bluuebunny/crossref_metadata_2025_split huggingface dataset:
Promoting Energy-Efficient Driving Using Associative Graphical Displays: Can a Cup of Coffee Encourage You to Drive More Smoothly?
Authors: Potvin-Bernal J. , Shu L. H. 
Published: November 2019

Abstract
Substantial energy savings during the use phase of internal-combustion and electric automobiles can be achieved by increasing eco-driving behavior, particularly reduced acceleration and braking. However, motivating widespread adoption of this behavior is challenging, with obstacles including incompatibility with drivers’ values and priorities, and disassociation between drivers’ actions and observable consequences. Efforts focused on informational approaches, e.g., training programs and educational campaigns, are both difficult to scale up and largely ineffective, with drivers reluctant to make long-term changes. Alternatively, behavior can be influenced by redesigning the context within which the behavior occurs. Such an intervention must be effective across demographics and underlying behaviors to achieve ubiquity. The current study investigates the perceived effect on driving style of a simple graphical dashboard display depicting an animated coffee cup. This display incorporates associative mental models and contextual relevance to increase the salience of inefficient vehicle movements and nudge drivers to adopt a smoother driving style. An online Amazon-Mechanical-Turk survey with 92 participants revealed a significant preference for the coffee cup over two other displays when controlling for demographic variables. This result offers preliminary evidence suggesting that greater success at promoting eco-driving may be achieved by using a behavioral nudge.
Designing Lightweight Tensegrity-Based Structures and Materials of Tailorable Thermal Expansion
Authors: Silverman Rochelle E. , Hernandez Edwin A. Peraza 
Published: November 2019

Abstract
In this work, we analyze and design structures and materials that possess custom thermal expansion. These structures and materials are composed of a base unit inspired by the tensegrity “D-bar” (or double-pyramid) topology. We derive, for the first time, analytical equations for the linearized and geometrically exact coefficients of thermal expansion (CTEs) of bi-material D-bar structures with arbitrary shape and complexity. Numerical results obtained using the geometrically exact CTE equations are compared with results obtained using the linearized CTE equations, showing that the latter are accurate only in cases where temperature changes are small. Further results show that D-bar structures of low complexity can produce a wide range of CTEs, including positive, zero, and negative values. These CTE values are exhibited for a range of materials that allows for easy manufacturing (materials with CTE ratios as low as 2). Thus, it is concluded that D-bar structures show promise for applications in aerospace engineering and other fields where new materials of tailorable thermal expansion are needed to decrease the substantial thermal stresses caused by large temperature changes.
On the Use of Multi-Disciplinary Optimisation Methods for Road Vehicle Passive Suspension Design
Authors: Yang Liunan , Ballo Federico , Previati Giorgio , Gobbi Massimiliano , Mastinu Gianpiero 
Published: November 2019

Abstract
Two widely used decomposition-based multi-disciplinary optimisation (MDO) methods, namely analytical target cascading (ATC) and collaborative optimisation (CO), are applied to the design of the suspension system of a road vehicle. Instead of directly optimising the spring stiffness and the damping coefficient, three parameters of the spring and three parameters of the damper are selected as design variables. Discomfort, road holding, and the total mass of the spring-damper system, are considered as objective functions. An investigation is completed to analyse the performance of the two decomposition methods compared with the conventional all-in-one (AiO) formulation in terms of efficiency and applicability.
Design of a Robotic Cannula for Robotic Lumber Discectomy
Authors: Ding Yang , Johnson Benjamin V. , Cappelleri David J. 
Published: November 2019

Abstract
In this paper, the design of the robotic cannula for minimally invasive robotic lumbar discectomy is presented. Lumbar discectomy is the surgery to remove the herniated disc material that is pressing on a nerve root or spinal cord. Recently, a robotic approach to performing this procedure has been proposed that utilizes multiple teleoperated articulated instruments inserted into the surgical workspace using a single cannula. In this paper, we propose a new robotic cannula system to work in conjunction with this new procedure. It allows for the independent teleoperated control of the axial position and rotation of up to three surgical instruments at the same time. The mechanical design, controller design, and prototype of new system are presented in this paper demonstrating a fully functioning device for this application. A novel worm gear and rack system allow for the instrument translation while and embedded gear trains produce the rotational movement. Steady-state errors of less than 50 μm for translation and less than 2° for rotational motion are obtained.
Influence of the Case Properties After Nitriding on the Load Carrying Capacity of Highly Loaded Gears
Authors: Sitzmann André , Tobie Thomas , Stahl Karsten , Schurer Stefan 
Published: November 2019

Abstract
The load carrying capacity of highly loaded gears can be increased by thermochemical surface treatments such as nitriding or case hardening. In contrast to case hardening, the nitriding treatment is carried out at lower process temperatures and therefore creates lower distortion. As a result, grinding after nitriding is usually not necessary. Nitrided gears are ordinarily characterized by a thin, high-hardness, a few micrometers thick compound layer of iron and alloy element nitrides directly on the surface and a subsequent diffusion layer reaching more deeply into the material. Nitriding, therefore, provides an alternative to case hardening for distortion-sensitive components and offers potential for cost savings in the production of highly loaded gears.
This publication will focus on the influence of nitriding on the load carrying capacity of highly loaded gears. In addition, this paper summarizes the current state of knowledge of nitrided gears and gives an insight into current research in the field of nitrided gears. In particular, the influence of the compound layer on the tooth root bending strength and the flank load carrying capacity achieved within the research project FVA 386 II is discussed.
Affine ICP for Fine Localization of Smart-AGVs in Smart Factories
Authors: Yılmaz Abdurrahman , Temeltaş Hakan 
Published: November 2019

Abstract
With the emergence of the concept of Industry 4.0, smart factories have started to be planned in which the production paradigm will change. Automated Guided Vehicles, abbreviated as AGV, that will perform load carrying and similar tasks in smart factories, Smart-AGVs, will try to reach their destinations on their own route instead of predetermined routes like in today’s factories. Moreover, since they will not reach their targets in a single way, they have to dock a target with their fine localization algorithms. In this paper, an affine Iterative Closest Point, abbreviated as ICP, based fine localization method is proposed, and applied on Smart-AGV docking problem in smart factories. ICP is a point set registration method but it is also used for localization applications due to its high precision. Affine ICP is an ICP variant which finds affine transformation between two point sets. In general, the objective function of ICP is constructed based on least square metric. In this study, we use affine ICP with correntropy metric. Correntropy is a similarity measure between two random variables, and affine ICP with correntropy tries to maximize the similarity between two point sets. Affine ICP has never been utilized in fine localization problem. We make an update on affine ICP by means of polar decomposition to reach transformation between two point sets in terms of rotation matrix and translation vector. The performance of the algorithm proposed is validated in simulation and the efficiency of it is demonstrated on MATLAB by comparing with the docking performance of the traditional ICP.
A Comparative Study of Metal Additive Manufacturing Processes for Elevated Sustainability
Authors: Min Wenbo , Yang Sheng , Zhang Ying , Zhao Yaoyao Fiona 
Published: November 2019

Abstract
Metal additive manufacturing (AM) processes have gone through a compound growth over the past decade, and the technology is widely applied in industries like aerospace, automobile and bio-medical fields. There is an increasing need to understand and improve its sustainability given the high profile of existing environmental challenges. This paper aims at developing a precise comparative model for the three major metal AM processes (including Laser Powder Bed Fusion (LPBF), Electron Beam Melting (EBM), and Direct Energy Deposition (DED)) with respect to environmental performance assessment with a future goal of providing closed-loop feedbacks for design optimization with elevated sustainability. To improve the precision of previously reported models, new factors including embodied impacts of machine and recycled powder, operation patterns, system lifespan and batch size, are considered. A topologically optimized rocket bracket made of Ti6Al4V is used as an example to investigate the environmental performance of the three processes. The results showed that given the same design solution, the EBM had the lowest environmental impacts for low batch size, while the DED excelled at production efficiency.
Requirement Generation: Lecture Intervention Impact on Variety and Novelty
Authors: Elena Maria Vittoria , Summers Joshua D. 
Published: November 2019

Abstract
The study presented here explores the influence that an educational intervention has on students in generating requirements during a design task. An experiment was performed in a fourth-year level mechanical engineering design course in which student participants are given a design problem for which they generated a list of requirements, a lecture on requirements in engineering design, and a second design problem for which they generated a second list of requirements. The results from the two problems were tested for determine whether the problems were similar in terms of variety and novelty of requirements generated. The effects of the lecture were evaluated by comparing results from the pre- and post-lecture activity with the two problems. Variety was assessed by sorting the individual requirements generated by each participant into eighteen categories. Novelty was evaluated on the level of uniqueness of the requirement against the complete set generated by all participants, based on both syntax and semantic filtering. The findings suggest that the lecture had a positive impact on the students in increasing the variety of the requirements generated individually by the participants. Further, all the identified novel requirements belonged to the activity performed after lecture.
Second Order Reliability Method for Time-Dependent Reliability Analysis Using Sequential Efficient Global Optimization
Authors: Hu Zhangli , Du Xiaoping 
Published: November 2019

Abstract
Reliability depends on time if the associated limit-state function includes time. A time-dependent reliability problem can be converted into a time-independent reliability problem by using the extreme value of the limit-state function. Then the first order reliability method can be used but it may produce a large error since the extreme limit-state function is usually highly nonlinear. This study proposes a new reliability method so that the second order reliability method can be applied to time-dependent reliability analysis for higher accuracy while maintaining high efficiency. The method employs sequential efficient global optimization to transform the time-dependent reliability analysis into the time-independent problem. The Hessian approximation and envelope theorem are used to obtain the second order information of the extreme limit-state function. Then the second order saddlepoint approximation is use to evaluate the reliability. The accuracy and efficiency of the proposed method are verified through numerical examples.
Modeling Known Design Concepts and Managing Their Functional Coupling Modules to Balance Upstream Objectives and Downstream Constraints for New Product Development
Authors: Wang Chu-Yi , Lu Stephen C.-Y. 
Published: November 2019

Abstract
Product development involves many trade-offs between upstream objectives (e.g., customer needs) and downstream constraints (e.g., modularity, manufacturability, etc.) Conceptual design, when design concepts are generated and improved, is the most important product development stage when these tradeoffs are made. However, when designers try to improve a known design concept of a current product during product development, it is often difficult for them to be both creative (i.e., meeting new objectives) and practical (i.e., incorporating existing constraints) at the same time. This paper presents a method that models known design concepts and manages their functional couplings for designers to achieve these difficult balances systematically during concept improvement. The modeling involves three steps. The first is to represent the known design concept as a dual-hierarchy by decomposing it into sub-concepts and identifying their functional requirements. The second is to find “functional schematics,” defined as a design matrix with minimal complexity, to manage functional couplings of the sub-concepts. The third is to build the executable modules based on the functional schematics. The model help designers achieve the most desirable level of upstream-downstream balance. A coffeemaker example is included to show how such a model of design concept is created and its design coupling modules are managed. The result also suggests that this approach can be used to create a “product family” from the base (or an existing) product to meet continually changing market demands.
Actuated Dual-Slip Model of Planar Slope Walking
Authors: Galindo Raul Lema , Weimholt Elise , Schmiedeler James P. 
Published: November 2019

Abstract
The planar dual spring-loaded inverted pendulum (dual-SLIP) model is a well-established passive template of human walking on flat ground. This paper applies an actuated extension of the model to walking on inclines and declines to evaluate how well it captures the behavior observed in human slope walking. The motivation is to apply the template to improve control of humanoid robot walking and/or intent detection in exoskeleton-assisted walking. Gaits of the actuated planar dual-SLIP model are found via the solution of a constrained nonlinear optimization problem in ten parameters. The majority of those parameters define the actuation scheme that injects energy for incline walking and absorbs energy for decline walking to achieve periodic, nonconservative gaits. Solution gaits across the speed range of 1.0 to 1.6 ms and slope range of −10 to 10 degrees exhibit some of the characteristics of human walking, such as the effect of slope on stance duration, step frequency, and step length. Efforts to reduce the number of parameters optimized by enforcing relationships observed in the solution gaits proved unsuccessful, suggesting that future work must trade off model complexity with fidelity of representation of human behavior.
Machine Learning Assisted Prediction of the Manufacturability of Laser-Based Powder Bed Fusion Process
Authors: Zhang Ying , Dong Guoying , Yang Sheng , Zhao Yaoyao Fiona 
Published: November 2019

Abstract
Laser-based powder bed fusion (LPBF) process is a type of additive manufacturing process which is able to produce complex metal geometries. The fast development of laser-based powder bed fusion process offers new opportunities to the industries. Comparing to the conventional manufacturing process, LPBF offers more freedom on the shape complexity and hierarchical complexity. Even though the LPBF process has many advantages, there are still many constraints on LPBF. At the current stage, LPBF process still has a very high threshold for industrial application. It requires designers to have extensive knowledge of LPBF process to make the design manufacturable. The need for the automatic manufacturability analysis in the early design stage is essential. In this paper, a novel approach on analyzing the manufacturability of LPBF process is introduced. The machine learning model is developed to predict the manufacturability of LPBF. The unique dataset is established as the training examples. The proposed method achieves very competitive accuracy on analyzing the manufacturability of LBPF. The limitation and future work will be discussed in the end.
Health State Assessment and Lifetime Prediction Based on Unsupervised State Estimation
Authors: Jihin Rosmawati , Söffker Dirk 
Published: November 2019

Abstract
Assessment of system health and prediction of remaining useful life can be performed effectively through the evaluation of degradation levels configured by multiple states. Commonly, degradation progression is modeled according to the specific configuration using existing algorithms with assuming numbers and state conditions. However, due to the complexity, especially in the case of a system with multiple hidden states, the proper configuration is hard to assign and to identify. The need for unsupervised state estimation process to assist degradation modeling preventing under or over assumption becomes obvious. Among the existing approaches is the application of clustering methods to classify data and to estimate the number of degradation states might exist. However, integration into the lifetime prediction framework is still infancy and often not considered. Therefore, in this work, a previously developed state machine lifetime model is extended to allow flexibility in configuring state topology based on K-means clustering algorithm and cluster validity index for the optimal number of states identification. Combining unsupervised state estimation process with a new state machine lifetime model has transformed it into a semi-supervised prognostic approach. For validation, hydraulic pressure data from tribology experiment are deployed for training and test the algorithm. Based on the evaluation, this approach demonstrates the ability to improve health state assessment and lifetime prediction in a more flexible way to address the variability in the system.
Programmable Logic Gates Based on Tunable Multistable Mechanisms
Authors: Zanaty Mohamed , Schneegans Hubert , Vardi Ilan , Henein Simon 
Published: November 2019

Abstract
Binary logic operations are the building blocks of computing machines. In this paper, we present a new programmable binary logic gate based on programmable multistable mechanisms (PMM), which are multistable structures whose stability behavior depends on modifiable boundary conditions as defined and analyzed in our previous work. The logical state of a PMM is defined by its stability and logical operations are implemented by modifying the stability behavior of the mechanism.
Our programmable logic device has two qualitatively different sets of inputs. The first set determines the logic function to be computed. The second set represents the logical inputs. The output is a single logical value, “true” if the mechanism changes state and “false” otherwise. In this way, we are able to mechanically implement a set of binary logical operations.
This implementation is validated using an analytical model characterizing the qualitative stability behavior of the mechanism. This was further verified using finite element analysis and experimental demonstration.
Low-Cost Real-Time Vision Platform for Spatial Temperature Control Research Education Developments
Authors: Viola Jairo , Radici Alberto , Dehghan Sina , Chen YangQuan 
Published: November 2019

Abstract
Temperature control is present in many industrial processes, making this skill mandatory for the control engineers. For this reason, different training temperature platforms have been created for this purpose. However, many of these platforms are expensive, require elaborate facility accommodations, and have higher heating and cooling times, making not suitable for teaching and training. This paper presents a low-cost educational platform for temperature control training. The platform employs a Peltier module as a heating element, which has lower heating and cooling time than other thermal system implementations. A low-cost real-time thermal camera is employed as a temperature feedback sensor instead of a standard thermal sensor. The control algorithm is developed in Matlab-Simulink and employs an Arduino board as hardware in the loop to manage the Peltier module. A temperature control experiment is performed to show that the platform is suitable for teaching and training experiences not only in the classroom but for engineers in the industry.
Embedding Cybersecurity Into Design Education: Increasing Designers’ Awareness of Cybersecurity Throughout the Design Process
Authors: Kim Euiyoung , Kwon Jieun , Yoon JungKyoon , Agogino Alice M. 
Published: November 2019

Abstract
As more digital devices with sensing capabilities are introduced into users’ daily lives, the risks of threats to data and privacy and security have increased. While cybersecurity has been acknowledged as an important concern in developing products with digital services, currently available design methodologies and practices offer limited effective guidance to designers to explicitly address cybersecurity issues. In this paper, we present a case study from a product design course at the University of California, Berkeley, where the course’s teaching team implemented an intervention in the form of cybersecurity-focused educational materials into the design process. The baseline and post-intervention survey results indicate that the cybersecurity intervention throughout the course had positively influenced the students’ awareness of cybersecurity (p &lt; 0.001, SD = 0.79, 26% increase in score, Cohen’s d = 0.81). The intervention provoked the designers to consider and include aspects of cybersecurity in developing their design solutions throughout most of the design process. However, their increased awareness aside, the extent of the student teams considering cybersecurity had tapered off over the 6-week design course with little noticeable influence in the final design.
Computational Design of 4D Printed Shape Morphing Multi-State Lattice Structures
Authors: Lumpe Thomas , Shea Kristina 
Published: November 2019

Abstract
4D printed structures can change their properties and functionalities as a response to a change in the environmental conditions, such as a change in the temperature. A heat stimulus can be used to trigger a transition between two states of a shape memory polymer. Specially designed structures made from these materials can transform into different shapes at different temperatures and can be useful for applications in morphing wings or car panels. Most of these structures, however, are still designed by hand and possess limited load carrying capabilities in at least one of their states. Here, it is shown how complex lightweight structures with multiple stable states can be designed using material modeling and structural optimization methods. By distributing different materials to different parts of the structure, local stiffness gradients are introduced, giving rise to architected global deformations under a single, locally applied load. The shape deformations can be either continuous over the whole structure or discrete only in small regions. The results demonstrate how active materials can be used in a new way to design shape morphing, lightweight lattice structures with different stable states and without sacrificing their structural capabilities.