bluuebunny/crossref_metadata_2025_split huggingface dataset:
Graph Representation of 3D CAD Models for Machining Feature Recognition With Deep Learning
Authors: Cao Weijuan , Robinson Trevor , Hua Yang , Boussuge Flavien , Colligan Andrew R. , Pan Wanbin 
Published: November 2020

Abstract
In this paper, the application of deep learning methods to the task of machining feature recognition in CAD models is studied. Four contributions are made:
1. An automatic method to generate large datasets of 3D CAD models is proposed, where each model contains multiple machining features with face labels.
2. A concise and informative graph representation for 3D CAD models is presented. This is shown to be applicable to graph neural networks.
3. The graph representation is compared with voxels on their performance of training deep neural networks to segment 3D CAD models.
4. Experiments are also conducted to evaluate the effectiveness of graph-based deep learning for interacting feature recognition.
Results show that the proposed graph representation is a more efficient representation of 3D CAD models than voxels for deep learning. It is also shown that graph neural networks can be used to recognize individual features on the model and also identify complex interacting features.
3D-Printed Polymeric Metamaterial Recovery Behavior After Large Deformation
Authors: Wu Siqi , Sancaktar Erol 
Published: November 2020

Abstract
Human-made lattice mechanical metamaterials have recently been shown to exhibit better stiffness or tunable properties than natural materials. We demonstrated that body-centered-cubic (BCC) metamaterials made by 3D-printer stereolithography (SLA) display good recovery properties after undergoing cyclic large compressive deformation. Our experimental results reveal that the strut thickness of BCC metamaterials affect the recovery and mechanical behaviors during compression.
Automated Design Tool for Automotive Control Actuators
Authors: Picard Cyril , Schiffmann Jürg 
Published: November 2020

Abstract
Automated design tools are seldom used in industry. Their potential, however, is high, especially in companies mostly active in variant design, where custom tools could help cut down development time in the early stages. The design of geared electro-mechanical actuators for the automotive industry is such a case. These actuators are simple examples of coupled multi-disciplinary systems that can be hard to design, since they need to follow strict specifications in terms of performance and packaging. This paper presents an automated design and optimization tool tailored for such systems based on an integrated modeling approach, multi-objective optimization and an interactive reporting tool. The focus is set on the impact of system-level constraints on the usability by industry of the generated designs. In two case studies, the tool is able to find competitive actuator candidates that are cheaper (−3.6% and −11%) and more compact than similar existing products in less than an hour on a state-of-the-art laptop computer. More powerful options or actuators using different technologies have also been proposed. Compared to optimizations done without system-level constraints, the generated actuators are immediately usable by engineers to get accurate insights into the design problem and promote informed decision-making.
Objective Reduction Using Axiomatic Design and Product-Related Dependencies: A Layout Synthesis of an Autonomous Greenhouse Case Study
Authors: Law-Kam Cio Yann-Seing , Ma Yuanchao , Vadean Aurelian , Beltrame Giovanni , Achiche Sofiane 
Published: November 2020

Abstract
Many-objective optimization problem (MaOP) is defined as optimization with more than 3 objective functions. This high number of objectives makes the comparing solutions more challenging. This holds true for design problems which are MaOPs by nature due to the inherent complexity and multifaceted nature of real-life applications. In the last decades, many strategies have attempted to overcome MaOPs such as removing objectives based on their impact on the optimization. However, from a design perspective, removing objectives could lead to an under optimal, unfeasible or unreliable design. Consequently, objective aggregation seems to be a better approach since objectives can be grouped based on design features controlled by the designers. The proposed methodology uses Axiomatic Design to decompose a system into subsystems or components, and Product-Related Dependencies Management to identify the dependencies between components and formulate the objectives. Then, these objectives are aggregated based on the subsystems found with the Axiomatic Design. The methodology, applied to the layout synthesis of an autonomous greenhouse, can trim down the number of objectives from 15 to 5. Then, using a modified non-dominated sorting genetic algorithm-II (NSGA-II) combined with the objective aggregation, we were able to increase the number of “good” concepts found from 9 to 33 out of a total of 50 obtained designs.
Development of Virtual Reality Training Scenario for Avalanche Rescue
Authors: Messina Michael Joseph , Teves Simon , Scurati Giulia Wally , Carulli Marina , Ferrise Francesco 
Published: November 2020

Abstract
As the popularity of winter outdoor sports is increasing, a growing number of users are engaging in activities in uncontrolled terrain, thus training for avalanche rescue is more important than ever. Traditional training takes place through workshops and in field sessions, presenting limitations to the training availability, since they require time, organization and specific weather conditions. This is problematic since the use of transceivers to locate buried individuals is not trivial and requires practice. Virtual Reality (VR) training has shown to be effective in several fields, especially in the context of hazardous conditions and emergencies, which require decision making under time pressure and management of complex tools in uncontrolled or unsafe environments. Examples include disaster medicine, military operations, and other fields in which actions must be performed precisely in short time frame. In this work, we present the development of an immersive VR system for avalanche rescue training as a complementary tool to the traditional techniques in order to prepare the trainee for field training sessions. We discuss the definition of the system requirements, the design and implementation of the tool, and considerations regarding hardware and software. Finally, we discuss possible limitations and future development.
Modelling Stick Balancing by Applying Switching-Type Control
Authors: Nagy Dalma J. , Bencsik László , Insperger Tamás 
Published: November 2020

Abstract
In today’s aging society special attention is devoted to the research of human balance control. Our research group focuses on understanding the control mechanism of the brain during balancing from an engineering point of view. The model of stick balancing is derived and the dynamical behaviour of the mechanical system is investigated numerically. The control force is modelled as a PD controller with predictor feedback without implementation uncertainty, which allows perfect delay compensation. However, the sensory dead zone of human perception is also accounted for in the model and thus, a delayed switching-type controller is applied. After deriving the mechanical model, numerical analysis is carried out in order to investigate the effect of time delay and the size of sensory dead zones.
Fuzzy Logic vs Equivalent Consumption Minimization Strategy for Energy Management in P2 Hybrid Electric Vehicles
Authors: Rahmeh Hadi , Bonfitto Angelo , Ruzimov Sanjarbek 
Published: November 2020

Abstract
This paper presents a comparison between a Fuzzy Logic and an Equivalent Consumption Minimization Strategy for the energy management of a Hybrid Electric Vehicle in P2 configuration, i.e. with the secondary energy converter located downstream the clutch. The design of the two methods is conducted aiming to minimize the fuel consumption. Although the adopted strategies are not charge sustaining, an additional goal of the techniques is to obtain a net energy extracted from the battery over a driving cycle that is not far from zero. The presented simulation results are obtained in the case of two homologation driving cycles, namely NEDC and WLTP. The objective of the study is to demonstrate that a non-optimal rule-based method can achieve a performance that is equivalent to a model-based optimal analytical approach.
Novel Design of a 3D Printed Anthropomorphic Soft Prosthetic Hand
Authors: Abbot Esme , de Oliveira Barros Amanda , Yang James 
Published: November 2020

Abstract
Human hands play a key role in almost all activities of daily living (ADLs) because it is an incredibly versatile tool capable of complex motion. For individuals who have had a complete loss of the hand, the ability to perform ADLs is impaired. Effective prosthetics accurately simulate the movements of a human hand by providing a high number of degrees of freedom, an efficient control system, and an anthropomorphic appearance. In this paper, the design and construction process of a highly anthropomorphic soft robotic prosthetic hand is outlined. The design specifications of the hand are based on feedback from current and former prosthetic users. The hand endoskeleton was 3D printed using fused deposition modeling techniques and was enclosed in a silicone coating modeled, after a real human hand. The hand presents anthropomorphic design in its realistic bone shapes and in its external covering that is like skin in texture and mechanical properties. The hand utilizes the flexibility of silicone instead of antagonistic tendons which would otherwise add complexity and weight to the prosthetic design. The prototype also includes adduction/abduction of the fingers, which is a common omitted movement in other prosthetics. Testing showed that the hand is capable of effective power and precision grasping.
Some Exploration of the Path-Dependence in the Contact Analysis
Authors: Chauda Gaurav , Segalman Daniel J. 
Published: November 2020

Abstract
One of the most frustrating features of joint mechanics is that all the important processes take place precisely where they cannot be seen or measured directly — the interface between contacting bodies. In order to achieve some insight into the mechanisms that give rise to the nonlinearities of joints one naturally turns to analytic or numerical models of interface mechanics. With such models, one can explore the significance of different assumptions of kinematics or surface mechanics and compare those with laboratory experiments on the integrated joints.
Among the limitations of such modeling strategies are the twin problems of 1) employing suitable models for friction and 2) solving the resulting equations. There is evidence that the commonly used Coulomb friction model is inconsistent with the experimentally observed behavior of lap joints; it is necessary to explore the use of more complex models. Additionally, even when computing contact and sliding with the relatively simple Coulomb friction model, capturing the evolution of traction fields from one load set to the next in a physically plausible manner has been a continuing challenge. Obtaining fidelity to such path dependence for more complex models would be consequently more difficult. This issue has motivated the research reported here on the source of the difficulty in modeling path-dependent contact and possible solutions.
A two-parameter Coulomb friction model is used to test a conventional contact algorithm and a newer one devised specifically to capture path dependence correctly. The evolution of lateral traction during cyclic loading is used to illustrate how the shear traction distribution at each load step evolves from that of the previous.
Design, Development and Characterization of a Wrap Spring Clutch/Brake Mechanism As a Knee Joint for an Assistive Exoskeleton
Authors: Subra Mani Vishnu Aishwaryan , Goldfarb Nathaniel , Fischer Gregory S. 
Published: November 2020

Abstract
Over the past decade, wearable robotics and exoskeletons have been gaining recognition in the field of medical, assistive and augmentative robotics and have led to numerous new innovative mechanisms and designs. Due to fast-paced research activities, the critical importance and performance of established mechanisms such as wrap spring clutch/brake, Wafer Disc Brakes have been overlooked or used ineffectively. This paper describes a practical design approach that will enable the designer to choose a mechanism based on the application of the device, which will promote overall growth in current technology. The Legged Anthropomorphic Robotic Rehabilitation Exoskeleton (LARRE) project used this approach to design, manufacture, and test the knee joint for ground-level walking. This paper provides the reasoning behind the selection of wrap spring clutch, its evaluation, and testing standards as the knee joint. A thorough literature review was conducted to understand the current state of the art. This project collected a rich set of biomechanical data to ensure that the mechanism will produce the right moments and range of motions during walking. To ensure that our mechanism meets the requirements, the mechanism was put through a wide range of stress tests. The paper establishes a methodology to choose a mechanism for an exoskeleton’s joint based on the desired requirements. The outcome of this paper is an analytical based design approach that can be used by other researchers to impart additional traits and weights, which will aid in the development of exoskeleton design.
Fresh in My Mind! Investigating the Effects of the Order of Presenting Opportunistic and Restrictive Design for Additive Manufacturing Content on Creativity
Authors: Prabhu Rohan , Miller Scarlett R. , Simpson Timothy W. , Meisel Nicholas A. 
Published: November 2020

Abstract
Additive manufacturing (AM) processes present designers with creative freedoms beyond the capabilities of traditional manufacturing processes. However, to successfully leverage AM, designers must balance their creativity against the limitations inherent in these processes to ensure the feasibility of their designs. This feasible adoption of AM can be achieved if designers learn about and apply opportunistic and restrictive design for AM (DfAM) techniques at appropriate stages of the design process. Researchers have demonstrated the effect of the order of presentation of information on the learning and retrieval of said information; however, there is a need to explore this effect within DfAM education. In this paper, we explore this gap through an experimental study involving 195 undergraduate engineering students. Specifically, we compare two variations in DfAM education: (1) opportunistic DfAM followed by restrictive DfAM, and (2) restrictive DfAM followed by opportunistic DfAM, against only opportunistic DFAM and only restrictive DfAM training. These variations are compared through (1) differences in participants’ DfAM self-efficacy, (2) their self-reported DfAM use, and (3) the creativity of their design outcomes. From the results, we see that only students trained in opportunistic DfAM, with or without restrictive DfAM, present a significant increase in their opportunistic DfAM self-efficacy. However, all students trained in DfAM — opportunistic, restrictive, or both — demonstrated an increase in their restrictive DfAM self-efficacy. Further, we see that teaching restrictive DfAM first followed by opportunistic DfAM results in the generation of ideas with greater creativity — a novel research finding. These results highlight the need for educators to account for the effects of the order of presenting content to students, especially when educating students about DfAM.
Computational Design Generation and Evaluation of Beam-Based Tetragonal Bravais Lattice Structures for Tissue Engineering
Authors: Arefin Amit M. E. , Egan Paul F. 
Published: November 2020

Abstract
The study and application of computational design is gaining importance in biomedical engineering as medical devices are becoming more complex, especially with the emergence of 3D printed scaffold structures. Scaffolds are medical devices that act as temporary mechanical support and facilitate biological interactions to regenerate damaged tissues. Past computational design studies have investigated the influence of geometric design in lattice structured scaffolds to investigate mechanical and biological behavior. However, these studies often focus on symmetric cubic structures leaving an opportunity for investigating a larger portion of the design space to find favorable scaffold configurations beyond these constraints. Here, tissue growth behavior is investigated for tetragonal Bravais lattice structured scaffolds by implementing a computational approach that combines a voxel-based design generation method, curvature-based tissue growth modeling, and a design mapping technique for selecting scaffold designs. Results show that tetragonal unit cells achieve higher specific tissue growth than cubic unit cells when investigated for a constant beam width, thus demonstrating the merits in investigating a larger portion of the design space. It is seen that cubic structures achieve around 50% specific growth, while tetragonal structures achieve more than 60% specific growth for the design space investigated. These findings demonstrate the need for continued adaption and use of computational design methodologies for biomedical applications, where the discovery of favorable solutions may significantly improve medical outcomes.
Scalable Set-Based Design Optimization and Remanufacturing for Meeting Changing Requirements
Authors: Al Handawi Khalil , Andersson Petter , Panarotto Massimo , Isaksson Ola , Kokkolaras Michael 
Published: November 2020

Abstract
Engineering design problems often have open-ended requirements, especially in the early stages of development. Set-based design is a paradigm for exploring, and keeping under consideration, several alternatives so that commitment to a single design can be delayed until requirements are settled. In addition, requirements may change over the lifetime of a component or a system. Novel manufacturing technologies enable designs to be remanufactured to meet changed requirements. By considering this capability during the set-based design optimization process, solutions can be scaled to meet evolving requirements and customer specifications even after commitment. Such an ability can also support a circular economy paradigm based on the return of used or discarded components and systems to working condition. We propose a set-based design methodology to obtain scalable optimal solutions that can satisfy changing requirements through remanufacturing. We first use design optimization and surrogate modeling to obtain parametric optimal designs. This set of parametric optimal designs is then reduced to scalable optimal designs by observing a set of transition rules for the manufacturing process used (additive or subtractive). The methodology is demonstrated by means of a structural aeroengine component that is remanufactured by direct energy deposition of a stiffener to meet higher loading requirements.
A Weighted Confidence Metric to Improve Automated Functional Modeling
Authors: Edmonds Katherine , Mikes Alex , DuPont Bryony , Stone Robert B. 
Published: November 2020

Abstract
Expanding on previous work of automating functional modeling, we have developed a more informed automation approach by assigning a weighted confidence metric to the wide variety of data in a design repository. Our work focuses on automating what we call linear functional chains, which are a component-based section of a full functional model. We mine the Design Repository to find correlations between component and function and flow. The automation algorithm we developed organizes these connections by component-function-flow frequency (CFF frequency), thus allowing the creation of linear functional chains. In previous work, we found that CFF frequency is the best metric in formulating the linear functional chain for an individual component; however, we found that this metric did not account for prevalence and consistency in the Design Repository data. To better understand our data, we developed a new metric, which we refer to as weighted confidence, to provide insight on the fidelity of the data, calculated by taking the harmonic mean of two metrics we extracted from our data, prevalence, and consistency. This method could be applied to any dataset with a wide range of individual occurrences. The contribution of this research is not to replace CFF frequency as a method of finding the most likely component-function-flow correlations but to improve the reliability of the automation results by providing additional information from the weighted confidence metric. Improving these automation results, allows us to further our ultimate objective of this research, which is to enable designers to automatically generate functional models for a product given constituent components.
Rule of Mixtures Model for 3D Printed Kevlar Reinforced Nylon: Determination of Volume Fraction Using Thermal Gravimetric Analysis
Authors: Amaria Anosh P. , Armstrong Jason N. , Pasquali Felipe M. , Hall John F. 
Published: November 2020

Abstract
An experimental procedure to determine the elastic modulus and tensile strength of kevlar reinforced nylon composites is discussed. Thermal Gravimetric Analysis (TGA), has been performed to determine the volume fraction of fiber and matrix components. TGA is a robust method to determine the volume fraction. It is also less labor intensive as compared to other methods. Samples with varying kevlar-nylon layer ratio were additively manufactured using fused deposition modelling (FDM) based on ASTM D3039 standards. MarkForged Mark X7 3D printer was used to manufacture samples. Elastic and tensile tests of the samples were conducted. The relation between volume fraction and elastic modulus of the composite can indeed be fit into the rule of mixtures model. However, its applicability for ultimate tensile strength for high fiber ratio composites has been put to question. The direction of fibers in the additively manufactured samples has been kept parallel to the loading direction. In this paper we will give the readers a deeper understanding of how additively manufactured composite samples behave under loading, further facilitating the design process for materials produced by additive manufacturing.
Artificial Neural Network Prediction of Deflection Maps for Cable-Driven Robots
Authors: Notash Leila 
Published: November 2020

Abstract
In this paper, the learning models of cable-driven robots are developed applying the artificial neural network (ANN). For known input and output data and known relationships (regression problem), the deflection maps of cable-driven parallel robots are predicted utilizing a multi-layer ANN. Two cable robots, a planar robot and a translational spatial robot, are examined to evaluate their models. The deflection maps of these cable robots are generated using the ANN and a non-linear optimization method. The predicted deflections of the ANN models, using much less number of poses for training, are highly satisfactory and comparable to the results obtained by a nonlinear optimization method throughout the pertinent discretized workspaces. In addition, ANN models could predict the deflections for poses that the nonlinear optimization methods may not. Moreover, with variations in robot/task parameters, such as payload, ANN models may predict accurate deflections.