bluuebunny/crossref_metadata_2025_split huggingface dataset:
Investigation of the Stability of Axially Moving Beams With Discrete Masses
Authors: Ntarladima Konstantina , Pieber Michael , Gerstmayr Johannes 
Published: November 2021

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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.