bluuebunny/crossref_metadata_2025 huggingface dataset:
Perceived Complexity of 3D Shapes for Spatial Visualization Tasks: Humans vs Generative Models
Authors: McShane Lucy , Lopez Christian
Published: November 2023

The objective of this work is to study the perceived complexity of 3D shapes from a human and a large generative model (i.e., ChatGPT) point of view. This work helps to better understand what makes 3D shapes, which are frequently used in spatial visualization tasks, perceived as complex. It also explores how well ChatGPT can capture the consensus of humans as to what makes shapes perceived as complex. Spatial visualization skills are correlated to success in many STEM fields. To enhance Virtual Reality applications aimed at developing spatial visualization skills, models capable of automatically generating shapes of varying complexities could be used to tailor tasks according to users’ skill levels. However, it is important to first understand how humans perceive the complexity of 3D shapes, and how this relates to their performance in spatial visualization tasks. The results of this work indicate that some visual features of 3D shapes, like symmetry, are correlated to their perceived complexity and the performance of individuals on spatial visualization tasks. More importantly, the results show that ChatGPT can generate shapes that are perceived as having different degrees of complexity by humans. The findings support the capabilities of large generative models, like ChatGPT, to capture aspects of human consensus, even in subjective matters such as the perceived complexity of 3D shapes. Hence, these models could potentially be used to automatically generate content, like for VR applications, which are tailored to an individual.
Selection Method of Molding Condition for Self-Adhesive Products Using Only Bamboo Fibers and Powder Extracted by a Machining Center
Authors: Tanaka Kaito , Kitazaki Reo , Hirogaki Toshiki , Aoyama Eiichi , Nobe Hiromichi
Published: November 2023

In this study, we attempt to create self-adhesive moldings using only bamboo fibers extracted by a machining center to solve global environmental problems and ecological destruction caused by bamboo. This molded body utilizes lignocellulose binder-less adhesion. This adhesion has the advantages of having a low environmental impact and of avoiding competition with food crops because it can be made from a large number of plants. However, the mechanical properties of molded products are still insufficient, and improvement of these properties is key to future utilization. In this study, FTIR will be used to determine the differences in composition depending on the size of the chips used as raw material. The results showed that smaller chips (bamboo powder) had more lignin, an adhesive component, and that their tensile strength increased when mixed with larger chips (bamboo fiber) and molded. Further, we attempt to determine the molding conditions that maximize the tensile strength of the molded product by setting the diffusion parameter to PD’, which represents the degree of lignin flow with reference to a manufacturing process model for fiber-reinforced composites. As a result, we were able to create a powder-mixed compact with tensile strength equivalent to that of commodity plastics.
Evolutionary Co-Mention Network Analysis via Social Media Mining
Authors: Gavino Phillip A. O. , Xiao Yinshuang , Cui Yaxin , Chen Wei , Sha Zhenghui
Published: November 2023

The immense volume of user-generated content on social media provides a rich data source for big data research. Comentioned entities in social media content offer valuable information that can support a broad range of studies, from product market competition to dynamic social network mining and modeling. This paper introduces a new approach that combines named entity recognition (NER) and network modeling to extract and analyze co-mention relationships among entities in the same domain from unstructured social media data. This approach contributes to design for market systems literature because little research has investigated product competition via co-mention networks using large-scale unstructured social media data. In particular, the proposed approach provides designers with a new way to gain insight into market trends and aggregated customer preferences when customer choice data is insufficient. Moreover, our approach can easily support the evolution analysis of co-mention relationships beyond cross-sectional analysis of co-mention networks in a single year due to the abundance of social media data in multiple years. To demonstrate the approach to supporting multi-year product competition analysis, we perform a case study on mining co-mention networks of car models with Twitter data. The result shows that our approach can successfully extract the co-mention relationships of car models in multiple years from 2016 to 2019 from massive Twitter content; and enables us to conduct evolutionary co-mention network analysis with temporal network modeling and descriptive network analysis. The analysis confirmed that the co-mention network is capable of identifying frequently discussed entities and topics, such as car model pairs that often involve in competition and emerging vehicle technologies such as electric vehicles (EV). Furthermore, conducting evolutionary co-mention network analysis provides designers with an efficient way to monitor shifts in customer preferences for car features and to track trends in public discussions such as environmental issues associated with EVs over time. Our approach can be generally applied to other studies on co-mention relationships between entities, such as emerging technologies, cellphones, and political figures.
Projected Barycentric Densities for Multi-Material Topology Optimization Using Shape Function With Penalization Approach
Authors: Singh Nikhil , Saxena Anupam
Published: November 2023

The paper proposes and implements modifications to the Shape Function with Penalization (SFP) approach to solve multi-material topology optimization (MMTO) problems. The SFP approach employs barycentric shape functions from the Finite Element literature, to evaluate material densities allowing one to solve MMTO requiring two or three variables per cell. Barycentricity, i.e., non-negativity and partition of unity amongst densities is the key for a cell to be assigned one of the specified materials and therefore no other. We show that applying filtering to SFP evaluated material densities leads to barycentric filtered density fields. Applying projection further to these filtered fields leads to loss of this property. We demonstrate next that applying filtering and projection to the design variables directly helps retain barycentricity in the resulting material densities. The corresponding MMTO problem formulation is developed followed by sensitivity analysis. The obtained formulation is applied on some well known topology optimization problems demonstrating the method’s ability to yield solutions with up to seven materials employing only 3 design variables per cell. Solutions elucidate that the modified projection operator successfully generates crisp transition regions between material (and void) phases.
Adaptation and Challenges in Human-AI Partnership for the Design of Complex Engineering Systems
Authors: Xu Zeda , Hong Chloe , Soria Zurita Nicolás F. , Gyory Joshua T. , Stump Gary , Nolte Hannah , Cagan Jonathan , McComb Christopher
Published: November 2023

Exploring the opportunities for incorporating Artificial Intelligence (AI) to support team problem solving has been the focus of intensive ongoing research. However, while the incorporation of such AI tools into human team problem solving can improve team performance, it is still unclear what modality of AI integration will lead to a genuine human-AI partnership capable of mimicking the dynamic adaptability of humans. This work unites human designers with AI Partners as fellow team members who can both reactively and proactively collaborate in real-time towards solving a complex and evolving engineering problem. Team performance and problem-solving behaviors are examined using the HyForm collaborative research platform. The problem constraints are unexpectedly changed midway through problem solving to simulate the nature of dynamically evolving engineering problems. This work shows that after the shock is introduced, human-AI hybrid teams perform similarly to human teams, demonstrating the capability of AI Partners to adapt to unexpected events. Nonetheless, hybrid teams do struggle more with coordination and communication after the shock is introduced. Overall, this work demonstrates that these AI design Partners can participate as active partners within human teams during a large, complex task, showing promise for future integration in practice.
Investigate Customer Preferences Using Online Video Reviews - Preliminary Results
Authors: Lin Kangcheng , Kim Harrison
Published: November 2023

The wealth of online reviews has grown exponentially, attracting the attention of many researchers who recognize their potential as a valuable source of customer feedback. Leveraging online reviews enables product designers to gain a deeper understanding of customer preferences and make informed decisions to improve product design. Traditionally, the major source of online reviews comes from e-commerce websites, such as Amazon and eBay. However, as social media platforms gain popularity, video reviews from these platforms are becoming an increasingly important source of customer feedback. Video reviews have several advantages over traditional textual reviews. They are more comprehensive, less likely to be fake, have greater coverage of the customer base, and contain more interaction in the form of comments. The paper presents a four-stage methodology to analyze video reviews from social media platforms as an alternative source of customer feedback. This involves collecting and preprocessing video reviews, extracting product features using latent Dirichlet allocation (LDA) models, analyzing sentiment using the valence aware dictionary and sentiment reasoner (VADER) package, and computing feature importance using SHAP values. To the best knowledge of the authors, very few literatures have investigated the feasibility of using video reviews as a substitute for traditional online reviews. Therefore, this paper contributes to the growing literature by demonstrating the viability of video reviews as an alternative source of online reviews to understand customer preferences and their implications for engineering design.
Application of the Ellipse of Elasticity to an Hybrid-Chain Compliant Mechanism
Authors: Sorgonà Orazio , Giannini Oliviero , Verotti Matteo
Published: November 2023

In the last decades, the development of compliant mechanisms has been constantly increasing and these systems have found applications in numerous engineering fields. In line with this growth, many efforts have been made to define analytical procedures and design strategies for modeling their deflections. In this investigation, the theory of the ellipse of elasticity is applied to the linear kinetostatic analysis of a compliant mechanism. The proposed method takes advantage of the properties of antiprojective polarity transformations with respect to the conic associated to the flexible system. The main advantage of the approach lies on the fact that the deflection analysis problem is reduced to a geometric problem with straightforward solution. The compliant mechanism under study is designed considering flexures with different geometric features, such as initial or variable curvature, and non-uniform cross-section. From a kinematic point of view, the mechanism is obtained from a hybrid chain composed of two closed chains and of one open chain. The ellipse of elasticity associated to the compliant system is determined as a composition of sub-chains in series and parallel arrangements. The kinetostatic analysis of the compliant mechanism is therefore performed by exploiting the antiprojective properties related to the ellipse, leading to the definition of the field of displacements of the end-effector.
Design and Analysis of a Series-Parallel Hybrid 3-S

P

S-U Mechanism
Authors: Venkateswaran Swaminath , Chablat Damien , Creusot Denis
Published: November 2023

This article presents the design and analysis of a seriesparallel hybrid tensegrity mechanism. The mechanism has two stages which consist of a fixed base, an intermediate mobile platform, and a mobile end-effector. Each stage is connected by three tension springs and a universal joint in the center. By correlating to a 3-SPS-U architecture, the geometrical equations for the mechanism are generated in the Euler space and the Tilt & Torsion space. To simplify the computations, the Tilt & Torsion space is employed for the analysis of the mechanism. A stability analysis is carried out initially to identify the design parameters of the mechanism in the static mode through an optimization approach. By employing algebraic methods, the singularity analysis is then carried out on the hybrid mechanism. The results of this analysis helped in identifying the feasible workspace and the maximum tilt limits of the architecture. A mapping equation that demonstrates the relation between the Tilt & Torsion angles and the Euler angles is then presented using the results of singularity analysis. A numerical simulation is then demonstrated to validate the results of the analysis. The mechanism under study is then proposed to be integrated into a piping inspection robot for passing through elbows and T-sections.
Gathering and Communicating Empathic User Understanding in Product Development
Authors: Borgman Teresia , Hölttä-Otto Katja , Hyvärinen Jaana
Published: November 2023

Empathic design has been shown to be beneficial for finding users’ latent needs in the early stages of product development. It is useful in gaining a deep understanding of the users’ thoughts, feelings, and behavior. While there is significant research on empathy, less attention has been given to the communication of the findings onwards to the rest of the development team. Proper communication is essential, given that in most projects, the product development team is too large to be involved directly with the user. Further, for some engineering systems, like the one studied here, the end user may not always be the direct customer. This paper examines how user understanding is currently gathered and communicated in a global engineering and service company to identify the current methods and what challenges they face. A thematic analysis is conducted on ten interviews with employees involved either in gathering or receiving user information in product development. The results show that the main communication challenges were caused by lack of understanding toward colleagues due to e.g., their different disciplinary backgrounds, lack of common time and the number of handovers of empathic information. Another issue was the lack of a proper way of storing and finding the user information from past projects.
Modeling the Dynamics of Customer Demand to Determine the Optimal Time to Release Product Updates: A Cognitive Approach
Authors: Walter Ian , Paré Philip E. , Panchal Jitesh H.
Published: November 2023

The nature of modern products is changing, and design processes need to change accordingly. As products become increasingly software-enabled and with the rise of software-as-a-service models, customer-facing updates can be released more frequently than was previously possible. The development pipelines for software-enabled products are shifting towards agile processes, which emphasize regular product updates, choosing the timing of these updates is important, and requires an understanding of how demand will be influenced by each release. However, the existing discrete-choice models used to predict demand do not capture the dynamic aspects of consumer decision making. To address this gap, we propose a dynamic demand model and demonstrate how it can be used to determine the optimal time to release product updates. The demand model is based on decision field theory (DFT), which enables the modeling of the dynamic and time-varying behavior of human decision makers. The primary contribution in this paper is a product strategy for update release schedules which accounts for the dynamic and time-varying behavior of potential customers. We demonstrate the method using simulations of multiple scenarios.
Digital Twin Based Development of Mobile Robot Assistant in Wind Turbines Manufacturing
Authors: Malik Ali Ahmad
Published: November 2023

The thrust for increased rating capacity of wind turbines has resulted in larger generators, longer blades, and taller towers. Presently, up to 16MW wind turbines are available in the market which is nearly a 60% increase in the design capacity over the last five years. Manufacturing of these turbines involves assembling gigantic-sized components. Due to the frequent design changes and the variety of tasks involved, conventional automation is not feasible, making it a labor-intensive activity. However, the handling and assembling of large components are challenging human capabilities. Additionally, it results in longer lead times and increased overall cost of sustainable energy. The article proposes the use of mobile robotic assistants for the hybrid automation of wind turbine manufacturing. The robotic assistant can result in reduced production costs, improved product quality, and better work conditions. The article presents the development of a robot assistant for human operators to effectively perform the assembly of wind turbines. The technology of the digital twin is explored for the commissioning and reconfiguration of the robot assistant. The digital twin also helped in speeding up the design and validation of the robot device. The case of the world’s leading wind turbine manufacturer is studied. The developed system is also applicable to other cases of large component manufacturing involving intensive manual effort.
Prediction of Human Reaching Pose Sequences in Human-Robot Collaboration
Authors: Flowers Jared , Wiens Gloria
Published: November 2023

In Human-Robot Collaboration (HRC), robots and humans must work together in shared, overlapping, workspaces to accomplish tasks. If human and robot motion can be coordinated, then collisions between robot and human can seamlessly be avoided without requiring either of them to stop work. A key part of this coordination is anticipating humans’ future motion so robot motion can be adapted proactively. In this work, a generative neural network predicts a multi-step sequence of human poses for tabletop reaching motions. The multi-step sequence is mapped to a time-series based on a human speed versus motion distance model. The input to the network is the human’s reaching target relative to current pelvis location combined with current human pose. A dataset was generated of human motions to reach various positions on or above the table in front of the human starting from a wide variety of initial human poses. After training the network, experiments showed that the predicted sequences generated by this method matched the actual recordings of human motion within an L2 joint error of 7.6 cm and L2 link roll-pitch-yaw error of 0.301 radians on average. This method predicts motion for an entire reach motion without suffering from the exponential propagation of prediction error that limits the horizon of prior works.
Electronics Design and Verification for Robots With Actuation and Sensing Requirements
Authors: Chen Dongsheng , Huang Zonghao , Sung Cynthia
Published: November 2023

Robot design is a challenging problem involving a balance between the robot’s mechanical design, kinematic structure, and actuation and sensing capabilities. Recent work in computational robot design has focused on mechanical design while assuming that the given actuators are sufficient for the task. At the same time, existing electronics design tools ignore the physical requirements of the actuators and sensors in the circuit. In this paper, we present the first system that closes the loop between the two, incorporating a robot’s mechanical requirements into its circuit design process. We show that the problem can be solved using an iterative search consisting of two parts. First, a dynamic simulator converts the mechanical design and the given task into concrete actuation and sensing requirements. Second, a circuit generator executes a branch-and-bound search to convert the design requirements into a feasible electronic design. The system iterates through both of these steps, a process that is sometimes required since the electronics components add mass that may affect the robot’s design requirements. We demonstrate this approach on two examples — a manipulator and a quadruped — showing in both cases that the system is able to generate a valid electronics design.
A Comparison of Bayesian Acquisition Functions for Use In Surrogate Multi-Objective Feasibility Robust Optimization With Interval Uncertainty
Authors: Kania Randall J. , Azarm Shapour
Published: November 2023

Engineering design optimization problems are commonly multi-objective, involve computationally expensive simulations, and have uncertainty. In these problems, consideration of uncertainty can produce feasibly robust optimized design solutions, i.e., optimized and feasible under uncertainty. In general, methods for solving multi-objective robust optimization problems can be computationally expensive. To ease the computational burden and make these methods more accessible, a surrogate-based bi-objective Bayesian feasibility robust optimization method from the literature has been extended in this paper to higher numbers of objectives. A key component of the proposed method is a new acquisition function. The acquisition function proposed in this paper uses a Monte Carlo approach to approximate the expected centroidal improvement of the solution set. The proposed acquisition function is compared against exact (closed form) acquisition function-based approaches and other methods from the literature. The experimental results from this study suggest the multi-objective robust optimization framework is insensitive to the selection of acquisition function.
Self-Supervised Learning of Temporally Varying Process Parameter Models for Direct Ink Writing
Authors: Yoon Yeo Jung , Yang Yang , Gupta Satyandra K.
Published: November 2023

Direct ink writing (DIW) is an additive manufacturing (AM) process known for its flexibility in printing a variety of materials. However, ink drying is one of the common issues in DIW process. Time-dependent changes in ink properties require us to use a temporally varying process parameter models to efficiently utilize ink. To address this problem, we propose a learning approach for constructing reliable process parameter models that compensate for the temporal changes in ink properties. In the study, we use a mixture of silicones as the ink and a robotic arm with a fluid dispenser system as the experimental setup. We begin by printing test artifacts and collecting initial data with various combinations of process parameters. This data is then used to construct surrogate models for the DIW process and estimate the proper ranges of process parameters to use over time. With self-supervised learning approach and the process parameter adjustment, our method maximizes ink utilization. Additionally, we apply image processing techniques to analyze the printed artifacts. Our results demonstrate the accuracy of the models, compare the DIW process outcomes for actual artifacts with and without temporal adjustment, and estimate the feasible duration for successful printing.
A Conceptual Design of Natural Gas Downhole Drilling and Production Equipment on RFKD Model
Authors: Jiang Yiwei , Zhao Wu , Guo Xin , Zhang Kai , Yu Miao , Hu Huicong
Published: November 2023

The stable operation of natural gas downhole drilling and production equipment is an important guarantee for the gradual transition to sustainable energy in the future. Since the equipment is in the environment of high-pressure natural gas impact, continuous change of gas pressure, and sand-gas mixing, it has higher requirements on reliability. Therefore, in the conceptual design stage, how to complete the solution process from requirement constraints to scheme configuration, and design high-reliability downhole equipment is an important part. New challenges are presented in the designer’s understanding of equipment functions and constraints and completing functional knowledge configuration when designing such stable-performance equipment. To solve this problem, a requirement function knowledge deployment (RFKD) model for equipment conceptual design is extended. Through this model, the conflicts faced by the main function of the equipment are obtained and solved, and the knowledge constraint rules that more accurately match the main function are proposed. The other functions of the equipment are standardized and decomposed into sub-functions, and the structured design knowledge is matched with the sub-functions through function-structure. The feature-knowledge mapping method is extended on the RFKD model. In this paper, the extended model is validated through a conceptual design of a multi-stage downhole throttle that can accommodate large pressure differences.
Reducing the Barriers to Designing 3D-Printable Prosthetics In Resource-Constrained Environments
Authors: Lee Junghun , Chesang Andrew , Gichane Michael , Busogi Moise , Byiringiro Jean , Tucker Conrad
Published: November 2023

Prosthetic devices remain unaffordable to many patients in resource-constrained environments. The design process of personalized prosthetic devices is sophisticated and requires specialized equipment and trained professionals who may have limited availability. The situation is further exacerbated by the fact that the resources are frequently situated away from underserved communities, compounding the issue of accessibility. Lowering the barriers to accessing required equipment and minimizing the involvement of trained professionals in designing and manufacturing prosthetic devices can potentially minimize the required visits to the prosthetists and lower the associated costs. Traditional prosthetic and orthotic device creation methods can take up to several months. There are attempts to leverage advanced technologies to enhance the design process, but they require special equipment such as 3D scanners and MRI equipment in addition to the active involvement of specialists.

This work proposes a computer vision method for creating a virtual model of the affected human limb. We also propose an automated approach for generating the socket from the virtual model and scaling other parts of the prosthetic device. The prosthetic device designed through the proposed process is realized via a 3D printer. We present a case study demonstrating the feasibility of our approach, using a mobile phone and open-source software to reconstruct a virtual representation of an upper limb and customize a prosthetic hand with minimal input from a specialist.
Digital Twin and Virtual Reality for Process Verification of Resistance Spot Welds in Automotive Body in White Manufacturing
Authors: Lekarczyk Elizabeth , Malik Ali Ahmad
Published: November 2023

Automotive body-in-white (BIW) manufacturing facilities utilize human inspectors to manually validate the quality of resistance spot welds (RSW). The process usually involves an articulated robotic arm that holds the component being inspected at its end effector, and presents the component to the human inspector. During the inspection process, the robot re-orientates the component for each weld location to make the inspection easier for the human inspector. Due to the complexity of human-robot interaction, several trials are made before a robot system is ready for serial production. Conventionally, robot coding, process validation, human ergonomic assessment and safety validations are performed once the robot has been commissioned at the production floor. To reduce the time required for document development and ensure complete visibility of all applied resistance spot welds, a digital twin was implemented. The digital twin also combines a digital human mannequin for validation. Utilizing Process Simulate software, the weld locations were highlighted and presented to the inspector in virtual reality (VR) and added to the quality control plan documents. Once robot placement was verified, the joint coordinates were downloaded to the offline program, for implementation; resulting in decreased programming time and inspection documentation development.
Revisiting Mechanical Advantage of Compliant Mechanisms: A Case-Study of a Compliant Gripper Using the Spring-Lever Model
Authors: Dixit Shreyas , Ananthasuresh G. K.
Published: November 2023

It is well known that mechanical advantage (MA) of compliant mechanisms is less than that of their rigid-body equivalents because a part of the input energy is used to deform elastic segments. Furthermore, MA of compliant mechanisms depends on the gap before engaging with the workpiece and its stiffness. To address this issue, we revisit the seminal work of Salamon and Midha with a view to minimize the sensitivity of MA with respect to workpiece stiffness and gap while designing compliant mechanisms. We do this by considering the spring-lever (SL) model which includes three parameters that capture the kineto-elasto-static behavior of the compliant mechanism. We first optimize the kineto-elasto-static parameters for desired MA and obtain a closed-form solution for the optimum parameters. Next, we interactively change the geometry to attain optimized parameters. We illustrate our two-step method of designing a compliant gripper whose MA is maximized and is nearly insensitive to workpiece stiffness and gap.
Transferability Analysis of Data-Driven Additive Manufacturing Knowledge: A Case Study Between Powder Bed Fusion and Directed Energy Deposition
Authors: Safdar Mutahar , Xie Jiarui , Ko Hyunwoong , Lu Yan , Lamouche Guy , Zhao Yaoyao Fiona
Published: November 2023

Data-driven research in Additive Manufacturing (AM) has gained significant success in recent years. This has led to a plethora of scientific literature to emerge. The knowledge in these works consists of AM and Artificial Intelligence (AI) contexts that haven’t been mined and formalized in an integrated way. Moreover, no tools or guidelines exist to support data-driven knowledge transfer from one context to another. As a result, data-driven solutions using specific AI techniques are being developed and validated only for specific AM process technologies. There is a potential to exploit the inherent similarities across various AM technologies and adapt the existing solutions from one process or problem to another using AI, such as Transfer Learning. We propose a three-step knowledge transferability analysis framework in AM to support data-driven AM knowledge transfer. As a prerequisite to transferability analysis, AM knowledge is featurized into identified knowledge components. The framework consists of pre-transfer, transfer, and post-transfer steps to accomplish knowledge transfer. A case study is conducted between flagship metal AM processes. Laser Powder Bed Fusion (LPBF) is the source of knowledge motivated by its relative matureness in applying AI over Directed Energy Deposition (DED), which drives the need for knowledge transfer as the less explored target process. We show successful transfer at different levels of the data-driven solution, including data representation, model architecture, and model parameters. The pipeline of AM knowledge transfer can be automated in the future to allow efficient cross-context or cross-process knowledge exchange.
Three-Dimensional Steady-State and Transient Response Modelling of Geared Systems Under Stick-Slip Motion Generated Excitations
Authors: Ozdemir Enes Timur , Inalpolat Murat , Lee Hyun Ku , Kim Moo Suk
Published: November 2023

This paper presents a general procedure for three-dimensional steady state and transient response analysis of geared systems under stick-slip motion generated excitations. In geared systems, stick-slip motion based excitations are mainly generated within the clutch systems. Thus, the dynamic model includes not only geared shafts and bearings, but also a single clutch stage. Three-dimensional shaft models are dynamically coupled with any number of gears and bearings at any design location of interest. Gear pairs are modeled using a simplified three dimensional dynamic model. Bearings are modeled using lumped compliance elements. The clutch has two interconnected modeling stages: a nonlinear torque characteristics model and a clutch disk model. The nonlinear torque transmission characteristics of the clutch are modelled as a piecewise linear function, while the dynamics of the clutch disk in 3D are modelled using a specifically driven Finite Element Model (FEM) utilizing thick plate elements. Finally, the clutch disk model is coupled with the input shaft to create the complete dynamic model. Nonlinearly transmitted torque is applied on clutch plate surface, and a Newmark’s time-step marching based integration method is used to determine transient response of the system. The capabilities of the model is highlighted through a benchmarking case study, where the baseline model would be a torsional dynamic model borrowed from literature.
Topological Insulator-Based Electroacoustic Transistors
Authors: Kuchibhatla Sai Aditya Raman , Leamy Michael J.
Published: November 2023

We propose an electroacoustic transistor enabled by reconfigurable topological insulators (TIs). The underlying structure of the device is a hexagonal lattice with a unit cell consisting of piezoelectric disks bonded to an aluminum substrate. First, we study the dispersion of flexural waves in the reconfigurable TI to identify Dirac cones in the band structure of a unit cell possessing C6v-symmetry. A topological bandgap can be opened by breaking inversion symmetry in the unit cell. This is achieved by altering the elastic response of one of the affixed piezoelectric disks using a negative impedance shunt circuit. Next, we analyze various topological states formed by interfacing mirror-symmetric unit cells. Sublattices with interface states are then combined to construct a transistor supercell which hosts at least two topologically protected channels for wave propagation. The amplitude of an incoming acoustic signal propagating in one of the topological channels, referred to as the ‘Gate’, is used to switch on or off a second topological channel between a wave source and receiver, mimicking the behavior of a field effect transistor in electronics. We employ finite element analysis to study the harmonic response of the transistor structure demonstrating the OFF and ON states of the device. Further, we present a mock-up of an electrical circuit which enables the switching of the topological channel between a wave source and receiver. The design of the proposed wave-based transistor promises the advantage of topological protection and may find applications in wearable devices, edge computing, and sensing in harsh environments.
Enhancing Part Quality Management Using a Holistic Data Fusion Framework in Metal Powder Bed Fusion Additive Manufacturing
Authors: Yang Zhuo , Kim Jaehyuk , Lu Yan , Jones Albert , Witherell Paul , Yeung Ho , Ko Hyunwoong
Published: November 2023

Metal, powder-bed-fusion-based, additive manufacturing (AM) processes have gained widespread adoption for the ability to produce complex geometries with high performance. However, a multitude of factors still affect the build process, which significantly impacts the adoption rate. This, in turn, leads to great challenges in achieving consistent and reliable part quality. To address this challenge, simulations and measurements have been progressively deployed to provide valuable insights into the quality of individual builds. This paper proposes an AM data-fusion framework that combines data sources beyond a single-part, development cycle. Those sources include the aggregation of measurements from multiple builds and the outputs from their related models and simulations. Both can be used to support decision makings that can improve part quality. The effectiveness of the holistic AM data fusion framework is illustrated through three use case scenarios: one that fuses process data from a single build, one that fusses data from a build and simulation, and one that fuses data from multiple builds. The case studies demonstrate that a data-fusion framework can be applied to effectively detect over-melting scan strategies, to monitor material melting conditions, and to predict down-skin surface defects. Overall, the proposed method provides a practical solution for enhancing part-quality management when individual data sources or models have intrinsic limitations.
The Effect of Tyre Models on the Stability and Ride Dynamics of Two-Wheeled Vehicles: MF-Tyre Vs. MF-Swift
Authors: Mottola Matteo , Lovato Stefano , Lot Roberto , Massaro Matteo
Published: November 2023

In the simulation of road vehicles dynamics, the tyre model should be tailored to the type of analysis. For stability and ride analyses, the MF-Tyre model is the most common choice, which is considered reliable up to 8–15 Hz. Forces are generated from tyre slips and normal load, the carcass dynamics are neglected, the lagged-response is accounted using relaxation equations or spring/damper elements. The MF-Swift model is another well-known option, this tyre model builds upon the MF-Tyre by including a rigid ring in order to simulate the carcass dynamics and extend the validity range up to 60–100 Hz. The aim of this work is to assess the effect of these two different tyre models on the stability and ride dynamics of two-wheeled vehicles. The lateral stability is investigated through the analysis of weave and wobble vibration modes. The ride response to impulses is investigated through wheelie/stoppie manoeuvres and with the vehicle running over a cleat, while the response to pseudo-random excitations is investigated through simulations on rough ISO roads. It was found that the carcass dynamics have some impact on the lateral stability. Conversely, the carcass dynamics have an impact on the in-plane responses at high frequency, both to impulsive and pseudo-random excitations.
Modeling and Co-Simulation of Hybrid Off-Road Machinery
Authors: Goswami Giota , Nutakor Charles , Sopanen Jussi
Published: November 2023

Co-simulation is a modeling approach that can be used to simulate systems that combine multiple physical domains in a computationally efficient manner. Hybrid off-road machinery can be modeled with required complexity by using multibody dynamics approach. The model of the machinery body can be combined with a hybrid electric powertrain which includes electrical and mechanical components in the powertrain through co-simulation. This paper describes a co-simulation platform that combines system-level hybrid electric powertrain models with detailed models of off-road machinery with hydraulic implements. This platform can help in comparing various powertrain concepts for off-road machinery. As a case example, two powertrain concepts for a tractor with a hydraulically operated front loader are compared. The dynamic traction and hydraulic loading of the tractor are simulated from user inputs given by a human operator and the resulting interactions of the tractor with a deformable terrain. A double planetary gear powertrain is combined with hydraulic actuators that are coupled, in the first case, directly to the engine, and in the second case, to the battery through a second electric motor. The model with electrically coupled hydraulics is observed to consume lower energy and fuel due to efficient distribution of the demanded power between the diesel engine and the electric battery.
Design and Virtual Prototyping of a Constant Force Gripper for Food Handling and Harvesting
Authors: Baggetta Mario , Hao Guangbo , Berselli Giovanni
Published: November 2023

This paper presents the design and virtual prototyping of a constant force gripper for food handling and harvesting, aimed at reducing the production costs associated with manual labor performed by human workers. The gripper has been designed using advanced Computer-Aided Design (CAD) software to be easily fabricated using additive manufacturing techniques. The solution involved the use of a lumped compliant element to obtain the desired constant force output, resulting in a gripper that is easily maintainable and cost-effective. The use of a constant force mechanism eliminates the need for dedicated sensors and complex control algorithms to avoid object damage, as completely closing the grasp is sufficient for every operation. The optimization of the compliant element shape has been crucial for achieving the desired constant force output and has required a Matlab software optimization framework using the pseudo-rigid body model equations and an fmincon algorithm. The virtual prototyping approach, using a flexible multi-body dynamics software, has enabled fast and easy testing of the CAD design, leading to an optimized final design with high performance and precision. The study results have demonstrated the potential of virtual prototyping for the design and optimization of advanced robotic systems in cost-sensitive industries, particularly in the food harvesting sector.
Form Attributes to Measure and Understand Aesthetic Preferences
Authors: Saadi Jana I. , Yang Maria C. , Chong Leah
Published: November 2023

The aesthetics of a product is critical to its desirability, and can be described in terms of syntactics and semantics. Syntactic aesthetics is an objective description based on the form and configuration of a product, while semantic aesthetics is a subjective interpretation of the form and gestalt of a product. This study seeks to identify a set of syntactic attributes to describe form and understand if an individual’s preferences for a form are consistent from one product to another. Form attributes from previous literature were expanded upon to create a consistent vocabulary for syntactic aesthetics that can be used to describe multiple products. Combinations of four selected attributes are utilized to describe a diverse set of designs for two products: vases and canopies. Conjoint analysis is used to quantitatively measure the form preferences of individuals towards different combinations of attribute levels of the objects. Results from conjoint analysis applied to vase and canopy designs indicate a 61.3% consistency of individual form preferences between the products. It is hoped that this methodology can help designers develop aesthetically consistent products that align with users’ preferences by quantifying users’ aesthetic preferences towards products through a vocabulary for syntactic attributes.
Optimization of 3D Printing While Traveling En Route to Extend Range of UAS for Multi-Location Mission Scenarios
Authors: Dickerson Tevin , Salmon John L. , Mattson Christopher A.
Published: November 2023

The nexus of two relatively recent technologies, additive manufacturing and unmanned aircraft systems (UAS), has enabled new and unique capabilities that have only started to be realized in integrated systems. This paper explores and quantifies the impact of 3D printing parts for UAS, or entire UAS systems, on an agent platform while it travels to multiple locations as part of a mission objective. The printed or enhanced UAS can then be released at launch points farther away from the goal locations. This, in turn, can accelerate mission completion times and reduce travel costs. The methodology and optimization problem are developed by making use of Apollonius circles extensively with interesting examples presented of the integration of these two technologies. Results indicate that based on the print capability and agent travel speed, using the sequence provided by a Traveling Salesman’s problem solution is not always optimal. Thousands of scenarios are optimized across the design space with some interesting phenomena discussed in the analysis.
Exploring the Manifestation of Design for Manufacturing Axioms in Students’ Early-Stage Engineering Design Concepts
Authors: Pearl Seth , Meisel Nicholas
Published: November 2023

Additive Manufacturing (AM) is a technology capable of producing designs that challenge those from traditional manufacturing methods. AM is of high interest for advanced capabilities such as leveraging free complexity and having the ability to manufacture multi-part products that are manufactured as a single assembled. By leveraging design heuristics for AM, the final design can be manufactured in a shorter timeframe with less material consumption while still maintaining the initial engineering goals of the design. Despite the promising potential of AM, there is a growing concern that designers are not utilizing the design heuristics that embody successful AM. When designers resort to using design heuristics for Traditional Manufacturing (TM) with the unintentional purpose of translating these heuristics to AM, they are not creating efficient designs for AM and are unable to reap the benefits of using AM. To remedy this problem, intervening early in the design process can help address any concerns regarding the use of AM design heuristics. This work explores the design heuristics that students use in creating designs in the context of TM and AM. Once the common design heuristics students use in their designs are identified, future studies will further investigate the specific features that these students are using to address them through early interventions. This work found that incorporating complex shapes and geometries and considering the minimum feature size are significant axioms for influencing the manufacturability of a design for both TM and AM.
Efficient Object Manipulation in Confined Space Using Compliant Based Gripper
Authors: Gabani Krushang , Ji Yuhe Oswin , Esfahani Ehsan T.
Published: November 2023

Manipulating small objects within a confined space is one of the challenging scenarios that industry 4.0 is currently facing. This is specifically important if the task on hand is done in human-robot collaboration. In such scenarios, the human operator may not necessarily place all the items in the most optimum locations. In such cases, it is necessary for the robotic system to identify the objects and isolate any items that are colliding. This should be followed by the priority identification for relocating the scattered objects within the given space to accommodate any incoming objects. This paper addresses the above-mentioned issue by providing a machine vision algorithm where the object added by a human collaborator are identified and then prioritized for the robotic manipulator to relocate them and provide space for any incoming objects. An optimization problem is formulated to identify the most suitable place for any of the objects that need to be replaced by the robotic manipulator. Using an adjustable stiffness gripper, we then introduce four basic actions (inner/outer grasp, rigid push, compliant push) to perform the manipulation task while avoiding any collision with the environment and other objects. The efficiency of the proposed work is demonstrated in several different cases of object manipulations.