ccm/publications huggingface dataset:
A Quantitative Approach to Evaluate the Application of the Extended  Situational Teaching Model in Engineering Education
Authors: Fariborz M Tehrani and Christopher McComb and Sherrianna Scott
Published in: Stats, 2021

The extended situational teaching model is a variation of situational teaching, which itself has roots in situational leadership. Application of situational leadership in education requires the teacher to lead students through various stages of the learning process. This paper presents the relationship between performance measures of extended situational teaching and common pedagogical tools in engineering classrooms. These relationships outlined the response of students at different preparation levels to the application of various course components, including classroom activities and out-of-classroom assignments, in respect to task and relationship behaviors. The results of a quantitative survey are presented to support the existence of such a relationship and to demonstrate the effectiveness of the extended situational teaching model. The survey covered 476 engineering students enrolled in nine different courses over a four-year period within the civil engineering program. The statistical analysis of the survey responses proceeded in two stages. The first stage of the analysis evaluates whether the survey tool can resolve meaningful differences between the categories of the situational teaching model, and provides aggregate recommendations for each category. In the second stage of the analysis, the specific instantiation of these categories is broken down according to academic standing (grade point average) and academic level, offering support for an extended situational teaching model. Conclusions discuss the statistical characteristics of the results and correlations between selected pedagogical tools and performance measures.
Deriving Metamodels to Relate Machine Learning Quality to Repository Characteristics in the Context of Additive Manufacturing
Authors: Glen Williams and Nicholas A Meisel and Timothy W Simpson and Christopher McComb
Published in: ASME 2020 International Design Engineering Technical Conferences and …, 2020

The widespread growth of additive manufacturing, a field with a complex informatic “digital thread”, has helped fuel the creation of design repositories, where multiple users can upload distribute, and download a variety of candidate designs for a variety of situations. Additionally, advancements in additive manufacturing process development, design frameworks, and simulation are increasing what is possible to fabricate with AM, further growing the richness of such repositories. Machine learning offers new opportunities to combine these design repository components’ rich geometric data with their associated process and performance data to train predictive models capable of automatically assessing build metrics related to AM part manufacturability. Although design repositories that can be used to train these machine learning constructs are expanding, our understanding of what makes a particular design repository useful as a machine learning training dataset is minimal. In this study we use a metamodel to predict the extent to which individual design repositories can train accurate convolutional neural networks. To facilitate the creation and refinement of this metamodel, we constructed a large artificial design repository, and subsequently split it into sub-repositories. We then analyzed metadata regarding the size, complexity, and diversity of the sub-repositories for use as independent variables predicting accuracy and the required training computational effort for training convolutional neural networks. The networks each predict one of three additive manufacturing build metrics: (1) part mass, (2) support material mass, and (3) build time. Our …
Decision-Making In The Prescription Of Orthotics And Prosthetics For Partial-Foot Amputees
Authors: Pratima Saravanan and Charity Hippie and Jingxin Wang and Christopher McComb and Jessica Menold
Published in: ASME 2019 International Design Engineering Technical Conferences and …, 2019

Prosthetists face a daunting number of decisions that directly affect an amputee’s ability to walk and indirectly affect the overall quality of life of that amputee. In addition, the lack of resources in low-income countries provides a barrier to receive care after an amputation, and approximately 80% of amputees in low-income countries lack appropriate prosthetic care. In this research, we are motivated to understand what factors affect the decision-making strategies of prosthetists and podiatrists when prescribing prosthetics and orthotics to partial foot amputees. This work establishes a decision-making framework as a step towards automated methods that may reduce the complexities and decision-making burden of prosthetic prescription, ultimately increasing the efficiency of prosthetic prescription in low-resourced areas. A decision-making model is proposed based on an extensive literature review of over 100 …
Helping Teams Expand the Breadth of Their Solution Space Through Product Dissection: A Simulation Based Investigation
Authors: Mohammad Alsager Alzayed and Christopher McComb and Samuel T Hunter and Scarlett R Miller
Published in: ASME 2018 International Design Engineering Technical Conferences and …, 2018

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 as a component of larger engineering design projects, the research on the effectiveness of product dissection activities has been primarily limited to individuals. Thus, the goal of this study was to investigate the impact of the type (s) of product dissected in a team environment on the breadth of the design space explored and the underlying influence of educational level on these effects. This was accomplished through a computational simulation of 7,000 nominal brainstorming teams generated by a statistical bootstrapping technique that accounted for all possible team configurations. Specifically, each team was composed of four team members based on a design repository of 463 ideas generated by first-year and senior engineering design students after a product dissection activity. The results of the study highlight that simulated senior engineering design teams explored a larger solution space than simulated first-year teams and that dissecting different types of products allowed for the exploration of a larger solution space for all of the teams. The results also showed that dissecting two analogically far and two simple products was most effective in expanding the solution space for simulated senior teams. The findings presented in this study can lead to a better understanding of how to most effectively deploy product dissection modules in engineering design education in order to maximize the solution space explored.
Improving Irrigation in Remote Areas: Multi-objective Optimization of a Treadle Pump
Authors: Pablo S. Santaeufemia and Nathan G Johnson and Christopher McComb and Kenji Shimada
Published in: ASME 2014 International Design Engineering Technical Conferences and …, 2014

Water-lifting technologies in rural areas of the developing world have enormous potential to stimulate agricultural and economic growth. The treadle pump, a human-powered low-cost pump designed for irrigation in developing countries, can help farmers maximize financial return on small plots of land by ending their dependency on rain-fed irrigation systems. The treadle pump uses a suction piston to draw groundwater to the surface by way of a foot-powered treadle attached to each suction piston. Current treadle pump designs lift water from depths up to 7 meters at a flow-rate of 1–5 liters per second. This work seeks to optimize the design of the Dekhi style treadle pump, which has gained significant popularity due to its simplicity. A mathematical model of the working fluid and treadle pump structure has been developed in this study. Deterministic optimization methods are then employed to maximize the flow rate …
Exploring the Potentials and Challenges of Cyber-Physical-Social Infrastructure Systems for Achieving Human-Centered Objectives
Authors: Maral Doctorarastoo and Katherine Flanigan and Mario Bergès and Christopher McComb
Published in: 10th ACM International Conference on Systems for Energy-Efficient Buildings …, 2023

 For decades, cyber-physical systems (CPSs) have transformed the design and management of infrastructure systems, improving their performance through the combination of sensing, computing, and control. However, while the existing CPS paradigm supports infrastructure system actuation to achieve desired economic objectives (e.g., efficiency, safety), it fails to account for possible input-output relationships between physical system actuation and human-centered (or “social”) benefits and objectives. There are entirely unexplored social benefits to be derived from infrastructure systems that have yet to be scientifically understood and exploited. For instance, the impact of public space design and management on social interactions. To address this untapped potential, we first highlight the existing gaps and challenges to reimagining CPS theory capable of measuring, modeling, and actuating social objectives within …
A Data-driven Approach for Multi-Lattice Transitions
Authors: Martha Baldwin and Nicholas A Meisel and Christopher McComb
Published in: 2022 International Solid Freeform Fabrication Symposium, 2022

Additive manufacturing is advantageous for producing lightweight components while maintaining function and form. This ability has been bolstered by the introduction of unit lattice cells and the gradation of those cells. In cases where loading varies throughout a part, it may be necessary to use multiple lattice cell types, also known as multi-lattice structures. In such structures, abrupt transitions between geometries may cause stress concentrations, making the boundary a primary failure point; thus, transition regions should be created between each lattice cell type. Although computational approaches have been proposed, smooth transition regions are still difficult to intuit and design, especially between lattices of drastically different geometries. This work demonstrates and assesses a method for using variational autoencoders to automate the creation of transitional lattice cells. In particular, the work focuses on identifying the relationships that exist within the latent space produced by the variational autoencoder. Through computational experimentation, it was found that the smoothness of transition regions was higher when the endpoints were located closer together in the latent space.
The problem with printing pitch: challenges in designing 3D printed claves
Authors: Emmeline Evans and Christopher McComb
Published in: Rapid Prototyping Journal, 2022

Although music education has been proven to benefit students, music programs are often cut when public school funding is reduced. The cost of instruments is a significant financial burden on music programs, which restricts student access to high-quality instruments that would benefit their learning. Therefore, this paper aims to develop additively manufactured, polylactic acid (PLA) claves that could reproduce the sound of wooden claves and be printed by students in schools at a reduced cost to promote equal access to music education regardless of a school’s financial status.This study developed a dual approach for analyzing clave vibration using mathematical analysis in MATLAB and SolidWorks finite element frequency simulation to predict the natural frequencies of 15 claves with varying geometries. To evaluate the performance of both models, the authors obtained …
Assessing Machine Learnability Of Image And Graph Representations For Drone Performance Prediction
Authors: Binyang Song and Christopher McComb and Faez Ahmed
Published in: International Design Conference - DESIGN 2022, 2022

Deep learning (DL) from various representations have succeeded in many fields. However, we know little about the machine learnability of distinct design representations when using DL to predict design performance. This paper proposes a graph representation for designs and compares it to the common image representation. We employ graph neural networks (GNNs) and convolutional neural networks (CNNs) respectively to learn them to predict drone performance. GCNs outperform CNNs by 2.6-8.1% in predictive validity. We argue that graph learning is a powerful and generalizable method for such tasks.
Addressing Challenges to Problem Complexity: Effectiveness of AI Assistance During the Design Process
Authors: Binyang Song and Nicolas F Soria Zurita and Hannah Nolte and Harshika Singh and Jonathan Cagan and Christopher McComb
Published in: ASME 2021 International Design Engineering Technical Conferences and …, 2021

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 is 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 …
Comparing Attribute- and Form-Based Machine Learning Techniques for Component Prediction
Authors: Glen Williams and Lucas Puentes and Jacob Nelson and Jessica Menold and Conrad Tucker and Christopher McComb
Published in: ASME 2020 International Design Engineering Technical Conferences and …, 2020

Online data repositories provide designers and engineers with a convenient source of data. Over time, the wealth and type of readily-available data within online repositories has greatly expanded. This data increase permits new uses for machine learning approaches which rely on large, high-dimensional datasets. However, a comparison of the efficacies of attribute-based data, which lends itself well to traditional machine learning algorithms, and form-based data, which lends itself to deep machine learning algorithms, has not fully been established. This paper presents one such comparison for an exemplar dataset. As the efficacy of different machine learning approaches may be dependent on the specific application, this method is intended to lay the groundwork for future studies that produce more extensive comparisons. Specifically, this work makes use of a manufactured gear dataset for sale price …
Using Deep Image Colorization to Predict Microstructure-Dependent Strain Fields
Authors: Pranav Milind Khanolkar and Aaron Abraham and Christopher McComb and Saurabh Basu
Published in: North American Manufacturing Research Conference, 2020

The microstructure of a material governs mechanical properties such as strength and toughness. Various finite element analysis (FEA) software packages are used to perform structural analyses such as predicting the flow of strain or strain fields in a microstructure. Engineers frequently operate these software packages to evaluate mechanical behavior and predict failure. Even though these FEA software packages provide highly accurate analyses, they are computationally intensive, taking a significant amount of time to produce a solution. The time required by the FEA software packages to achieve accurate results largely depends on microstructure details and mesh resolution, thus providing a trade-off between fidelity and computation time. This research proposes the use of Deep Learning algorithms to achieve a significant reduction in the time required to predict high-accuracy strain fields in a two-dimensional …
Assessing The Impact Of Cognitive Assistants On Mental Workload In Simple Tasks
Authors: Torsten Maier and Vincent Donghia and Chaoran Chen and Jessica Menold and Christopher McComb
Published in: ASME 2019 International Design Engineering Technical Conferences and …, 2019

Cognitive assistants, such as Apple’s Siri or Amazon’s Alexa, are increasingly becoming routine elements of our daily lives, yet the effects of such devices on the humans using them has been largely unstudied. While much research has proposed new intelligent systems for specific contexts, few studies have characterized the dynamics between cognitive assistants and humans. In an effort to address this gap, this research study investigates the effect of cognitive assistants on mental workload. Participants perform the Wisconsin Card Sorting Task while working with a fake cognitive assistant (Mary) and concurrently completing a secondary speed and accuracy test. Performance with the cognitive assistant was found to be higher but with no significant difference in mental workload. Participants working with the cognitive assistant were found to be less frustrated than those completing the task without it.
Critical Thinking In The Design Classroom: An Analysis Of Student Design Reflections
Authors: Emmeline Evans and Jessica Menold and Christopher McComb
Published in: ASME 2019 International Design Engineering Technical Conferences and …, 2019

Within the domain of education, the term “critical thinking” is widely understood to mean the various skills that comprise an individual’s logical and reasoning abilities. It is critical that designers possess these abilities so that they can solve the complex problems of an increasingly interconnected world. In order to better understand patterns in engineering students’ critical thinking, this research applies the classifications of the 2001 revision of Bloom’s Taxonomy to 49 reflections written by first-year engineering students on a two-hour design practicum. Reflections were thematically coded to identify when students operated in different levels of the cognitive process and knowledge dimensions. Using k-means clustering analysis, genres of reflection were then determined. Four unique clusters of responses were identified. Notable trends in clusters included application and evaluation of procedural knowledge …
Design Practica as Authentic Assessments in First-year Engineering Design Courses
Authors: Christopher McComb and Catherine GP Berdanier and Jessica Menold
Published in: First-Year Engineering Experience Conference, 2018

This paper describes the design and evaluation of a novel assessment for first-year engineering design courses that is rooted in an authentic design challenge. This approach modifies the traditional written-exam approach typically found in engineering courses, which is inherently inauthentic and cannot easily capture the exploratory nature of engineering design. Our assessment improves alignment with common learning objectives found in first-year engineering design courses and additionally prepares students for the type of case study interviews that are increasingly common for entry-level engineering jobs. To evaluate our assessment, 50 first-year students completed the engineering design self-efficacy instrument once before beginning the assessment and a second time approximately 48 hours later upon completion of a reflection assignment. In addition, students retrospectively reported their perceived change in self-efficacy during the assessment. Analysis shows that students perceived a large retrospective increase in skill level, despite only a small increase in directly measured self-efficacy. These results are analyzed in light of the Dunning-Kruger effect and we posit that the assessment helps to align students’ self-efficacy with their actual skill level. Increased alignment of self-efficacy with skill level may minimize student frustration when encountering challenging tasks in the future, potentially increasing retention of engineering students as well as facilitating the development of lifelong learning attitudes.
Eliciting Configuration Design Heuristics with Hidden Markov Models
Authors: Christopher McComb and Jonathan Cagan and Kenneth Kotovsky
Published in: International Conference on Engineering Design, 2017

Configuration design problems, characterized by the selection and assembly of components into a final desired solution, are common in engineering design. Although a variety of theoretical approaches to solving configuration design problems have been developed, little research has been conducted to observe how humans naturally attempt to solve such problems. This work mines the data from a cognitive study of configuration design to extract helpful design heuristics. The extraction of these heuristics is automated through the application of hidden Markov models. Results show that, for a truss configuration problem, designers proceed through four procedural states in solving configuration design problems, transitioning from topology design to shape and parameter design. High-performing designers are distinguished by their opportunistic tuning of parameters early in the process, enabling a heuristic search process similar to the A* search algorithm.
Heterogeneous Simulated Annealing Teams: An Optimizing Search Algorithm Inspired by Engineering Design Teams
Authors: Christopher McComb and Jonathan Cagan and Kenneth Kotovsky
Published in: International Conference on Engineering Design, 2015

Although insights uncovered by design cognition are often utilized to develop the methods used by human designers, using such insights to inform computational methodologies also has the potential to improve the performance of design algorithms. This paper uses insights from research on design cognition and design teams to inform a better simulated annealing search algorithm. Simulated annealing has already been established as a model of individual problem solving. This paper introduces the Heterogeneous Simulated Annealing Team (HSAT) algorithm, a multi-agent simulated annealing algorithm. Each agent controls an adaptive annealing schedule, allowing the team develop heterogeneous search strategies. Such diversity is a natural part of engineering design, and boosts performance in other multi-agent algorithms. Further, interaction between agents in HSAT is structured to mimic interaction between members of a design team. Performance is compared to several other simulated annealing algorithms, a random search algorithm, and a gradient-based algorithm. Compared to other algorithms, the team-based HSAT algorithm returns better average results with lower variance.
Modeling Human Behavior in Cyber-Physical-Social Infrastructure Systems
Authors: Maral Doctorarastoo and Katherine Flanigan and Mario Bergès and Christopher McComb
Published in: 10th ACM International Conference on Systems for Energy-Efficient Buildings …, 2023

 The concept of cyber-physical-social infrastructure systems (CPSISs) has been introduced to offer a paradigm shift necessary for including human-centered, or “social,” objectives in traditional cyber-physical systems (CPSs). In this paper we focus on one of the greatest challenges of developing CPSISs: modeling and predicting human behavior. Traditional approaches like rule-based models (e.g., agent-based models) and stochastic methods fall short in capturing the complex, multi-objective, and dynamic nature of human behavior. We propose the novel use of Hierarchical Imitation and Reinforcement Learning to combine the strengths of imitation learning and reinforcement learning for this purpose. This approach offers a cost-effective and efficient way of modeling human behavior, reducing reliance on extensive community surveys and expert opinions and instead supporting the integration of in-situ data. This …