bluuebunny/crossref_metadata_2025 huggingface dataset:
Parameter Extraction From Images Using Multilabel Supervised Learning
Authors: Ezemba Jessica , Cunningham James D. , Tucker Conrad S.
Published: November 2023

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.
Optimal Structural Design of the Composite Chassis of an High Energy Efficient Vehicle
Authors: Stabile Pietro , Ballo Federico Maria , Gobbi Massimiliano , Mastinu Gianpiero
Published: November 2023

This paper presents the optimal structural design of the composite chassis of a high energy efficient quadricycle. The chassis consists of three main parts, realised by means of sandwich structures. The bottom frame, has an expanded polypropylene (EPP) foam core and carbon fiber-reinforced plastic (CFRP) external skins. Aramid honeycomb and CFRP skins are used for the sandwich structures of the body and the bulkhead that separates driver’s compartment and energy compartment. An optimization procedure, suitable for laminated composite structures, is employed to determine the optimal stacking sequence, i.e. shape, number and orientation of each ply. The sum of the weighted compliance evaluated for several load cases is set as objective function to minimize. Optimization constraints are related to the limit mass and to structural stiffness, strength and manufacturing aspects. The optimization procedure relies on three phases. Free-size phase aims to determine the optimum shape and continuous thickness distribution for each ply. The second phase involves a discrete optimization procedure, to determine the optimal number of plies. Finally, the optimal stacking sequence of the laminates is defined in order to meet manufacturability requirements. Compared with the non-optimized design of the chassis, the optimization approach adopted leads to a chassis featuring lower mass, with same compliance. Strength and stiffness related properties are also improved.
Augmented Reality for the Positioning of Fasteners and Adhesives in Sheet Metal Joining Processes in the Automotive Industry
Authors: Schwenk Rebecca , Smith Shana
Published: November 2023

The manual sheet metal joining process in the automotive industry relies on visual guides to locate standardized joint positions. Traditionally, operators place physical templates on the workpiece to mark the exact positions of each joint prior to operating the machinery. In attempts to eliminate this step, applications of augmented reality (AR) that provide positional information through digital means have been introduced. AR offers additional functionalities such as displaying animations and tolerances; however, existing works on the topic often rely on markers in the workspace and face limitations due to ergonomics and workpiece complexity. This paper introduces an AR solution that uses model targets and digital templates to overlay the positional information of joints on the workpiece, enhancing manufacturing visualization. In addition, the positional and diametrical relative accuracies of joints are measured and provided to the user in real-time. Functionalities such as animations, a user interface (UI), and a progress tracking mechanism additionally enhance the process for the user. Unity and Vuforia Engine together with a sample workpiece were used to demonstrate the concept. The resulting solution is free of physical markers in the workspace, resistant to accidental occultation, and suitable for a variety of sheet metal joining processes and workpiece complexities.
Use of Data-Mined Customer Reviews to Inform Design Evaluation and Testing of Bassinets
Authors: Singhose William , Adams Christopher , Rana Anjnee , Kim Dooroo , Li Wayne
Published: November 2023

This paper describes a method to use data-mined customer reviews to identify potential product defects. The process involves locating negative reviews of a specific product and then extracting comments that have potential connections with the product design. The extracted comments are then categorized and correlated with features of the product. Given that the customer comments are generally not specifically tied to engineering requirements, or even posed in engineering terms, this correlation requires some degree of engineering analysis to establish a correlation. After these complaint-feature correlations are established, then engineering tests directed at understanding the possible defects are performed. This data-mining process effectively harnesses the massive amount of in-situ testing and evaluation that is performed by customers of the product. The process is illustrated via a case study of an infant bassinet. Customer reviews of the product were studied to identify and categorize key complaints about the bassinet. These complaints were correlated with features and potential defects in the product, including assembly difficulties, as well as a sleeping surface that tilts and causes infants to roll and press into the mesh side wall while sleeping. Then, assembly and sleeping surface deflection tests were conducted. The sleeping surface deflection tests include measurements of how bassinet leg separation affects the deflection, as well as long-duration progressive deflection that occurs from repeated use of the product. The results of the engineering testing confirm the presence of defects in the bassinet and its assembly instructions, as suggested by the reviews. This case study illustrates how data-mined customer reviews provide a valuable source of engineering data and indications of product defects.
Automated Evaluation of Written Assignments in Engineering Using Task-Independent Features and Rubric-Guided Features
Authors: Xu Wanyu , McAdams Daniel A. , Polycarpou Andreas A.
Published: November 2023

The increasing student enrollment in engineering and the growing importance of written communication motivate us to implement automated evaluation of written technical documents. This study aims to enhance our understanding of the automatic grading of written assignments in engineering and provides insights into effectively evaluating technical communication. We collected a graded dataset from a mechanical engineering course and manually selected features based on its grading rubric. Manually selected features include both task-independent features and task-dependent features. We evaluated the performance of the selected features on a classification model using grades provided by human graders. The combination of task-independent features and rubric-guided topic features outperformed any other single features or their combinations and reached a moderate agreement with human graders on 3 out of 5 datasets. The results suggest a new way to evaluate students’ assignments based on pre-defined rubrics and provide a significant first step toward building an automated evaluation framework that can provide detailed feedback on students’ complex assignments.
The Synthesis of P-Drivable Single Degree of Freedom Spatial Mechanisms
Authors: Liu Jian , Khan Mohammad Z. , Myszka David H. , Murray Andrew P.
Published: November 2023

For a single degree of freedom spatial mechanism, a reference frame attached to any of its links produces a continuous motion of this frame. Given the progression of this frame from the start through the end of the mechanism’s motion, this paper seeks to identify specific points relative to this moving reference frame. The points of interest are those that can be coupled with a second point determined in the fixed frame to act as the end joint locations for a spherical-prismatic-spherical (SPS) driving chain. If the selection of the point pair is made such that the change in distance between them as the mechanism moves is strictly monotonic, then the SPS chain they define is potentially capable of driving the mechanism over the desired range of motion. This motion is referred to as P-drivable. This avoids singularities encountered by actuating the mechanism at one of its original joints. The proposed approach enables the dimensional synthesis of a single degree-of-freedom mechanism to focus on creating circuit-defect-free solutions. The actuating chain can then be determined as a separate step in the synthesis process. This paper also considers motions that are not P-drivable and the specialization to planar systems with the synthesis of a P-drivable RPR chain.
Sensor Network Design for Permanent Magnet Synchronous Motor Fault Diagnosis
Authors: Kohtz Sara , Zhao Junhan , Renteria Anabel , Lalwani Anand Vikas , Zhang Xiaolong , Haran Kiruba Sivasubramaniam , Senesky Debbie , Wang Pingfeng
Published: November 2023

Optimal sensor placement is a challenge in many engineering design applications, especially within the field of prognostics and health management. Recently, data-driven approaches have become a staple for solving and addressing these challenges. Machine learning techniques have been applied to solve complex optimization problems in the field of signals processing. However, these methods require a substantial amount of data, which can be difficult to obtain. In addition, the design space may be extremely large, so a deterministic approach may not be possible. Therefore, there is a need for probabilistic frameworks that can simultaneously train a classifier for detection of faults as well as selecting new designs for optimal placement. In this paper, the proposed methodology contains a genetic algorithm embedded with a clustering algorithm to simultaneously train the classifier and determine a sensor network. This novel structure is implemented for detecting short-winding faults of a permanent magnet synchronous motor using magnetic field sensors. The training data is simulated using a finite element model, and the design space is extremely large. Nonetheless, the results of the proposed methodology show accuracy for detection of faults.
Understanding the Relation Between Designer Search Strategies and Designer Learning During Design Space Exploration
Authors: Song Hyeonik , Selva Daniel
Published: November 2023

Design Space Exploration (DSE) is a knowledge acquisition technique used from early-stage to detailed engineering design. During DSE, designers systematically generate a range of design alternatives and compare them by their design criteria. DSE allows designers to learn about the design problem, e.g., about design decisions or features that are more common among good designs than among other designs (“driving features”). To help designers learn the driving features, AI-assisted design tools with advanced analysis techniques have been developed. Yet, there has been scant research attention on understanding designers’ exploratory and learning behaviors during DSE, which has the potential to contribute to the development of more effective AI-assisted design tools. To address the research gap, we examine designer learning behaviors in real-time using data from a human subject study (N = 24) that studies human-AI collaboration in DSE. We examine design search strategies (i.e., number of generated designs, and scale of design moves made during design generation) and their relations to designer learning (i.e., knowledge about driving features, and ability to use driving features to generate designs). In this work, we show that the designer’s ability to use driving features to create new designs is associated with their knowledge about the driving features. Also, we observe that designers who generate many designs (mindless creation) achieved lower designer learning than those who generated few designs. We discuss the findings’ potential implications on the development of future AI-assisted design tools.
Exploring How the Design Hierarchy of Needs Explains Correlations Between Design Decisions and Online Customer Review Ratings
Authors: Retzlaff Lisa , Ferguson Scott
Published: November 2023

Understanding and meeting customer needs plays a significant role in the success of released products. Consumer shopping behavior has also moved toward an online experience, where customers learn of, read about, and make selections based on what they see online. Customer reviews offer a wide range of commentary about the shopping, delivery, and product use experience. The comments specific to product use can provide significant insights into where a product is failing to meet (or exceeds) expectations. In this paper, we establish a frame that the impact of design decisions is often reflected in these comments, and that the star rating a product receives can be correlated to how well a product fulfills the Design Hierarchy of Needs. The Design Hierarchy of Needs is an adaptation of Maslow’s Hierarchy of Needs, where needs are associated with the levels of functionality, reliability, usability, proficiency, and creativity. The tenet of the Design Hierarchy of Needs is that a design must first serve lower-level needs (functionality) before addressing higher-level needs (creativity). In this paper, we explore five hypotheses that are posed with the goal of exploring how the Design Hierarchy of Needs can be used for explaining online review star ratings by identifying where products fail to address (or exceed) needs. Reviews from five products across multiple star ratings are coded, and these codes are analyzed to determine the frequency of categories where negative comments about needs are being addressed. We show that the number of negative comments addressing functionality and reliability decrease as the star ratings increase, and that 1- and 2-star reviews are dominated by negative comments about these need levels. We also find that as the star ratings increase, negative comments are focused on the hierarchical levels of proficiency and usability, reflecting the tradeoffs that customers perceive themselves making while using the product. Finally, we also show that positive comments about proficiency and creativity primarily exists only in 4- and 5-star reviews. We believe that these findings can help drive conversations around design decisions when considering the extent to which needs should be addressed, particularly when tied to product cost, and the tradeoffs that are made when addressing mid-tier levels of the hierarchy. This work further establishes that design decisions must be made in the context of a market system, where need fulfilment, cost and performance, and the online shopping experience must be simultaneously considered.
Capturing Local Temperature Evolution During Additive Manufacturing Through Fourier Neural Operators
Authors: Chen Jiangce , Xu Wenzhuo , Baldwin Martha , Nijhuis Björn , Boogaard Ton van den , Gutiérrez Noelia Grande , Narra Sneha Prabha , McComb Christopher
Published: November 2023

High-fidelity, data-driven models that can quickly simulate thermal behavior during additive manufacturing (AM) are crucial for improving the performance of AM technologies in multiple areas, such as part design, process planning, monitoring, and control. However, the complexities of part geometries make it challenging for current models to maintain high accuracy across a wide range of geometries. Additionally, many models report a low mean square error (MSE) across the entire domain (part). However, in each time step, most areas of the domain do not experience significant changes in temperature, except for the heat-affected zones near recent depositions. Therefore, the MSE-based fidelity measurement of the models may be overestimated.

This paper presents a data-driven model that uses Fourier Neural Operator to capture the local temperature evolution during the additive manufacturing process. In addition, the authors propose to evaluate the model using the R2 metric, which provides a relative measure of the model’s performance compared to using mean temperature as a prediction. The model was tested on numerical simulations based on the Discontinuous Galerkin Finite Element Method for the Direct Energy Deposition process, and the results demonstrate that the model achieves high fidelity as measured by R2 and maintains generalizability to geometries that were not included in the training process.
Ocean Wave Energy Conversion With a Spar-Floater System Using a Nonlinear Inerter Pendulum Vibration Absorber
Authors: Gupta Aakash , Tai Wei-Che
Published: November 2023

The inerter pendulum vibration absorber is connected with a power take-off mechanism (called IPVA-PTO) to study its wave energy conversion potential. The resulting IPVA-PTO system is integrated between a spar and a floater (torus) using a ballscrew mechanism. The hydrodynamic stiffness, added mass and radiation damping effects on the spar-floater system are characterized using boundary element method via Ansys Aqwa. It has been observed that a 1:2 internal resonance between the spar-floater system and the pendulum is responsible for nonlinear energy transfer between the two systems. This nonlinear energy transfer occurs when the primary harmonic solution of the system becomes unstable, and a secondary solution emerges in the system characterized by harmonics of frequency half the excitation frequency. As a result of this energy transfer, the vibration of the spar-floater system is suppressed, and the energy is transferred to the pendulum. The focus of this paper is to analyze this 1:2 internal resonance phenomenon near the resonant frequency of the spar. The IPVA-PTO system, when integrated with the spar-floater system, is compared to a linear coupling between the spar and the floater in terms of the response amplitude operator (RAO) of the spar and the energy conversion capability defined by the capture width of the energy converter.
An Experimental Investigation of the Effects of Lubrication on Rattling Motions and Noise of a Gear Pair
Authors: Donmez Ata , Kahraman Ahmet , Handschuh Michael , O’Toole Michael
Published: November 2023

Vibro-impacts are common in various automotive power transmission applications. They lead to noise issues known as rattling. Input or output toque fluctuations cause gear teeth to separate and commit to complex sequences of drive and coast-side impacts spanning the entire backlash zone. Several recent experimental and theoretical studies characterized the underlying nonlinear structure of the rattling behavior of gear systems, ignoring any influences of lubrication conditions. Those rattle models with no lubricant provisions were shown to compare well with the dry contact measurements. This experimental study aims at complementing these earlier studies providing acoustics and torsional vibration measurements from a single gear pair excited by input torque fluctuations as it operates dry, jet-lubricated and dip-lubricated conditions. A rattle test machine equipped with a lubrication system is presented. Corresponding vibro-impact and sound pressure measurements are provided for a wide range of operating and lubrication conditions. Noise and torsional vibration data under different lubrication conditions are compared directly to quantify the influence of lubrication on the resultant rattle behavior. Presented results with dry, dip and jet lubrication show that lubricant provides some additional damping in the process reducing the noise level to a certain extent while overall vibro-impact behavior remains intact.
Non-Smooth Reduced Interface Models and Their Use in Co-Simulation of Mechanical Systems
Authors: Raoofian Ali , Dai Xu , Kövecses József
Published: November 2023

In a co-simulation setup, the entire system is decomposed into a collection of individual subsystems that are interfaced together, with each subsystem being modelled and integrated separately according to its own requirements. To maintain the interconnectivity and consolidation of the primary system, these subsystems must communicate with each other through the interface and transfer certain information at the end points of a defined time interval termed macro time step. Inside the macro time step, the evolution of the interface variables has to be approximated as information about them will only be available again at the end of the step. In real-time simulations, the size of the macro time step and the accuracy of the approximated interface variables are critical factors; if the interface variables are approximated accurately, the size of the macro time step can be kept large enough to provide interactive rates without loss of accuracy and stability. This work focuses on systems where unilateral contact interactions are important and proposes reduced interface model concepts for such non-smooth systems. The use of the proposed reduced interface model (RIM) is demonstrated in co-simulation to provide model-based approximation of the interface variables. The advantages of the proposed method are demonstrated through two representative case studies.
Modeling Semisubmersible Wind Turbine With Inerter-Based Structural Control in the Platform
Authors: Okuda Ryan , Zuo Lei
Published: November 2023

With growing awareness of climate change and an increased interest in renewable energy, resources like offshore wind are projected to grow in the near future. One key issue within offshore wind is how to stabilize the floating system when it experiences large wind and wave forces that impact its performance and shorten its operating life. Researchers have been exploring structural control methods and creating modeling tools to evaluate the performance of the control methods. One such tool is Open-FAST, which is the industry standard for modeling wind turbine dynamics. The goal of this paper is to build the Inerter-based structural control upon the existing capabilities of OpenFAST. Inerter-based structural control methods offer arguably better performance than traditional vibration absorbers, and the configuration proposed in this paper also offers the ability to use a generator as an element in the structural controller. This allows extra energy to be generated along with the improvement in vibration absorption. Through this study, this inerter-based control method is explored through the lens of an established modeling tool to provide the validation for the model to explore which load cases the inerter performs best in and what design considerations must be made. In addition, the energy harvesting potential of the inerter system is evaluated and shown to increase the system’s capabilities especially under stormy ocean conditions.
A Combined Simple/Complex Terramechanics Representation Part 1: Using Machine Learning to Identify DEM Soil Properties From Bevameter Test Data
Authors: Thornton Ben , Pesheck Eric , Jayakumar Paramsothy
Published: November 2023

This study seeks to improve upon existing methods of characterizing soft-soils for mobility simulations which use the Discrete Element Method. The study presents a novel approach for characterizing DEM properties based on Bevameter test data. The proposed method involves training a Reduced Order Model (ROM) to predict Bevameter results from DEM particle properties and using the ROM within a multi-objective optimization framework to determine the DEM properties which best fit a target Bevameter dataset.

To demonstrate the efficacy of the approach, the trained ROM was used to generate DEM models meant to mimic the Bevameter response for a variety of field-tested soils. The accuracy of the resulting DEM models was evaluated using two validation methods. One in which the error in the resulting DEM properties is quantified, and one in which the error in the resulting DEM Bevameter response is quantified.

The findings of this study provide compelling evidence that the proposed approach is a promising method for characterizing the properties of soil for DEM simulations using Bevameter data. The results indicate that certain enhancements are necessary for further refinement of the approach. However, once refined, this method is expected to offer a dependable and efficient means of soil model characterization.
Collecting Product Design Data: Examining the Applicability of Utilizing Gaze Data to Obtain Customer Feedback on Product Designs
Authors: Vrampas George , Kado Corey , Yang Xiaoou , Kames Elisabeth , Morkos Beshoy
Published: November 2023

Studies have shown gaze path data to be helpful in gaining consumer feedback. This study examines the applicability of utilizing eye tracking in collecting consumer preference data on a product’s design. During the study, the subjects are asked to indicate their preferences textually and verbally; concurrently, eye-tracking software collects gaze data from the test subjects. Both datasets are compared for correlation. The robustness of the study is tested against three variables: (1) product type; (2) design fidelity; and (3) product features. The study determines whether gaze data can be applied to a range of different products. The specific sample products used are cars, cellular phones, and microwaves. This tests viability when applied to a variety of products. The study also investigates preference data on various design fidelities: a sketch generated early in the design process (low fidelity), a drawing at an intermediate stage in the process (medium fidelity), or a picture/rendering of the final product (high fidelity). The last variable in the study examines the impact of the number of features that could be altered on each of the products. Due to the diversity of the product set, many features can be changed and analyzed. By comparing the preference data to the gaze data collected for these variables, we find that all product types can be examined; however, it is important to note that lower interest products presented as sketches (lower fidelity) with two changed features most accurately predicted preferences data of consumers.