bluuebunny/crossref_metadata_2025 huggingface dataset:
Lessons Learned From Three Iterative Studies on Creativity Interventions
Authors: Sahar A. , Shu L. H.
Published: November 2021

Previous work by the authors suggested that performing conflict-processing tasks improved subsequent creative output on the Alternative Uses Test (AUT). Although a positive relationship was established, the number of conflict levels was limited, i.e., previous work included only conflict and no-conflict conditions. Two online follow-up studies included an additional high-conflict level to better understand the relationship between conflict processing and creative performance. These two follow-up studies did not replicate the previous study’s results, but revealed similar, yet non-significant trends.

The current paper compares the three studies, emphasizing differences between them, including study environments, instructions, types of tasks used as interventions, and participant backgrounds, etc. Key conclusions relevant to future, particularly online, studies in design creativity and beyond are as follows.

Effective in-person studies may not translate well to online studies, where participant distraction and lack of motivation are more difficult to detect, monitor and control. Imposing a minimum number of correct responses to complete study tasks may reduce the effects of distraction and lack of motivation. Without in-person presence of both the researcher and the study participant, enhanced feedback for online responses may promote comprehension of instructions. However, enabling online participants to ask questions directly can further reduce confusion and improve task completion.
Prediction of Melt Pool Geometry Using Deep Neural Networks
Authors: Milaat Fahad Ali , Yang Zhuo , Ko Hyunwoong , Jones Albert T.
Published: November 2021

Selective laser melting (SLM) is modernizing the production of highly complex metal parts across the manufacturing industry. However, achieving material homogeneity and controlling thermal deformation remain major challenges for metal-based, additive manufacturing. Therefore, adequate control systems are needed to monitor build processes, and ensure part quality throughout production. Traditionally, control designs relied on physics-based knowledge in analyzing, characterizing, and modeling complex, nonstationary patterns. Recent advancements in machine learning techniques harness the abundance of data to discover effective control designs. In this paper, we investigate the efficacy of a data-driven approach towards in-situ modeling of melt-pool geometry. Specifically, we propose a new methodology that uses a deep neural network architecture to predict melt pool geometries with linear regression models, which manifest during in-situ processes. Experimental results show that our deep neural network model with multiple input features produced 84% goodness of fit score, outperforming the model with a single feature that scored 37% for the given dataset, and the monitored regression models. These outcomes promote further investigation into new and efficient ways for acquiring real-time data from in-situ processes. Our contribution complements the way we understand properties of in-situ data, and predict patterns of melt pools, based on artificial cognition.
An Application of Machine Learning to Predict Stiffness Discrimination Thresholds Using Haptics
Authors: Karadoğan Ernur
Published: November 2021

The effectiveness of our interaction with the computer-generated environments is subject to our physical limitations in real life such as our ability of discriminating differences in stiffness or roughness. This ability, represented by Weber fractions, is usually quantified by means of psychophysical experimentation. The experimentation process is tedious and repetitive as it requires the same task to be completed by participants until the mastery at a certain stimulus level can be ensured before moving onto the next level. Moreover, these thresholds are dependent on the tested standard stimulus level and, therefore, need to be identified by separate experiments for every possible standard stimulus level. The purpose of the current study is to reduce the amount of experimentation and predict the thresholds for stiffness discrimination of individuals after being tested at a single stimulus level. The prediction models tested provide a moderate level of prediction power, but more features, potentially physical and demographical in nature, are needed to increase their effectiveness. The procedure described herein can be extended to any modality other than stiffness and, therefore, has the potential to predict overall palpation effectiveness of an individual after a feasible amount of data is obtained through experimentation.
Comparing Change Management Processes for Requirements and Manufacturing: An Interview Based Study
Authors: Sutton Meredith , Summers Joshua D.
Published: November 2021

The study presented in this paper compares requirement and manufacturing change management processes to determine if similar processes can be used for both types of changes. A literature review is used to identify prescribed process stages. Ten stages are identified for both requirements and manufacturing change management. A series of interviews are then conducted with three different population groups to determine the process stages actually used in the field. The resulting process models are compared with the process models from the literature. Further, a thematic analysis is performed on the interview findings. Ultimately, differences are found between the prescribed and practiced change management models for both types of changes. Formal documentation stages are more prevalent for the manufacturing domain, though documentation in practice is less than what is prescribed. This includes the issuance of change requests and change orders in manufacturing change management that are not present in requirement change management processes. Significant differences were also found between the two change types; namely, requirement changes deal with more abstract concepts and as such can afford more informal documentation, whereas manufacturing changes deal with existing artifacts and require solid documentation. Additional research thrusts are identified to help reconcile change management processes across the life cycle.
A Family of Novel Compliant Linear-Motion Mechanisms Based on Compliant Rolling-Contact Element Pivot
Authors: Huo Tonglong , Yu Jingjun , Zhao Hongzhe , Wei Xian
Published: November 2021

Compliant linear-motion mechanisms are of great use in precision machines, due to their excellent performances such as infinite resolution and low cost. The accuracy of the mechanisms is an important consideration for mechanical design in applications, especially in the case of large working load. Considering Compliant Rolling-contact Element (CORE) pivot is characterized with high bearing capacity, the paper adopts it as a building block to design a family of compliant linear-motion mechanisms for heavy load applications. These mechanisms are achieved by replacing four rigid pivots in parallel four-bar mechanism with CORE pivots, and the motion accuracy is improved by means of contacting surfaces design of four CORE pivots. Firstly, the CORE pivot is introduced and five extended arrangements for bearing heavy load are given. Meanwhile, configuration of the compliant linear-motion mechanisms constructed by CORE pivots is obtained. In addition, kinematics of the mechanisms is analyzed and parametric design condition for achieving rectilinear motion is modeled. Based on the condition, detailed topological structures of the mechanisms are designed. Finally, motion simulations are implemented to verify accuracy of the proposed mechanisms. The results demonstrate that the mechanisms proposed in this paper are capable of offering a high-precision linear motion and providing a promising application prospect in precision machines.
Understanding the Energy Behavior of Building Occupants Through the Chronology of Their Energy Interactions
Authors: Preziuso Danielle , Kaminski Gregory , Odonkor Philip
Published: November 2021

The energy consumption of buildings has traditionally been driven by the consumption habits of building occupants. However, with the proliferation of smart building technologies and appliances, automated machine decisions are beginning to impart their influence on building energy behavior as well. This is giving rise to a disconnect between occupant energy behavior and the overall energy consumption of buildings. Consequently, researchers can no longer leverage building energy consumption as a proxy for understanding human energy behavior. This paper addresses this problem by exploiting the habitual and sequential nature of human energy consumption. By studying the chronology of human energy actions, the results of this work present a promising new approach for non-intrusively learning about human energy behavior directly from building energy demand data.
Analysis and Synthesis of Conical Coil Springs
Authors: Patel Harshkumar , Zhou Hong
Published: November 2021

Springs are mechanical devices that are employed to resist forces, store energy, absorb shocks, mitigate vibrations, or maintain parts contacting each other. Spring wires are commonly coiled in the forms of helixes for either extension or compression. Helical springs usually have cylindrical shapes that have constant coil diameter, constant pitch and constant spring rate. Unlike conventional cylindrical coil springs, the coil diameter of conically coiled springs is variable. They have conical or tapered shapes that have a large coil diameter at the base and a small coil diameter at the top. The variable coil diameter enables conical coil springs generate desired load deflection relationships, have high lateral stability and low buckling liability. In addition, conical compression springs can have significantly larger compression or shorter compressed height than conventional helical compression springs. The compressed height of a conical compression spring can reach its limit that is the diameter of the spring wire if it is properly synthesized. The height of an undeformed conical coil spring can have its height of its spring wire if the spring pitch is chosen to be zero. The variable coil diameter of conical coil springs provides them with unique feature, but also raises their synthesis difficulties. Synthesizing conical coil springs that require large spring compression or small deformed spring height or constant spring rate is challenging. This research is motivated by surmounting the current challenges facing conical coil springs. In this research, independent parameters are introduced to control the diameter and pitch of a conical coil spring. Different conical coil springs are modeled. Their performances are simulated using the created models. The deflection-force relationships of conical coil springs are analyzed. The results from this research provide useful guidelines for developing conical coil springs.
Event-Triggered Boundary Control Strategy for a Time Fractional Wave Equation Subject to Boundary Disturbance
Authors: Yuan Zhan-Mei , Zhou Hua-Cheng
Published: November 2021

In this paper, we investigate the event-triggered boundary feedback control problem for an unstable time fractional wave equation with unknown perturbation at the boundary. To cope with the instability of system when there is no disturbance, the backstepping method is adopted to convert the original unstable system into a stable system. An UDE-based estimator based on low-pass filter is proposed to estimate unknown time-varying input disturbance. With the estimation of disturbance, the event-triggered boundary feedback controller is proposed. It is shown that the event-triggered strategy could asymptotically stabilize system and a positive lower bounded of minimum inter-event time is ensured to exclude the Zeno phenomenon.
Evaluating Heuristics in Engineering Design: A Reinforcement Learning Approach
Authors: ElSayed Karim A. , Bilionis Ilias , Panchal Jitesh H.
Published: November 2021

Heuristics are essential for addressing the complexities of engineering design processes. The goodness of heuristics is context-dependent. Appropriately tailored heuristics can enable designers to find good solutions efficiently, and inappropriate heuristics can result in cognitive biases and inferior design outcomes. While there have been several efforts at understanding which heuristics are used by designers, there is a lack of normative understanding about when different heuristics are suitable. Towards addressing this gap, this paper presents a reinforcement learning-based approach to evaluate the goodness of heuristics for three sub-problems commonly faced by designers: (1) learning the map between the design space and the performance space, (2) acquiring sequential information, and (3) stopping the information acquisition process. Using a multi-armed bandit formulation and simulation studies, we learn the suitable heuristics for these individual sub-problems under different resource constraints and problem complexities. Additionally, we learn the optimal heuristics for the combined problem (i.e., the one composing all three sub-problems), and we compare them to ones learned at the sub-problem level. The results of our simulation study indicate that the proposed reinforcement learning-based approach can be effective for determining the quality of heuristics for different problems, and how the effectiveness of the heuristics changes as a function of the designer’s preference (e.g., performance versus cost), the complexity of the problem, and the resources available.
Addressing Challenges to Problem Complexity: Effectiveness of AI Assistance During the Design Process
Authors: Song Binyang , Soria Zurita Nicolás F. , Nolte Hannah , Singh Harshika , Cagan Jonathan , McComb Christopher
Published: November 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 exhibits a reduced advantage in addressing highly complex objectives. During the design process, the individual designers working with AI assistance employ a relatively explorative search strategy, while the individual designers working without AI assistance devote more effort to parameter design.
The Bus Factor in Conceptual System Design: Protecting a Design Process Against Major Regional and World Events
Authors: Van Bossuyt Douglas L. , Arlitt Ryan M.
Published: November 2021

We introduce a method to help protect against and mitigate possible consequences of major regional and global events that can disrupt a system design and manufacturing process. The method is intended to be used during the conceptual phase of system design when functional models have been developed and component solutions are being chosen. Disruptive events such as plane crashes killing many engineers from one company traveling together, disease outbreaks killing or temporarily disabling many people associated with one industrial sector who travel to the same conference regularly, geopolitical events that impose tariffs or complete cessation of trade with a country that supplies a critical component, and many other similar physical and virtual events can significantly delay or disrupt a system design process. By comparing alternative embodiment, component, and low-level functional solutions, solutions can be identified that better pass the bus factor where no one disruptive event will cause a major delay or disruption to a system design and manufacturing process. We present a simplified case study of a renewable energy generation and storage system intended for residential use to demonstrate the method. While some challenges to immediate adoption by practitioners exist, we believe the method has the potential to significantly improve system design processes so that systems are designed, manufactured, and delivered on schedule and on budget from the perspective of significant disruptive events to design and manufacturing.
Goal-Oriented Inverse Design (GoID) of Feedstock Filament for Fused Deposition Modeling
Authors: Deka Angshuman , Nellippallil Anand Balu , Hall John
Published: November 2021

Additive manufacturing (AM) can produce complex geometrical shapes and multi-material parts that are not possible using typical manufacturing processes. The properties of multi-material AM parts are often unknown. For multi-material parts made using Fused Deposition Modeling (FDM), these properties are driven by the filament. Acquiring the properties of the products or the filament necessitates experiments that can be expensive and time-consuming. Thus, there is a need for simulation-based design tools that can determine the multi-material properties of the filament by exploring the complex process-structure-property (p-s-p) relationship.

In this paper, we present a Goal-Oriented Inverse Design (GoID) method to produce feedstock filament for FDM process with specific property goals. Using this method, the designers connects the structure and property in the p-s-p relationship by identifying satisficing material composition for specific property goals. The filament properties identified in the problem are percentage elongation, tensile strength, and Young’s Modulus. The problem is formulated using a generic decision-based design framework, Concept Exploration Framework. The solution space exploration for satisficing solutions is performed using the compromise Decision Support Problem (cDSP). The forward information flow is first established to generate the necessary mathematical relationships between the composition and the property goals. Next, the target property goals of the filament are set. The cDSP is used for solution space exploration to identify satisficing solutions for material composition for the target property goals. While the results are interesting, the focus of our work is to demonstrate, and refine, the goal-oriented, inverse design method for the AM domain.
Can Machine Learning Tools Support the Identification of Sustainable Design Leads From Product Reviews? Opportunities and Challenges
Authors: Saidani Michael , Kim Harrison , Yannou Bernard
Published: November 2021

The increasing number of product reviews posted online is a gold mine for designers to know better about the products they develop, by capturing the voice of customers, and to improve these products accordingly. In the meantime, product design and development have an essential role in creating a more sustainable future. With the recent advance of artificial intelligence techniques in the field of natural language processing, this research aims to develop an integrated machine learning solution to obtain sustainable design insights from online product reviews automatically. In this paper, the opportunities and challenges offered by existing frameworks — including Python libraries, packages, as well as state-of-the-art algorithms like BERT — are discussed, illustrated, and positioned along an ad hoc machine learning process. This contribution discusses the opportunities to reach and the challenges to address for building a machine learning pipeline, in order to get insights from product reviews to design more sustainable products, including the five following stages, from the identification of sustainability-related reviews to the interpretation of sustainable design leads: data collection, data formatting, model training, model evaluation, and model deployment. Examples of sustainable design insights that can be produced out of product review mining and processing are given. Finally, promising lines for future research in the field are provided, including case studies putting in parallel standard products with their sustainable alternatives, to compare the features valued by customers and to generate in fine relevant sustainable design leads.
Recommended Methods Supporting Adoption of the Agile Philosophy for Hardware Development
Authors: Peterson Matthew , Summers Joshua D.
Published: November 2021

The objective of this research is to understand the historical evolution of software development, identify desirable characteristics of methods supporting agile for hardware, and recommend potential methods enabling agile development of hardware products. As technology and markets change, product development increasingly operates in a volatile, uncertain, complex, and ambiguous (VUCA) environment. While originally applied to software development, organizations are starting to see opportunity in adapting the agile philosophy for hardware development. A comparison of philosophies is made between waterfall, spiral, and agile development. The historical evolutions of software development, after agile, including Continuous Integration Continuous Deployment (CICD), Development and Operations (DevOps), and Development Security and Operations (DevSecOps) is presented. Benefits and challenges in the application of agile methods for hardware are presented. Benefits include improvements in flexibility in response to change and soft factors such as team communication, transparency and commitment. However, many challenges still remain. These are grouped into theme areas including lack of product flexibility, difficulty in separating deliverables, challenges with breaking down tasks within a sprint, changes needed in culture and mindset, difficulty scaling beyond pilot programs, team distribution, and development of an integrated approach across the product lifecycle. Potential methods to aid in the adoption of agile for hardware are discussed using the phases of the hardware development lifecycle as a framework. Recommended methods include the adaptation of Model Based Systems Engineering (MBSE) for problem definition, the use of generative methods for design, the application of Virtual Reality (VR) for prototyping, leveraging additive manufacturing for production, and favoring software defined systems to help in operations. By reducing both the duration and person-hours, these methods enable higher iteration rates for hardware products needed for an agile philosophy. The paper concludes with a discussion on future research efforts supporting the enabling agile development of hardware.
Design and Kinematic Simulation of a Novel Leg Mechanism for Multi-Legged Robots
Authors: Asci Simone , Zhang Ketao
Published: November 2021

Among mobile robotic research field, legged locomotion is largely applied for advanced robotic systems due to the higher degree of versatility compared to wheeled robots, which allows them to successfully move and interact in unstructured environments; nevertheless, legged robots present several designing problems and require a much more complex control system. Based on an effective robotic leg, this paper presents a novel design, which integrates a cam joint, aimed to improve the versatility performances minimizing changes in the original model and without increasing the control complexity. Furthermore, the design strategy aims to exploit the coupled action of two actuators, which are disposed in a novel configuration so to gain versatility advantage while maintaining velocity performances of legs equipped with a single actuator. The model is presented through a kinematic analysis, followed by the simulation of the leg mechanism trajectory and a comparison with the original configuration.
Methods for Shape Fitting in Morphing Compliant Mechanisms
Authors: Yellowhorse Alden , Rommers Jelle , Amoozandeh Ali , Herder Just L.
Published: November 2021

While compact folding is desirable for applications such as deployable mechanisms, achieving this with compliant mechanisms can be challenging. One reason for this is that the relaxed and stressed states of the mechanism are known and the loads producing the transition are unknown. The relaxed state is determined by the desired, deployed state and the stressed geometry is determined by the storage space. Approaches for solving this problem often require significant software development or cannot address problems in three dimensions. To address this problem, this work describes a method for designing 3D compliant mechanisms that can fold compactly. If the stressed and relaxed geometry are specified, an algebraic method can be used to find loads which best approximate the desired geometry. A least-squares approach is used to minimize error. A simplification of this method in two dimensions is also described. To further enhance the accuracy of the shape approximation, a method for varying the beam bending stiffness is described. For comparison, an inverse finite-element solver was implemented and paired with an optimizer and used to solve the same problem. Both methods were used to design a compliant, compactly folding beam. These results were compared with results from a commercial, finite-element software package.
Modification of Robotics Curriculum for Project-Based First Year Engineering Design Course
Authors: Nolte Hannah , Tan Xiaomei , Weaver Alexander , Starkey Elizabeth
Published: November 2021

Texas Instruments (TI) currently offers a comprehensive curriculum to supplement their Robotics System Learning Kit MAX (TI-RSLK MAX). While designed for all college students, TI would like to provide a modification of the curriculum specifically for first-year engineering design courses. The goal of this project was to modify the current TI-RSLX MAX curriculum, making it easier to digest for first-year engineering students and more applicable to shorter duration projects (∼8 weeks), while still being information dense. To better understand the user experience of the current TI-RSLK MAX curriculum and determine what users would want from a modified curriculum, user interviews were conducted and analyzed using inductive qualitative analysis techniques. Results showed that while the current TI robotics project has many positives, first-year students primarily struggled with the coding aspect of the project. These results directed and informed the prototype development of a modified curriculum module focused on teaching students code. Low- and medium-fidelity prototypes were developed and tested with users. Data from the usability study were analyzed using descriptive analysis techniques and results indicate that the medium-fidelity prototype helped students learn robotics coding. This research has implications for a high-fidelity modified TI-RSLX MAX curriculum and robotics education for first-year engineering design students.
Model Order Reduction for a Piecewise Linear System Based on Dynamic Mode Decomposition
Authors: Saito Akira
Published: November 2021

This paper presents a data-driven model order reduction strategy for nonlinear systems based on dynamic mode decomposition (DMD). First, the theory of DMD is briefly reviewed and its extension to model order reduction of nonlinear systems based on Galerkin projection is introduced. The proposed method utilizes impulse response of the nonlinear system to obtain snapshots of the state variables, and extracts dynamic modes that are then used for the projection basis vectors. The equations of motion of the system can then be projected onto the subspace spanned by the basis vectors, which produces the projected governing equations with much smaller number of degrees of freedom (DOFs). The method is applied to the construction of the reduced order model (ROM) of a finite element model (FEM) of a cantilevered beam subjected to a piecewise-linear boundary condition. First, impulse response analysis of the beam is conducted to obtain the snapshot matrix of the nodal displacements. The DMD is then applied to extract the DMD modes and eigenvalues. The extracted DMD mode shapes can be used to form a reduction basis for the Galerkin projection of the equation of motion. The obtained ROM has been used to conduct the forced response calculation of the beam subjected to the piecewise linear boundary condition. The results obtained by the ROM agree well with that obtained by the full-order FEM model.
Investigate the Influence of Online Ratings and Reviews in Purchase Behavior Using Customer Choice Sets
Authors: Lin Kangcheng , Kim Harrison M.
Published: November 2021

The exponentially growing online reviews have become a great wealth of information into which many researchers have started tapping. Using online reviews as a source of customer feedback, product designers are able to better understand customers’ preferences and improve product design accordingly. However, while predicting future product demand as a function of product attributes and customer heterogeneity has proved to be effective, not many literatures have studied the impact of non-product-related features, such as number of reviews and average ratings, on product demand using a large-scale dataset. As such, this paper proposes a data-driven methodology to investigate the influence of online ratings and reviews in purchase behavior by using discrete choice analysis. In the absence of information about the true customer choice set, we generate an estimated customer choice set based on a probability sampling using customer clustering and product clustering. In order to examine the effect of number of reviews and average rating, we have computed, for all the laptops in the choice set of each customer, the number of reviews and thus average rating at the date of this particular customer’s review. Using laptops for our case study, our experiment has shown that the number of reviews and average ratings are statistically significant, and the inclusion of these features will greatly improve the predictive ability of the model.
Exploration of the Digital Innovation Process in the Smart Product-Service System
Authors: Saymeh Haneen A. F. , Zhang Xiangying , Peng Tao , Tang Renzhong , Wang Zuoxu , Zheng Pai
Published: November 2021

The dissemination of digital technologies enabled massive data generation and new forms of cooperation. Hence, a new type of innovation named digital innovation emerged. Digital innovation has evolved in different domains and contexts, one of which is called Smart Product-Service System (Smart PSS). Many research works have been done on the development of Smart PSS by leveraging various digital technologies and exploiting its digital capabilities along its lifecycle to generate personalized and innovative solutions. However, there are scarcely any works to explore how the digital innovation process can be manifested to fully exploit digital capabilities throughout Smart PSS development. Aiming to fill this gap, this research provides a theoretical basis for digital innovation in Smart PSS and investigates how it can be in line with the development of Smart PSS from an engineering lifecycle perspective. This work also provides a definition of Smart PSS from the context of digital innovation. A case study on smart office chair is employed to demonstrate the digital innovation process in the usage stage. This work can provide insight and guidance for Smart PSS development and further harness its digital innovation process.
There Is No “I” in Team but There Is in Innovation: How Individual Attributes Impact Team Ideation and Selection Practices
Authors: Peng Aoran , Hunter Sam , Miller Scarlett R.
Published: November 2021

While teaming is a vital component of engineering, it is important to remember that there is no team without individuals and individual behavior can drive team outputs. One of these individual factors that may manifest itself at the team level is individual risk-taking attitudes, which can be impacted by personality and preferences for creativity. However, a gap exists in research on the impact of the team composition in these factors on creative outputs, as previous research has found that team composition plays a key role in team performance. The current work was developed to examine how the elevation and diversity of team personality and preferences for creativity impact the novelty and quality of ideas generated and selected by the team through a simulation study of 60,831 simulated teams. The results of this study show that the novelty and quality of ideas generated and selected can be predicted by the elevation and diversity of the simulated teams’ personality and PCS factors. In addition, it was found that the impact of the individual elevation and diversity factors can vary depending on the ideation trait (novelty and quality) and design stage (concept generation and selection). However, there was one variable that had a consistent positive impact on team behavior — the elevation of the Creative Confidence of the Simulated Team Members. These findings provide preliminary evidence on how individual attributes manifest themselves at the team level to impact team ideation and selection performances.
Toward Computer Aided Visual Analogy Support (CAVAS): Augment Designers Through Deep Learning
Authors: Zhang Zijian , Jin Yan
Published: November 2021

The goal of this research is to develop a computer-aided visual analogy support (CAVAS) framework that can augment designers’ visual analogical thinking by providing relevant visual cues or sketches from a variety of categories and stimulating the designer to make more and better visual analogies at the ideation stage of design. The challenges of this research include what roles a computer tool should play in facilitating visual analogy of designers, what the relevant and meaningful visual analogies are at the sketching stage of design, and how the computer can capture such meaningful visual knowledge from various categories through analyzing the sketches drawn by the designers. A visual analogy support framework and a deep clustering model, called Cavas-DL, are proposed to learn a latent space of sketches that can reveal the shape patterns for multiple categories of sketches and at the same time cluster the sketches to preserve and provide category information as part of visual cues. The latent space learned serves as a visual information representation that captures the learned shape features from multiple sketch categories. The distance- and overlap-based similarities are introduced and analyzed to identify long- and short-distance analogies. Extensive evaluations of the performance of our proposed methods are carried out with different configurations, and the visual presentations of the potential analogical cues are explored. The evaluation results and the visual organizations of information have demonstrated the potential of the usefulness of the Cavas-DL model.
Forward Kinematics for Suspended Under-Actuated Cable-Driven Parallel Robots: A Neural Network Approach
Authors: Mishra Utkarsh A. , Caro Stéphane
Published: November 2021

Kinematic analysis of under-constrained Cable-Driven Parallel Robots has been a topic of interest because of the inherent coupling between the loop-closure and static equilibrium equations. The paper proposes an unsupervised neural network algorithm to perform real-time forward geometrico-static analysis of such robots in a suspended configuration under the action of gravity. The formulation determines a non-linear function approximation to model the problem and proves to be efficient in solving for consecutive and close waypoints in a path. The methodology is applied on a six-degree-of-freedom (6-DOF) spatial under-constrained suspended cable-driven parallel robot. Specific comparison results to show the effectiveness of the proposed method in tracking a given path and degree of constraint satisfaction are presented against the results obtained from non-linear least-square optimization.
Towards a Rational, Narrative-Based Design Framework for Navigating Radical Uncertainty in Engineering Design
Authors: Bryden Kenneth M. , Ferguson Scott
Published: November 2021

This paper examines decision making under radical uncertainty in engineering design, that is, engineering decision making in those situations where it is not possible to know the outcomes and/or construct the utility functions and probabilities needed to support rational-human decision making. In these situations, despite being faced with radical uncertainty, engineers do (and must) proceed forward in a linear, clear, and predictable manner. Yet, they may not proceed in a manner that is well described by current engineering design frameworks. Examining the role of decision making in business and other social enterprises, Tuckett and Nikolic [1] have proposed conviction narrative theory (CNT) to describe how rational decision-makers confronted with situations in which insufficient information is available to support traditional decision-making tools use narrative and intuition to reach convincing and actionable decisions. This paper proposes that, in a manner similar to what is described in CNT, narrative and engineering judgment play a critical role in engineering design situations dominated by radical uncertainty. To that end, this paper integrates the traditional rational-human view of decision making as expressed by Hazelrigg in the well-known Decision-Based Design (DBD) framework and CNT as proposed by Tuckett and Nikolic. In the resulting rational, narrative-based design framework, narrative structures are used to describe and develop design alternatives and provide the ideas, beliefs, and preferences needed by the DBD framework. The resulting preferred design is expressed as a narrative and tested using engineering judgement. Specifically, the goal of the design process is expressed as a high-level guiding narrative that fosters the development of design narratives (design alternatives), and ultimately results in a convincing narrative that describes the preferred design. The high-level guiding narrative outlines the event(s), entity(s), preferences, and beliefs needed to support the design. The design narratives are narrative fragments that are nested within the high-level narrative and include the proposed action (idea), the specific challenges that the design faces, and the possible (but not yet verified) outcomes. The convincing narrative is the validated, preferred option that results from the DBD analysis and optimization process and is reviewed using engineering judgement. Following development of the rational, narrative-based design framework, the value of the framework is discussed within the context of practical engineering design.
Is Expensive More Environment Friendly? Comparative LCA of Three Home Appliances
Authors: Shaukat M. Mobeen , Masood Hammad , Merah Neçar , Al-Badour Fadi
Published: November 2021

Due to rise in income and living standards in the developing world, there is a substantial growth in the use of home appliances. This growth is responsible for several environmental issues. Hence, there is a growing demand for energy efficient, environment friendly, and sustainable appliances. This study compares the environmental impacts of three home appliances using Life Cycle Assessment (LCA). Three irons of same power rating but with different prices (low, medium, and high) were selected for this study. First, energy consumption of these irons was measured and then they were disassembled to generate a detailed list of components, materials, and processes used to manufacture them. Next, LCA was conducted using SimaPro to compare the environmental impacts of these irons. Life cycle stages including material extraction, component manufacturing, assembly, distribution, and use were considered in LCA. Both ReCiPe mid-point and end-point environmental impacts were calculated. The results of this study showed that high-price iron was more environment friendly than the low-price iron.
Frequency Tunable Phononic Crystal Flat Lens for Subwavelength Imaging
Authors: Danawe Hrishikesh , Tol Serife
Published: November 2021

In this paper, we present a thickness-contrast based flat lens for subwavelenth imaging in an aluminum plate. The lens is made of phononic crystal (PC) with a triangular lattice arrangement of through holes drilled over an aluminum plate. Subwave-length imaging is achieved by exploiting the concept of negative refraction of A0 plate mode for the optical dispersion branch of the PC. The wavenumbers are matched at a design frequency by creating a step change in the thickness of the PC-lens and host plate. The thickness-contrast results in refractive index of minus one at the interface of the lens and host plate. Negative refraction-based lens overcomes the diffraction limit and enables focusing of flexural waves in an area less than a square wavelength. We validate the flat lens design at a single design frequency through numerical simulations and experiments. Further, we numerically demonstrate the tunability of the lens design over a broadband frequency range by modifying the thickness-contrast between the lens and host plate. The proposed frequency tunable design is promising for many applications such as ultrasonic inspection, tetherless energy transfer, and energy harvesting, where the localization of wave energy in a small spot is desirable.
The Mixed-Body Model: A Method for Predicting Large Deflections in Stepped Cantilever Beams
Authors: Sargent Brandon S. , Ynchausti Collin R. , Nelson Todd G. , Howell Larry L.
Published: November 2021

This paper presents a method for predicting endpoint coordinates, stress, and force to deflect stepped cantilever beams under large deflections. This method, the Mixed-Body Model or MBM, combines small deflection theory and the Pseudo-Rigid-Body Model for large deflections. To analyze the efficacy of the model, the MBM is compared to a model that assumes the first step in the beam to be rigid, to finite element analysis, and to the numerical boundary value solution over a large sample set of loading conditions, geometries, and material properties. The model was also compared to physical prototypes. In all cases, the MBM agrees well with expected values. Optimization of the MBM parameters yielded increased agreement, leading to average errors of < 0.01 to 3%. The model provides a simple, quick solution with minimal error that can be particularly helpful in design.
Reduction of Whole Body Vibration in a Wide Frequency Range Using Inflation Pressure Control of Air Bladder Cushion
Authors: Nuthi Pavan , Gu Yixin , Nasirian Aida , Lindsay Alexandra , Purandare Himanshu , Haldipurkar Nischita , Shah Kashish , Wijesundara Muthu B. J.
Published: November 2021

Several types of interfaces like foam and inflated air cells exist to reduce the effect of mechanical vibration experienced in human-machine interfaces in different scenarios such as transportation. However, their vibration attenuation performance in a wide frequency range relevant to whole body vibration (1–80 Hz) leaves much to be desired. In this study, we investigate the effect of inflation pressure on the vibration attenuation behavior of an air cell cushion. An experimental setup capable of conducting frequency sweep tests and regulating inflation pressure in an air cell array cushion was developed. Frequency sweep tests were conducted at various inflations and the vibration transmissibilities at static inflations were plotted. A dynamic inflation scheme was developed based on the apriori knowledge of inflation dependent transmissibilities. Furthermore, the closed loop behavior of the inflation scheme was evaluated with a frequency sweep test. The resulting closed loop transmissibility indicated better vibration attenuation performance than any single static inflation for the air cell array cushion in the range of frequencies relevant to whole body vibration. This result lays the groundwork for potential air cell cushions which modify their inflation dynamically through a direct feedback from sensors like accelerometers to attenuate vibration in a wide frequency range.
An Enhanced Squared Exponential Kernel With Manhattan Similarity Measure for High Dimensional Gaussian Process Models
Authors: Xu Yanwen , Wang Pingfeng
Published: November 2021

The Gaussian Process (GP) model has become one of the most popular methods and exhibits superior performance among surrogate models in many engineering design applications. However, the standard Gaussian process model is not able to deal with high dimensional applications. The root of the problem comes from the similarity measurements of the GP model that relies on the Euclidean distance, which becomes uninformative in the high-dimensional cases, and causes accuracy and efficiency issues. Limited studies explore this issue. In this study, thereby, we propose an enhanced squared exponential kernel using Manhattan distance that is more effective at preserving the meaningfulness of proximity measures and preferred to be used in the GP model for high-dimensional cases. The experiments show that the proposed approach has obtained a superior performance in high-dimensional problems. Based on the analysis and experimental results of similarity metrics, a guide to choosing the desirable similarity measures which result in the most accurate and efficient results for the Kriging model with respect to different sample sizes and dimension levels is provided in this paper.
Reflections on Designing in the Wild: How Theories of Design Information Manifest in Practice
Authors: Damen Nicole B. , Toh Christine A.
Published: November 2021

Information acquisition, utilization and communication are integral to the design process, but systematic investigation of information behavior is complicated by its variety and the ways in which designers engage with information throughout the design process. Our previous work developed a theoretical framework to categorize the various types of information used during the design process, known as the Information Archetypes Framework. This paper explores how these information dimensions manifest in design practice, as reflected on by experienced practicing designers. Deep qualitative analysis of eight interviews with practicing designers revealed that the designers intentionally adapt their behavior to match situation specific needs, and navigate the tensions between information dimensions through trajectories and loops.
Examining Goal Congruence on Engineering Design and Innovation Student Teams
Authors: Beckman Sara , Jian Alan , Sabharwal Ahan , Goucher-Lambert Kosa
Published: November 2021

Goal congruence, defined as agreement by all members of a team on a common set of objectives, has been positively associated with team cohesion, team performance and team outcomes, including grades earned. Yet there is little in-depth study at scale and across types of engineering design and innovation classes in higher education that examines the goals students set for their work together. This research explores goal congruence in 857 teams involving 1470 students across 18 classes over four years. To examine goal congruence, we use student assessments of their level of agreement on their goals as well as evaluations of their written goal statements. Machine learning techniques are used to automatically identify goal types and congruence between goals. We find that goal congruence on student teams is relatively low, even when they assess it as high, partly due to variety in the types of goals they identify. We categorize the goals students articulate for their teams into grade-, completion-, teaming-, learning-, problem-, output- and outcome-oriented goals and report variance in the types of goals identified in different pedagogical settings. Our findings have implications for how faculty design their classes, link learning outcomes to team projects and facilitate goal setting on student teams.