bluuebunny/crossref_metadata_2025_split huggingface dataset:
Application of a Customized Optical Force Sensor to a Cable-Driven Leg Exoskeleton
Authors: Huang Jiaxu , Hidayah Rand , Agrawal Sunil , Diez Jorge A. , García-Aracil Nicolas 
Published: November 2021

Abstract
In this paper, a customized optical force sensor is developed for an application with a cable-driven leg exoskeleton. Sensors are vital components of cable-driven, exoskeletal robotic systems, which require real-time and accurate measurement of cable tensions. While these systems’ accuracy is a consideration, the added weight, volume, and complexity of the system must is an essential part of widespread adoption for wearable applications. These sensors can also be costly, which is undesirable. An optical force sensor has the advantages of being lightweight, lower in manufacturing cost, and easy to incorporate within the exoskeleton architecture. We designed a sensor to accommodate the expected force profiles and magnitudes during gait while wearing the Cable-Driven Active Leg Exoskeleton (C-ALEX) during a walking task. We carried out four different calibration tests with dynamic loading of the sensors from 10N to 40N. Once a sensor calibration was established, the optical sensor’s performance was compared to a traditional load cell in validation tests. Both components were used in an assist-as-needed force controller in a walking task with a user, with the optical force sensor incorporated into the C-ALEX exoskeleton arms. Comparing the sensor responses to the command tension forces, the results show a root-mean-square error (RMSE) of 0.9595N ± 0.5360N.
Optimal Allocation of Design Margins in Additive Remanufacturing
Authors: Al Handawi Khalil , Panarotto Massimo , Andersson Petter , Isaksson Ola , Kokkolaras Michael 
Published: November 2021

Abstract
Often, coping with changing requirements results in substantial overdesign, because of the ways in which design margins are allocated at the beginning of the design process. In this paper, we present a design optimization method for minimizing overdesign using additive manufacturing. We use recently defined constituents of design margins (buffer and excess) as metrics in a design optimization problem to minimize overdesign. The method can be used to obtain optimal design decisions for changing requirements. We demonstrate our method by means of a turbine rear structure design problem where changes in the temperature loads are met by depositing different types of stiffeners on the outer casing. The optimal decisions obtained by optimization minimize overdesign but ensure that requirements are met throughout the product’s lifecycle.
Design Concept Generation With Variational Deep Embedding Over Comprehensive Optimization
Authors: Fujita Kikuo , Minowa Kazuki , Nomaguchi Yutaka , Yamasaki Shintaro , Yaji Kentaro 
Published: November 2021

Abstract
This paper proposes a framework for generating design concepts through the loop of comprehensive exploitation and consequent exploration. The former is by any sophisticated optimization such as topology optimization with diversely different. The latter realization is due to the variational deep embedding (VaDE), a deep learning technique with classification capability. In the process of design concept generation first, exploitation through computational optimization generates various possibilities of design entities. Second, VaDE learns them. This learning encodes the clusters of similar entities over the latent space with smaller dimensions. The clustering result reveals some design concepts and identifies voids where as-yet-unrecognized design concepts are prospective. Third, the decoder of the learned VaDE generates some possibilities for new design entities. Forth such new entities are examined, and relevant new conditions will trigger further exploitation by the optimization. In this paper, this framework is implemented for and applied to the conceptual design problem of bridge structures. This application demonstrates that the framework can identify voids over the latent space and explore the possibility of new concepts. This paper brings up some discussion on the promises and possibilities of the proposed framework.
Exploration of Support Structure Design for Additive Manufacturing at a Major OEM: a Case Study
Authors: Morand Lucas , Summers Joshua D. , Pataky Garrett J. 
Published: November 2021

Abstract
The support structures required in many forms of additive manufacturing are often seen as waste that is tolerated as necessary. In metal additive processes, cost is frequently reduced by minimizing the amount of support structures needed to produce a part so that in turn, material use is decreased. However, there still exists the challenge of generating parts that are not deformed by the stresses created in the process. In this case study, support structures were leveraged to address deformation. A part was printed via direct metal laser melting with supports with a high grouping density in areas of high anticipated deformation in order to stiffen the part to prevent deformation. Then, they were printed again with a low grouping density to allow the part to relax and reduce stress. Combinations of support strategy and leaving supports on during post processing were used to investigate the effects of keeping or removing the supports in post-print operations such as surface treatment. The two optimized support strategies saw a lower deformation than the baseline approach to supports, and the releasing strategy was closest to the reference solid model with a 26% reduction in average deformation. The results suggest that the support structures in additively manufactured parts have a different impact on the part than the original intent of the supports to simply alleviate a process requirement. The support structures should be used to impact the final part geometry.
Multisensory VR for Delivering Training Content to Machinery Operators
Authors: Bordegoni Monica , Carulli Marina , Spadoni Elena 
Published: November 2021

Abstract
The issue of training operators in the use of machinery is topical in the industrial field and in many other contexts, such as university laboratories. Training is about learning how to use machinery properly and safely.
Beyond the possibility of studying manuals to learn how to use a machine, operators typically learn through on-the-job training. Indeed, learning by doing is in general more effective, tasks done practically are remembered more easily, and the training is more motivating and less tiresome. On the other hand, this training method has several negative factors. In particular, safety may be a major issue in some training situations.
An approach that may contribute overcoming negative factors is using Virtual Reality and digital simulations techniques for operators training.
The research work presented in this paper concerns the development of a multisensory Virtual Reality environment for training operators to properly use machinery and Personal Protective Equipment (PPE). The context selected for the study is a university laboratory hosting manufacturing machinery. It has been developed an application that allows user to navigate the laboratory, to approach a machine and learn about how to operate it and also what PPE to use while operating. Specifically, the paper describes the design and implementation of the application.
Gait Prediction and Mechanism Design for 1-DOF Lower Limb Rehabilitation Devices Based on Machine Learning
Authors: Song Wanbing , Zhang Yating , Ge Zhaojie , Zhao Ping 
Published: November 2021

Abstract
A task motion trajectory usually needs to be determined for the training process and mechanism design for rehabilitation patients since they are not capable of providing a normal motion. In this paper, a machine-learning-based approach of gait trajectory prediction for lower limb rehab patients is proposed to provide the basis for the design of simple 1-degree-of-freedom (DOF) rehab mechanisms. First, a large amount of gait trajectories from various healthy volunteers are collected along with their body parameters, and a normalization method is presented to trim/expand these trajectory samples to a standard length and timing while retaining their shape and velocity information. Then, these normalized gait samples are clustered and regressed into a limited number of representative trajectories with K-means algorithm, and the cluster index is recorded as the label for each trajectory. Next, a genetic-algorithm-optimized support vector machine method is adopted to train a classifier for the trajectories, obtaining the correspondence between body parameters and cluster labels of gait trajectories. As a result, once a group of body parameters are input into the classifier, it can predict a most suitable gait trajectory for the specific patient. It shows that the accuracy of trajectory prediction reaches 96% both on training set and test set which verifies the effectiveness of the method. In the end, a 1-DOF gait rehab mechanism design example is provided to illustrate the application of the proposed method. Taking the predicted result from the classifier as the task motion trajectory for the synthesis of mechanisms, a 1-DOF six-bar mechanism is designed and the patient-mechanism matching can be realized.
Classification of 3-DOF 3-UPU Translational Parallel Mechanisms Based on Constraint Singularity Loci Using Gröbner Cover
Authors: Kong Xianwen 
Published: November 2021

Abstract
A 3-UPU translational parallel mechanism (TPM) is one of typical TPMs. Several types of 3-UPU TPMs have been proposed in the literature. Despite comprehensive studies on 3-UPU TPMs in which the joint axes on the base and the moving platform are coplanar, only a few 3-UPU TPMs with a skewed base and moving platform have been proposed. However, the impact of link parameters on singularity loci of such TPMs has not been systematically investigated. The advances in computing CGS (comprehensive Gröbner system) or Gröbner cover of parametric polynomial systems provide an efficient tool for solving this problem. This paper presents a systematic classification of 3-UPU TPMs, especially those with a skewed base and moving platform, based on constraint singularity loci. First, the constraint singularity equation of a 3-UPU TPM is derived. To simplify this equation, the coordinate frame on the base (or moving platform) is set up such that the centers of three U joints are located on different coordinate axes. Using Gröbner Cover, the 3-UPU TPMs are classified into 20 types based on the constraint singularity loci. Finally, a novel 3-UPU TPM is proposed. Unlike most of existing 3-UPU TPMs which can transit to two or more 3-DOF operation modes at a constraint singular configuration, the proposed 3-UPU TPM can only transit to one general 3-DOF operation mode in a constraint singular configuration. The singularity locus divides the workspace of this 3-UPU TPM into two constraint singularity-free regions. This work provides a solid foundation for the design of 3-UPU TPMs and a starting point for the classification of a general 3-UPU parallel mechanism.
An Improved Dynamic Model of the Mecanum Wheel for Multibody Simulations
Authors: Manzl Peter , Gerstmayr Johannes 
Published: November 2021

Abstract
Mobile robots and autonomous guided vehicles have become an indispensable part of modern industrial environments and are used for a wide range of handling operations. To fully use the potential of mobile platforms, omnidirectional platforms are a good choice. A prominent and widely used variant in the industry are Mecanum wheels, which allow arbitrary movement in any direction in the plane. In most applications only the kinematics is considered, however, dynamic models that take the geometry of the rollers into account are still missing. In this paper two models for Mecanum wheels with different degrees of detail are derived. The detailed model considers the rollers as single bodies undergoing contact and friction with the rolling plane. As the wheel consists of multiple rollers, a complex contact situation with temporal overlapping and additional vibrations occurs. The simplified model reproduces the overall kinematics of the rollers with orthotropic friction and only one rigid body for the wheel, thereby being computationally more efficient. Both models are well suited to reproduce essential dynamic effects of a mobile robotic platform, which can not be described by the conventional kinematics model. We implement and compare both models with experimental results, showing the good performance of both models.
Exploration of the Dynamics of Neuro-Cognition During TRIZ
Authors: Milovanovic Julie , Hu Mo , Shealy Tripp , Gero John 
Published: November 2021

Abstract
The Theory of Inventive Problem Solving (TRIZ) method and toolkit provides a well-structured approach to support engineering design with pre-defined steps: interpret and define the problem, search for standard engineering parameters, search for inventive principles to adapt, and generate final solutions. The research presented in this paper explores the neuro-cognitive differences of each of these steps. We measured the neuro-cognitive activation in the prefrontal cortex (PFC) of 30 engineering students. Neuro-cognitive activation was recorded while students completed an engineering design task. The results show a varying activation pattern. When interpreting and defining the problem, higher activation is found in the left PFC, generally associated with goal directed planning and making analytical. Neuro-cognitive activation shifts to the right PFC during the search process, a region usually involved in exploring the problem space. During solution generation more activation occurs in the medial PFC, a region generally related to making associations. The findings offer new insights and evidence explaining the dynamic neuro-cognitive activations when using TRIZ in engineering design.
Development of Line-to-Line Contact Formulation for Continuum Beams
Authors: Bozorgmehri Babak , Matikainen Marko K. , Mikkola Aki 
Published: November 2021

Abstract
A line-to-line beam contact formulation in the framework of the absolute nodal coordinate formulation (ANCF) is introduced in this paper. Higher- and lower-order ANCF beam elements employ the introduced beam contact formulation. The higher- and lower-order ANCF beam elements are compared in terms of their accuracy and performance in a large deformation contact problem. Efficiency of numerical integration of contact energy variation contribution to the system’s equations of motion is studied. The contacting elements’ surfaces of the ANCF beam elements are parameterized by segmentation of integration over the contact patch. Numerical results investigate the accuracy, robustness and efficiency of the developed line-to-line contact formulation by comparing against a solid element type using commercial finite element code. According to the numerical results, the higher-order ANCF beam element’s solution is closer than the lower-order ANCF beam element’s in accordance with the reference solution provided by a solid element type using commercial finite element code ABAQUS. Furthermore, the higher-order beam element is found to be more efficient than the lower-order beam with respect to the numerical integration of the contact energy variation. Expectedly, the higher-order ANCF beam element is able to capture the cross-section deformation in a large deformation contact problem, while the lower-order element fails to exhibit such cross-sectional deformation.
A New Triangle: Fractional Calculus, Renormalization Group, and Machine Learning
Authors: Niu Haoyu , Chen YangQuan , Guo Lihong , West Bruce J. 
Published: November 2021

Abstract
The emergence of the systematic study of complexity as a science has resulted from the growing recognition that the fundamental assumptions upon which Newtonian physics is based are not satisfied throughout most of science, e.g., time is not necessarily uniformly flowing in one direction, nor is space homogeneous. Herein we discuss how the fractional calculus (FC), renormalization group (RG) theory and machine learning (ML) have each developed independently in the study of distinct phenomena in which one or more of the underlying assumptions of Newtonian formalism is violated. FC has been shown to help us better understand complex systems, improve the processing of complex signals, enhance the control of complex networks, increase optimization performance, and even extend the enabling of the potential for creativity. RG allows one to investigate the changes of a dynamical system at different scales. For example, in quantum field theory, divergent parts of a calculation can lead to nonsensical infinite results. However, by applying RG, the divergent parts can be adsorbed into fewer measurable quantities, yielding finite results. To date, ML is a fashionable research topic and will probably remain so into the foreseeable future. How a model can learn efficiently (optimally) is always essential. The key to learnability is designing efficient optimization methods. Although extensive research has been carried out on the three topics separately, few studies have investigated the association triangle between the FC, RG, and ML. To initiate the study of their interdependence, herein the authors discuss the critical connections between them (Fig. 1). In the FC and RG, scaling laws reveal the complexity of the phenomena discussed. The authors emphasize that the FC’s and RG’s critical connection is the form of inverse power laws (IPL), and the IPL index provides a measure of the level of complexity. For FC and ML, the critical connections in big data, wherein variability, optimization, and non-local models are described. The authors introduce the derivative-free and gradient-based optimization methods and explain how the FC could contribute to these study areas. In the end, the association between the RG and ML is also explained. The mutual information, feature extraction, and locality are also discussed. Many of the cross-sectional studies suggest a connection between the RG and ML. The RG has a superficial similarity to deep neural networks (DNNs) structure in which one marginalizes over hidden degrees of freedom. The authors remark in the conclusions that the association triangle between FC, RG, and ML, form a stool on which the foundation to complexity science might comfortably sit for a wide range of future research topics.
Using Deep Learning to Simulate Multi-Disciplinary Design Teams
Authors: Stump Gary M. , Yukish Michael , Cagan Jonathan , McComb Christopher 
Published: November 2021

Abstract
Human subject experiments are often used in research efforts to understand human behavior in design. However, such research is often time-consuming, expensive, and limited in scope due to the need to experimentally control specific variables. This work develops an initial digital simulation of team-based multidisciplinary design, where the actions of individual team members are simulated using deep learning models trained on historical human design trends. The main benefit of this work is to simulate design session events and interactions without human participants, developing a complimentary method to rapidly perform digital team-based experiments. This research merges the benefits of purely data-driven modeling with minimal assumptions about process, along with the strengths of agent-based modeling in which it is possible to tailor agent behavior. Initial results show that the simulated design team sessions are able to replicate trends and distributions compared to human-based team sessions, but run approximately 21 times faster than equivalent human subject studies. The multi-disciplinary design problem currently simulated is loosely coupled, in the sense that agent behaviors can be modeled in isolation of other agents and yet replicate the behavior of the ensemble. Future work will extend the agents to sense and respond behaviors that can be used to model tightly coupled problems, and truly evaluate team formulations.
Knowledge Discovery for Early Failure Assessment of Complex Engineered Systems Using Natural Language Processing
Authors: Andrade Sequoia R. , Walsh Hannah S. 
Published: November 2021

Abstract
Emerging complex engineered systems may have unexpected safety issues due to novel operational environments, increasing autonomy, human-machine interaction, and other factors. To prevent failures in operation or testing that necessitate costly redesign, it is desirable to predict likely failure modes early in the design process. Information about past engineering failures in natural language format presents one possible solution by enabling the retrieval of information that can inform new designs. However, identifying documents containing usable information and extracting the required information can be prohibitively time-consuming when implemented at scale. In this research, an automated natural language processing (NLP) framework is proposed to discover relevant knowledge from documents containing failure-related design information. The framework is applied to NASA’s Lessons Learned Information System (LLIS), which is publicly available. Documents containing usable information are filtered using two different NLP-based models. Next, from the identified usable documents, a failure taxonomy is extracted using a partitioned hierarchical topic modeling approach. Partitions of the document describe different sections of the failure taxonomy — i.e., failure, cause of failure, and recommendations — as indicated by the structure of the original document. The extracted failure taxonomy can be leveraged in early design failure assessment methods. Moreover, the framework can be used to identify documents containing usable failure-related design information from other databases and extract relevant information from these documents.
Multi-Objective Structural Optimization of Vehicle Wheels
Authors: Stabile P. , Ballo F. , Gobbi M. , Previati G. 
Published: November 2021

Abstract
This work focuses on the development of an innovative design methodology for lightweight wheels of road vehicles. In particular, the activity is carried out for the specific case of a wheel designed for an ultra-efficient vehicle for Shell Eco-marathon competition, with the aim of finding preliminary design solutions. A simplified finite element model of the tire structure is employed for an accurate modelling of the forces acting at the tire/rim interface. The material properties of the tire structure are identified by means of experimental tests. The computed tire/rim force distribution is applied to the rim exploiting a simplified finite element model of the wheel rim. A multi-objective optimization problem is formulated, based on mass and compliance minimization. Several wheel design layouts are investigated, which differ in terms of number of spokes (i.e. 3, 5 and 7), spokes layout (i.e. straight and Y-shape) and spokes cross section (i.e. rectangular, C and I). Geometric quantities related to the cross section dimensions of the spokes and to the rim thickness are optimized. Design constraints related to structural stiffness and elastic stability (both global and local buckling) are taken into account. The developed finite-element based model of the wheel is used to train a set of neural networks to approximate the objective functions and the design constraints to reduce the computational effort. A multi-objective genetic algorithm is adopted to obtain the Pareto-optimal solutions. The implemented method has proved to be a valuable tool to support design engineers in taking critical decisions in the early stages of the design process.