bluuebunny/crossref_metadata_2025 huggingface dataset:
Kuwait household water demand in 2050: Spatial microsimulation and impact appraisal
Authors: Alazmi Hamad J. , Mitchell Gordon , Trigg Mark A.
Published: November 2023

Household water demand has increased dramatically in Kuwait over the last few decades, due to rapid population growth and changing lifestyles. Avoiding a water deficit through a supply‐side approach has been the default strategy in Kuwait, yet this approach is unsustainable, associated with declining groundwater levels, and reliance on desalination that results in major carbon emission and environmental impact and that takes a large and growing share of oil revenues. In this study, we forecast household water demand in Kuwait to 2050 under a Business‐As‐Usual (BAU) scenario and evaluate the economic and environmental impacts. A spatial microsimulation, constrained by the national population projection of the Kuwait Institute of Scientific Research (KISR), was developed to overcome data limitations in forecasting household demand. Results show a 45% increase in water demand by 2050, to 664.1 million cubic metres (MCM), relative to the 2019 base year. Annual production costs increase from 1.39 billion USD in 2019 to 1.99 billion USD by 2050, whilst carbon emissions increase from 10.85 to 15.54 million tonnes/year. These results should alert policymakers to the potential impacts of the growing water demand and provide further support for water conservation action to reduce demand.
A Parametrically Excited Mode-Localized Acceleration Threshold Sensor Using Supercritical Hopf Bifurcation
Authors: Tang Yinghai , Zhao Jian , Kacem Najib , Dong Zeyuan , Zheng Xianze
Published: November 2023

A parametrically excited mode-localized acceleration threshold sensor was designed by using the Supercritical Hopf (SCH) bifurcation with high robustness to the fluctuation of driving voltage and damping while maintaining high sensitivity. The multi-physical dynamic model is established considering both the mechanical and electrical nonlinearities. At the SCH bifurcation point, small acceleration will change the topological structure from SCH bifurcation to Saddle-Node (SN) bifurcation, and the corresponding sensitivity (amplitude ratio) can reach 10000%/g in a dynamic range of ±0.001g. Additionally, the transmission process of the amplitude ratio is similar to the Sign function, which can be used as a threshold switch.
A Task-Decomposed AI-Aided Approach for Generative Conceptual Design
Authors: Wang Boheng , Zuo Haoyu , Cai Zebin , Yin Yuan , Childs Peter , Sun Lingyun , Chen Liuqing
Published: November 2023

Generative algorithm-based conceptual design has been innovatively applied as an emerging digital design paradigm for early-stage design ideation. With powerful large language models (LLMs), designers can enter an initial prompt as a design requirement to generate using machine reasoning capability descriptive natural language content. The machine-generated output can be used as stimuli to inspire designers during design ideation. However, the lack of transparency and insufficient controllability of LLMs can limit their effectiveness when assisting humans on a generative conceptual design task. This generation process lacks theoretical guidance and a comprehensive understanding of design requirements, which may potentially lead to generated concepts that are mismatched or lack creativity. Inspired by the Function-Behavior-Structure (FBS) model, this paper proposes a task-decomposed AI-aided approach for generative conceptual design. We decompose a conceptual design task into three sub-tasks including functional reasoning, behavioral reasoning, and structural reasoning. Prompt templates and specification signifiers are specified for different steps to guide the LLMs to generate reasonable results, controllably. The output of each step becomes the input of the next, aiding in aggregating gains per step and embedding the selection preferences of human designers at each stage. A conceptual design experiment is conducted, and the results show that the conceptual design ideation with our method are more reasonable and creative in comparison to a baseline.
Bow Snap-Through Necessary Condition in Electrostatically Actuated Initially Curved and Latched Micro-Beams
Authors: Medina Lior
Published: November 2023

A curved bistable micro-beam, subjected to electrostatic loading from an electrode facing its concave side, may produce a snap-through response with almost half the voltage when compared to actuation emanating from an electrode facing the convex side of the beam. Such actuation has been dubbed “bow actuation” due to the similarity of preloading an arrow onto a bow, and the resulting shift in equilibrium. Under a certain elevation-to-thickness ratio, a bistable beam will also become latchable, possessing a zero point load (i.e., latching point), which allows the beam to maintain itself in its second stable state under zero load/voltage. In the current work, a necessary condition is found, describing the geometry and initial conditions required for static bow snap-through response. The condition allows one to design and produce a bow snap-through that will enable the beam to converge to its latching point, thus promoting efficient non-volatile, and low-power consumption bistable-based devices. The condition is found using an undamped dynamic single degree-of-freedom (DOF) reduced-order (RO) model, attained via Galerkin’s decomposition. Subsequent numerical calculations, conducted in the presence of ambient damping, show that the condition is necessary to attain bow snap-through, while also disclosing the behaviour of the model.
Physics Guided Machine Learning Approach to Safe Quasi-Static Impact Situations in Human-Robot Collaboration Following the Power and Force Limiting Method of the ISO/TS 15066 Standard
Authors: Kovinčić Nemanja , Gattringer Hubert , Müller Andreas , Brandstötter Mathias
Published: November 2023

Following the performance and force limitation method of the ISO/TS 15066 standard, human safety is evaluated in a quasi-static impact situation in a human-robot collaboration task. To achieve this, all potentially critical impact situations are first identified and then the corresponding impact forces and pressures are experimentally measured and compared with their limit values specified by ISO/TS 15066. If a collaborative application is evaluated as safe, once something is changed in the collaborative workspace, to obtain certainty again, one has to re-assess the safety properties and to re-measure the corresponding forces and pressures. This measurement-based safety assessment severely limits the flexibility of a collaborative process. In this paper, as a method for overcoming the flexibility problem, a physics guided machine learning ensemble for prediction of peak impact forces, within predefined modification dimensions of the collaborative application, is proposed. The input features for the ensemble are obtained using a simplified mathematical model of an impact between a robot and a human. Based on experimentally measured peak impact forces performed with the Universal Robots UR10e, peak impact forces covering an almost whole range of data distribution of interest defined by modification dimensions are obtained using interpolation. As an example of the presented methodology, a generic pick and place task with two modification dimensions is considered. As the most important application of the presented methodology, a map showing the maximal safe impact velocity for a grid of points in a part of a collaborative workspace of interest is shown.
StickBot: A Methodology for Building Robots and Other Functional Elements From Tree Branches and String
Authors: Carroll Devin , Ang Valerie , Yim Mark
Published: November 2023

There are times when robots or robot parts must be improvised from found materials. These times include disaster recovery scenarios, lunar or martian exploration or in areas that are highly resource constrained such as third world countries. For these situations, found materials can be used to construct new robots or robotic elements such as exo-skeletons or orthoses as well as repair damaged components or structures.

This paper quantifies the use of various fastening techniques for attaching together unstructured members, such as tree branches, with actuators to act as the building blocks for robotic links and joints. We quantify which fastener styles are ideal for fixed joints, or for revolute and spherical joints. The techniques considered range from more traditional methods such as diagonal and square lashings to using nuts and bolts to connect components together. In each area we analyze the pros and cons of the technique employed from a complexity, accuracy, and strength perspective. The techniques are then employed to build arm and wrist orthoses as well as a robotic gripper and walker. The range of motion of the orthoses are analyzed. The branch gripper has comparable performance to a commercially available gripper in terms of size and payload. In each case we discuss the pros and cons of these designs and how they can be improved upon.
Multimodal Sensor Fusion for Real-Time Location-Dependent Defect Detection in Laser-Directed Energy Deposition
Authors: Chen Lequn , Yao Xiling , Feng Wenhe , Chew Youxiang , Moon Seung Ki
Published: November 2023

Real-time defect detection is crucial in laser-directed energy deposition (L-DED) additive manufacturing (AM). Traditional in-situ monitoring approach utilizes a single sensor (i.e., acoustic, visual, or thermal sensor) to capture the complex process dynamic behaviors, which is insufficient for defect detection with high accuracy and robustness. This paper proposes a novel multimodal sensor fusion method for real-time location-dependent defect detection in the robotic L-DED process. The multimodal fusion sources include a microphone sensor capturing the laser-material interaction sound and a visible spectrum CCD camera capturing the coaxial melt pool images. A hybrid convolutional neural network (CNN) is proposed to fuse acoustic and visual data. The key novelty in this study is that the traditional manual feature extraction procedures are no longer required, and the raw melt pool images and acoustic signals are fused directly by the hybrid CNN model, which achieved the highest defect prediction accuracy (98.5 %) without the thermal sensing modality. Moreover, unlike previous region-based quality prediction, the proposed hybrid CNN can detect the onset of defect occurrences. The defect prediction outcomes are synchronized and registered with in-situ acquired robot tool-center-point (TCP) data, which enables localized defect identification. The proposed multimodal sensor fusion method offers a robust solution for in-situ defect detection.
Numerical Analysis of Phase Nonreciprocity in a Linear Spatiotemporally Modulated System
Authors: Wu Jiuda , Yousefzadeh Behrooz
Published: November 2023

Vibration transmissibility in a material with spatiotemporal modulation can be changed by just inverting the direction of transmission. This asymmetric feature of transmission can occur due to nonreciprocity. Various studies have demonstrated nonreciprocity in modulated materials by calculating or measuring the differences in amplitudes of response in opposite transmission directions. However, equal amplitudes do not indicate reciprocity because amplitude difference is blind to phase shift. In this work, we investigate nonreciprocal phase shifts (with equal transmitted amplitudes) in vibration transmission through a discrete one-dimensional periodic material subject to spatiotemporal modulation of grounding stiffness coefficients; the transmitted vibrations along the opposite directions have the same amplitude but undergo different phase shifts. We focus on the effects of the number of degrees of freedom (DoF) and modulation amplitude on vibration transmission nonreciprocity. We use the averaging method to calculate the steady-state response of the modulated system. Reciprocity bias and output norms are calculated to evaluate the degree of nonreciprocity in opposite transmission directions. Phase nonreciprocity is identified in cases where output norms are equal but reciprocity bias is not zero. While the response of the system is quasiperiodic, its envelope is periodic in time. We therefore use the response envelopes to help us identify response regimes that are characterized by nonreciprocal phase shifts. The analysis method based on the envelope of the steady-state response facilitates future parametric studies on phase nonreciprocity in discrete materials with spatiotemporal modulations.
Ceramic Bearing Endurance Within a Transmission
Authors: Riggs Mark R. , Hood Adrian A. , Pomplon William A. , Fetty Jason R. , Milligan Ryan , Zielinski John J.
Published: November 2023

Fully ceramic bearings have been demonstrated to provide a forty to fifty percent reduction in weight over steel bearings. They can also provide improved reliability due to increased corrosion protection. One of the primary challenges to incorporating ceramic bearings is the coefficient of thermal expansion mismatch between the ceramic bearing races and its mating surfaces. The application of a tolerance ring was developed to address these challenges. The tolerance ring was applied between the inner bearing race and the pinion shaft to retain the race and absorb the thermal expansion differences, while maintaining an acceptable fit across the thermal operating range. The application of a ceramic bearing within a transmission was evaluated through a 300-hour endurance experiment. The experiment transmitted shaft speeds and torques through the transmission with a vertical load. The ceramic bearing successfully transmitted torque and speed through the duration of the experiment, and vibration did not increase over time. This experiment gives confidence that fully ceramic bearings may be utilized to improve power density and corrosion resistance. In addition, this experiment also demonstrated the ability of the newly commissioned Vehicle Innovative Powertrain Experimental Research facility at Aberdeen Proving Ground, Maryland to successfully perform experiments with novel technologies incorporated into a transmission.
Design Considerations for 3RRR Parallel Robots With Lightweight, Approximate Static-Balancing
Authors: Del Giudice Giuseppe , Johnston Garrison L. H. , Simaan Nabil
Published: November 2023

Balancing parallel robots throughout their workspace while avoiding the use of balancing masses and respecting design practicality constraints is difficult. Medical robots demand such compact and lightweight designs. This paper considers the difficult task of achieving optimal approximate balancing of a parallel robot throughout a desired task-based dexterous workspace using balancing springs only. While it is possible to achieve perfect balancing in a path, only approximate balancing may be achieved without the addition of balancing masses. Design considerations for optimal robot base placement and the effects of placement of torsional balancing springs are presented. Using a modal representation for the balancing torque requirements, we use recent results on the design of wire-wrapped cam mechanisms to achieve balancing throughout a task-based workspace. A simulation study shows that robot base placement can have a detrimental effect on the attainability of a practical design solution for static balancing. We also show that optimal balancing using torsional springs is best achieved when all springs are at the actuated joints and that the wire-wrapped cam design can significantly improve the performance of static balancing. The methodology presented in this paper provides practical design solutions that yield simple, lightweight and compact designs suitable for medical applications where such traits are paramount.
Mobile Manipulation Platform for Autonomous Indoor Inspections in Low-Clearance Areas
Authors: Pearson Erik , Szenher Paul , Huang Christine , Englot Brendan
Published: November 2023

Mobile manipulators have been used for inspection, maintenance and repair tasks over the years, but there are some key limitations. Stability concerns typically require mobile platforms to be large in order to handle far-reaching manipulators, or for the manipulators to have drastically reduced workspaces to fit onto smaller mobile platforms. Therefore we propose a combination of two widely-used robots, the Clearpath Jackal unmanned ground vehicle and the Kinova Gen3 six degree-of-freedom manipulator. The Jackal has a small footprint and works well in low-clearance indoor environments. Extensive testing of localization, navigation and mapping using LiDAR sensors makes the Jackal a well developed mobile platform suitable for mobile manipulation. The Gen3 has a long reach with reasonable power consumption for manipulation tasks. A wrist camera for RGB-D sensing and a customizable end effector interface makes the Gen3 suitable for a myriad of manipulation tasks. Typically these features would result in an unstable platform, however with a few minor hardware and software modifications, we have produced a stable, high-performance mobile manipulation platform with significant mobility, reach, sensing, and maneuverability for indoor inspection tasks, without degradation of the component robots’ individual capabilities. These assertions were investigated with hardware via semi-autonomous navigation to waypoints in a busy indoor environment, and high-precision self-alignment alongside planar structures for intervention tasks.
Development of a Comprehensive Motorless Walking Assistance and Evaluation of Muscle Activity
Authors: Wu Xiuyuan , Wang Yuntian , Osawa Keisuke , Tanaka Eiichiro
Published: November 2023

Walking disorders are a major challenge for people with neuromuscular or musculoskeletal impairments, resulting in reduced mobility and quality of life. This paper addresses an approach to design a comprehensive mechanical walking assist that facilitates foot flexion and extension for the walking by applying the force at the ankle, while adjusting the walking motion and posture to a healthy pattern. The apparatus uses an offset slider crank mechanism to lift the foot and complete the dorsiflexion, and a four-bar linkage to assist the plantarflexion by using the action-reaction force from the lever kicks against the ground. The forefoot mechanical pressure sensor accommodates triggering to both motions, and the heel sensor acts as auxiliary releasing the lever. The experiments showed that both plantarflexion and dorsiflexion are assisted. The corresponding muscle activity for each motion is reduced, promoting a safer and healthier walking. The apparatus has the potential to improve the mobility and quality of life for who with walking disorders and impairments, and could be applied to other domains where mechanical assistance is required, promising a trend to walking rehabilitation with less cost, providing the reference for experiment configuration and further study.
The Effect of Geometrical Quality on the Stress-State in the Plastic Gear
Authors: Zorko Damijan , Vukašinović Nikola , Černe Borut
Published: November 2023

Gear manufacturing errors affect the load sharing between the meshing gears as also the load distribution along the individual tooth’s width. High quality grades are especially hard to achieve for injection molded plastic gears. On the other hand, plastic materials are much more flexible than metals and the teeth deflect and comply under load, leading to an improved load distribution. Larger deviations lead however to a localized contact area resulting in stress concentration and early failure. This study aims to investigate the effect of gear manufacturing quality on the mechanical state of the plastic gear, and consequently also on the gear-life. An original systematic combination of numerical and experimental methods was employed to study these effects. The deviation of geometrical quality parameters, i.e. lead profile and pitch were found to have a substantial effect on the stress (root and flank) state of the gear. The effect of lead deviation was found to be most pronounced for the quality grades Q10 to Q12, while improving the lead quality from Q10 to Q8 did not exhibit a substantial improvement in the load distribution and the corresponding stress. Same was observed for pitch deviation, where again the most pronounced effect was found when improving the quality grade from Q12 to Q10. Quality factors are proposed which can be considered when conducting the root stress and flank pressure calculations in order to include the effect of uneven load distribution in case of larger geometric deviations.
Development of a Validation Process, Overcoming the Challenges Associated With the Evaluation of Design Indicators
Authors: Mesa-López Ana María , Ruiz-Arenas Santiago
Published: November 2023

Different types of indicators have been developed to measure the value of design in companies. As with any tool developed for research, indicators must be developed according to a scientific approach. One of the important steps in this development is validation. The main validation problem for design indicators lies in the following challenges: Design often involves multiple variables and factors that interact with each other. This can make it difficult to isolate the impact of any one indicator on the overall design. Furthermore, design operates differently in each industry. These issues make benchmarking against standardized metrics problematic. Most existing design metrics are qualitative, subject to interpretation, or closed to the public. It is possible that there is no data collected in the companies to evaluate their performance. To the authors’ knowledge, the literature does not describe a systematic validation process for indicators that present difficulties like those exhibited by design indicators. Therefore, this paper presents a proposal for a validation process for design indicators that can be replicated during the indicator development process, which provides a possible solution to these challenges. Four dimensions are considered: face validity, content validity, criterion validity and construct validity. This research is part of a current project that aims to demonstrate the value that the application of design could bring to companies and provide design managers with the tools to support their decision-making.
Parametric Musculoskeletal Model for Human-Robot Interaction Simulation in Warfield Rescue: A Pilot Study
Authors: Lee Seunghun , Cheng Zhiqing , Yang James
Published: November 2023

A novel physics-based human-robot interaction method is proposed and applied to drag the wounded warfighter in the overhead direction in this study. This method predicts the rescue motion using a full-body skeletal model and forward dynamics in OpenSim. The ground reaction force (GRF) is calculated by the contact models in OpenSim, and external forces are applied to the skeletal model. The predicted motion data and a full-body musculoskeletal model are the input for inverse dynamics and static optimization, and the output is joint torques. The simulation results are validated by a kinematic and a joint torque range verification. The proposed model can analyze the rescue scenarios almost in real time and predicts the potential risk of injuries during rescue motion.
Emerging Methods for 2D and 3D Physics Simulation to Support Early Design Decisions
Authors: Chaisiriroj Pongchalat , Stone Robert B.
Published: November 2023

Current approaches to developing 2D and 3D dynamic simulations can be time-intensive to develop and run early in the design process. Recently, machine learning-driven methods have produced near-real-time predictions of design performance. As these approaches rely on backpropagation methods, designers cannot inspect the meaning of results as they pertain to a single target — for example, object class identification or efficiency estimation. In addition, machine learning does not usually generate 2D and 3D field results. This paper identifies and classifies emerging dynamic field simulation methods and previews a novel method for accurate and efficient dynamic simulation. A literature review of recent peer-reviewed journals and books, as well as an exploration of emerging simulation software, will be presented in this paper. A grounded theory approach will be used to extract and classify key concepts. Preliminary examples of interest include cellular automata, decision trees, consensus or crowd-based methods, and hybrid machine learning methods. This study is intended to provide engineers with a better understanding of the emerging capabilities of state-of-the-art simulations in industries parallel and orthogonal to mechanical engineering theory, including gaming, film, and architecture. The work examines a multi-physics challenge in designing electric propulsion systems for spacecraft as dynamic field models for this design space are computationally intensive to produce.
Lab-to-Market Design of an Electrodialysis-Based Home-Scale Water Desalination System
Authors: Floryan Marie , Bowers Quinn , Sternberg Zachary , Gembali Sahas , Goyal Akshita , Bessette Jonathan , Honarparvar Soraya , Winter Amos
Published: November 2023

Water scarcity is increasingly becoming a problem in India, so much so that a recently proposed piece of legislation would mandate home-scale water desalination systems to have a 60% recovery ratio [1], significantly higher than existing systems. We developed an electrodialysis-based home-scale desalination system for obtaining drinking water that will meet and surpass this new proposed recovery ratio. This project builds heavily on previous and current work by the MIT Global Engineering and Research (GEAR) Lab and was conducted with Eureka Forbes Limited (EFL), a major stakeholder in India’s home-scale desalination industry. Two critical design modules within the desalinator are explored — the system integration and fluid reversal mechanism and the electrodialysis stack end cap analysis. We developed a model framework that will output the validity of end cap designs based on established design requirements, designed fluid reversal mechanisms for distinct markets, and fabricated a prototype desalination cabinet that fits within the EFL’s design constraints. We also discuss the details of our collaboration with both academic and industrial partners, extract specific lessons learned about the lab-to-market design process, and discuss some of the specific challenges faced by a design group in our circumstances and the ways we resolved them.
Dynamic Characterizations of O- and X-Carbon Nanotube Reinforced Rings
Authors: Li Hongjie , Deng Yan , Fan Mu , Wang Dan , Tzou Hornsen
Published: November 2023

Carbon nanotube (CNT) reinforced materials exhibit great potential applications because their properties can be tailored by changing the distribution type. Ring structure has been widely used in aerospace, architecture and other fields. This study focuses on the dynamic characterizations of O- and X-CNT reinforced rings under two oscillation conditions. Different distribution types of CNTs, namely, X-distribution type (X), uniform distribution type (UD), and O-distribution type (O), are considered. The influence of embedded CNTs on the mechanical properties of the polypropylene ring is analyzed. To reflect the dynamic characteristics, both the membrane force induced component and bending moment induced component, i.e., transverse and circumferential actions, are studied respectively. The results suggest: (a) the CNTs can improve the mechanical properties of the ring; (b) for the transverse oscillation, the bending behavior of the X-distribution type is larger than that of the membrane behavior, resulting in the largest component induced by the bending moment; and (c) the O-distribution type has a great influence on the membrane behavior, and this component should be the largest in the circumferential oscillation where the membrane force induced component dominates. However, because the volume fraction of CNTs in the three distribution types does not change, the deformation of CNT reinforced ring is uniform.
Comparative Study of Approaches for an Ontology of Digital Artifacts
Authors: Šormaz Dušan , Kulvatunyou Boonserm , Drobnjaković Miloš , Seeharit Saruda
Published: November 2023

Modern business, engineering, and manufacturing are supported by many software tools. Those tools not only help create new products for customers but also enable delivering them in increasingly shorter time periods. To achieve both, the ability to transfer, exchange, integrate, and analyze digital data among supply chain participants with diverse software tools is needed. Creating that ability, however, requires a higher level of interoperability with support from semantics tools, such as knowledge graphs and ontologies. As many engineering and business tasks involve digital communication, it is important to develop ontologies for digital artifacts of various fidelity levels and various scopes of the product life cycle to enable the required interoperability. There are ontologies associated with the various stages of the product life cycle. Those ontologies have various fidelity levels that correspond to specific lifecycle stages and that enable the required interoperability within those stages, with very few enabling interoperability between the stages.

To address those ontology needs, the Industrial Ontology Foundry (IOF) has released the first version of its Core ontology. The Core provides a basis to represent information about some digital objects such as plan specification as well as physical objects such as occurrence of a process (that may or may not be proceeding according to the plan). While that is the case, there is still a need for the IOF Core to provide further guidance on how they should be related and how to scale to various types of digital and physical objects.

To address the issues of relations between physical and digital objects, this paper provides an overview of approaches from recent literature and efforts by the authors and collaborators. It then illustrates in more detail four approaches that are more aligned with the Basic Formal Ontology (BFO), on which the IOF Core is based. General and use case specific requirements are used to arrive at our initial observations. Finally, we discuss a plan for further analysis.
A Collaborative Robotic Screw Assembly System Using 6-PUS Parallel Mechanism
Authors: Sun Qiao , Gao Dalong , Chen Xianbao , Sun Jing , Huang Ninjian , Gao Feng
Published: November 2023

Screw fastening is an important process in the automobile assembly line, and it is difficult to be accomplished completely by automation. Collaborative screw fastening is a practical technique in automotive assembly. In this paper, a novel collaborative robotic system for screw tightening operations based on parallel mechanism is proposed, which is more suitable for high-torque screw tightening tasks while improving operator ergonomics. To this end, a 6-PUS parallel mechanism with a central passive limb is designed. In addition, a control framework for collaborative screw tightening tasks is developed. Its main idea is that the human operator is responsible for the error-prone initial mating stage of screws, while the robot is responsible for the screw-driving stage. The two stages can be performed simultaneously, increasing productivity and reducing the labor intensity. To this end, a multi-mode active compliance controller is proposed according to stages of the screw tightening process and the feedback of a 6-axis force sensor. Furthermore, a statistics based method that helps the robot to guess the next screw position is proposed to improve the fluentness of the collaborative process. A prototype of the proposed robotic system is built. Experiments of the collaborative screw installation process of an engine part are conducted to demonstrate the effectiveness of the proposed system.
Can the Attributes of a Waste Bin Improve Recycling? A Literature Review for Sensors and Actuators to Define Product Design Objectives
Authors: Ríos-Zapata David , Rendón-Vélez Elizabeth , Ruiz-Arenas Santiago , Arbeláez-Estrada Juan Carlos , Vallejo Paola , Tabares Marta Silvia
Published: November 2023

Sustainability efforts have experienced a significant boost in recent years, where the investment in designing systems that help people better separate waste is evident. These efforts are centred on developing smart bins that can increase recycling by impacting the correct separation rate. This article presents a literature review of the attributes of smart bins that have contributed to a higher separation rate. The review yielded three key findings. The first is the characterisation of the commercial sensors used to identify waste and the mechanical components used to segregate and separate waste according to their category. The second is an analysis of the implications of selecting components, sensors and actuators, currently used and their impact and influence on the design of a smart bin. The third is a classification proposal of smart bins in four categories: i) full automation: the system classifies and separates the waste automatically. ii) moderate autonomy: the system is able to re-classify waste, when the user fails to correctly dispose, and segregate it automatically. iii) low autonomy: the system identifies the waste and indicates to the user where to dispose. iv) manual: since technological aids are not available, the bin signage will suggest to the user where to dispose.
Finding Chain Nets of Solids for 3D Printability
Authors: Lawrence Matthew , Tomlinson Scott , Khoda Bashir
Published: November 2023

3D-printing or additive manufacturing (AM) can be a size-constrained and time-consuming manufacturing method compared to those traditionally used. Printing larger structures as a net could be a possible solution to this limitation. The intent behind this work is to establish a methodology for creating 3D structures with discretized planner segments using smaller printer envelopes. Nets of solids are capable of folding into an enclosed body from coplanar shapes. Nets can be utilized to create larger objects not otherwise possible due to space limitations in manufacturing methods. Chain nets are particularly useful for AM, which can avoid collision between the segmented part net and printer topology. To prevent interference with the printer, the shape must be unfolded in a chain net. Unfolding algorithms of this type can be simplified to a Hamiltonian pathfinding problem. This method can be particularly useful when used in conjunction with printing on a flexible substrate, i.e., fabric. Larger solids can be created by printing each side separately and using the fabric as a joint between the faces. When the sides of the solids are printed, the print bed no longer limits the size of solids that can be manufactured. This would also allow collaborative printing with multiple printer platforms to print different faces of the net simultaneously.
Applying Origami-Based Design Approaches to Foldable Furniture for Children
Authors: Parkinson Bethany , Skinner Mitchel , Halverson Denise , Bateman Terri , Magleby Spencer , Howell Larry
Published: November 2023

Origami-inspired design principles — including thickness accommodation techniques, surrogate folds, and a rare use of nonrigid panels — are applied here in a novel foldable furniture item known as the Hidey Cube for use in a daycare facility. Each of the design principles is applied to develop the Hidey Cube and to help it meet its design objectives, including first: the need to provide a safe space for children, and second: the desire for this safe space to be collapsible, transportable, risk-free, and play-enhancing. Through analysis and consideration of a variety of hinges commonly used in origami-based design, and by undergoing an iterative process, two variations on the Hidey Cube design are presented. These two variations — a wood composite version and a fabric-covered foam version — are described and demonstrated. The advantages and disadvantages of each design are enumerated. The fabric-covered foam version utilizes non-rigid panels in conjunction with membrane hinges, which requires an interesting upholstery-based manufacturing approach to allow for the desired actuation while limiting undesired parasitic motion, also presented here. Non-rigid panels are demonstrated to be viable for use in origami-inspired applications, and specific potential applications and recommended future work are discussed. In applications where some parasitic motion is allowable and where non-rigid panels present other functional benefit — such as in furniture, shrouds for use over a medical device, or in wearable technology — the techniques presented here are particularly beneficial.
Identification of a Lumped Element Modeled Ring Shaped Structure via Peak Picking Method
Authors: Seyrek Pelin , Arısoy Doğan Onur , Karapıstık Gökhan , Erözden Ahmet Kutay , Karataş Hamit Çağlar , Ünver Hakki Özgür
Published: November 2023

This paper presents an application of a well-known modal system identification method (i.e., Peak Picking Method) on ring shaped structures with imbalances in terms of natural frequency and damping ratio mismatches, in-silico. To the best of our knowledge, the application of this method to ring shaped structures is novel in the literature.

The system is modeled at secondary vibration pattern (n = 2) via zero dimension lumped element modeling approach. Hence the model has two orthogonal modes (i.e., 2-DOF) with coupling terms of natural frequency and time constant mismatch azimuth angles in-between. Natural frequencies and damping ratios in modal coordinates are estimated using Peak Picking Method based on Real and Imaginary parts of Direct and Cross Frequency Response Functions (FRFs). For this purpose, sine-sweep force signals are applied to the model and displacement values of the both modes are collected. Additionally, eigenvalue decomposition method is utilized to obtain the system characteristic as well as to derive system parameters and coupling terms in generalized coordinates. This identification approach is applied to model with varying quality factors (ranging between 2,500–1,000,000), natural frequencies (ranging between 500–3501 Hz) with numerous natural frequency differences of 0.2 Hz, 1 Hz, and 5 Hz, and different natural frequency azimuth mismatch angle values of π/6 and π/12 while keeping the time constant azimuth mismatch angle value at π/8 for performance evaluations.

Results show that Peak Picking Method can accurately estimate the natural frequencies in all scenarios, on the other hand damping ratio estimations present higher errors in lower quality factors. Also, both azimuth mismatch angle estimations always converge to the highest value given, which is a shortcoming of this method.
Design of a Robotic Testbed for Spherical Additive Manufacturing
Authors: Kim Christopher D. , DeVries Levi D. , Kutzer Michael D. M.
Published: November 2023

The use of additive manufacturing (AM) has become commonplace across a growing number of disciplines. Despite a wide variety of AM methods ranging from polymer extrusion to laser sintering of metals, the use of flat layers or “slices” is common for deposition planning and execution. While effective, the capabilities of AM can be expanded to improve the surface finish, inertial symmetry, and physical properties of fabricated parts with the introduction of conformal or “wrapped” layering. This paper presents the design of a robotic testbed to demonstrate conformal AM using spherical layers. Using this spherical layer constraint, we detail methods for 1) specifying hardware parameters to maximize print volume given an articulated industrial manipulator; and 2) mapping deposition trajectories into joint coordinates using an inverse kinematic solution. Results demonstrate the application of methods to a UR5 industrial robotic manipulator and documentation for the design of a physical testbed.
Selective Amplification and Suppression of Strain in a Multi-Axis Force Sensor Using Topology Optimization
Authors: Sung Myung Kyun , Lee Soobum , Burns Devin E. , Persia Jude Thaddeus
Published: November 2023

This paper proposes an innovative conceptual design of a wind tunnel balance axial section using topology optimization. A wind tunnel balance is a sensor that measures six force/moment components from a wind tunnel model. It is also a structural link between the wind tunnel model and supporting hardware. An axial section, one of the six measurement sections in the balance, is difficult to design because it is often required to resolve an axial force which is much smaller than other force components. Topology optimization is used in this paper to obtain a non-intuitive conceptual design of an axial section. To realize the design requirements, a new top-down symmetric design formulation is suggested to amplify the gauge reading under a small axial loading and to suppress the gauge reading under nonaxial loadings. The formulation assumes the use of a conventional full Wheatstone bridge circuit. The projection method is extensively used to consider manufacturing uncertainties. Then, a postprocessing strategy is used to generate a manufacturable geometry. The postprocessing leverages the Multi-Objective Genetic Algorithm function in ANSYS Workbench to ensure the design requirements of the balance structure are met while maintaining a smooth geometry profile. Satisfactory sensing performance is verified from the postprocessed model using commercial FEM software.
Efficient Airfoil Geometric Uncertainty Quantification Using Neural Network Models and Sequential Sampling
Authors: Koratikere Pavankumar , Leifsson Leifur
Published: November 2023

In this paper, a new and unique surrogate-based forward propagation algorithm for aerodynamic geometric uncertainty quantification (UQ) is proposed. The proposed algorithm extends the recent efficient global optimization with neural network (NN)-based prediction and uncertainty (EGONN) algorithm which was created for unconstrained optimization problems. The proposed extended EGONN algorithm for UQ (uqEGONN) constructs a global surrogate model of the aerodynamics characteristics using a NN based on data sampled from physics-based computational fluid dynamics (CFD) simulations. The NN model is adaptively enhanced using a NN model of the prediction uncertainty. In each sampling cycle, the prediction NN is used to compute the summary statistics with Monte Carlo simulations (MCS). The algorithm terminates when the absolute relative change in the summary statistics reach a specified tolerance, or when a specified maximum number of samples is reached. The algorithm is demonstrated on the UQ of the RAE 2822 airfoil at Mach 0.734, angle of attack 2.89 deg, and Reynolds number of 6.5 million. The geometric uncertainty is represented using twelve normally distributed parameters. The results show that the proposed algorithm yields comparable results as those obtained from direct CFD-based MCS with less than 100 CFD samples. The mean and standard deviation of the airfoil drag coefficient are within 0.45 drag counts, for the lift coefficient it is within 0.04 lift counts, and for the pitching moment coefficient it is within 0.11 (× 10−2).
Machine Learning-Based Model Bias Correction by Fusing CAE Data With Test Data for Vehicle Crashworthiness
Authors: Zeng Jice , Zhao Ying , Li Guosong , Gao Zhenyan , Li Yang , Barbat Saeed , Hu Zhen
Published: November 2023

Physics-based simulation and analysis have emerged as promising techniques for optimizing the number of physical prototypes for vehicle crashworthiness evaluation in frontal impact with rigid barriers. Nonetheless, one of the hurdles for vehicle crashworthiness virtual certification is the potential differences between the computer simulation predictions and physical test results. In this regard, this study aims at improving the prediction capability of the Computer-Aided Engineering (CAE) model for crashworthiness performance evaluation at speeds beyond those defined by current regulations and public domain testing protocols. One way of achieving this is by integrating data from a number of physical crash tests with the CAE data using machine learning models. A novel approach is proposed in the displacement domain (deceleration vs. displacement) to enable data fusion to help recover missing physics associated with the CAE model. A nonlinear spring-mass model is used in this study to simulate rigid-barrier vehicle frontal impact. The deceleration response is transformed from a function of time to a function of displacement, and a Gaussian process regression (GPR) model is applied to capture the model bias of the nonlinear spring constant under a dynamic analysis scheme. The training data for the GPR model are split into multiple clusters by a Gaussian mixture model to capture bias patterns under different speed regimes. After clustering a GPR model is trained for each group of data. The optimal GPR model, trained by a specific cluster exhibiting the highest probability of new data belonging to it, is utilized for prediction. This selected GPR model is integrated with the original CAE model to predict vehicle deceleration under a new crash speed during a vehicle deceleration dynamic analysis. The proposed approach is validated using physical vehicle crash tests and demonstrated improved accuracy of CAE model results and predictions.
AutoSurf: Automated Expert-Guided Meshing With Graph Neural Networks and Conformal Predictions
Authors: Heyrani Nobari Amin , Rey Justin , Kodali Suhas , Jones Matthew , Ahmed Faez
Published: November 2023

Computational Fluid Dynamics (CFD) is widely used in different engineering fields, but accurate simulations are dependent upon proper meshing of the simulation domain. While highly refined meshes may ensure precision, they come with high computational costs. Similarly, adaptive remeshing techniques require multiple simulations and come at a great computational cost. This means that the meshing process is reliant upon expert knowledge and years of experience. Automating mesh generation can save significant time and effort and lead to a faster and more efficient design process. This paper presents a machine learning-based scheme that utilizes Graph Neural Networks (GNN) and expert guidance to automatically generate CFD meshes for aircraft models. In this work, we introduce a new 3D segmentation algorithm that outperforms two state-of-the-art models, Point-Net++ and PointMLP, for surface classification. We also present a novel approach to project predictions from 3D mesh segmentation models to CAD surfaces using the conformal predictions method, which provides marginal statistical guarantees and robust uncertainty quantification and handling. We demonstrate that the addition of conformal predictions effectively enables the model to avoid under-refinement, hence failure, in CFD meshing even for weak and less accurate models. Finally, we demonstrate the efficacy of our approach through a real-world case study that demonstrates that our automatically generated mesh is comparable in quality to expert-generated meshes, and enables the solver to converge and produce accurate results. The code and data for this project is made publicly available at https://github.com/ahnobari/AutoSurf.