bluuebunny/crossref_metadata_2025 huggingface dataset:
Some (Team) Assembly Required: An Analysis of Collaborative Computer-Aided Design Assembly
Authors: Cheng Kathy , Olechowski Alison
Published: November 2021

Previous efforts in the area of collaborative computer-aided design (CAD) suggest that a team of designers working synchronously in a multi-user CAD (MUCAD) environment can produce CAD models faster than a single user. Our research is the among the first to investigate assemblies in MUCAD. Due to the lack of hierarchical feature dependency in assemblies, we propose that CAD teams can optimize assembly through modularization and parallel execution. In our study, 20 participants were tasked with assembling pre-modelled CAD parts of varying complexity in teams of one, two, three or four. We analyze audio recordings, team activity, and survey responses to compare the performance of individuals and virtual collaborative teams during assembly, while working with the same MUCAD platform.

This paper features a multimodal approach to analyze team trends in communication, workflow, task allocation and challenges to determine which factors are conducive to the success of a multi-user CAD team and which are detrimental. In our work, the success of a team is measured by its productivity score, which is the number of mates added by a team within a given time frame. We present evidence that teams can complete an assembly in less calendar time than a single user, but single users are more efficient based on person-hours, due to communications and coordination overheads. Surprisingly, paired contributors exhibit an assembly bonus effect. These findings represent a preliminary understanding of collaborative CAD assembly work. Our work supports the claim that collaborative assembly activities have the potential to improve the capabilities of modern product design teams, delivering products faster and at lower cost. We identify areas for future research, and highlight areas of improvement for collaborative CAD platforms and engineering design teams.
A New Paradigm for the Enjoyment and Exploitation of Cultural Heritage Based on Spatial Augmented Reality: The Case of the Ducal Palace of Urbino
Authors: Leopardi Alma , Ceccacci Silvia , Mengoni Maura
Published: November 2021

In the last years, museums have begun to apply new technological solutions to manage their exhibits in a more open, inclusive, and creative way, to improve the visitors’ experience to respond to the need to expand the audience. The main goal is to face the increasing competition in an economy referred to as the “Experience Economy”. To this end, Augmented Reality technology seems to represent a good solution for museum guide systems, to improve visitors’ learning and enjoyment.

In this context, the present paper proposes a museum guide system based on Spatial Augmented Reality powered by dynamic projection. The paper describes the overall HW and SW system architecture and reports in detail the developed process adopted to design and implement a museum guide and entertainment application, in the context of the “Studiolo of Federico da Montefeltro” in the Ducal Palace of Urbino. A preliminary survey has been carried out, which involved a total of 79 subjects, aimed at investigating the quality of visitor’s experience, aroused by the proposed application, in terms of the “Four Experience Realms” defined by Pine & Gilmore (1998).

Results suggest that the proposed application can be used to stage experiences that satisfy the visitors and may help to enable museums into the Experience Economy.
Nonlinear Analysis of a Class of Inversion-Based Compliant Cross-Spring Pivots
Authors: Li Shiyao , Hao Guangbo , Chen Yingyue , Zhu Jiaxiang , Berselli Giovanni
Published: November 2021

This paper presents a nonlinear model of an inversion-based generalized cross-spring pivot (IG-CSP) using the beam constraint model (BCM), which can be employed for the geometric error analysis and the characteristic analysis of an inversion-based symmetric cross-spring pivot (IS-CSP). The load-dependent effects are classified in two ways, including structure load-dependent effects and beam load-dependent effects, where the loading positions, geometric parameters of elastic flexures, and axial forces are the main contributing factors. The closed-form load-rotation relations of an IS-CSP and a non-inversion-based symmetric cross-spring pivot (NIS-CSP) are derived with consideration of the three contributing factors for analyzing the load-dependent effects. The load-dependent effects of IS-CSP and NIS-CSP are compared when the loading position is fixed. The rotational stiffness of the IS-CSP or NIS-CSP can be designed to increase, decrease, or remain constant with axial forces, by regulating the balance between the loading positions and the geometric parameters. The closed-form solution of the center shift of an IS-CSP is derived. The effects of axial forces on the IS-CSP center shift are analyzed and compared with those of a NIS-CSP. Finally, based on the nonlinear analysis results of IS-CSP and NIS-CSP, two new compound symmetric cross-spring pivots are presented and analyzed via analytical and FEA models.
Synthesis of Defect-Free Peaucellier Mechanism and Potential Implications for Energy Harvesting
Authors: Almandeel Ali , Aladwani Abdulaziz , Ali Hessein
Published: November 2021

Cantilevered beams with piezoceramic layers are typically used to generate electrical energy; hence, a base excitation on a harvester is required. This work investigates the use of a link-type mechanism called the Peaucellier mechanism to actuate piezoelectric energy harvesters. The Peaucellier mechanism is known to trace an exact straight line, providing harmonic motion, which is exploited here for exciting a bimorph piezoelectric cantilever beam. To generate the required base excitation, a function generation synthesis methodology for designing a defect-free Peaucellier mechanism driven by a dyad (PMD) is proposed, in addition to an example being provided to confirm the efficacy of the method. The harmonic motion involves two design variables (frequency and amplitude) which are key parameters and can be tuned to generate the required electrical power. It was determined that PMD could excite the energy harvester, generating an electrical power of approximately 4.52 μ W at low frequency. The synthesis generated a mean absolute error of 0.061 m/s2 confirming an excellent match between the points of the input-output and desired acceleration. The results confirm that the Peaucellier mechanism is suitable for the actuation of energy harvesters where parasitic power harvesting is required in different practical applications, including robotics and stationary machines.
Generation of Continuous Toolpaths for Additive Manufacturing Using Implicit Slicing
Authors: Steuben J. C. , Michopoulos J. G. , Iliopoulos A. P.
Published: November 2021

The generation of footpaths for additive manufacturing (AM), a process commonly known as “slicing,” has a strong impact on the performance of both the associated hardware systems and the resulting objects. Available slicers invariably produce discontinuous tootpaths, featuring jumps or so-catted “travel moves” during which the deposition of material or/and energy must be hatted. For AM processes using slowly solidifying feedstock materials, such as thermosetting polymers or cementitious mixtures such as concrete, these tootpath discontinuities are highly undesirable due to the artifacts they generate. This renders existing sticers difficult to use in such applications, and presents a road-block to the adoption of AM for such material systems. In the present work, this difficulty is addressed by the development of a simple geometric criterion for the existence of continuous tool-paths that are capable of producing a specified input geometry. This development is based on the principles of morphological geometric analysis and graph theory. It is shown that, for any geometric feature with a characteristic thickness at least twice the extrusion width, a continuous toolpath exists. Furthermore, a general-purpose algorithm for continuous toolpath generation, for arbitrarily shaped objects satisfying this criterion, is developed and demonstrated on a representative test problem. Finally, conclusions and the path forward for the usage of this approach with existing AM systems is explored.
A Feature Encoding Approach and a Cloud Computing Architecture to Map Fishing Activities
Authors: Galdelli A. , Mancini A. , Frontoni E. , Tassetti A. N.
Published: November 2021

Monitoring fish stocks and fleets’ activities is key for Marine Spatial Planning. In recent years Vessel Monitoring System and Automatic Identification System have been developed for vessels longer than 12 and 15m in length, respectively, while small scale vessels (< 12m in length) remain untracked and largely unregulated, even though they account for 83% of all fishing activity in the Mediterranean Sea. In this paper we present an architecture that makes use of a low-cost LoRa/cellular network to acquire and process positioning data from small scale vessels, and a feature encoding approach that can be easily extended to process and map small scale fisheries. The feature encoding method uses a Markov chain to model transitions between successive behavioural states (e.g., fishing, steaming) of each vessel and classify its activity. The approach is evaluated using k-fold and Leave One Boat Out cross-validations and, in both cases, it results in significant improvements in the classification of fishing activities. The use of a such low-cost and open source technology coupled to artificial intelligence could open up potential for more integrated and transparent platforms to inform coastal resource and fisheries management, and cross-border marine spatial planning. It enables a new monitoring strategy that could effectively include small-scale fleets and support the design of new policies oriented to the optimal use of marine resources.
Optimal Modular Remanufactured Product Configuration and Harvesting Planning for End-of-Life Products
Authors: Kim Jinju , Park Seyoung , Kim Harrison M.
Published: November 2021

Since remanufacturing requires additional processes compared to the production process of new products, various factors need to be considered. First, it is necessary to decide which end-of-life (EoL) product parts/modules to use among the EoL products available for the remanufactured product. At this stage, it is crucial to understand the future customer demand and requirements for each part. Next, it is also necessary to figure out whether selective disassembly is possible to disassemble a specific target component without completely disassembling the product. With the increasing number of product designs that are difficult to disassemble, the disassembly sequence and level should be considered for the efficiency of the overall remanufacturing process. This study proposes an integrated model to (i) find configuration design suitable for remanufactured products that can maximize customer utility based on current EoL products, and (ii) establish a harvest plan that determines the optimal operations and levels. This proposed model can be used as a tool that helps product designers find the appropriate design of remanufactured products while increasing the efficiency of the remanufacturing process.
Nonlinear Dynamics Simulation of Bending Deflection for Composite Laminated Plate Under Varied Temperature Using Lyapunov Exponent Parameter
Authors: Yousuf Louay S.
Published: November 2021

This paper study the nonlinear dynamics behavior of the bending deflection of composite laminated plate based on largest Lyapunov exponent parameter. Wolf algorithm is used to quantify largest Lyapunov exponent in the presence of aspect ratios and fiber volume fractions. A power spectrum analysis has been added using the amplitude of Fast Fourier Transform (FFT) to detect the non-periodic motion of the bending deflection of the composite plate. The simulation process is done using ANSYS software Ver. 18.2. The temperature gradient of thermal shock is varied between (60C° and −15C°) through the laminate thickness. The experiment setup has been done through heating and cooling rig test environment. The non-periodic motion of the bending deflection is decreased with the increasing of aspect ratios, while the non-periodic motion of the bending deflection is increased wit the increasing of fiber volume fractions.
Test Bench for Characterization and Durability Tests of Motorbike Clutches
Authors: Previati G. , Gobbi M.
Published: November 2021

In the paper, a new test bench for characterization and durability assessment of multi disc wet clutches for motorbike applications is presented. The design of the new test bench is inspired by the current SAE standards J2490 and J286 that refer to the testing of friction material for automatic transmissions wet clutches. Differently from the test bench described in the two standards, which is mainly designed to test the friction material, the test bench presented in this paper has been designed for testing the whole clutch, comprising all discs and the actuation mechanism. The lubrication system of the clutch utilized on the test bench is very similar to the one present on the actual motorbike. The design and instrumentation of the new test rig is analysed and discussed in the paper. The test bench has been used for the characterization of different motorbike clutches. The bench has proved to be able to measure the clutch characteristic for different temperatures and clutch wear levels. A very good repeatability of the tests has been achieved allowing for a comparison of different clutches design, friction material properties and operating conditions (temperature, ..). Some experimental results are presented and discussed in the paper.
Kinematic Design of Deployable Structures With Low Actuation Requirements Based on Pop-Up Folding
Authors: Montano Eduardo E. , Peraza Hernandez Edwin A.
Published: November 2021

This paper presents the kinematic modeling and design of deployable structures inspired by pop-up books. These pop-up structures can exhibit large changes in area and volume through deployment motion that resembles opening the pages of a book. The pop-up structures have a modular topology and are formed by multiple parallelepiped units, here termed as pop-up units. The analysis of the kinematics of single pop-up units and assemblies of these that form larger structures is presented. An algorithm that integrates multiple pop-up units to form structures that approximate two-dimensional and three-dimensional target shapes when deployed is subsequently devised. The algorithm ensures that the structures formed by the assemblies of multiple pop-up units retain the single degree of freedom of a pop-up unit. The stored strain energy of these structures, which can provide the means to deploy them in practice, is also analyzed. Finally, various examples showing the applicability of the design algorithm in the synthesis of pop-up structures that approximate a diverse set of two-dimensional and three-dimensional target shapes are provided. The pop-up structures can be applied to a large spectrum of applications that need extensive deployment from small volumes while requiring a low number of degrees of freedom. These applications may include aerospace structures and MEMS.
A New Robotic Hand Based on the Design of Fingers With Spatial Motions
Authors: Hamon Pol , Chablat Damien , Plestan Franck
Published: November 2021

This article presents a new hand architecture with three under-actuated fingers. Each finger performs spatial movements to achieve more complex and varied grasping than the existing planar-movement fingers. The purpose of this hand is to grasp complex-shaped workpieces as they leave the machining centres. Among the taxonomy of grips, cylindrical and spherical grips are often used to grasp heavy objects. A combination of these two modes makes it possible to capture most of the workpieces machined with 5-axis machines. However, the change in grasping mode requires the fingers to reconfigure themselves to perform spatial movements. This solution requires the addition of two or three actuators to change the position of the fingers and requires sensors to recognize the shape of the workpiece and determine the type of grasp to be used. This article proposes to extend the notion of under-actuated fingers to spatial movements. After a presentation of the kinematics of the fingers, the problem of stability is discussed as well as the transmission of forces in this mechanism. The complete approach for calculating the stability conditions is presented from the study of Jacobian force transmission matrices. CAD representations of the hand and its behavior in spherical and cylindrical grips are presented.
Investigation of the Stability of Axially Moving Beams With Discrete Masses
Authors: Ntarladima Konstantina , Pieber Michael , Gerstmayr Johannes
Published: November 2021

The present paper addresses axially moving beams with co-moving concentrated masses while undergoing large deformations. For the numerical modeling, a novel beam finite element is introduced, which is based on the absolute nodal coordinate formulation extended with an additional Eulerian coordinate to represent the axial motion. The resulting formulation is well known as Arbitrary Lagrangian Eulerian (ALE) method, which is often used for axially moving beams and pipes conveying fluids. As compared to previous formulations, the present formulation allows us to introduce the Eulerian part by an independent coordinate, which fully incorporates the dynamics of the axial motion, while the shape functions remain independent of the beam coordinates and are thus constant. The proposed approach, which is derived from an extended version of Lagrange’s equations of motion, allows for the investigation of the stability of axially moving beams for a certain axial velocity and stationary state of large deformation. A multibody modeling approach allows us to extend the beam formulation for co-moving discrete masses, which represent concentrated masses attached to the beam, e.g., gondolas in ropeway systems, or transported masses in conveyor belts. Within numerical investigations we show that a larger number of discrete masses behaves similarly as the case of (continuously) distributed mass along the beam.
Does It Matter Where Design Teams Come From in Design Studies?
Authors: Milovanovic Julie , Gero John , Becker Kurt
Published: November 2021

Professionals need to collaborate with multiple stakeholders in product development to stay competitive and to innovate. Through their values and mission, companies develop a specific working environment that can lead to the development of design methods and tools. In this article, we study design team dynamics of professional engineers working in two different organizations. We aim at identifying differences in team behaviors between teams drawn from two different organizations. The goal is twofold. At a theoretical level, we aim at gaining a better understanding of the effect of work culture on design team behaviors. At a methodological level, we explore whether grouping teams from different organizations into a single larger sample to obtain better reliability is relevant. To do this, we compared two cohorts of teams based on which company engineers worked at. Both companies are international organizations employing more than 50,000 collaborators worldwide. Teams of three engineers worked on designing a next-generation personal assistant and entertainment system for the year 2025. We analyzed each team’s design interactions and behaviors using quantitative tools (Multiple Factor Analysis and Correspondence Analysis). Results from this exploratory analysis highlight different behaviors between cohorts as well as a common overall approach to team design thinking.
Gradient-Enhanced Multifidelity Neural Networks for High-Dimensional Function Approximation
Authors: Nagawkar Jethro , Leifsson Leifur
Published: November 2021

In this work, a novel multifidelity machine learning (ML) algorithm, the gradient-enhanced multifidelity neural networks (GEMFNN) algorithm, is proposed. This is a multifidelity extension of the gradient-enhanced neural networks (GENN) algorithm as it uses both function and gradient information available at multiple levels of fidelity to make function approximations. Its construction is similar to the multifidelity neural networks (MFNN) algorithm. The proposed algorithm is tested on three analytical functions, a one, two, and a 20 variable function. Its performance is compared to the performance of neural networks (NN), GENN, and MFNN, in terms of the number of samples required to reach a global accuracy of 0.99 of the coefficient of determination (R2). The results showed that GEMFNN required 18, 120, and 600 high-fidelity samples for the one, two, and 20 dimensional cases, respectively, to meet the target accuracy. NN performed best on the one variable case, requiring only ten samples, while GENN worked best on the two variable case, requiring 120 samples. GEMFNN worked best for the 20 variable case, while requiring nearly eight times fewer samples than its nearest competitor, GENN. For this case, NN and MFNN did not reach the target global accuracy even after using 10,000 high-fidelity samples. This work demonstrates the benefits of using gradient as well as multifidelity information in NN for high-dimensional problems.
3D Printed Food Design and Fabrication Approach for Manufacturability, Rheology, and Nutrition Trade-Offs
Authors: Mahmoud Rahmatul , Nguyen Quang , Christopher Gordon , Egan Paul F.
Published: November 2021

3D printing enables the production of personalized designs that are desirable in the medical industry for applications including orthopedics, tissue engineering, and personalized nutrition. Currently, the design process relies on trial-and-error approaches, especially for biomaterial development, and there is a need for methodologies to streamline the design process to facilitate automation. Here, we investigate a design methodology for printing foods by mixing novel biomaterial combinations informed by rheological measurements that indicate printability. The process consists of first printing basic designs with chocolate, marzipan, and potato biomaterials known to print consistently. Rheological measurements are collected for these materials and compared to a novel pumpkin biomaterial. The pumpkin had a higher complex modulus and lower mechanical loss tangent than all other biomaterials, therefore motivating the addition of rheological agents to reach more favorable properties. Varied concentrations of corn starch and guar gum were added to the pumpkin to improve printability while altering the nutrient distribution. A 4% inclusion of guar gum provided the most consistent pumpkin prints. A complex 3D object was fabricated with the 4% guar gum pumpkin material, therefore demonstrating the merits in using rheological properties to inform printability for use in design automation routines. The design approach enabled comparisons of relative nutrition and printability trade-offs to demonstrate a proof-of-concept user interface for design automation to facilitate customized food production. Further research to develop a complete design methodology for linking rheological properties to printability would promote consistent prediction of print quality for novel formulations to support design automation, with potential generalizability for diverse biomaterials.
Scalable3-BO: Big Data Meets HPC - A Scalable Asynchronous Parallel High-Dimensional Bayesian Optimization Framework on Supercomputers
Authors: Tran Anh
Published: November 2021

Bayesian optimization (BO) is a flexible and powerful framework that is suitable for computationally expensive simulation-based applications and guarantees statistical convergence to the global optimum. While remaining as one of the most popular optimization methods, its capability is hindered by the size of data, the dimensionality of the considered problem, and the nature of sequential optimization. These scalability issues are intertwined with each other and must be tackled simultaneously. In this work, we propose the Scalable3-BO framework, which employs sparse GP as the underlying surrogate model to scope with Big Data and is equipped with a random embedding to efficiently optimize high-dimensional problems with low effective dimensionality. The Scalable3-BO framework is further leveraged with asynchronous parallelization feature, which fully exploits the computational resource on HPC within a computational budget. As a result, the proposed Scalable3-BO framework is scalable in three independent perspectives: with respect to data size, dimensionality, and computational resource on HPC. The goal of this work is to push the frontiers of BO beyond its well-known scalability issues and minimize the wall-clock waiting time for optimizing high-dimensional computationally expensive applications. We demonstrate the capability of Scalable3-BO with 1 million data points, 10,000-dimensional problems, with 20 concurrent workers in an HPC environment.
Predicting a Paradigm Shift: Exploring the Relationship Between Cognitive Style and the Paradigm-Relatedness of Design Solutions
Authors: Cole Courtney , Marhefka Jacqueline , Jablokow Kathryn , Mohammed Susan , Ritter Sarah , Miller Scarlett
Published: November 2021

Nearly 60 years ago, Thomas Kuhn revolutionized how we think of scientific discovery and innovation when he identified that scientific change can occur in incremental developments that improve upon existing solutions, or it can occur as drastic change in the form of a paradigm shift. In engineering design, both types of scientific change are critical when exploring the solution space. However, most methods of examining design outputs look at whether an idea is creative or not and not the type of creativity that is deployed or if we can predict what types of individuals or teams is more likely to develop a paradigm-shifting idea. Without knowing how to identify who will generate ideas that fit a certain paradigm, we do not know how to build teams that can develop ideas that better explore the solution space. This study provides the first attempt at answering this question through an empirical study with 60 engineering design student teams over the course of a 4- and 8-week design project. Specifically, we sought to identify the role of cognitive style using KAI score, derived from Kirton’s Adaption-Innovation (A-I) theory, on the paradigm-relatedness of ideas generated by individuals and teams. We also sought to investigate the role of crowdsourcing for measuring the paradigm-relatedness of design solutions. The results showed that KAI was positively related to a greater likelihood of an individual’s idea being categorized as paradigm-breaking. In addition, the team KAI diversity was also linked to a greater likelihood of teams’ ideas being categorized as paradigm-challenging. Finally, the results support the use of crowdsourcing for measuring the paradigm-relatedness of design solutions.
A Probabilistic Approach for Estimating the Environmental Impact of Novel Product Concepts
Authors: Ferrero Vincenzo , Hoyle Chris , DuPont Bryony
Published: November 2021

Global concerns about climate change and resource management have escalated the need for sustainable consumer products. In light of this, sustainable design methodologies that supplement the product design process are needed. Current research focuses on developing sustainable design curricula, adapting classical design methods to accommodate environmental sustainability, and sustainability tools that are applicable during the early design phase. However, concurrent work suggests that sustainability-marketed and innovative products still lack a reduction of environmental impact compared to conventional products. Life cycle assessment (LCA) has proven to be an exceptional tool used to assess the environmental impact of a realized product. However, LCA is a reactive tool that does not proactively reduce the environmental impact of novel product concepts. Here we develop a novel methodology, the PeeP method, using historical product LCA data with kernel density estimation to provide an estimated environmental impact range for a given product design. The PeeP method is tested using a series of case studies exploring four different products. Results suggest that probability density estimations developed through this method reflect the environmental impact of the product at both the product and component level. In the context of sustainable design research, the PeeP method is a viable methodology for assessing product design environmental impact prior to product realization. Our methodology can allow designers to identify high-impact components and reduce the cost of product redesign in practice.
Research on Multi-Dimensional Information Service Oriented to Innovative Process Planning
Authors: Li Jun , Guo Xin , Zhao Wu
Published: November 2021

The importance of process planning in manufacturing systems has been widely recognized. Process planning is a complex process with abundant information, intensive knowledge and miscellaneous experience, which leads to many challenges in its innovation. Innovation has been supported by innovative methods and information integration. However, the existing research on innovative design theory and information integration only focused on product design but rarely process planning. Therefore, it is urgent to study how to systematically use multi-dimensional information to influence process planning and realize innovative process design. An model for innovative process planning was proposed. And the general strategy of process innovation design was established. The process knowledge management and application methods were put forward, and the knowledge service system for innovative process was established. In order to support process innovation in practice, the framework of innovative process design service platform was established. Finally, a case is taken to illustrate the feasibility of the proposed method. The result shows that the proposed method can guide the designers to produce innovative process plans rapidly to solve the shortcomings of traditional process plans.
Leveraging Design Heuristics for Multi-Objective Metamaterial Design Optimization
Authors: Suresh Kumar Roshan , Srivatsa Srikar , Silberstein Meredith , Selva Daniel
Published: November 2021

Design optimization of metamaterials and other complex systems often relies on the use of computationally expensive models. This makes it challenging to use global multi-objective optimization approaches that require many function evaluations. Engineers often have heuristics or rules of thumb with potential to drastically reduce the number of function evaluations needed to achieve good convergence. Recent research has demonstrated that these design heuristics can be used explicitly in design optimization, indeed leading to accelerated convergence. However, these approaches have only been demonstrated on specific problems, the performance of different methods was diverse, and despite all heuristics being “correct”, some heuristics were found to perform much better than others for various problems. In this paper, we describe a case study in design heuristics for a simple class of 2D constrained multiobjective optimization problems involving lattice-based metamaterial design. Design heuristics are strategically incorporated into the design search and the heuristics-enabled optimization framework is compared with the standard optimization framework not using the heuristics. Results indicate that leveraging design heuristics for design optimization can help in reaching the optimal designs faster. We also identify some guidelines to help designers choose design heuristics and methods to incorporate them for a given problem at hand.
Supporting Designer Learning and Performance in Design Space Exploration: A Goal-Setting Approach
Authors: Chaudhari Ashish M. , Suresh Kumar Roshan , Selva Daniel
Published: November 2021

Design space exploration (DSE) is an important knowledge discovery process in the early design phase of complex systems. The outcomes of this process generally include the performance of the designs generated and designer learning. The latter broadly refers to the designer’s knowledge of the mapping between the design space and the objective space. Despite the integration of visual and data analytics in DSE, there is a lack of emphasis on a human designer’s learning as a basis for increasing the effectiveness of DSE. To address this gap, we investigate the use of goal-setting as a motivating factor to improve DSE outcomes. Previous research suggests that the goal of designing (i.e., finding good designs) and the goal of learning (i.e., learning useful knowledge) are inextricably interlinked. We test the hypothesis that giving designers an explicit goal of learning vs an explicit goal of designing generates different learning and performance outcomes, despite the two goals being interlinked. To this hypothesis, we conduct a between-subject experiment in which participants (N = 14) use a DSE tool to explore mechanical metamaterial designs. Subjects in the first conditions are incentivized to maximize the number of correct answers in learning tests administered after using the tool. Subjects in the second condition are incentivized to maximize the performance of designs they generate. The results show that the subjects with the goal of learning perform better on the learning tests, with a large but mildly significant effect. Whereas, the subjects with the designing goal generate better design performance, with a small but significant effect. This study suggests that there may exist a trade-off between the designing and learning goals, despite their interconnections, and designers can target one at the expense of the other through goal-setting.
Using an SPH-Based Continuum Representation of Granular Terrain to Simulate the Rover Mobility
Authors: Hu Wei , Zhou Jason , Serban Radu , Negrut Dan
Published: November 2021

We use the Smoothed Particle Hydrodynamics (SPH) method to determine the dynamics of granular material in its interaction with a four-wheel rover. The goal of the simulation is to investigate the mobility of the rover while operating on granular terrains. In order to speed up the simulation, we employ a continuum model to capture the dynamics of the deformable terrain. The rover wheel geometry is defined through a mesh. The granular material is modeled as an elasto-plastic continuum that dynamically interacts with the rigid wheels of the rover in a Chrono [1] co-simulation setup. The interaction between each wheel and the granular terrain is handled through so-called Boundary Conditions Enforcing (BCE) particles which are attached to the rover wheel. Several simulations are performed to assess the rover robustness for operation in flat (with obstacles), uphill, downhill, and side-tilted mobility scenarios.
Designing and 3D Printing Lab Equipment for Mechanical Vibrations Course and Laboratory: Work in Progress
Authors: Lewis Josh , Estrada Benjamin , Pena Paul , Garcia Martin , Tekes Ayse
Published: November 2021

Undergraduate mechanical engineering students struggle in comprehending the fundamentals presented in an introductory level mechanical vibrations course which eventually affects their performance in the posterior courses such as control theory. One salient factor to this is missing the visualization of the concept with hands-on learning since the vibrations and control laboratory course is offered in the following semester. This study presents the design, development of three portable and 3D-printed compliant vibratory mechanisms actuated by a linear motor and their implementation in vibrations course and vibrations and control laboratory. The proposed setups consist of flexible and compliant springs, sliders, and base support. Mechanisms are utilized to demonstrate free and forced vibrations, resonation, and design of a passive isolator. In addition to the 3D-printed, portable lab equipment, we created the Matlab Simscape GUI program of each setup so instructors can demonstrate the fundamentals in the classroom, assign homework, project, in-class activity or design laboratory.
A Deep Learning Approach for Product Detection in Intelligent Retail Environment
Authors: Pazzaglia Giulia , Mameli Marco , Frontoni Emanuele , Zingaretti Primo , Pietrini Rocco , Manco Davide , Placidi Valerio
Published: November 2021

A planogram is the graphical representation of the way a given number of products are positioned within the shelves in a store. The creation of a correct planogram is a fundamental tool for a store’s performance: it helps to increase sales and achieve maximum customer satisfaction by reducing out-of-stocks. To this end, this work aims to provide an automatic object recognition based system that allows the operator to verify the correctness of a planogram. For image acquisition, either low-cost battery-powered cameras positioned on the opposite side of the shelf or simply a tablet with a dedicated app can be used. These tools are connected to the cloud where the detection and matching phases are performed. The experimental results come from a real environment.
SDF-Based Inverse Process Design of Solar Cells Using Molecular Dynamics Simulations
Authors: Ghumman Umar Farooq , van Beek Anton , Munshi Joydeep , Chien TeYu , Balasubramanian Ganesh , Chen Wei
Published: November 2021

Despite their lower weight, cheaper production cost, and diminished demand of metals, Organic Photovoltaic Cells (OPVCs) have not replaced conventional solar cells. The reason for this is their relatively low power conversion efficiency that could be a consequence of a lack in understanding of the underlying physics. More specifically, the influence of processing conditions and microstructure morphology on OPVC performance is still an open research area. In a previous study, we presented a spectral density function (SDF)-based design framework to optimize material performance with respect to its quasi-random microstructure (such as an OPVC). However, to guarantee manufacturability, the influence of processing conditions to material performance needs to be considered. In this study we present a two-step inverse design scheme which first identifies the optimal key microstructure descriptor(s) and then the optimal processing conditions. Dividing the design problem in two steps greatly benefits tractability as it allows the use of SDF reconstruction to reduce the dimensionality of the processing conditions. Subsequently, fewer costly high fidelity coarse-grained molecular dynamics simulations (or physical experiments) are required to identify the optimal processing conditions. We apply the introduced framework to optimize the performance of an OPVC with respect to volume fraction (i.e., chemical compositions) and annealing temperature. The inverse design approach results in a potential ten-fold decrease in the computational cost compared to direct process-performance optimization.
Evolutionary Grasp Planning for Sheet Metal Parts With Multi-Type Grippers
Authors: Ali Tribaldos Jicmat , Sen Chiradeep
Published: November 2021

Grasping sheet metal objects for manufacturing operations requires custom-made robot-mounted end-effectors to grip the parts. Modern end-effectors use multi-type grasp where a combination of gripper types such as suction cups, magnets, and fingers may be used. This paper presents a genetic algorithm-based approach of grasp design automation. The algorithm first generates an option space of possible grasping locations by analyzing the geometry of the sheet metal part and then uses a genetic algorithm to optimize the grasp using up to five magnets and suction cups. The algorithm includes as fitness criteria the factor of safety of the total gripping force against part weight, the unbalanced moment created by the gripping forces and part weight, the cost of the grasp, and three combinations of these parameters. The GA features asexual reproduction, mutation, and elitism. The algorithm is implemented in the Siemens NX™ Knowledge Fusion language and on Microsoft VBA code. The paper presents detailed test results and sensitivity analyses that indicate that genetic algorithms can produce viable solutions for multi-type grasp configurations and that the algorithm behaves in response to varying its control parameters in ways that are logically anticipated.
When the Going Gets Tough: Exploring Changes in the Startup Landscape Due to the Challenges of 2020
Authors: Mahan Tobias , Saravanan Pratima , Krishnakumar Sandeep , Nolte Hannah , McComb Christopher , Menold Jessica
Published: November 2021

Crowdfunding platforms like Kickstarter provide new product developers with a novel and readily accessible platform to interact with potential customers, seek funding, and gain valuable feedback from target users. Successful startup design processes are often praised due to startups’ agility and ability to adapt to changing markets under extreme resource constraints. The COVID-19 pandemic changed the market in a number of significant ways, and publicly available data on Kickstarter provides valuable insight into the fundamental ways that product developers adapt to volatiles markets created by a global crisis. Leveraging linguistic analysis, topic modelling, k-means clustering, and principal component analysis, startup data was analyzed to identify overarching trends in startup launch campaigns across years. Specifically, this work identifies emerging trends in the Kickstarter market in 2020 to understand how external factors affect launch practices. Subtle yet significant changes in Kickstarter campaigns show that new clusters of campaigns emerged during 2020, not present in prior years, indicating that product developers adapted language to meet the needs of a changing market. Findings motivate a more thorough investigation of the relationships between startup market trends and external events to build a deeper understanding of the fundamental ways developers adapt design processes due to external stimuli.
The Human Error and Functional Failure Reasoning Framework: How Does It Scale?
Authors: Irshad Lukman , Demirel H. Onan , Tumer Irem Y.
Published: November 2021

The goal of this research is to demonstrate the applicability of the Human Error and Functional Failure Reasoning (HEFFR) framework to complex engineered systems. Human errors are cited as a root cause of a majority of accidents and performance losses in complex engineered systems. However, a closer look would reveal that such mishaps are often caused by complex interactions between human fallibilities, component vulnerabilities, and poor design. Hence, there is a growing call for risk assessments to analyze human errors and component failures in combination. The HEFFR framework was developed to enable such combined risk assessments. Until now, this framework has only been applied to simple problems, and it is prone to be computationally heavy as complexity increases. In this research, we introduce a modular HEFFR assessment approach as means of managing the complexity and computational costs of the HEFFR simulations of complex engineered systems. Then, we validate the proposed approach by testing the consistency of the HEFFR results between modular and integral assessments and between different module partitioning assessments. Next, we perform a risk assessment of a train locomotive using the modular approach to demonstrate the applicability of the HEFFR framework to complex engineered systems. The results show that the proposed modular approach can produce consistent results while reducing complexity and computational costs. Also, the results from the train locomotive HEFFR analysis show that the modular assessments can be used to produce risk insights similar to integral assessments but with a modular context.
Not Good Enough? Exploring Relationships Between Students’ Empathy, Their Attitudes Towards Sustainability, and the Self-Perceived Sustainability of Their Solutions
Authors: Prabhu Rohan , Alsager Alzayed Mohammed , Starkey Elizabeth
Published: November 2021

Empathy plays an important role in designers’ ability to relate to problems faced by others. Several researchers have studied empathy development in engineering design education; however, a majority of this work has focused on teaching designers to empathize with primary users. Little attention in empathy development research is given to empathizing with those affected in a secondary and tertiary capacity. Moreover, little research has investigated the role of students’ empathy in influencing their emphasis on sustainability, especially in the concept evaluation stage. Our aim in this paper is to explore this research gap through an experimental study with engineering students. Specifically, we introduced first-year engineering students at a large public university in the northeastern United States to a short workshop on sustainable design. We compared changes in their trait empathy and attitudes towards sustainability from before to after participating in the workshop. We also compared the relationship between students’ trait empathy, attitudes towards sustainability, and the self-perceived sustainability of their solutions in a design task. From our results, we see that students reported an increase in their beliefs and intentions towards sustainability and a decrease in their personal distress from before to after participating in the workshop. Furthermore, students’ trait empathy correlated negatively with the self-perceived sustainability of their solutions. These findings highlight the need for future work studying the role of empathy in encouraging a sustainable design mindset among designers.
Sketch-Based Mechanism Simulation Using Machine Learning
Authors: Nurizada Anar , Purwar Anurag
Published: November 2021

This paper presents a machine learning approach for building an object detector for interactive simulation of planar linkages from handmade sketches and drawings found in patents and texts. Touch- and pen-input devices and interfaces have made sketching a more natural way for designers to express their ideas, especially during early design stages, but sketching existing complex mechanisms can be tedious and error-prone. While there are software applications available to help users make drawings, including that of a linkage mechanism, it is both educational and instructive to see existing sketches come to life via automated simulation. However, texts and patents present rich and diverse styles of mechanism drawings, which makes automated recognition difficult. Modern machine learning algorithms for object recognition require an extensive number of training images. However, there are no data sets of planar linkages available online. Therefore, our first goal was to generate images of sketches similar to hand-drawn ones and use state-of-the-art deep generation models, such as β-VAE, to produce more training data from a limited set of images. The latent space of β-VAE was explored by linear and spherical interpolations between sub-spaces and by varying latent space’s dimensions. This served two-fold objectives — 1) examine the possibility of generating new synthesized images via interpolation and 2) develop insights in the dependence of latent space dimension on bar linkage parameters. t-SNE dimensionality reduction technique was implemented to visualize the latent space of a β-VAE in a 2D space. Training images produced by animation rendering were used for fine-tuning a real-time object detection system — YOLOv3.
Search for exotic spin-dependent interactions with a spin-based amplifier
Authors: Su Haowen , Wang Yuanhong , Jiang Min , Ji Wei , Fadeev Pavel , Hu Dongdong , Peng Xinhua , Budker Dmitry
Published: November 2021

Advanced quantum sensor enables a search for spin physics beyond the standard model.
The lungfish cocoon is a living tissue with antimicrobial functions
Authors: Heimroth Ryan Darby , Casadei Elisa , Benedicenti Ottavia , Amemiya Chris Tsuyoshi , Muñoz Pilar , Salinas Irene
Published: November 2021

The mucus cocoon of terrestrialized African lungfish is a living tissue with potent antimicrobial functions.