bluuebunny/crossref_metadata_2025_split huggingface dataset:
Parameter Extraction From Images Using Multilabel Supervised Learning
Authors: Ezemba Jessica , Cunningham James D. , Tucker Conrad S. 
Published: November 2023

Abstract
In this work, we propose an approach to predict multiple design parameters of products using 2D images and supervised learning techniques. Fully parametric 2D or 3D vector representations have a high degree of flexibility since they are generally capable of morphing between all designs in the design space. A reverse design method is applied to parametrically designed products to extract design knowledge from existing designs and apply it to future designs. The goal of this research is to develop an accurate and efficient predictive model for multiple design parameters. The paper introduces several novel aspects, including learning parametric patterns from a 2D model dataset and using them to predict design parameters for new engineering systems. Additionally, the paper proposes a finetuning multilabel approach for predicting multiple design parameters of different types simultaneously, including binary and continuous parameters. The results demonstrate that the proposed approach achieves high prediction accuracy and suggest that the proposed approach can be used as a reliable tool for predicting multiple design parameters of products in practical engineering design scenarios from only 2D images, potentially allowing designer to iterate through more existing engineering designs.
A Framework for Origami Flasher Pattern Optimization to Mitigate Rigid-Foldability Issues
Authors: Varela Katie , Coleman Nathan , Skinner Mitchel , Howell Larry L. , Magleby Spencer P. 
Published: November 2023

Abstract
Origami is increasingly popular in engineering for its ability to deploy to a large area from a compact volume. In addition to its circular nature and fixed central polygon, the origami Flasher pattern has several advantages that make it a desirable candidate for many deployable systems, including space applications such as LiDaR telescopes, solar arrays, or reflectarray antennas. Some complications with the Flasher pattern that limit its application are panel interference due to multiple high degree vertices and its inability to rigidly fold. This work builds on the Cross-Frame design by Varela et al., which was implemented as a thickness accommodation technique, as well as a solution for the interference issues. Modifications of the Cross-Frame design for the Flasher origami pattern are presented, which address interference and the rigid-foldability issues within the Flasher pattern. Three frame designs, the modified cross, the diamond, and the Z design, are presented and trade-offs for each design regarding stiffness, either localized or generalized, are explored. Methods for optimizing each frame design to accommodate for rigidfoldability are introduced and algorithms and constraints for this topological optimization are discussed. Results of optimizations for stiffness and length are shown, and further modifications for future research are discussed.
The Dynamics of a System of N Coupled MEMS-Colpitts Oscillators
Authors: Rahimpour Shaghayegh , Bajaj Nikhil , Shah Shreyas Y. , Pyles Conor , Weinstein Dana , Rhoads Jeffrey F. , Quinn D. Dane 
Published: November 2023

Abstract
Networks of coupled oscillatory components have been used to represent phenomena in a broad range of areas, including biological systems and social networks, as well as applications in engineering, physics, and neuromorphic computing. This work focuses on the dynamics of a network of N resistive, globally coupled MEMS-Colpitts oscillators. The simulation of the network’s behavior requires the solution of 3 N coupled differential equations, which can be time-consuming and require substantial storage capacity. An alternative method for studying the network is to use perturbation techniques. In this work, we apply the method of multiple scales, leading to slow timescale dynamics that are solved using the harmonic balance method. This results in a set of nonlinear algebraic equations that can be addressed with common solvers for systems of equations. This approach captures important behaviors in coupled oscillator systems, such as frequency pulling and locking. The approach is also able to capture both phase and amplitude dynamics, which is a critical requirement as they are both important to the behavior. The approach is demonstrated on systems of N = 2 and N = 5 coupled oscillators. The solution offers accurate results and significant computational efficiency (10–50×) as compared to direct numerical integration. Because of the computational benefits, this novel approach can be an important tool as future systems move to progressively larger networks of oscillators.
A Two-Timescale Reinforcement Learning Approach for Control Co-Design Problems
Authors: Sadat Eddieb , Lotfalian Saremi Mostaan , Bayrak Alparslan Emrah 
Published: November 2023

Abstract
Design of smart (or active) systems that perform automated tasks intelligently based on the interaction with their environments requires a collective solution of the physical and control system design problems together. In this paper, we present a model-free on-policy reinforcement learning approach to solve control co-design problems for such smart systems. This approach uses a discrete two timescale reinforcement learning that addresses the control system design in an inner loop with a fast time scale and the physical system design in an outer loop with a slower time scale. Both design problems use the same temporal difference-based Q-learning formulation. We apply this two-time-scale reinforcement approach to the online video game EcoRacer where the physical system involves the design of a gear ratio for an electric vehicle and the control system involves acceleration and braking decisions over time to finish a track with minimum energy consumption within a limited time. The results show the ability of the proposed approach to find the system optimal solution for the EcoRacer case study within a reasonable computation time without requiring any knowledge of the physics governing the system. The proposed method is generalizable and has the potential to take advantage of the ongoing developments in the field of reinforcement learning.
LCAD: A Framework for Coupling Computer-Aided Design and Life Cycle Analysis Visualizations
Authors: Vernica Teodor , Aher Gaurav , Veluri Badrinath , Ramanujan Devarajan 
Published: November 2023

Abstract
Understanding the processes contributing to a product’s lifecycle environmental impact is critical for discovering sustainability-focused redesign opportunities. However, there is currently a gap between the tools and data representations used by product designers and life cycle analysis (LCA) practitioners, which can obstruct identifying such redesign opportunities. Previous efforts of communicating life cycle analysis results to product designers via computer-aided designs (CAD) models map these data at a coarse-grained, whole component-level which lacks the necessary detail for informing meaningful product geometry and process changes. In this work, we propose a framework to generate an integrated LCA-CAD representation, in which LCA data is volumetrically mapped to individual geometric features of a component, using a set of functions designed to select relevant surfaces and volumes from the model. The resulting representation is then used to generate a three-dimensional visualization given a specified visualization scheme. The framework is demonstrated using two scenarios that highlight the potential benefits of the proposed representation. The visualizations generated through this method present more detailed information than previous approaches and can map causal relationships between geometric features on a product and processes contributing to its environmental impact.
Robust Design for Product Adaptation Considering Changes in Configurations and Parameters
Authors: Deabae Reza , Xue Deyi 
Published: November 2023

Abstract
This paper introduces a robust design method for product adaptation considering uncertainties in both product configurations and parameters. In this study, probability of product adaptation in the operation stage and influence of the probability on the optimal design solution are investigated. In this work, an AND-OR tree is used to model feasible design candidates and their adaptations, where each node represents a partial solution for the original design or the adapted design. Design candidates are generated from the AND-OR tree through tree-based search, and a design candidate can be defined by variation nodes that are used for potential product adaptations. A multi-level optimization method is applied to obtain the optimal values of design parameters for each design candidate and the best design solution from all feasible candidates. Both evaluation measures and their variations are considered in this robust design method.
Quantifying Resilience Trade-Offs for Small-Scale Farms: A System Optimization Study in Uganda
Authors: Austin-Breneman Jesse , Chakravarthula Praneet Nallan , Kimbowa Alvin B. , Oviroh Peter Ozaveshe , Boahen Samuel , Miyingo Emmanuel Wokulira , Papalambros Panos Y. 
Published: November 2023

Abstract
Resilience is broadly understood as the capacity of a system to absorb, adapt, and transform in response to significant changes in its external environment. Improving resilience requires design trade-offs, for example, increases in equipment capacity and capital costs. This paper explores resilience trade-offs for food security and crop production of agricultural systems representing the small-scale farms dominant in low-income regions of the world. In this study, resilience assessment metrics are incorporated into a water-energy-food nexus optimization method to quantify resilience trade-offs. As an illustrative case, the previously developed system modeling and optimization framework INRCD-OPT is applied to the case study of small-scale farming in the Kampala region of Uganda to examine trade-offs between investments in irrigation driven by renewable energy microgrids and farm resilience during drought years. Optimal farm designs for baseline climate parameters show that a tailored microgrid capacity and irrigation strategy is needed to maximize profit under different climate conditions. Results optimizing for average profit across varying climate conditions show that a “resilient” farm design balances a smaller microgrid with a more aggressive irrigation strategy, navigating the trade-off between capital investment, operating cost, and crop yields under drought conditions. This type of analysis can inform long-term planning for resilience and the value of investments to achieve increased food security and crop production stability.
Designing Immersive Experiences in Virtual Reality for Training and Working With Digital Artifacts in Design for Additive Manufacturing Processes
Authors: Mathur Jayant , Miller Scarlett R. , Simpson Timothy W. , Meisel Nicholas A. 
Published: November 2023

Abstract
Adopting additive manufacturing (AM) in product design is a challenge for organizations due to a shortage of skilled designers who can effectively use AM to address emerging engineering problems. The development of institutional talent in AM and design for AM (DfAM) is crucial for organizations looking to leverage AM capabilities. Therefore, designers need meaningful active experiences that highlight the advantages and limitations of AM technologies to enable informed artifact generation during the design process. However, limited access to active in-person learning experiences with industrial AM systems and knowledge to support the design of AM artifacts is a major hurdle to AM talent development. Digital modalities of AM education, such as computer-aided (CAx) and virtual reality (VR) tools, can therefore increase access to experiential learning to cultivate DfAM intuition. There is a gap in the literature, however, that investigates and compares immersive and non-immersive modalities in AM and studies their effectiveness in training and artifact generation. Additionally, there is a scarcity of information on the design process behind creating immersive experiences that can support these research endeavors. It is essential to investigate design guidelines and heuristics for creating immersive experiences that are meaningful and effective for AM and DfAM training. This research is thus motivated to explore the design and application of immersive and non-immersive digital modalities in AM and DfAM. The presented work here investigates the design of immersive VR user experiences for this purpose with support from the literature and findings in previously published work.
Higher Order Kinematic Analysis of Long-Dwell Mechanisms
Authors: Lanni Chiara , Figliolini Giorgio , Tomassi Luciano 
Published: November 2023

Abstract
This paper deals with the higher order kinematic analysis of a long-dwell mechanism, which is synthesized by applying the dead-points superposition method. In particular, a crank-rocker four-bar linkage, a centered slider-crank mechanism, an orthogonal Cardan mechanism and an offset slider-rocker mechanism, are connected in series to give a crank-driven 10-bar long dwell mechanism, which kinematic analysis is formulated for the first time, up to the sixth-order and thus, including velocity, acceleration, jerk, jounce or snap, crackle and pop. The proposed formulation was validated by significant graphical and numerical results, which show the long dwell-time of the output rocker link for a constant angular velocity of the driving crank. Other multi-link mechanisms can be assembled in different way by using the dead-points superposition method and thus, obtaining a long-dwell mechanism.
Vector Field Based Volume Peeling for Multi-Axis Machining
Authors: Dutta Neelotpal , Zhang Tianyu , Fang Guoxin , Yigit Ismail E. , Wang Charlie C. L. 
Published: November 2023

Abstract
This paper presents an easy-to-control volume peeling method for multi-axis machining based on the computation taken on vector fields. The current scalar field based methods are not flexible and the vector field based methods do not guarantee the satisfaction of the constraints in the final results. We first conduct an optimization formulation to compute an initial vector field that is well aligned with those anchor vectors specified by users according to different manufacturing requirements. The vector field is further optimized to be an irrotational field so that it can be completely realized by a scalar field’s gradients. Iso-surfaces of the scalar field will be employed as the working surface layers for multi-axis volume peeling in the rough machining. Algorithms are also developed to remove and process singularities generated on the scalar field. Our method has been tested on a variety of models and verified by physical experimental machining.
Modular Origami Horn and Claw Chains
Authors: Xing David , You Zhong 
Published: November 2023

Abstract
The origami horn is a rigidly foldable tubular origami model designed to match the shape of an arbitrary curve. While it has potential for engineering applications, the origami horn comes with its drawbacks. It has no self-locking design, and the model might collide with itself as it folds. This paper discusses the construction of two origami tessellations designed to match the shape of a given non-intersecting planar curve. These are the modular origami horn chain, MOHC, and the modular origami claw chain, MOCC. Both origami tessellations solve the issues found in the origami horn. The MOHC is a concatenation of horn units, and the MOCC is a concatenation of claw units. Both units are flat-foldable and rigidly foldable. An algorithm is proposed to convert any non-intersecting planar curve to either the MOHC or MOCC. Both origami tessellations possess a property allowing copies of them to stack and form rigidly foldable stacked structures. MOHCs and MOCCs are also classified into families, in which two origami tessellations in the same family can stack to form self-locking stacked structures. Each stacked structure folds with one DOF, enabling it to deploy with simple controls. Paper prototypes are constructed to demonstrate that the models deploy as designed. Furthermore, the origami tessellations presented here can be used to create origami robot arms and snake-like deployable structures.
Shape-Dependent Motion Interpolants for Planar Objects
Authors: Liu Huan , Ge Qiaode Jeffrey , Langer Mark P. 
Published: November 2023

Abstract
Rigid body kinematics is most commonly about movements of unbounded spaces. This paper deals with the kinematics of bounded shapes in a plane and focuses on the problem of motion interpolation of a planar object with its shape taken into consideration. It applies and extends a shape-dependent distance measure between two positions in the context of motion interpolation. Instead of using a fixed reference frame, a shape-dependent inertia frame of reference is used for formulating the distance between positions of a rigid object in a plane. The resulting distance function is then decomposed in two orthogonal directions and is used to formulate an interpolating function for the distance functions in these two directions. This leads to a shape-dependent interpolation of translational components of a planar motion. In difference to the original concept of Kazerounian and Rastegar that comes with a shape-dependent measure of the angular motion, it is assumed in this paper that the angular motion is shape independent as the angular metric is dimensionless. The resulting distance measure is not only a combination of translation and rotation parameters but also depends on the area moments of inertia of the object. It derives explicit expressions for decomposing the shape-dependent distance in two orthogonal directions, which is then used to obtain shape-dependent motion interpolants in these directions. The resulting interpolants have similarities to the well-known spherical linear interpolants widely used in computer graphics in that they are defined using sinusoidal functions instead of linear interpolation in Euclidean space. The path of the interpolating motion can be adjusted by different choice of shape parameters. Examples are provided to illustrate the effect of object shapes on the resulting interpolating motions.
The Generation of Novel Art Using Collaborative ML Models
Authors: Short Ada-Rhodes 
Published: November 2023

Abstract
In the past year there have been paradigm shifting developments in the feasibility and availability of machine learning tools for the creation of visual and textual works. Two of the most prominent examples of this has been Large-Language models like chatGPT and methods like stable diffusion for generating art from text prompts. Both visual and language arts are often thought of as human activities, so exploring the possibilities and limitations of these tools is important for both understanding automation and improving our understanding of human cognition. In this paper I use a Large-Language Model and stable diffusion in tandem to develop an understanding of what new possibilities exist in computational cognition and design automation through their application. While no single model can recreate the complexities of a biological brain at this time, they can be thought of as analogous to individual neurological structures. For example, a Large-Language Model that is able to reason out and communicate the solutions to simple logic puzzles could recreate some of the functionality of the frontal lobe of the cerebrum. Additionally approaches like stable diffusion can recreate some of the functions of the occipital and parietal lobes. By combining them more complex behaviors and capabilities can be achieved than are possible from the individual parts. This work is in its early stages but is foundational for later developments in design automation, robotics, and computational cognition.
Conceptual Design Generation Using Large Language Models
Authors: Ma Kevin , Grandi Daniele , McComb Christopher , Goucher-Lambert Kosa 
Published: November 2023

Abstract
Concept generation is a creative step in the conceptual design phase, where designers often turn to brainstorming, mindmapping, or crowdsourcing design ideas to complement their own knowledge of the domain. Recent advances in natural language processing (NLP) and machine learning (ML) have led to the rise of Large Language Models (LLMs) capable of generating seemingly creative outputs from textual prompts. The success of these models has led to their integration and application across a variety of domains, including art, entertainment, and other creative work. In this paper, we leverage LLMs to generate solutions for a set of 12 design problems and compare them to a baseline of crowdsourced solutions. We evaluate the differences between generated and crowdsourced design solutions through multiple perspectives, including human expert evaluations and computational metrics. Expert evaluations indicate that the LLM-generated solutions have higher average feasibility and usefulness while the crowdsourced solutions have more novelty. We experiment with prompt engineering and find that leveraging few-shot learning can lead to the generation of solutions that are more similar to the crowdsourced solutions. These findings provide insight into the quality of design solutions generated with LLMs and begins to evaluate prompt engineering techniques that could be leveraged by practitioners to generate higher-quality design solutions synergistically with LLMs.
Fatigue Prediction for Repetitive Lifting
Authors: Barman Shuvrodeb , Xiang Yujiang , Rakshit Ritwik , Yang James 
Published: November 2023

Abstract
In this study, the fatigue progression and optimal motion trajectory during repetitive lifting task is predicted by using a 10 degrees of freedom (DOFs) two-dimensional (2D) digital human model and a three-compartment controller (3CC) fatigue model. The numerical analysis is further validated by conducting an experiment under similar conditions. The human is modeled using Denavit-Hartenberg (DH) representation. The task is mathematically formulated as a nonlinear optimization problem where the dynamic effort of the joints is minimized subjected to physical and task specific constraints. A sequential quadratic programming method is used for the optimization process. The design variables include control points of (1) quartic B-splines of the joint angle profiles; and (2) the three compartment sizes profiles for the six physical joints of interest — spine, shoulder, elbow, hip, knee, and ankle. Both numerical and experimental liftings are performed with a 15.2 kg box as external load. The simulation reports the human joint torque profiles and the progression of joint fatigue. The joint torque profiles show periodic trends. A maximum of 17 cycles are predicted before the repetitive lifting task fails, which also reasonably agrees with that of the experimental results (16 cycles). This formulation is also a generalized one, hence it can be used for other repetitive motion studies as well.