bluuebunny/crossref_metadata_2025 huggingface dataset:
Kinematic Calibration of a 3rRPS Metamorphic Parallel Mechanism
Authors: Chai Xuheng , Nurahmi Latifah , Dai Jian S. , Gan Dongming
Published: November 2020

In this paper, a kinematic calibration method is developed for a 3rRPS metamorphic parallel mechanism with respect to all unknown parameters. Each limb of the 3rRPS mechanism is composed of (rR), P, and S joints. Two joints are actuated, namely P and r joints, hence the mechanism is able to switch between the 3RPS parallel mechanism and 3US parallel mechanism. The geometric constraint equations of the 3rRPS mechanism are initially established. Then, the optimization problems for the base, platform and actuated prismatic lengths during given trajectory are formulated by using the global search optimization algorithm. A physical model of the 3rRPS metamorphic parallel mechanism is built and an experiment is setup to validate the proposed calibration and optimization models. The external device, i.e., the OptiTrack is used during the experiment for motion capture system. All unknown parameters are identified and optimized by dint of the geometric properties of this mechanism and nonlinear optimization algorithms. The experimental results demonstrate that the proposed calibration method is valid and effective.
Towards Creating Inter-Finger Kinematic Models for Natural Coordination Derived From Grasping of Virtual Objects
Authors: Robson Nina , Chavez Brianda , Won Jong-Seob
Published: November 2020

This paper builds up on a recently developed novel planar inter-joint finger kinematic model for human hand configurations, which applies to the realization of a naturalistic human finger motion. The model is a function of the finger anthropomorphic data and is derived based on the geometry of a hand shape grasping a virtual cylindrical object. Unlike well-known inverse kinematics models, the inter-joint model is capable of describing the natural rotation configuration of the joints of a long finger independently by a single parameter, the radius of the cylindrical object. A novel inter-finger coordination model, based on the inter-joint model is proposed and experimentally tested. The preliminary results show that the model has the potential to simulate naturalistic human hand grasping motion. The merit of the proposed inter-finger coordination model is in its simplicity when used in hand-exoskeleton design assessment and naturalistic hand trajectory planning applications, among others. We would like to note that this paper is the first step towards exploring future simplified combined design-control strategies for the development and assessment of mechanical limbs for naturalistic movement.
Designing Deep Transfer Networks for Bearing Fault Diagnosis With Heterogeneous Data Fusion
Authors: Su Yunsheng , Wang Zequn
Published: November 2020

Accurate fault defection of bearing is critical in condition-based maintenance to improve system reliability and reduce operational cost. This paper introduces a deep transfer learning-based approach for bearing fault diagnosis by fusing heterogeneous information from multiple sources. Convolutional neural networks (CNN) are first designed to extract critical features by mapping extremely high-dimensional signals such as vibration and images to a much lower dimensional latent space. By partially retaining the resultant CNN architectures and parameters, it becomes possible to transfer and fuse the knowledge gained from multiple heterogeneous sources to improve the robustness and accuracy of fault diagnosis of bearings. With the prior knowledge, a deep transfer learning (DTL) architecture is designed to incorporate the heterogeneous data and trained to detect bearing faults. To future improve the performance of bearing fault diagnosis, a performance-driven optimization approach is developed to optimize the validation accuracy of bearing diagnosis by successively designing the architectures of the deep transfer networks. The CWRU experimental data is utilized to demonstrate the performance of the proposed approach.
A Set Based Design Method Using Bayesian Active Learning
Authors: Shintani Kohei , Sugai Tomotaka , Ishizaki Keisuke , Knudde Nicolas , Couckuyt Ivo , Dhaene Tom
Published: November 2020

The purpose of this paper is to propose a new Set-based concurrent engineering method using Bayesian active learning and to show an application to a multi-disciplinary design optimization problem. In the early stages of the system design process, it is required to set a target value considering the uncertainty of design conditions. If any change of design condition occurs by an external factor in the later development process, the predefined target value cannot be held, and critical rework can be inevitable. To avoid this issue, it is important in the early design stage to solve not only a single target solution but also feasible design solutions that satisfy all multi-disciplinary requirements. In order to discover the feasible region with limited resources, an efficient sampling strategy using CAE simulation is necessary. In this study, a sampling strategy based on Bayesian active learning is proposed to discover a feasible region of multi-disciplinary constraints concurrently. In the proposed method, Gaussian Process models of the multi-disciplinary constraints are trained. Based on posterior distributions of trained Gaussian Processes, new acquisition function by combining two different types of acquisition functions, Probability of Feasibility and Entropy Search is proposed and maximized to generate new sampling points to improve the prediction accuracy of feasible region effectively. To show the effectiveness of the proposed Set-based concurrent engineering method to a multi-disciplinary design problem, a suspension design problem is demonstrated.
Attention Routing: Track-Assignment Detailed Routing Using Attention-Based Reinforcement Learning
Authors: Liao Haiguang , Dong Qingyi , Dong Xuliang , Zhang Wentai , Zhang Wangyang , Qi Weiyi , Fallon Elias , Kara Levent Burak
Published: November 2020

In the physical design of integrated circuits, global and detailed routing are critical stages involving the determination of the interconnected paths of each net on a circuit while satisfying the design constraints. Existing actual routers as well as routability predictors either have to resort to expensive approaches that lead to high computational times, or use heuristics that do not generalize well. Even though new, learning-based routing methods have been proposed to address this need, requirements on labelled data and difficulties in addressing complex design rule constraints have limited their adoption in advanced technology node physical design problems. In this work, we propose a new router — attention router, which is the first attempt to solve the track-assignment detailed routing problem by applying reinforcement learning. Complex design rule constraints are encoded into the routing algorithm and an attention-model-based REINFORCE algorithm is applied to solve the most critical step in routing: sequencing device pairs to be routed. The attention router and its baseline genetic router are applied to solve different commercial advanced technologies analog circuits problem sets. The attention router demonstrates generalization ability to unseen problems and is also able to achieve more than 100× acceleration over the genetic router without severely compromising the routing solution quality. Increasing the number of training problems greatly improves the performance of attention router. We also discover a similarity between the attention router and the baseline genetic router in terms of positive correlations in cost and routing patterns, which demonstrate the attention router’s ability to be utilized not only as a detailed router but also as a predictor for routability and congestion.
Risk-Averse Optimization for Resilience Enhancement Under Uncertainty
Authors: Wu Jiaxin , Wang Pingfeng
Published: November 2020

With the growth of complexity and extent, large scale interconnected network systems, e.g., transportation networks or infrastructure networks, become more vulnerable towards external disturbances. Hence, managing potential disruptive events during design, operating, and recovery phase of an engineered system therefore improving the system’s resilience is an important yet challenging task. In order to ensure system resilience after the occurrence of failure events, this study proposes a mixed integer linear programming (MILP) based restoration framework using heterogenous dispatchable agents. Scenario based stochastic optimization (SO) technique is adopted to deal with the inherent uncertainties imposed on the recovery process from the nature. Moreover, different from conventional SO using deterministic equivalent formulations, additional risk measure is implemented for this study because of the temporal sparsity of the decision making in applications such as the recovery from extreme events. The resulting restoration framework involves with a large-scale MILP problem and thus an adequate decompaction technique, i.e., modified Langragian Relaxation, is also proposed in order to achieve tractable time complexity. Case study results based on the IEEE 37-buses test feeder demonstrate the benefits of using the proposed framework for resilience improvement as well as the advantages of adopting SO formulations.
Hybrid Soft-Rigid Deployable Structure Inspired by Thick-Panel Origami
Authors: Liu Chenying , Maiolino Perla , Yang Yunfang , You Zhong
Published: November 2020

Recently, the techniques of origami have become the subject of scientific research. Such methods of folding plates are suitable for practical engineering applications. This paper proposes a novel structure, inspired by thick-panel origami, with hybrid rigid bodies and flexible hinges. Able to be expanded, flipped, and rotated, the waterbomb origami pattern has been chosen to produce a large number of configurations. The mechanism and motion analysis of a single unit and its basic assembly are conducted theoretically and also simulated. An additive fabrication method based on 3D printing makes it a one-step process to achieve a balance between rigidity and flexibility in the structure. Different configurations are demonstrated in three assemblies that exhibit good transformability, reconfigurability, and scalability. With the expansion/packaging ratio ranging from 0.11 to 7.2 in a modular unit, a mechanical metamaterial of negative Poisson’s ratio can be obtained at any spatial size. In addition, our design’s potential for robotic applications is also validated by an adaptable gripper with tendon-driven systems.
Designing Excitation Maneuvers With Maximal Parameter Sensitivity for an X-by-Wire Autonomous Tricycle
Authors: Chen Yi-Ping , Chan Kuei-Yuan
Published: November 2020

Validation in vehicle engineering identifies and quantifies the differences between simulation models and experiment data. In this work we consider these differencesthe lack of ability to model uncertainties and to identify unknown parameters values, especially for coupled complex systems such as vehicles. Effects of unknown model parameters vary under different maneuvers and the ability to excite a source of uncertainty is the focus of this study. We propose an optimization method to generate a proper maneuver that maximize the sensitivity of uncertain parameters based on global sensitivity analysis (GSA). Sensitivities with respect to individual uncertain parameters and those that consider coupled effects are all included. We utilize Kriging-based metamodels to improve the efficiency of the GSA problems with computationally expensive simulations. The optimal design of excitation maneuvers to create the most sensitive performances can then be obtained. The applicability and the accuracy of the proposed method are assessed via a math model and a practical application on a x-by-wire autonomous tricycle. Results show that our proposed method can assist in providing a suitable maneuver as an alternative validation to uncertain parameters in a vehicle system.
Analysis of Extrusion Parameters for the Fused Deposition Modeling Process
Authors: Yu Wenqiang , Nie Zhengwei , Lin Yuyi , Jiang Huakai
Published: November 2020

Fused deposition modeling (FDM) is an additive manufacturing technology commonly used for three-dimensional printing. The screw rod is an important part of the extrusion nozzle in the FDM manufacturing. The technical parameters of the screw rod, such as the screw pitch, screw angle, groove width, and length-diameter ratio (or aspect ratio), etc., directly determine the speed of extrusion and the precision of modeling. To improve the printing speed and quality of FDM models, this work analyzed the influence of extrusion screw’s parameters on the extrusion flow rate and proposed the best parameters for the screw rod of the FDM process. The results of this work have a crucial significance on the modeling speed and accuracy of the FDM.
Task Taxonomy for Autonomous Unmanned Aerial Manipulator: A Review
Authors: Coulombe Charles , Gamache Jean-François , Barron Olivier , Descôteaux Gabriel , Saussié David , Achiche Sofiane
Published: November 2020

The development of unmanned aerial manipulators (UAMs) allows a novel class of flying robots to carry out a wide variety of tasks in difficult environments due to their versatility and autonomy. However, the different tasks that can be carried out might call for different control strategies. To this end, one needs to categorize the possible tasks accomplishable by UAMs. This paper proposes a novel taxonomy, which is the result of a video information acquisition methodology combined with a review of research works in the literature. The different elements of the taxonomy are separated using a higher level of abstraction in a way that the general description of the tasks are considered and not its operational details. To illustrate the fact that algorithms must adapt to different tasks, a description of the usual UAM architecture is carried out. Four categories of criteria are used in the taxonomy to differentiate all possible tasks. These categories are the interaction type, the actual task definition, the environment condition and the time sensitivity of the task. This taxonomy forms the basis for possible machine-learning-based task classifiers that could be used in autonomous UAMs control and mission planning. Multiple tasks defined in the taxonomy can be combined to accomplish complex missions.
Optimal Trajectory Generation Using an Improved Probabilistic Road Map Algorithm for Autonomous Driving
Authors: Feraco Stefano , Bonfitto Angelo , Khan Irfan , Amati Nicola , Tonoli Andrea
Published: November 2020

This paper presents a technique based on the probabilistic road map algorithm for trajectory planning in autonomous driving. The objective is to provide an algorithm allowing to compute the trajectory of the vehicle by reducing the distance traveled and minimizing the lateral deviation and relative yaw angle of the vehicle with respect to the reference trajectory, while maximizing its longitudinal speed. The vehicle is considered as a 3 Degree-of-Freedom bicycle model and a Model Predictive Control algorithm is implemented to control the lateral and longitudinal dynamics. Both the control and trajectory generation algorithms take the road lane boundaries as the only input from the surrounding environment exploiting a simulated camera. The performance of the technique is compared with the case in which the reference trajectory is the central line between the lane boundaries. The proposed algorithm is validated in a simulated driving scenario.
On Supporting the Learning of Biomechanics Using Multidisciplinary Physical Prototyping
Authors: Hernandez Sebastian , Achiche Sofiane , Spooner Daniel , Vadean Aurelian , Raison Maxime
Published: November 2020

Over the last decades, the use of multibody dynamics in biomechanics research has grown considerably and holds significant promises for the health and biomedical industries. Nowadays, it allows estimating internal data of the body that would be impractical or impossible to obtain experimentally, e.g. individual muscle forces. Also, multibody dynamics simulation allows one to constrain virtually any apparatus to the musculoskeletal system, helping to understand and improve the patient’s dynamic interactions with the device. The modeling and validation of human multibody models remain a tedious task to achieve for the research community and can vary significantly depending on the applications. Despite the advantages offered by the multibody modeling of the human body, its introduction in the biomedical engineering curriculum is not widespread. The present paper aims to evaluate the feasibility and the interest of introducing multibody modeling into multidisciplinary, real-world projects using 3D printed prototypes to add an experimental understanding of the difficulties and validation of the human body modeling. The proposed methodology is based on a literature review of the multibody dynamics teaching methods used in mechanical engineering, followed by a first pilot project and feedback from students and professors of the community through interviews. Finally, a project is proposed, using physical prototyping to support the learning.
A Model Predictive Control-Based Energy Management Strategy Considering Electric Vehicle Battery Thermal and Cabin Climate Control
Authors: Liu Yuanzhi , Zhang Jie
Published: November 2020

The energy management strategy plays a critical role in scheduling the operations and enhancing the overall efficiency for electric vehicles. This paper proposes an effective model predictive control-based (MPC) energy management strategy to simultaneously control the battery thermal management system (BTMS) and the cabin air conditioning (AC) system for electric vehicles (EVs). We aim to improve the overall energy efficiency, while retaining soft constraints from both BTMS and AC systems. It is implemented by optimizing the operation and discharging schedule to avoid peak load and by directly utilizing the regenerative power instead of recharging. Compared to the systematic performance without any control coordination between BTMS and AC, results reveal that there are a 4.3% reduction for the recharging energy, and a 6.5% improvement for the overall energy consumption that gained from the MPC-based energy management strategy. Overall the MPC-based energy management is a promising solution to enhance the efficiency for electric vehicles.
Lattice Structure Optimization With Orientation-Dependent Material Properties
Authors: Sharpe Conner , Seepersad Carolyn Conner
Published: November 2020

Advances in additive manufacturing techniques have enabled the production of parts with complex internal geometries. However, the layer-based nature of additive processes often results in mechanical properties that vary based on the orientation of the feature relative to the build plane. Lattice structures have been a popular design application for additive manufacturing due to their potential uses in lightweight structural applications. Many recent works have explored the modeling, design, and fabrication challenges that arise in the multiscale setting of lattice structures. However, there remains a significant challenge in bridging the simplified computational models used in the design process and the more complex properties actually realized in fabrication. This work develops a design approach that captures orientation-dependent material properties that have been observed in metal AM processes while remaining suitable for use in an iterative design process. Exemplar problems are utilized to investigate the potential design changes and performance improvements that can be attained by taking the directional dependence of the manufacturing process into account in the design of lattice structures.
Lightweight Design of a Multi-Material Suspension Lower Control Arm
Authors: Messana Alessandro , Sisca Lorenzo , Ferraris Alessandro , Airale Andrea Giancarlo , Carello Massimiliana
Published: November 2020

This paper is focused on the design, analysis and testing of a multi-material (carbon fibre and steel) Lower Control Arm (LCA) of a McPherson suspension for a C segment vehicle. Therefore, starting from the existing component (made of steel), the LCA mass has been reduced by using a hybrid technology, diminishing the steel thickness and adding a carbon fiber tailored cover without compromising the mechanical performance in terms of stiffness and stress distribution. In so doing, it has been possible to evaluate the potential and the capabilities of the hybridization without re-designing the component totally. In particular, it has been developed a specific methodology that combines both virtual and experimental procedures to face the hybridization challenges of mechanical coupling, safety and lightweight. For these reasons, the multi-material lower control arm represents a noticeable case study in which this methodology has been applied, correlated and validated.
A LIDAR-Based Clustering Technique for Obstacles and Lane Boundaries Detection in Assisted and Autonomous Driving
Authors: Feraco Stefano , Bonfitto Angelo , Amati Nicola , Tonoli Andrea
Published: November 2020

This paper presents a clustering technique for the detection of the obstacles and lane boundaries on a road. The algorithm consists of two nested clustering stages. The first stage is based on hierarchical clustering, and the second on k-means clustering. The method exploits a preliminary ground-plane filtering algorithm to process the raw LIDAR point cloud, that is based on the semantic segmentation of point clouds. The clustering algorithm estimates the position of the obstacles that define the race track. Once the race track is sensed, the lane boundaries are detected. The method is validated experimentally on a four-wheel drive electric vehicle participating in the Formula SAE events. The validation environment is structured with traffic cones to define the race track.
Optimizing an Algorithm for Data Mining a Design Repository to Automate Functional Modeling
Authors: Mikes Alex , Edmonds Katherine , Stone Robert B. , DuPont Bryony
Published: November 2020

The purpose of this research is to find the optimum values for threshold variables used in a data mining and prediction algorithm. We also minimize and stratify a training set to find the optimum size based on how well it represents the whole dataset. Our specific focus is automating functional models, but the method can be applied to any dataset with a similar structure. We iterate through different values for two of the threshold variables in this process and cross-validate to calculate the average accuracy and find the optimum values for each variable. We optimize the training set by reducing the size by 78% and stratifying the data, whereby we achieve an accuracy that is 96% as good as the whole training set and takes 50% less time. These optimum values can be used to better predict the functions and flows of any future product based on its constituent components, which can be used to generate a complete functional model.
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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.