computing and Cognition’24, 2024

Deep Reinforcement Learning (RL) has emerged as a promising technique for automating configuration design because of its capacity for sequential decision-making. However, it faces challenges in learning stability when complex engineering simulations compose the reward function. This diminishes the practicality of deep RL for configuration design. To address this challenge, this work integrates configuration design heuristics in a deep RL framework to enhance stability and efficiently converge to high performance solutions. Specifically, we shape the reward based on symmetry, a deep-rooted heuristic that is widely applicable and frequently used in engineering design practice. This approach is empirically tested on a truss design problem wherein the RL agent employs a symmetry detection method during the design process. The results reveal that the proposed symmetry-guided approach consistently yields …
To Centralize or Decentralize? Examining Cyber-Physical Control on the Manufacturing Shop Floor
Authors: Akash Agrawal and Sung Jun Won and Tushar Sharma and Mayuri Deshpande and Christopher McComb
Published in: Engineering Archive, 2024

Multi-agent Reinforcement Learning (RL) frameworks for job scheduling and navigation control of autonomous mobile robots are becoming increasingly common as a means of increasing productivity in manufacturing. Centralized and decentralized frameworks have emerged as the two dominant archetypes for these systems. However, the tradeoffs of these competing archetypes in terms of efficiency, stability, robustness, accuracy, generalizability, and scalability are not well-understood. This work investigates the time efficiency, learning stability, and robustness to operational disruptions of an exemplar decentralized RL framework in comparison to a centralized RL framework. Specifically, several policies with increasing computational budgets are trained using both frameworks and then evaluated on the throughput and safety of the shop floor in static and dynamic tests. We observe that the decentralized framework yields a high performing policy at a significantly lower training budget than the centralized one. However, the centralized framework exhibits superior learning stability as well as robustness to the initialization of robot locations in static testing. Furthermore, we compare the robustness of the frameworks in dynamic tests to find that the decentralized framework provides better compensation for processing delays and failures in real time.
Understanding Collaboration in Sub-Structured Teams Through a Computational Model of Set-Based Concurrent Engineering
Authors: Sean Rismiller and Jonathan Cagan and Christopher McComb
Published in: Journal of Engineering Design, 2024

Coordinating the collaboration within sub-structured teams created for large engineering projects presents several challenges. Set-based concurrent engineering (SBCE), by maintaining several candidate designs for each subproblem throughout the process, may alter the best ways to coordinate subteams in these projects, compared to point-based concurrent engineering (PBCE) processes in which only one candidate design is maintained for each subproblem. Previous studies have introduced SBCE to industry with positive results and have corroborated its stated benefits through computational study, but researchers have yet to systematically study how SBCE interacts with sub-structured teams. This work augments a multi-agent model developed to simulate and study SBCE, the Point/Set-Organized Research Teams (PSORT) platform, to model sub-structured teams with different forms of intra-subteam …
Arctic Coastal Hazard Index (ACHI): A Framework for Assessing Vulnerability to Permafrost Thawing, Coastal Erosion, and Flooding in the Arctic
Authors: Ming Xiao and Ziyi Wang and Dmitry Nicolsky and Vladimir E Romanovsky and Christopher McComb and Louise Melanie Farquharson
Published in: AGU23, 2023

The Arctic's permafrost thaw, leading to coastal land loss, flooding, and ground subsidence, poses significant threats to civil infrastructure and coastal communities. Addressing these challenges necessitates effective hazard assessment tools, which have been lacking for the Arctic coast. In response, we developed a comprehensive framework called the Arctic Coastal Hazard Index (ACHI) to assess the vulnerability of Arctic coasts to permafrost thawing, coastal erosion, and flooding. To quantify the coastal permafrost thaw potential (PTP), we employed a regional assessment approach using a ground settlement index based on a numerical permafrost thermal model developed by the Geophysical Institute's Permafrost Laboratory (GIPL) at the University of Alaska Fairbanks. This model provided data on future regional mean annual ground temperature, active layer thickness, and talik thickness. The predicted thaw …
Delimiting Design: Expanding the Limits of Concurrency through Set-Based Concurrent Engineering
Authors: Sean Rismiller and Jonathan Cagan and Christopher McComb
Published in: Journal of Engineering Design, 2023

Engineering design problems are often decomposed and distributed among design teams so that their components may be developed concurrently in a project. Set-based concurrent engineering (SBCE) details a development process that reduces the need for communication and rework, often considered limiting factors to concurrency, by maintaining many candidate designs for each subproblem throughout the design process. This ensures that candidates have some compatible options to combine with as the project progresses rather than needing constant coordination and adaptation. Previous studies of SBCE found positive results in industry and corroborated its claimed benefits with computational models but have yet to study how it interacts with problem decomposition. This work uses the Point/Set-Organised Research Teams (PSORT) platform, a computational model developed to simulate and study SBCE …
Dreaming of Data: Examining Data Augmentation for Machine Learning in Additive Manufacturing
Authors: Glen Williams and Martha Baldwin and Timothy W Simpson and Nicholas A Meisel and Christopher McComb
Published in: 34th Annual International Solid Freeform Fabrication Symposium, 2023

The data generated during additive manufacturing (AM) practice can be used to train machine learning (ML) tools to reduce defects, optimize mechanical properties, or increase efficiency. In addition to the size of the repository, emerging research shows that other characteristics of the data also impact suitability of the data for AM-ML application. What should be done in cases for which the data in too small, too homogeneous, or otherwise insufficient? Data augmentation techniques present a solution, offering automated methods for increasing the quality of data. However, many of these techniques were developed for machine vision tasks, and hence their suitability for AM data has not been verified. In this study, several data augmentation techniques are applied to synthetic design repositories to characterize if and to what degree they enhance their performance as ML training sets. We discuss the comparative advantage of these data augmentation techniques across several canonical AM-ML tasks.
AddLat2D the 2D Lattice Generator
Authors: Martha Baldwin and Nicholas A Meisel and Christopher McComb
Published in: Software Impacts, 2023

This work addresses the challenges of acquiring additive manufacturing data, given the complexities and design possibilities of such structures. Researchers in additive manufacturing struggle with scarcity and unsuitability of 2D datasets which pose further difficulties. To overcome these concerns, this research presents an application, AddLat2D, for generating 2D lattice structure datasets tailored to user specifications. Building upon a previous version of the application (Baldwin et al., 2023, 2022), this work highlights our development and usage of AddLat2D to generate datasets that have custom image size and pixel intensity values.
Promoting Data Science in Mechanical Engineering Research and Education: An Exploration of the Hackathon Mechanism
Authors: Zhenghui Sha and Yan Lu and Christopher McComb and Zhuo Yang and Anh Tran and Dehao Liu and Faez Ahmed and Binyang Song and Bryan O'Halloran and Brandon Lane and Anant Kumar Mishra and Ho Yeung and Michael Lawton and Lucia Mirabella and Rahul Rai and Dazhong Wu
Published in: Emotional Engineering 9, 2023

In 2020, the American Society of Mechanical Engineers successfully held the first series of hackathon events in its two primary conferences. The first was held from August 15–16 as a pre-conference event of the ASME 2020 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference (IDETC/CIE), and the second was held from November 14–15 at the International Mechanical Engineering Congress and Exposition (IMECE). These ASME Hackathons aim to provide an open mechanism for researchers to explore new statistical and machine-learning techniques appropriate for unstructured data in design and manufacturing, as well as to develop new educational pathways for training the next generation of data-literate mechanical engineers. In this chapter, we summarize and share insights from planning and conducting these hackathons, the first-ever organized …
Simulating Heterogeneous Portfolio Choices and Financial Market Outcomes
Authors: Sebastian Benthall and Christopher D. Carroll and Zachary David and John Liechty and Alan Lujan and Christopher McComb and Nicholas Skar-Gislinge
Published in: AMPM 2022: 2nd Workshop on Agent-based Modelling and Policy-Making, 2022

Explaining the causes of financial crises requires relaxing some of the assumptions traditionally made by macroeconomic theory. We are able to simulate market crashes when consumer households have heterogeneous expectations of asset prices. These consumers earn, consume, and allocate their savings to either a risky or risk-free asset. As a departure from most macroeconomic models, the price of the risky asset is determined by a realistic agent-based simulation of a stock market, rather than through an idealized frictionless market model. Only a subset of agents in the market, the institutional investors, price the asset according to a rational expectations strategy based on an observed signal of the asset’s fundamental value. The consumers try to infer this fundamental value from the noisy price signal under bounded rationality conditions that limit their memory of and access to the price process. The consumers pass all of their buy and sell orders to the market in aggregate through a broker. This dynamic process of strategic interaction between heterogeneous macroeconomic agents and the agent-based financial market determines a new equilibrium price. We find that when the consumers have enough market power to overwhelm the institutional investors, the market will fail by driving the price of the risky asset to zero. This shows how one form of bounded rationality–ignorance about fundamental asset value–can contribute to financial crises. We also find that increasing the consumer market power leads to an increase in volatility (and a decrease in the price) of the risky asset. This suggests that there is an asymmetry whereby …
A Real-Time Artificial Intelligence Process Manager for Engineering Design
Authors: Joshua T Gyory and Nicolas F Soria Zurita and Jay Martin and Corey Balon and Christopher McComb and Kenneth Kotovsky and Jonathan Cagan
Published in: ASME 2022 International Design Engineering Technical Conferences and …, 2022

Managing the design process of teams has been shown to considerably improve problem-solving behaviors and resulting final outcomes. Automating this activity presents significant opportunities in delivering interventions that dynamically adapt to the state of a team to reap the most impact. In this work, an Artificial Intelligence (AI) agent is created to manage the design process of engineering teams in real time, tracking features of teams’ actions and communications during a complex design and path-planning task with multidisciplinary team members. Teams are also placed under the guidance of human process managers for comparison. Regarding outcomes, teams perform equally as well under both types of management, with trends towards even superior performance from the AI-managed teams. The managers’ intervention strategies and team perceptions of those strategies are also explored …
Process Monitoring: Distinguishing Defect Shapes by Strain Field Signatures
Authors: Saurabh Basu and Christopher McComb and Mustafa Rifat and Hongtao Sun and Soundar Kumara
Published in: North American Manufacturing Research Conference, 2022

In this research, a theoretic physics-based framework for identification of defects via analysis of strain fields is presented. This framework comprises identification of self-similarity of strain fields followed by their dimensionality reduction using kernel based principal component analysis. The efficacy of this framework is tested qualitatively, by visual analysis, and quantitatively, using numerical classification algorithms. We see high (> 95%) accuracy of classification via cross-validation studies using support vector machine algorithm. These results suggest that strain field can provide a viable approach for constructing highly robust in line defect detection system in modern manufacturing environments.© 2022 Society of Manufacturing Engineers (SME). Published by Elsevier Ltd. All rights reserved.
Infrastructure and Community Resilience in the Changing Arctic: Status, Challenges, and Research Needs
Authors: Ming Xiao and Lilian Alessa and Louise Farquharson and Dmitry Nicolsky and Benjamin M. Jones and Vladimir E. Romanovsky and Anne Jensen and Christopher McComb and Xiong Zhang and Min Liew and Xiaohang Ji
Published in: https://www.thearcticinstitute.org/infrastructure-community-resilience …, 2021

Permafrost thaw is one of the world’s most pressing climate problems, already disrupting lifestyles, livelihoods, economies, and ecosystems in the north, and threatening to spill beyond the boundaries of the Arctic as our planet continues to warm. To examine the effects of permafrost degradation, and increase our understanding of what this phenomenon means for the future of the region (and the world), The Arctic Institute’s new two-part permafrost series aims to analyze the topic from scientific, security, legal, and personal perspectives. The second installment of our series features eight new articles on permafrost degradation and its effects on Arctic life, research, and the world at large. But before, check out the seven articles from our first installment, starting with the. Intro
Multi-Objective Model-Based Optimization of Pilot Decision Making for Urban Air Mobility
Authors: Nicholas Holowsko and Christopher McComb
Published in: ASME 2021 International Mechanical Engineering Congress and Exposition, 2021

The emergence of an Urban Air Mobility (UAM) market utilizing fleets of vertical takeoff and land (VTOL) aircraft has the potential to shorten commutes, alleviate traffic congestion, and transform the way people interact with cities. Both industry and academic effort has been aimed towards predicting and simulating fleet activity for the purpose of informing vehicle design decisions and infrastructure planning activities. However, little effort has been targeted at analyzing aircraft fleet performance and sensitivity to pilot decision making, infrastructure availability, and changing vehicle characteristics. In this work, we utilize an existing proprietary, industry developed, discrete-event simulation tool that calculates air vehicle fleet performance with user specified vehicle and demand parameters. Building on this simulation, we then apply an optimization and sensitivity analysis, identifying the relative importance of different …
Image collection of 3D-printed prototypes and non-3D-printed prototypes
Authors: Matthew Li and Ninad Mahajan and Jessica Menold and Christopher McComb
Published in: Data in Brief, 2019

Image processing refers to the use of computer algorithms to manipulate and enhance digital images to improve their quality or to make them more suitable for tasks such as classification. Common benchmarking datasets in this field include the imagenet, CIFAR-100, and MNIST datasets. This dataset is a collection of images that are particularly relevant to engineering and design, consisting of two categories: 3D-printed prototypes, and non-3D-printed prototypes This data was collected through a hybrid approach that entailed both web scraping and direct collection from engineering labs and workspaces at Penn State University. The initial data was then augmented using several data augmentation techniques including rotation, noise, blur, and color shifting. This dataset is potentially useful to train image classification algorithms or attentional mapping approaches. This data can be used either by itself or used to …
Ethical Design of Gendered Products
Authors: Olivia Roy and Christopher McComb
Published in: 13th Engineering, Social Justice and Peace Conference, 2018

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Taylor Series Error Correction Network for Super-Resolution of Discretized Partial Differential Equation Solutions
Authors: Wenzhuo Xu and Christopher McComb and Noelia Grande Gutierrez
Published in: Available at SSRN 4731728, 0

High-fidelity engineering simulations can impose an enormous computational burden, hindering their application in design processes or other scenarios where time or computational resources can be limited. An effective up-sampling method for generating high-resolution data can help reduce the computational resources and time required for these simulations. However, conventional up-sampling methods encounter challenges when estimating results based on low-resolution meshes due to the often non-linear behavior of discretization error induced by the coarse mesh. In this study, we present the Taylor Expansion Error Correction Network (TEECNet), a neural network designed to efficiently super-resolve partial differential equations (PDEs) solutions via graph representations. We use a neural network to learn high-dimensional non-linear mappings between low-and high-fidelity solution spaces to approximate the effects of discretization error. The learned mapping is then applied to the low-fidelity solution to obtain an error correction model. Building upon the notion that discretization error can be expressed as a Taylor series expansion based on the mesh size, we directly encode approximations of the numerical error in the network design. This novel approach is capable of correcting point-wise evaluations and emulating physical laws in infinite-dimensional solution spaces. Additionally, results from computational experiments verify that the proposed model exhibits the ability to generalize across diverse physics problems, including heat transfer, Burgers' equation, and cylinder wake flow, achieving over 96% accuracy by mean squared error …
Adaptation through communication: Assessing human–artificial intelligence partnership for the design of complex engineering systems
Authors: Zeda Xu and Chloe Soohwa Hong and Nicolás F Soria Zurita and Joshua T Gyory and Gary Stump and Hannah Nolte and Jonathan Cagan and Christopher McComb
Published in: Journal of Mechanical Design 146 (8), 2024

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 toward solving a complex and evolving engineering problem. Team performance and problem-solving behaviors are examined using the HyForm collaborative research platform, which uses an online collaborative design environment that simulates a complex interdisciplinary design problem. The problem constraints are unexpectedly changed midway through …
HM-SYNC: A Multimodal Dataset of Human Interactions with Advanced Manufacturing Machinery
Authors: John Martins and Cheyu Lin and Katherine Flanigan and Christopher McComb
Published in: Journal of Mechanical Design, 2025

A deep understanding of manufacturing processes is essential for advancing manufacturing-oriented design and engineering complex systems. As advanced manufacturing technologies evolve, systems have grown more complex, and human interaction has become a vital component of both operation and design. This shift introduces new challenges, as human roles within these systems extend beyond traditional boundaries and are not yet fully understood in current design processes. Characterizing human interactions within manufacturing systems is therefore critical to supporting further advancements. Additionally, human behavior plays a significant role in many engineered systems beyond manufacturing, underscoring the value of developing methodologies to better analyze human behavior and interactions within complex environments. These methodologies can broadly support and enhance diverse …