bluuebunny/crossref_metadata_2025 huggingface dataset:
Digital Twin Based Interactive Mechatronics Lab Development for Remote Lab Offering and Evaluation
Authors: Viola Jairo , Guc Furkan , Chen YangQuan , Calderon Mauricio
Published: November 2021

Mechatronics and control education is supported by laboratory intensive assignments that allow students acquire software and hardware skills to solve real world problems. However, COVID-19 force many schools to switch into remote learning complicating the instruction of practical assignments. This paper presents a novel proposal for interactive remote teaching of the laboratory component of the course ME-142: Mechatronics at the University of California, Merced using Digital Twins (DT) and the flipped classroom methodology. Each lab experience is composed by a set of on-demand supporting materials with the foundations of mechatronics simulation using MATLAB/Simulink to enhance and adapt the learning experience of the students. Once the students acquire advanced simulation skills, a set of Digital Twin models are provided to the students in order to begin their interaction with virtual representations of real systems for identification, analysis, controller design and validation, which are available online for remote access. By the end of the course, students were able not only to gain valuable experience with mechatronic systems but also interact and build advanced modelling techniques as Digital Twin, contributing to compensate the lack of remote hardware interaction.
Multiscale Topology Optimization With Gaussian Process Regression Models
Authors: Najmon Joel C. , Valladares Homero , Tovar Andres
Published: November 2021

Multiscale topology optimization (MSTO) is a numerical design approach to optimally distribute material within coupled design domains at multiple length scales. Due to the substantial computational cost of performing topology optimization at multiple scales, MSTO methods often feature subroutines such as homogenization of parameterized unit cells and inverse homogenization of periodic microstructures. Parameterized unit cells are of great practical use, but limit the design to a pre-selected cell shape. On the other hand, inverse homogenization provide a physical representation of an optimal periodic microstructure at every discrete location, but do not necessarily embody a manufacturable structure. To address these limitations, this paper introduces a Gaussian process regression model-assisted MSTO method that features the optimal distribution of material at the macroscale and topology optimization of a manufacturable microscale structure. In the proposed approach, a macroscale optimization problem is solved using a gradient-based optimizer The design variables are defined as the homogenized stiffness tensors of the microscale topologies. As such, analytical sensitivity is not possible so the sensitivity coefficients are approximated using finite differences after each microscale topology is optimized. The computational cost of optimizing each microstructure is dramatically reduced by using Gaussian process regression models to approximate the homogenized stiffness tensor. The capability of the proposed MSTO method is demonstrated with two three-dimensional numerical examples. The correlation of the Gaussian process regression models are presented along with the final multiscale topologies for the two examples: a cantilever beam and a 3-point bending beam.
Systematic Enumeration and Identification of Unique Spatial Topologies of 3D Systems Using Spatial Graph Representations
Authors: Peddada Satya R. T. , Dunfield Nathan M. , Zeidner Lawrence E. , James Kai A. , Allison James T.
Published: November 2021

Systematic enumeration and identification of unique 3D spatial topologies of complex engineering systems such as automotive cooling layouts, hybrid-electric power trains, and aero-engines are essential to search their exhaustive design spaces to identify spatial topologies that can satisfy challenging system requirements. However, efficient navigation through discrete 3D spatial topology options is a very challenging problem due to its combinatorial nature and can quickly exceed human cognitive abilities at even moderate complexity levels. Here we present a new, efficient, and generic design framework that utilizes mathematical spatial graph theory to represent, enumerate, and identify distinctive 3D topological classes for an abstract engineering system, given its system architecture (SA) — its components and interconnections. Spatial graph diagrams (SGDs) are generated for a given SA from zero to a specified maximum crossing number. Corresponding Yamada polynomials for all the planar SGDs are then generated. SGDs are categorized into topological classes, each of which shares a unique Yamada polynomial. Finally, for each topological class, one 3D geometric model is generated for an SGD with the fewest interconnect crossings. Several case studies are shown to illustrate the different features of our proposed framework. Design guidelines are also provided for practicing engineers to aid the utilization of this framework for application to different types of real-world problems.
A Multi-Scale Topology Optimization Approach for Optimal Macro-Layout and Local Grading of TPMS-Based Lattices
Authors: Strömberg Niclas
Published: November 2021

The use of lattice structures in design for additive manufacturing has quickly emerged as a popular and efficient design alternative for creating innovative multifunctional lightweight solutions. In particular, the family of triply periodic minimal surfaces (TPMS) studied in detail by Schoen for generating frame-or shell-based lattice structures seems extra promising. In this paper a multi-scale topology optimization approach for optimal macro-layout and local grading of TPMS-based lattice structures is presented. The approach is formulated using two different density fields, one for identifying the macro-layout and another one for setting the local grading of the TPMS-based lattice. The macro density variable is governed by the standard SIMP formulation, but the local one defines the orthotropic elasticity of the element following material interpolation laws derived by numerical homogenization. Such laws are derived for frame- and shell-based Gyroid, G-prime and Schwarz-D lattices using transversely isotropic elasticity for the bulk material. A nice feature of the approach is that the lower and upper additive manufacturing limits on the local density of the TMPS-based lattices are included properly. The performance of the approach is excellent, and this is demonstrated by solving several three-dimensional benchmark problems, e.g., the optimal macro-layout and local grading of Schwarz-D lattice for the established GE-bracket is identified using the presented approach.
A Framework to Study Human-AI Collaborative Design Space Exploration
Authors: Viros-i-Martin Antoni , Selva Daniel
Published: November 2021

This paper presents a framework to describe and explain human-machine collaborative design focusing on Design Space Exploration (DSE), which is a popular method used in the early design of complex systems with roots in the well-known design as exploration paradigm. The human designer and a cognitive design assistant are both modeled as intelligent agents, with an internal state (e.g., motivation, cognitive workload), a knowledge state (separated in domain, design process, and problem specific knowledge), an estimated state of the world (i.e., status of the design task) and of the other agent, a hierarchy of goals (short-term and long-term, design and learning goals) and a set of long-term attributes (e.g., Kirton’s Adaption-Innovation inventory style, risk aversion). The framework emphasizes the relation between design goals and learning goals in DSE, as previously highlighted in the literature (e.g., Concept-Knowledge theory, LinD model) and builds upon the theory of common ground from human-computer interaction (e.g., shared goals, plans, attention) as a building block to develop successful assistants and interactions. Recent studies in human-AI collaborative DSE are reviewed from the lens of the proposed framework, and some new research questions are identified. This framework can help advance the theory of human-AI collaborative design by helping design researchers build promising hypotheses, and design studies to test these hypotheses that consider most relevant factors.
Operational Modal Analysis of a Rotating Structure Subject to Random Excitation Using a Tracking Continuously Scanning Laser Doppler Vibrometer via an Improved Demodulation Method
Authors: Lyu Linfeng , Zhu Weidong
Published: November 2021

A new operational modal analysis (OMA) method is developed for estimation of modal parameters (MPs) of a rotating structure (RS) subject to random excitation using a nonuniform rotating beam model, an image processing method, and an improved demodulation method. The solution to the governing equation of a nonuniform rotating beam is derived, which can be considered as the response of the beam measured by a continuously scanning laser Doppler vibrometer (CSLDV) system. A recently developed tracking CSLDV system can track and scan the RS. The image processing method determines the angular position of the RS so that the tracking CSLDV system can sweep its laser spot along a time-varying path on it. The improved demodulation method obtains undamped mode shapes (UMSs) of the RS by multiplying its measured response by sinusoids whose frequencies are its damped natural frequencies (DNFs) that are obtained from the fast Fourier transform of the measured response. Experimental investigation of the OMA method using the tracking CSLDV system is conducted, and MPs of a rotating fan blade (RFB), including DNFs and UMSs, with different constant speeds and its instantaneous MPs with a non-constant speed are estimated. Estimated first DNFs and UMSs of the stationary fan blade and RFB are compared with those from the lifting method that was previously developed by the authors.
Cost of Controls for Multi-Rotor Drones
Authors: Hoang Samantha , Shen I. Y.
Published: November 2021

This paper focuses on the energy costs of using a control system to follow a specific trajectory as opposed to directly choosing the inputs in an open-loop sense to achieve a specific trajectory. Results show that the PID controller will use 7–13% more energy than the open-loop system for the small-amplitude trajectories and 62–74% more energy for the high-amplitude trajectories while maintaining trajectory errors of approximately 5% and 19%, respectively. The PD controller will use 0.03–0.04% less energy than the open loop system for the small-amplitude trajectories and 0.35% less energy for the high-amplitude trajectories while maintaining similar trajectory errors to the PID controller. The two trajectories have different trajectory errors (5% vs. 19%). Therefore, the controller does not have to move the rotor speeds to the same level, thus causing more energy saved for the large-amplitude trajectory. Based on these simulations, it can be concluded that the PD controller will be significantly more energy efficient than the PID controller while maintaining similar trajectory errors throughout the entire flight. This is because the integral control term in the PID controller accumulates the error so any initial large errors in the position will result in large controller inputs later in the flight. The accumulation of error in the integral term then results in large variations in rotor speed which often leads to a higher energy usage. Thus, a PD controller may be a more energy efficient choice while maintaining effectiveness in trajectory-tracking. These results highlight the importance of considering and comparing the energy costs of various control systems when designing controllers for multi-rotor drones.
Intelligent Design Prediction Aided by Non-Uniform Parametric Study and Machine Learning in Feature Based Product Development
Authors: Ramnath Satchit , Ma Jiachen , Shah Jami J. , Detwiler Duane
Published: November 2021

Automotive body structure design is critical to achieve lightweight and crash worthiness based on engineers’ experience. In the current design process, it frequently occurs that designers use a previous generation design to evolve the latest designs to meet certain targets. However, in this process the possibility of adapting design ideas from other models is unlikely. The uniqueness of each design and presence of non-uniform parameters further makes it difficult to compare two or more designs and extract useful feature information. There is a need for a method that will fill the missing gap in assisting designers with better design options. This paper aims to fill this gap by introducing an innovative approach to use a non-uniform parametric study with machine learning in order to make valuable suggestions to the designer. The proposed method uses data sets produced from experiment design to reduce the number of parameters, perform parameter correlation studies and run finite element analysis (FEA), for a given set of loads. The response data generated from this FEA is then used in a machine learning algorithm to make predictions on the ideal features to be used in the design. The method can be applied to any component that has a feature-based parametric design.
Service Recommendation Based on Dynamic User Portrait: An Integrated Approach
Authors: Tang Yuqi , Li Shanshan , Song Wenyan , Zhou Caibo , Niu Zixuan
Published: November 2021

The rapid development of computer science and internet technology has enabled the prevailing digital transformation. Nowadays, more and more service companies have built up their smart service platforms. Due to the rapid growth in services, consumers may not get their personalized needs accurately. So companies have concentrated more on smart services than before, such as proactive recommending services. However, few studies focus on smart service recommendation, and few recommendation methods are suitable for service recommending. This paper proposes a smart service recommendation method based on user dynamic portrait and collaborative filtering. Theoretically, this method improves the accuracy of recommendation and reflects the change of users’ preference. Finally, we use the users’ data from a science and technology service platform to verify the effectiveness of the method.
CreativeGAN: Editing Generative Adversarial Networks for Creative Design Synthesis
Authors: Heyrani Nobari Amin , Rashad Muhammad Fathy , Ahmed Faez
Published: November 2021

Modern machine learning techniques, such as deep neural networks, are transforming many disciplines ranging from image recognition to language understanding, by uncovering patterns in big data and making accurate predictions. They have also shown promising results for synthesizing new designs, which is crucial for creating products and enabling innovation. Generative models, including generative adversarial networks (GANs), have proven to be effective for design synthesis with applications ranging from product design to metamaterial design. These automated computational design methods can support human designers, who typically create designs by a time-consuming process of iteratively exploring ideas using experience and heuristics. However, there are still challenges remaining in automatically synthesizing ‘creative’ designs. GAN models, however, are not capable of generating unique designs, a key to innovation and a major gap in AI-based design automation applications. This paper proposes an automated method, named CreativeGAN, for generating novel designs. It does so by identifying components that make a design unique and modifying a GAN model such that it becomes more likely to generate designs with identified unique components. The method combines state-of-art novelty detection, segmentation, novelty localization, rewriting, and generative models for creative design synthesis. Using a dataset of bicycle designs, we demonstrate that the method can create new bicycle designs with unique frames and handles, and generalize rare novelties to a broad set of designs. Our automated method requires no human intervention and demonstrates a way to rethink creative design synthesis and exploration. For details and code used in this paper please refer to http://decode.mit.edu/projects/creativegan/.
Reliability Constrained Optimal Design of Distributed Generators in Power System Under Load and Wind Turbine Generation Uncertainty
Authors: Chen Zhetao , Xi Zhimin
Published: November 2021

Power systems are designed to meet power demands of the communities with high reliability. Distributed generators (DGs) could play an essential role in improving the power system reliability and resilience. To date, influence of the uncertainty of the DGs to power system reliability has not been well addressed. Consequently, placement of the DGs considering reliability constraints may not be optimally conducted. This paper proposes reliability analysis and design of power systems under time-dependent load uncertainty and wind power generation uncertainty using an efficient uncertainty quantification (UQ) method, i.e., the eigenvector dimension reduction (EDR) method. Furthermore, binary particle swarm optimization (B-PSO) is proposed to address the optimal placement of DGs considering the reliability constraint. Two case studies, including an IEEE 14-bus power system and an IEEE 57-bus power system, are used to demonstrate the effectiveness of the proposed methodology.
A Mixed Sideslip Yaw Rate Stability Controller for Over-Actuated Vehicles
Authors: Gimondi Alex , Corno Matteo , Savaresi Sergio M.
Published: November 2021

Electronic stability control (ESC) has become a fundamental safety feature for passenger cars. Commonly employed ESCs are based on differential braking. Nevertheless, electric vehicles’ growth, particularly those featuring an over-actuated configuration with individual wheel motors, allows for maintaining driveability without slowing down the vehicle. Standard control strategies are based on yaw rate tracking. The reference signal is model-based and needs precise knowledge of the friction coefficient. To increase the system robustness, more sophisticated approaches that include vehicle sideslip are introduced. Still, it is unclear how the two signals have to be weighted, and rarely proposed controllers have been experimentally validated. In this paper, we present a mixed sideslip and yaw rate stability controller. The mixed approach allows to address the control design as a single-input single-output problem simplifying the tuning process. Furthermore, we explain the rationale behind the choice of the weighting parameter. Eventually, the proposed ESC is validated following EU regulation in simulation and with an experimental vehicle on dry asphalt and snow. The results obtained in all the performed tests demonstrate that the proposed control strategy is robust and effective. The mixed approach is able to halve the sideslip in critical conditions with respect to a pure yaw rate approach.
Variable Stiffness Design and Analysis of Flexure Hinge Based on ID-LEJ
Authors: Li Yanlin , Qiu Lifang , Zhou Kang , Li Chongxiang
Published: November 2021

In recent years, compliant mechanisms have attracted more and more attention of scholars at home and abroad, and achieved rapid development. The introduction of flexible variable stiffness design in flexible mechanism can not only improve the safety of human-computer interaction, but also improve the adaptability of the machine. Because the ID-LEJ (Inside-Deployed Lamina Emergent Joint) is a kind of LEMs and has very good bending performance. In this paper, the rotary ID-LEJ flexure hinge is proposed based on ID-LEJ hinge, to maximize the bending capacity of the hinge. In order to realize variable stiffness of rotary ID-LEJ, four sliders are arranged in the rotary ID-LEJ to change the stiffness of the hinge. The variable stiffness of the hinge is analyzed by Equivalent system and Finite element analysis. When the slider is symmetrically divided (yl1=yl2=yr1=yr2) the bending equivalent constant of the variable stiffness hinge varies continuously from 30.8 n · mm / rad ∼ 38.2n · mm / rad. And when the slider is asymmetrically distributed (yl1=yl2≠yr1=yr2) The bending equivalent constant of the variable stiffness hinge varies continuously from 30.8n · mm / rad to 34.2 · mm / rad. The results show that the variable stiffness performance is very flexible and stable.
Challenges and Strategies in Remote Design Collaboration During Pandemic: A Case Study in Engineering Education
Authors: Belanger Elise , Bartels Caroline , She Jinjuan
Published: November 2021

The COVID-19 pandemic forced college education to shift from face-to-face to online instruction. This effort is particularly challenging for freshmen and sophomore students, in engineering design projects where collaborations are needed. The study aims to qualitatively understand challenges and possible strategies revealed by students in remote design collaboration through the lens of an undergraduate-level engineering design introduction class. The authors closely observed team members’ struggles and how they handled them through bi-weekly and final reflections in a semester-long project. The challenges and strategies from 11 teams (42 students) were analyzed and implications for future engineering design education were discussed. The findings provide insights to experimentations that aim to establish a successful remote learning environment that reaches core education objectives of engineering design while also helping students adapt to a geographically distributed engineering workforce in future. The study also illustrated the usefulness of reflections as a tool to capture students’ learning dynamics.
Evolutionary Discrete Multi-Material Topology Optimization Using CNN-Based Simulation Without Labeled Training Data
Authors: Yang Xingtong , Li Ming , Zhu Liangchao , Zhong Weidong
Published: November 2021

Multi-material topology optimization problem under total mass constraint is a challenging problem owning to the incompressibility constraint on the summation of the usage of the total materials. A novel optimization approach is proposed here that utilizes the wide search space of the genetic algorithm, and greatly reduced computational effects achieved from the direct structure-performance mapping. The former optimization is carefully designed based on our recent theoretical insights, while the latter simulation is derived via a novel convolutional neural network based simulation which does not rely on any labeled simulation data but is instead designed based on a physics-informed loss function. As compared with results obtained using latest approach based on density interpolation, structures of better compliances are observed under acceptable computational costs, as demonstrated by our numerical examples.
Techno-Economic and Environmental Assessment of Dairy Products: A Case Study in Southeast Idaho, USA
Authors: Van Slyke Brekke , Mirkouei Amin , McKellar Michael
Published: November 2021

Idaho was the 3rd largest milk producer in the United States in 2019, and the dairy industry remains one of the most considerable sections of the state’s economy. The dairy industry itself has many effects on the environment, and there are many opportunities within this industry to improve its environmental impacts. This paper explores a dairy processing facility (under current operating norms and an improved set of operating conditions) to assess techno-economic aspects, determine the gate-to-gate environmental impacts, and identify critical process parameters. In this study, the environmental impact was determined using the life cycle assessment method to evaluate greenhouse gas emissions in kg CO2 equivalents per kg of packaged milk. The economic assessment was performed, using a life cycle costing analysis method for estimating the net present value, payback period, and total profit of the various scenarios, as well as determining the major cost drivers to the process. The results show that the total environmental impact of 1 kg of packaged milk was between 0.0102 to 0.0125 kg CO2 equivalents. It was also determined that the proposed adjustments to the operating conditions could reduce the heating costs by 84% and the overall annual costs by 16.3%. This study can help provide justification for further research when determining the optimum operating conditions and energy sources for dairy processing equipment and facilities. This includes investigating both real-world and theoretical models when making plans for improving dairy processes.
Automatically Discovering Mechanical Functions From Physical Behaviors via Clustering
Authors: Chiu Kevin N. , Anderson David , Fuge Mark D.
Published: November 2021

Computational design methods provide opportunities to discover novel and diverse designs that traditional optimization approaches cannot find or that use physical phenomena in ways that engineers have overlooked. However, existing methods require supervised objectives to search or optimize for explicit behaviors or functions — e.g., optimizing aerodynamic lift. In contrast, this paper unpacks what it means to discover interesting behaviors or functions we do not know about a priori using data from experiments or simulation in a fully unsupervised way. Doing so enables computers to invent or re-invent new or existing mechanical functions given only measurements of physical fields (e.g., pressure or electromagnetic fields) without directly specifying a set of objectives to optimize.

This paper explores this approach via two related parts. First, we study clustering algorithms that can detect novel device families from simulation data. Specifically, we contribute a modification to the Hierarchical Density-Based Spatial Clustering of Applications with Noise algorithm via the use of the silhouette score to reduce excessively granular clusters. Second, we study multiple ways by which we preprocess simulation data to increase its discriminatory power in the context of clustering device behavior. This leads to an insight regarding the important role that a design’s representation has in compactly encoding its behavior.

We test our contributions via the task of discovering designs that function as fluidic logic gates. We generate synthetic data that mimics fluidic devices and show that our proposed contributions better discover logic gates, as measured by adjusted Rand score. Specifically, combining our Resolution Selection preprocessing and principal component analysis resulted in the highest and tightest spread of adjusted Rand scores on our tested datasets. This opens up new avenues of research wherein computers can automatically explore different types of physics and then derive new device functions, behaviors, and structures without the need for human labels or guidance.
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

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

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

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

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

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

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

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

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

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

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

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

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.