bluuebunny/crossref_metadata_2025_split huggingface dataset:
Methods for Creating Additive Printing Paths on Nonplanar Surfaces
Authors: Rudd Liam , Faghihrasoul Zahra , Campbell Matthew I. 
Published: November 2023

Abstract
One of the ways that additive manufacturing technology continues to improve is the addition of a fourth or fifth degree-of-freedom. Adding rotational axes to the printer head or to the print bed can lead to improvement in the time, cost of printing as well as the quality of the print. We have witnessed similar improvements in subtractive CNC machining as well. The added axes require new computational methods to automatically define toolpaths. In additive, slicer software creates planar cross-sections through the part and automatically defines these toolpaths. With added rotational axes, toolpaths on nonplanar surfaces will be required to fully utilize the advantages of multi-axis printing. This paper reviews current work in nonplanar toolpaths and introduces three new concepts that advance the field. These three concepts are presented in three separate sections — each with their own related work and results. Firstly, we present methods for projecting or unwrapping a nonplanar surface to 2D polygons so that paths can be defined by conventional means. Then, the paths are projected or wrapped back onto the 3D surfaces. Next, two approaches are presented for creating perimeter paths and infill paths that do not transform the slices to 2D planes. These iterative methods simulate the toolpath as particles reacting to virtual forces that guide them to ideal positions. The third builds on an algorithm for creating biologically inspired undulating paths or membranes called differential line growth. These methods can be useful in defining paths for surfaces with high curvature since converting to 2D and back would likely produce too much distortion and paths would not maintain near-equal geodesic distances.
Build Orientation Optimization for Five-Axis 3D Printing
Authors: Alonayni Ghazi , Campbell Matthew I. 
Published: November 2023

Abstract
It is well understood that printing orientation plays a critical role in the quality of a final printed product and the total print time. In five-axis 3D printers, the added rotations can further reduce time and material, but initial orientation is still a significant factor. The build orientation is a critical factor in the printed part’s surface quality and the need for additional material as support structures. In this research, we develop build orientation sampling techniques and evaluate them based on their computational cost and accuracy. The evaluation of a build orientation is based on three criteria: the volume of the added support structure, the surface quality, and the stability of the part while rotating and tilting on the printing platform. The creation of the support structure uses Boolean polygon operations to compute the amount of material required as support. The surface quality and stability of a part at a given orientation are also calculated. Our proposed optimization scheme was tested on seven 3D parts and was found to only require a small number of evaluations to achieve a Pareto-optimal set of build orientations. This method can be utilized as a first step in planning the fabrication of parts printed on five-axis printers.
How Does Agency Impact Human-AI Collaborative Design Space Exploration? A Case Study on Ship Design With Deep Generative Models
Authors: Khan Shahroz , Kaklis Panagiotis , Goucher-Lambert Kosa 
Published: November 2023

Abstract
Typical parametric approaches restrict the exploration of diverse designs by generating variations based on a baseline design. In contrast, generative models provide a solution by leveraging existing designs to create compact yet diverse generative design spaces (GDSs). However, the effectiveness of current exploration methods in complex GDSs, especially in ship hull design, remains unclear. To that end, we first construct a GDS using a generative adversarial network, trained on 52,591 designs of various ship types. Next, we constructed three modes of exploration, random (REM), semi-automated (SAEM) and automated (AEM), with varying levels of user involvement to explore GDS for novel and optimised designs. In REM, users manually explore the GDS based on intuition. In SAEM, both the users and optimiser drive the exploration. The optimiser focuses on exploring a diverse set of optimised designs, while the user directs the exploration towards their design preference. AEM uses an optimiser to search for the global optimum based on design performance. Our results revealed that REM generates the most diverse designs, followed by SAEM and AEM. However, the SAEM and AEM produce better-performing designs. Specifically, SAEM is the most effective in exploring designs with a high trade-off between novelty and performance. In conclusion, our study highlights the need for innovative exploration approaches to fully harness the potential of GDS in design optimisation.
An Improved Shape Annealing Algorithm for the Generation of Coated DNA Origami Nanostructures
Authors: Babatunde Bolutito , Cagan Jonathan , Taylor Rebecca E. 
Published: November 2023

Abstract
In recent years, the field of structural DNA nanotechnology has advanced rapidly due to transformative design tools. Although these tools have been revolutionary, they still bear one overall limitation of requiring users to fully conceptualize their designs before designing. Recently, a simple computational casting technique was developed using generative optimization strategies to automate the design of DNA origami nanostructures. This approach employs a shape annealing algorithm, which creates a formal language of honeycomb DNA origami nanostructures with shape grammars and drives designs from the language towards a desired configuration using simulated annealing. This initial demonstration of the approach can generate novel scaffold routing schemes for creating solid or hollow structures constrained by boundaries of polyhedral meshes. The results from the initial approach, particularly from the hollow structures, reveal a challenging design space. This simple technique generates novel scaffold routing schemes that do not replicate the overall polyhedral mesh shape and is limited in its ability of controlling scaffold path exploration in the design space. This paper demonstrates an approach for varying effective wall thickness and improving quality of polyhedral mesh coverage for hollow structures that can be tuned and optimized by introducing a more refined computational casting technique. We achieve these improvements through changes in the simulated annealing algorithm by adding a Hustin move set algorithm that dynamically adjusts the performance of the overall design and redefining how these hollow designs are articulated. The results in this work illustrate how the shape annealing algorithm can navigate a challenging design space to generate effective hollow designs.
Safe Autonomous Vehicle Cooperative Cruise Control With Deep Reinforcement Learning
Authors: Yang James , Negash Natnael M. 
Published: November 2023

Abstract
Recently, intelligent control approaches through reinforcement learning have achieved robust performance. The practical complication of reinforcement learning in approximating complex functions is resolved through the introduction of deep learning into the framework. We developed such a deep reinforcement learning-based optimal vehicle cruise control. The most commonly used algorithm, deep deterministic policy gradient (DDPG), overestimates Q-functions that can lead to sub-optimal agent policy. We proposed the twin-delayed DDPG (TD3) algorithm, the extension of the DDPG algorithm. We have compared our proposed method with model-based control approaches and non-self-learning as physics-based car-following models to see the effectiveness of the proposed approach. The TD3 agent has achieved a minimum spacing error than MPC and PID. Moreover, the TD3 agent has shown effective tracking performance compared to physics-based models and risk assessments.
Route Based Fuel Savings of Freight Train Hybridization
Authors: Ahuja Nitisha , Martin Jay D. , Yukish Michael A. , Stump Gary M. , Bennett Lorri A. , Schlake Bryan W. , Anstrom Joel R. , Rahn Christopher D. 
Published: November 2023

Abstract
Adding battery locomotives to traditional diesel locomotives to form a hybrid consist has the potential to reduce fuel consumption and emissions in freight rail operations. This paper provides round trip, route-based estimates of diesel (hpd/ton) and battery (hpb/ton) power requirements and diesel (gal/ton) and battery (hpbhr/ton) energy requirements for hybrid consist. Given the route-specific power profile, the power split between the battery pack and the diesel engine is optimized to minimize fuel consumption by running the diesel engine at maximum efficiency during the entire trip. The results from 100-mile out/back routes between Chicago and Harrisburg indicate that the required diesel power for hybrid consist is maximum in low-gradient regions at 1.17 (hpd/ton) and the required battery power is maximum in hilly regions at 2.04 (hpb/ton) for discharge and 9.43 (hpb/ton) for charge. Maximum fuel efficiency gains of approximately 60% are seen in hilly routes, where 2.09 (hpbhr/ton) of battery energy is needed. Minimum fuel economy gains are observed in flat regions, with a 20% fuel consumption reduction. This ideal case analysis of hybrid consist can serve as an easy-to-use tool for the US freight rail sector to estimate fuel savings and sort routes based on the potential results for electrification, without requiring substantial computation.
Improving Change Management by Quantifying the Relationships Between Margins and Design Variable Flexibility
Authors: Jacobson Lindsey , Ferguson Scott 
Published: November 2023

Abstract
Managing system changes is one of the most challenging and important tasks facing today’s engineering designers. Beyond the numerous design revisions that occur during the design process, accelerating rates of technological and societal growth strain existing systems, and those that will produce the greatest value are the systems that can be adapted and evolved. The complexity of modern engineered systems requires that significant resources be invested so that changes can be managed throughout the design and redesign processes. Implementing even relatively small modifications can require extensive design effort in systems with many coupled and entangled relationships. There is significant evidence that design margins can effectively limit change propagation, yet the relationship between margins and change propagation has not been fully studied. In this paper, we take a step toward quantifying how margins create flexibility at a component level in an engineered system. We define a decision variable’s (DV) Complex Flexibility Range (CFR) as the extent by which a variable can change while maintaining feasibility with respect to all requirements and constraints. A high-powered rocket’s recovery system is modeled, and the CFRs are calculated for multiple DVs. We use the quantified CFRs to demonstrate how margins create flexibility at the level of DVs and how changing a decision variable impacts margins and DV flexibility. Several applications for CFRs are identified, including 1) predicting change propagation, 2) identifying change absorbers, 3) communicating changes in margin/flexibility, and 4) negotiating DV selection and requirements definition. This work provides a foundation for further study of how margins can be allocated and for studying how systems can be efficiently, and effectively, adapted and evolved.
A Novel Mass Sensor Incoporating Multiple Internal Resonances in Coupled Resonators Under Electrostatic Actuation
Authors: Sun Rongjian , Zhao Jian , Kacem Najib , Dong Zeyuan , Lyu Ming 
Published: November 2023

Abstract
Nonlinearly coupled vibrations in microelectromechanical sensors have received much attention for their great potential in improving sensor performance. In this paper, a novel mass sensor based on multiple internal resonances of three electrostatically coupled resonators is proposed. The mathematical model of the sensor is established by using Euler-Bernoulli theory, and the dynamic response before and after mass perturbation is solved by using the method of multiple scales, and verified by the harmonic balance method combined with the asymptotic numerical method. The relative shift of the amplitude sum induced by the mass perturbation at the saddle-node bifurcation point is used as the output sensitivity, and the sensitivity is enhanced by three orders of magnitude compared with the relative shift of frequency. Finally, the effect of driving voltage on the sensitivity of the mass sensor is analyzed.
Performance Assessment of Fractional-Order Control of Non-Minimum Phase Power Converter Systems
Authors: Nwoke Justus , Viola Jairo , Chen YangQuan 
Published: November 2023

Abstract
Non-minimum phase (NMP) systems present unique controller design challenges like the reduced stability region or an elevated noise and disturbance sensitivity due to its limited bandwidth caused by the presence of one or more right half plane zeros. In power electronics, architectures like the flyback converter exhibit this behavior, increasing the complexity in designing closed-loop controls for regulation tasks. This work present the assessment of several fractional-order controllers as an alternative for the control of NMP systems in the presence of external disturbances and random noise. In this case, a non-ideal flyback converter in regulation tasks is used as the benchmark system to compare the performance of several fractional-order control methods against its integer-order version in the presence of external disturbances and random noise. The obtained results shows that using fractional-order controllers can improve the power converters transient response and its response against external events.
Model Architecture Exploration Using ChatGPT for Specific Manufacturing Applications
Authors: Villena Toro Javier , Tarkian Mehdi 
Published: November 2023

Abstract
Selecting an appropriate machine learning model architecture for manufacturing tasks requires expertise in both computer science and manufacturing. However, integrating state-of-the-art machine learning models and manufacturing processes is often challenging due to the distance between these fields. OpenAI’s popular language model, ChatGPT, has the potential to bridge this gap.
This paper proposes guidelines and questions to explore model architecture options and extract valuable information from ChatGPT’s natural language processing capabilities. While ChatGPT is a powerful tool, it is important to verify any answers obtained against reliable sources before making any decisions. The guidelines compose a flowchart with four queries to give ChatGPT enough context and exisiting input data information. ChatGPT suggestions will be directed towards input processing, output, and architecture proposals. The last query produces keywords based on the chat for a background study on the topic.
A manufacturing case study was conducted to demonstrate the effectiveness of these guidelines. The study involved creating a model to forecast fixturing locations for welding processes in the automotive sector. After conducting four separate interviews with ChatGPT, the authors discuss the selection of architecture based on ChatGPT suggestions and contrast it with previous literature.
The proposed guidelines are expected to be useful in a variety of manufacturing contexts, as they offer a structured approach to exploring model architecture options using ChatGPT’s capabilities, ultimately leading to new and innovative applications of machine learning in this field.
Adding a Novel Olfactory Dimension to Multisensory Experience in Virtual Reality Training
Authors: Ferrise Francesco , Dozio Nicolò , Spadoni Elena , Rossoni Marco , Carulli Marina , Bordegoni Monica 
Published: November 2023

Abstract
Nowadays, industrial training is gaining popularity, and in particular, Virtual Reality (VR) technologies are often adopted to recreate training experiences that can transform the learning process by exploiting the concept of learning by doing. Moreover, VR allows us to recreate realistic and immersive environments that provide trainees with hands-on experience in a safe and controlled setting. One of the advantages of using VR technologies to perform training simulations lies in the ability that these tools offer to mix different senses as if they were independent variables and to manipulate them one at a time. In this way, the effects of using one sense on the specific task under analysis can be discovered in a controlled scenario in which the others are controlled. One of the least used senses in VR is undoubtedly that of smell. In this paper, we intend to use a new olfactory device specifically designed to be adopted with VR technologies, integrate it with an immersive VR training application, and propose a series of uses allowed by this combination. In the paper, we report the details of the multisensory approach that integrates the different senses. Additionally, the paper presents the findings of a pilot experiment conducted to validate the adopted approach and assess the results. The study demonstrates that the developed Olfactory Display had no adverse impact on users’ interaction with VR content.
Preliminary Concepts for Magnetic Actuation and Stabilization of Origami-Based Arrays
Authors: Pruett Hunter , Coleman Nathan , Magleby Spencer 
Published: November 2023

Abstract
Five genres of magnetic hinge concepts are presented; a simple bistable magnetic hinge, a monostable magnetic hinge from the simple bistable magnetic hinge, a hinge based on diametrically magnetized gears, a hinge composed of pairs of magnets, and a hinge based on the strategic placement of a single pair of cuboid magnets. Particular focus is given to the hinge composed of a single pair of cuboid magnets, as this genre of hinge can be low-profile and used in thin panels, which can yield a greater deployed area given a stowed volume. A framework for the generalization of such a hinge is presented and various design variables are defined. Two novel self-actuating and self-stabilizing hinges (the MaLO and MaLDO hinges) based on this framework are presented. The characterization of these hinges is defined and two key additional offset design variables are presented. The effect on bistability of varying one offset is explored in the MaLDO hinge and a hypothesized relationship for estimating when the hinge will be bistable is presented and discussed. Both hinges are shown in selected applications; the MaLDO hinge is shown implemented in a deployable truss and the MaLO hinge is shown implemented in a Miura-ori-based reflectarray antenna design.
Curvature Analysis of Linear and Circular Tractrices for the Planar Motion of Vehicles
Authors: Lanni Chiara , Figliolini Giorgio , Angeles Jorge 
Published: November 2023

Abstract
The curvature analysis of the linear and circular tractrix curves for the planar motion of vehicles is formulated as based on the application of the classical differential geometry, along with the Bresse and return circles. In particular, a two-wheel (bicycle) model of four wheels vehicles was assumed and the evolutes of the corresponding fixed centrodes, being the related moving centrodes represented by straight lines, are determined, along with the evolutes of the linear and circular tractrix curves.
The proposed formulation has been implemented in Matlab, in order to simulate and analyze the vehicle behavior in terms of curvature, when the front wheel center follows a straight line or a circle. Significant numerical and graphical results allowed the validation of the proposed formulation in different conditions.
Document Understanding-Based Design Support: Language Model Based Design Knowledge Extraction
Authors: Qiu Yunjian , Jin Yan 
Published: November 2023

Abstract
Design knowledge in the vast amount of design reports and documents can be a great resource for designers in their practice. However, capturing such domain-specific information embedded in long-length unstructured texts is always time-consuming and sometimes difficult. Therefore, it is highly desirable for a computer system to automatically extract the main knowledge points and their corresponding inner structures from given documents. In this study of document understanding for design support (DocUDS), a design-perspective knowledge extraction approach is proposed that uses phrase-level domain-specific labeled datasets to finetune a Bidirectional Encoder Representation from Transformers (BERT) model so that it can extract design knowledge from documents. The BERT model finetuning attempts to blend in the domain-specific knowledge of well-recognized domain concepts and is based on the datasets generated from design reports. The model is utilized to map the captured sentences to the main design entities &lt;requirement&gt;, &lt;function&gt;, and &lt;solution&gt;. In addition, this approach uncovers inner relationships among the sentences and constructs overall structures of documents to enhance understanding. The definitions of design perspectives, inter-perspective relations, and intra-perspective relations are introduced, which together capture the main design knowledge points and their relations and constitute an understanding of the design domain knowledge of a text. The case study results have demonstrated the proposed approach’s effectiveness in understanding and extracting relevant design knowledge points.
Control of a Bipedal Walking Using Partial Feedback Linearization and Gaussian Process Regression-Based of the Step-to-Step Map
Authors: Torres Daniel , Hernandez Hinojosa Ernesto , Bhounsule Pranav A. 
Published: November 2023

Abstract
Bipedal robots with small and point feet find it difficult to balance instantaneously due to underactuation, but can be relatively easy to control over the time scale of a step. Thus, models of step-to-step dynamics provide an effective means for the development of step-to-step controllers. Such models are analytically intractable because they are obtained from integrating nonlinear equations of motion. Our hypothesis is that obtaining approximations of such models can be effective for real-time computations of control inputs. We use partial feedback linearization to reduce the system dimensions for the step-to-step map. We formulate and solve an optimal control problem with the approximations to compute control for tracking a reference velocity and walking on stepping stones. These solutions demonstrate the need for bipedal robots to achieve specific control objectives, such as walking speed and foot placement, in order to navigate through restricted environments such as the stepping stones. We provide simulations on a 5-link biped to show the efficacy of the approach. A video link is provided: https://www.youtube.com/watch?v=p2TLU3Jzul8.