bluuebunny/crossref_metadata_2025 huggingface dataset:
A pnh-Adaptive Refinement Procedure for Numerical Optimal Control Problems
Authors: Bartali Lorenzo , Gabiccini Marco , Guiggiani Massimo
Published: November 2022

This paper presents an automatic procedure to enhance the accuracy of the numerical solution of an optimal control problem (OCP) discretized via direct collocation at Gauss-Legendre points. First, a numerical solution is obtained by solving a nonlinear program (NLP). Then, the method evaluates its accuracy and adaptively changes both the degree of the approximating polynomial within each mesh interval and the number of mesh intervals until a prescribed accuracy is met. The number of mesh intervals is increased for all state vector components alike, in a classical fashion. Instead, improving on state-of-the-art procedures, the degrees of the polynomials approximating the different components of the state vector are allowed to assume, in each finite element, distinct values. This explains the pnh definition, where n is the state dimension. Instead, in the literature, the degree is always raised to the highest order for all the state components, with a clear waste of resources.

Numerical tests on three OCP problems highlight that, under the same maximum allowable error, by independently selecting the degree of the polynomial for each state, our method effectively picks lower degrees for some of the states, thus reducing the overall number of variables in the NLP. Accordingly, various advantages are brought about, the most remarkable being: (i) an increased computational efficiency for the final enhanced mesh with solution accuracy still within the specified tolerance, (ii) a reduced risk of being trapped by local minima due to the reduced NLP size.
Control Co-Design Under Uncertainties: Formulations
Authors: Azad Saeed , Herber Daniel R.
Published: November 2022

This article explores various uncertain control co-design (UCCD) problem formulations. While previous work offers formulations that are method-dependent and limited to only a handful of uncertainties (often from one discipline), effective application of UCCD to real-world dynamic systems requires a thorough understanding of uncertainties and how their impact can be captured. Since the first step is defining the UCCD problem of interest, this article aims at addressing some of the limitations of the literature by identifying possible sources of uncertainties in a general UCCD context and then formalizing ways in which their impact is captured through problem formulation alone (without having to immediately resort to solution strategies). We first develop a universal UCCD formulation and discuss its fundamental elements. Issues such as the treatment of the objective function, the challenge of the analysis-type equality constraints, and various formulations for inequality constraints are discussed. Then, more specialized problem formulations such as the risk-neutral and risk-averse stochastic, worst-case robust, probabilistic robust, fuzzy expected value, and possibilistic chance-constrained UCCD formulations are presented. Key concepts from these formulations, along with insights from closely-related fields, such as robust and stochastic control theory, are discussed, and future research directions are identified.
Optimizing Intentional Islanding Design Strategies for Enhanced Failure Resilience of Power Systems
Authors: Wu Jiaxin , Chen Xin , Zhang Jie , Wang Pingfeng
Published: November 2022

Establishing cleaner energy generation therefore improving the sustainability of power systems is a crucial task in this century. One of the key strategies being pursued is to shift the dependence on fossil fuel to renewable technologies such as wind, solar, and nuclear. However, with the increasing number of heterogeneous components included in the power system, the complexity of the hybrid energy system becomes more significant, which makes it prone to system failures. The complex system imposes a more stringent requirement of the contingency plan to enhance the overall system resilience against potential system disruptive events. Among different strategies to ensure a resilient system, intentional islanding is commonly applied in practical applications for power systems. In this study, we address the challenges of intentional islanding design considering high penetration of renewable energy sources, and develop a hierarchical clustering-based design method for optimized intentional islanding strategies at the transmission level of a power system. To incorporate the renewable generation that relies on the inverter technology, the frequency measurements are considered to represent the transient response and further used as embedded information in the clustering algorithm. And in the case study, the stabilized post-disruption performance of the modified IEEE-9 bus test system demonstrates the capability of the proposed method for resilience enhancement.
Explore the Beauty of Cam Geometry: The Necessary and Sufficient Design Conditions for the Cam Mechanism With Flat Face Follower
Authors: Chen Yen-Nien , Hsu Kuan-Lun
Published: November 2022

Cam, a mechanism usually used to transform a rotary motion into desired output motion, has been commonly used in modern industry. In practice, a variety of practical methods have been discussed to improve the cam mechanism. In theory, however, the mobility of cam mechanism is seldom mathematically addressed. This paper mathematically discusses the necessary and sufficient design conditions for two kinds of form-closed cam mechanism (a cam with a translating flat-faced follower and a cam with an oscillating flat-faced follower) to be mobile. For the former cam mechanism, the relation between the design conditions and the cam mobility is derived by proving the identical cam contour enveloped by the flat faces. The result shows that the cam mechanism is mobile if and only if it satisfies the design conditions: (a) distance between the two follower flat faces is a constant value (twice base circle radius + total stroke of follower) and (b) summation of the displacement functions being 180 degrees out of phase must be the total stroke of the follower. For the later cam mechanism, the relation is derived by transforming the cam contour into a geometric layout associated with orthoptic curves. The result shows that the cam mechanism is mobile if and only if it satisfies the design conditions: (a) the flat faces are orthogonal, (b) the cycle of the displacement function has to be 180 degrees, (c) low dwell period and high dwell period are identical, (d) difference of rising and returning period has to be twice the stroke of follower, and (e) only one of the rising or falling period can be designed.
Task Modification Scheme for a Collaborative Robot
Authors: Singh Hardeep , Kumar Rajesh , Saha Subir Kumar
Published: November 2022

The paper formulates an algorithm for online modification of the trajectory of a collaborative robot. A sliding mode control algorithm is developed to identify the human intent, under ignorance of the mass and inertia of the element attached to the end effector of the collaborative robot (cobot) and also under ignorance of the human behaviour while applying force to the end effector of the cobot. The sliding mode control methodology allows a human user to modify the trajectory of the cobot while in motion. The controller is developed based on the assumption that the force applied by the human agent is an outcome of a Proportional-Derivative (PD) based dynamics. Although, the human dynamics is assumed, the parameters of the assumed PD control is unknown. However, the boundedness of the human dynamics’ gain matrices lead to the need of a sliding mode controller. Subsequently, the traditional Dynamic Motion Primitives (DMPs) have been used to rapidly train the desired behaviour of a human user. Then, the local planner is modified based on the task modification. The utility of the sliding mode control algorithm and the proposed DMP approach is demonstrated by experiments on the Kinova Gen2 cobotic Arm. Also, the sliding mode control algorithm is shown to converge asymptotically under bounded human action.
Does Synchronous Collaboration Improve Collaborative Computer-Aided Design Output: Results From a Large-Scale Competition
Authors: Deng Yuanzhe , Marion Tucker , Olechowski Alison
Published: November 2022

With the growing demand for distributed collaboration for large and complex design in modern engineering, the collaboration inefficiencies of traditional computer-aided design (CAD) tools are increasingly conspicuous. Emerging cloud-based multi-user computer-aided design (MUCAD) platforms bring a new working style for CAD in the form of real-time synchronous collaboration. Little research exists to characterize collaboration in CAD, and specifically the synchronicity of collaboration has yet to be examined. In this study, we analyzed the backend action logs of 101 teams’ design processes from a large-scale virtual robotic design competition, where all designs were modelled in a commercially available MUCAD platform. Metrics of interest were analyzed with regression and mediation analyses to uncover factors that correlated to a team’s success in the competition. Results show that team size is a positive predictor of team performance. Large teams, which tend to see a large amount of time commitment from members, were more likely to perform more CAD actions and achieve high scores from the competition. This suggests that the benefits of collaboration (e.g., economies of division of labor, learning) outweigh the potential downsides (e.g., coordination overhead, free riding) in this context. While controlling for team size, increased synchronous collaboration occurrences were observed to negatively correlate to teams’ performance — a novel finding which we discuss in detail. Thus, we conclude that although large teams benefited from the MUCAD environment, a tendency for synchronous real-time collaboration did not coincide with higher performance. This study provides important evidence in the ongoing design and innovation research fields aiming to better understand collaboration. Future research should investigate the characteristics of effective collaboration strategies in MUCAD environments to develop best practice for the increasing number of design teams moving to such tools.
Partition-Aware Stability Control for Humanoid Robot Push Recovery
Authors: Song Hyunjong , Peng William Z. , Kim Joo H.
Published: November 2022

For successful push recovery control in response to unpredicted perturbations, a humanoid robot must select the appropriate stabilizing action from a wide range of available strategies. Existing approaches are restricted to a limited range of balancing motions because they are often derived from reduced-order models and ignore system-specific aspects such as swing leg dynamics or joint kinematic/actuation limits. In this work, a novel partition-aware push recovery controller is introduced that can select between the ankle, hip, and captured stepping strategies for balance by evaluating the robot’s center-of-mass (COM) state with respect to different levels of criteria. The criteria are the partition-based stability regions in the augmented COM state space, which are numerically constructed for each balancing strategy. For stepping, both free- and fixed-arm capturability regions were obtained to quantify the effect of arm momenta on balancing capability. The regions are pre-computed for control with an optimization-based method that incorporates whole-body system dynamics, contact interactions with the ground, system-specific characteristics, and requirements of the corresponding strategy. Through simulation experiments, the proposed approach was demonstrated to allow the controller to fully exploit a humanoid robot’s balancing capability and validate the use of pre-computed stability regions as explicit criteria to initiate a proper balancing motion, in contrast to the use of incomplete or implicit criteria in existing controllers.
Long-Range Risk-Aware Path Planning for Autonomous Ships in Complex and Dynamic Environments
Authors: Hu Chuanhui , Jin Yan
Published: November 2022

Path planning and collision avoidance are common problems for researchers in vehicle and robotics engineering design domains. In the case of autonomous ships, the navigation is guided by the Regulations for Preventing Collisions at Sea (COLREGs). However, COLREGs do not provide specific guidance for collision avoidance, especially for multi-ship encounter situations, which is a challenging task even for humans. In short-range path planning and collision avoidance problems, the motion of target ships is often considered as moving at a constant velocity and direction, which cannot be assumed in long-range planning and complex environments. The research challenge here is how to factor in the uncertainty of the motion of the target ships when making long-range path plans. In this paper, we introduce a long-range path planning algorithm for autonomous ships navigating in complex and dynamic environments to reduce the risk of encountering other ships during the future motion. Based on the information of position, speed over ground, and course over ground of other ships, our algorithm can estimate the intentions and future motions of them based on the probabilistic roadmap algorithm and use a risk-aware A* algorithm to find the optimal path that has low accumulated risk of encountering other ships. A case study is carried out on the real Automatic Identification Systems (AIS) datasets, and the result shows that our algorithm can help reduce multi-ship encounters in long-term path planning.
LINKS: A Dataset of a Hundred Million Planar Linkage Mechanisms for Data-Driven Kinematic Design
Authors: Heyrani Nobari Amin , Srivastava Akash , Gutfreund Dan , Ahmed Faez
Published: November 2022

In this paper, we introduce LINKS, a dataset of 100 million one degree of freedom planar linkage mechanisms and 1.1 billion coupler curves, which is more than 1000 times larger than any existing database of planar mechanisms and is not limited to specific kinds of mechanisms such as four-bars, six-bars, etc. which are typically what most databases include. LINKS is made up of various components including 100 million mechanisms, the simulation data for each mechanism, normalized paths generated by each mechanism, a curated set of paths, the code used to generate the data and simulate mechanisms, and a live web demo for interactive design of linkage mechanisms. The curated paths are provided as a measure for removing biases in the paths generated by mechanisms that enable a more even design space representation. In this paper, we discuss the details of how we can generate such a large dataset and how we can overcome major issues with such scales. To be able to generate such a large dataset we introduce a new operator to generate 1-DOF mechanism topologies, furthermore, we take many steps to speed up slow simulations of mechanisms by vectorizing our simulations and parallelizing our simulator on a large number of threads, which leads to a simulation 800 times faster than the simple simulation algorithm. This is necessary given on average, 1 out of 500 candidates that are generated are valid (and all must be simulated to determine their validity), which means billions of simulations must be performed for the generation of this dataset. Then we demonstrate the depth of our dataset through a bi-directional chamfer distance-based shape retrieval study where we show how our dataset can be used directly to find mechanisms that can trace paths very close to desired target paths. Furthermore, we discuss how we plan to expand LINKS to include more complex mechanical components and expand the dataset in the future. our work is available at https://github.com/ahnobari/LINKS. We believe LINKS will enable a vast array of computational approaches in kinematic design.
Design and Modeling of a New Variable Stiffness Robotic Finger Based on Reconfigurable Beam Property Change for Flexible Grasping
Authors: Xu Wei , Fu Jiaming , Gan Dongming
Published: November 2022

Flexible grippers can provide fine grasping and manipulation to various objects and environment interactions. However, most current mechanisms can not change the stiffness in a short time, which limits the application scenario of the flexible grippers. This paper presents a novel variable stiffness robotic finger that can adapt to soft and rigid gripping objects by continuously changing its stiffness over a wide range in a short period of time. The principle is to change the second area moment of inertia of the finger by changing the filling ratio of the cavity between two parallel beams. A complete theoretical stiffness model is developed and compared with the finite element analysis (FEA) model. Effects of multiple design parameters on finger stiffness performance are compared and analyzed, and the accuracy of the theoretical model is verified, with a maximum error of less than 6.5%. The performance of the finger is further evaluated through an experimental prototype, which proved that the finger can safely perform a wide range of daily object-grasping tasks with adaptable compliance. The proposed stiffness-varying mechanism can adjust stiffness in a short time with a very large ratio (around 1:37). The design provides a new direction in developing variable-stiffness robotic grippers for flexible grasping.
Reconfigurable Robot Design Aided With Design Cards
Authors: Kalimuthu Manivannan , Hayat Abdullah Aamir , Elara Mohan Rajesh , Wood Kris L.
Published: November 2022

Reconfigurable products and systems, or more broadly transformer products and systems, change their state or morphology to perform multiple functions depending on the needs. Such systems continue to play a vital role in developing a wide range of autonomous robotic solutions in the areas of surveillance, cleaning and maintenance, and rescue. Current reconfigurable robotic systems emphasize software, control, and electrical module development, and there is a lack of design aids or suitable mechanisms resulting in reconfiguration. The main research question for this study is: How can the design of re-configurable robot development be assisted in the early stages, focusing on suitable mechanism recommendations for transformation? Prior published work on reconfigurable systems identified three main transformation principles: expand/collapse, expose/cover, fuse/divide, and twenty facilitators. However, how these underlying principles and facilitators can aid in selecting suitable mechanisms for the required reconfiguration was not addressed. We propose a set of design card mapping the facilitators and principles to aid designers with generating innovative and novel reconfigurable robots. Using the empirical method and observing the motion generated by the range of mechanisms across domains were associated with respective underlying principles and facilitators, resulting in a set of design cards. The limited-scale pilot design study is carried out in this work which poses a design problem to the participants. The proposed design card’s usefulness and limitations are established and discussed by comparing the solutions generated with and without the use of design cards.
Exploring the Effects of Partnership and Inventory for Supply Chain Resilience Using an Ecological Network Analysis
Authors: Wilson Tyler , Chatterjee Abheek , Layton Astrid
Published: November 2022

Traditional supply chain policies and design efforts, such as lean-manufacturing, prize efficiency over all other factors. These traditional design principles are being challenged as supply chains around the globe struggle to bounce back from an array of recent disruptions. This brings up the longstanding battle between profit driven efficiency goals and redundancy efforts that support demand in times of need. Supply chain design guidelines that address resilience thus require a balanced approach between efficiency and redundancy, one that presents flexibility in a way that acknowledges profit requirements. Ecologists have used Ecological Network Analysis to reveal a unique balance of pathway efficiency and redundancy characteristic of biological ecosystems. This balance results in efficient steady-state operations and survival despite unexpected disruptions. Supply chain networks are here evaluated using the same ecological analysis, providing quantitative design guidelines for achieving an ecologically similar system-level resilience. This study builds on past bio-inspired supply chain work to simulate profit-based supply chain performance during disruptions from an ecological perspective. Significant insights include that the validity of ecologically inspired supply chain design is contingent on the supply chain’s properties and that under the right conditions, the ecological balance of efficiency and redundancy can vastly improve the performance of supply chains during disruptions.
Design of Fourbar Linkages Using a Reinforcement Learning Optimization Method
Authors: Gallego Juan A. , Muñoz Juan M. , Viquerat Jonathan , Aguirre Milton E.
Published: November 2022

This work studies the feasibility of using Reinforcement Learning and Policy Based Optimization, on a continuous action space, for straight line path generation of four bar linkages. Based on the path radius of curvature, an optimization method introduces a reward function that is calculated by the inflection circle and the Euler-Savary equation to train policy based agents. Two experiments are defined, which run 200 agents in each setting. Results are compared to demonstrate the process and outcome of using this design method. In experiment one the starting point is fixed at the center of the design space. In experiment two the agents start from a random point in the design space. We conclude that agents trained in both experimental settings are capable of creating linkages that generate quasi-straight line segments (high radii of curvature). The results across 400 distinct agents show that more than 50% of the generated designs are reasonably straight, such that the linkages are suitable in applications for quasi-straight line path generation.
Sustainability in an Age of Space Trash: Adapting Ion Thrusters for Use on Cubesats
Authors: Ashley Aaron , Clarke-Sather Abigail R.
Published: November 2022

With the advent of commercial space launches for satellites, orbital space near earth is increasingly crowded. An estimated half million objects are being tracked by ground-based sensors. These objects include both operational and defunct satellites, discarded rocket stages from previous launches, and debris from former spacecraft. Simultaneously, small satellites, e.g., cubesats, have the potential to make basic space infrastructure such as communications and weather tracking far more accessible to institutions worldwide. Cubesats are small satellites composed of one or more modular units. With the number of objects, including cubesats, in orbit increasing, some way to address satellites at end-of-life is needed. End-of-life for satellites can involve moving the object to a so-called graveyard orbit or to de-orbit down towards earth to be incinerated in the atmosphere upon re-entry. This paper compares two methods for providing propulsion of cubesats at end-of-life, direct current electron bombardment (DC) and radiofrequency (RF) ion thrusters. These two types of ion thrusters are compared for both impact on spacecraft lifespan and on Δv or change in velocity delivered to a spacecraft. A three unit cubesat was modeled using CAD software Solidworks and exit velocity of plasma generated was modeled using ANSYS Fluent 2019 software. A 12% increase in Δv was calculated for the modeled RF as compared to the DC electron bombardment ion thruster and most importantly the Δv exceeded orbital velocity. This finding implies that RF ion thrusters can effectively de-orbit small cubesats allowing incineration upon reentry to earth’s atmosphere as an end-of-life option.
Rolling Locomotion of Hexagonal Kinematic Chain Robot
Authors: Stanley Joshua , Schroeder Adam , Trease Brian
Published: November 2022

Chain robots are composed of a series of discrete links connected by actuated revolute joints in a kinematic chain or loop. They possess the ability to roll by constraining and changing their shape, causing the robot to advance to adjacent panels. They also have the advantage of being able to change their driving geometry to improve their speed, efficiency, ride quality, or ability to handle rough terrain. This is especially useful for extra-planetary exploration applications, for which the robot can fold down to a small packing size for transport. In this work, a hexagonal chain robot was designed and constructed. A variety of locomotion types were created, classified as either a ‘static’ or ‘dynamic’ gait, and experimentally tested. In addition to the distinct gaits, two different tread geometries were used to determine their effect on the performance parameters of the different gaits. Through experimentation, we found that the dynamic gaits selected performed better in terms of speed and efficiency, particularly when using the round treads. Conversely, the static gaits performed better when it came to incline and terrain navigation and ride quality, especially with the flat treads. The true advantage of this platform is its ability to adapt it locomotion depending on the terrain and its priorities.
Customer Journey Mapping Using Stochastic Models
Authors: Ding Yiqing , MacDonald Erin F.
Published: November 2022

A customer journey map is a visual representation of the process that a person goes through when interacting with a product or a service and it is often related to human-centered design. The process of which customer journey maps are built is referred to as customer journey mapping (CJM) and traditionally this process includes techniques such as observations, interviews, and surveys. However, the emergence of new data collection techniques such as interactive mobile applications has made richer data available for service designers. This emerging data availability poses both challenges and opportunities for CJM. In this paper, we propose an innovative stochastic-based method to tackle these challenges while preserving the advantages of traditional CJM. Specifically, the proposed method models user-generated customer experiences as Markov chains and amalgamate the large quantities of experiences into a small number of customer journey maps which are easier to analyze for service designers. This method is based on a clustering algorithm that achieves the maximum posterior likelihood post-clustering. By employing our method on collected data, service designers can discover key components in customers’ experiences in addition to some edge cases. We will demonstrate the effectiveness of this method using sample data from the 2017 National Household Travel Survey (NHTS).
Can I Get a Word In? the Impact of Turn-Taking and Gender on Engineering Design Students’ Psychological Safety
Authors: O’Connell Abby , Cole Courtney , Mohammed Susan , Jablokow Kathryn , Miller Scarlett
Published: November 2022

Psychological safety and turn-taking have both been listed as key factors needed for collaboration in teams to emerge. Specifically, prior work has shown that increased communication in teams can lead to high psychological safety. Prior work on turn-taking as a measure of communication has mostly focused on its inclusivity in a team rather than its frequency. While the gender composition of the team can impact both participation as well as team psychological safety, there is a lack of research at the individual level. As such, this study provides the first attempt at connecting turn-taking, gender, and psychological safety through the analysis of members of fifteen engineering design student teams during the concept generation stage of their project. Specifically, we gathered video data to study how the number of turns and self-turns in a team impact psychological safety at both the individual and the team levels. We also examined how gender impacts participation and individual perceptions of psychological safety. The results found that turns and self-turns have a significant positive impact on an individual’s perception of the team’s psychological safety. However, no such relationship was found at the team level, indicating that there may be additional underlying factors in team level psychological safety. While we found that gender did not impact individual turn-taking, it did affect an individual’s perception of their psychological safety. These results provide quantitative evidence of the role of team communication on psychological safety.
Automatic Power Plane Generation With Genetic Optimization and Multilayer Perceptron
Authors: Liao Haiguang , Patil Vinay , Dong Xuliang , Shanbhag Devika , Fallon Elias , Hogan Taylor , Spasojevic Mirko , Kara Levent Burak
Published: November 2022

We present an automatic power plane generation method to accelerate the design of printed circuit boards (PCB). In PCB design, while automatic solvers have been developed to predict important indicators such as the IR-drop, power integrity, and signal integrity, the generation of the power plane itself still largely relies on laborious manual methods. Our automatic power plane generation approach is based on genetic optimization combined with a multilayer perceptron and is able to automatically generate power planes across a diverse set of problems with varying levels of difficulty. Our method consists of an outer loop genetic optimizer (GO) and an inner loop multi-layer perceptron (MLP) that generate power planes automatically. The critical elements of our approach include contour detection, feature expansion, and a distance measure to enable island-minimizing complex power plane generation. We compare our approach to a baseline solution based on A*. The A* method consisting of a sequential island generation and merging process which can produce less than ideal solutions. Our experimental results show that our method outperforms A* in 71% of the design problems with varying levels board layout difficulty. We also present ablation studies demonstrating the influence of various algorithmic choices. Finally, we provide insights into how the power planes evolve with our model parameters to form feasible and desirable space partitions.
Interactive Visualization of Spatial Triangles
Authors: Larochelle Pierre M.
Published: November 2022

This article presents a novel software tool for the interactive visualization of spatial triangles. These triangles serve as the geometric foundation for spatial mechanism design; both synthesis and analysis. Moreover, the conceptualization and visualization of triangles facilitate the mastery of the synthesis and analysis of spatial mechanisms. This interactive visualization tool has been developed to facilitate the teaching and learning of spatial mechanisms at the graduate and undergraduate levels. The trigonometry employed and the interactive MATLAB implementation are presented. It is hoped that the dissemination of this software tool will facilitate the learning and advancement of spatial mechanism design techniques.
Exploring Rules of Sustainable Design With Compositional Manufacturing
Authors: Bjorni Jacob , Dinar Mahmoud
Published: November 2022

The need for creating new manufacturing methods for sustainability is apparent given the harmful environmental effects of perpetual mining of raw materials. There are two common themes that dominate the work on sustainable design and manufacturing. One is using checklists during design to ensure reusability in the future by designing products with sustainability built into its life cycle. The other is developing design guidelines for designers to follow. The aim of this study was to explore a novel method of sustainable design through compositional manufacturing which enables resynthesizing nonfunctional pieces of scrap or waste material into functioning products. We conducted an exploratory case study with casting, where risers, gates, and miscast parts were resynthesized in a more sustainable and environmentally friendly way than remelting them. To do this, parts with arbitrary functions were created using various geometric properties taken from a selection of scrap parts. Based on initial observations, we identified few heuristics. One of the main discoveries was that groups of scrap parts with greater geometric differences resulted in final parts with more varied functions. The data gleaned from more targeted implementations of this process are required to create a more robust understanding of how effectively scrap parts can be utilized and how sustainable this process is compared to other design for sustainability methods.
Measuring Risk Attitudes for Strategic Decision-Making in a Collaborative Engineering Design Process
Authors: Avşar Alkim Z. , Stern Jordan L. , Grogan Paul T.
Published: November 2022

This paper evaluates a questionnaire-based risk attitude assessment method to quantify individual risk attitudes for strategic, multi-actor design decisions. A lottery-equivalence questionnaire elicits a utility curve for risky payoffs which is fit to a Constant Absolute Risk Aversion (CARA) model. Secondary data from a multi-actor design experiment provides observations of strategic decisions in two-actor design games for validation. 124 participants complete the risk attitude questionnaire and a series of 29 experimental tasks. Assuming participants follow the risk dominance equilibrium selection criterion, a risk-neutral utility function accurately predicts 62.2% of decisions. Incorporating risk attitudes elicited from the questionnaire only increases the accuracy to 63.3% while incorporating risk attitudes inferred from observations increases the accuracy to 77.5%. While participants exhibit differential risk attitudes in design tasks, results show the lottery-equivalent questionnaire does not provide risk attitudes consistent with strategic design decisions. Results support findings that risk in the engineering domain is contextual. This paper concludes that risk attitude is an important factor in understanding strategic decisions in interactive engineering design settings and understanding risk attitudes can help create more efficient design processes.
Dynamically Weighted Ensemble of Diverse Learners for Remaining Useful Life Prediction
Authors: Nemani Venkat , Thelen Adam , Hu Chao , Daining Steve
Published: November 2022

A traditional ensemble approach to predicting the remaining useful life (RUL) of equipment and other assets has been constructing data-driven and model-based ensembles using identical estimators. This ensemble approach may perform well on quality data collected from a design-of-experiments test but may ultimately fail when deployed in the field because of higher-than-expected noise, missing measurements, and different degradation trends. In such work environments, the high similarity of the learners can lead to large under/overestimates of RUL, where the ensemble is only as accurate as the learner which under/overestimates RUL the least. In response to this, we investigate whether an ensemble of diverse learners might be able to predict RUL consistently and accurately by aggregating the predictions of various algorithms which are found to perform differently under the same conditions. We propose improving ensemble model performance by 1) using a combination of diverse learning algorithms which are found to perform differently under the same conditions and 2) training a data-driven model to adaptively estimate the prediction weight each learner receives. The proposed methods are evaluated using open-source run-to-failure datasets from two popular systems of prognostics research: lithium-ion batteries and rolling element bearings. Results for the lithium-ion battery case study indicate that the proposed method achieves a 12% improvement in RUL prediction accuracy and a 27% decrease in the average uncertainty calibration error when compared to ensembles without dynamic weighting, and an even larger improvement when compared to individual models.
Analyzing Expert Design Cost Estimation for Additive Manufacturing
Authors: Williams Glen , Meisel Nicholas A. , Simpson Timothy W. , McComb Christopher
Published: November 2022

Compared to conventional fabrication, additive manufacturing enables production of far more complex geometries with less tooling and increased automation. However, despite the common perception of AM’s “free” geometric complexity, this freedom comes with a literal cost: more complex geometries may be challenging to design, potentially manifesting as increased engineering labor cost. Being able to accurately predict design cost is essential to reliably forecasting large-scale design for additive manufacturing projects, especially for those using expensive processes like laser powder bed fusion of metals. However, no studies have quantitatively explored designers’ ability to complete this forecasting. In this study, we address this gap by analyzing the uncertainty of expert design cost estimation. First, we establish a methodology to translate computer-aided design data into descriptive vectors capturing design for additive manufacturing activity parameters. We then present a series of case study designs, with varied functionality and geometric complexity, to experts and measure their estimations of design labor for each case. Summary statistics of the cost estimates and a linear mixed effects model predicting labor responses from participant and design attributes was used to estimate the significance of factors on the responses. A task-based, CAD model complexity calculation is then used to infer an estimate of the magnitude and variability of normalized labor cost to understand more generalizable attributes of the observed labor estimates. These two analyses are discussed in the context of advantages and disadvantages of relying on human cost estimation for additive manufacturing forecasts as well as future work that can prioritize and mitigate such challenges.
Transforming Hand-Drawn Sketches of Linkage Mechanisms Into Their Digital Representation
Authors: Purwar Anurag , Nurizada Anar
Published: November 2022

This paper presents a deep neural network based approach for interactive digital transformation and simulation of n-bar planar linkages consisting of both revolute and prismatic joints from hand-drawn sketches. Instead of taking a pure computer vision approach, we combine the output of a convolutional deep neural network with the topological knowledge of linkage mechanisms to create a framework for recognition of hand-drawn sketches. To accomplish this, we first synthetically generate a dataset of images of linkage mechanism sketches similar to hand-drawn ones and then fine-tune a state of the art deep neural network capable of detecting discrete objects. While the network had previously been exposed to only a general class of images of every-day objects, it was for the first time trained with a set of building blocks of linkage mechanisms, viz. joints and links. Thereafter, we present a novel algorithm, which performs topological analysis on the set of detected objects to create a kinematic model of the sketched mechanisms. The results show that this algorithm performs well on hand-drawn sketches and could help with conversions of such sketches to their digital representation for effective communication, analysis, cataloging, and classification.
Towards Creating Robust Walking Multi-Legged Robots for Traversing Different Terrains
Authors: Robson Nina , Audrey Vanessa
Published: November 2022

This paper explores the kinematic synthesis, design and pilot experimental testing of a six-legged walking robotic platform able to traverse through different terrains. The design approach builds up incrementally starting with studying the basic human leg walking trajectory and then defining a “relaxed” kinematic task. The “relaxed” kinematic task consists only of two contact locations (toe-off and heel-strike) with higher order motion task specifications compatible with foot-terrain(s) contact and curvature constraints in the vicinity of the two contacts. As a next step, an eight-bar leg image is created based on the “relaxed” kinematic task and incorporated within a six-legged walking robot. Pilot experimental tests explore if the proposed approach results in an adaptable behavior which allows the platform to incorporate different walking foot trajectories and gait styles coupled to each environment. The results suggest that the proposed “relaxed” higher order motion task combined with the leg morphological properties and feet material allowed the platform to walk stably on the different terrains.
Adaptive Slicing Based on Accurately Assessing the Variations of the Model’s Geometry for Staircase Effect and Dimensional Deviation Mitigation
Authors: Yang Qinkai , Myant Connor
Published: November 2022

Current additive manufacturing (AM) technologies commonly cause geometric inaccuracies (e.g. staircase effect and dimensional deviation) in the printed parts. These geometric inaccuracies significantly limit the industry potential of AM as they are the primary cause of reduced energy efficiency (e.g. turbine blade and wind tunnel testing models) and failed implants (e.g. hip and cranial implants and dental prostheses).

To improve the geometric accuracy while balancing the build time in AM, previous studies have proposed adaptive slicing. It works by varying decreasing layer thickness in sections of high curvature. However, current adaptive slicing methodologies have all faced difficulties adjusting layer thickness precisely according to the variations in the model’s geometry, limiting the geometric accuracy improvement.

This paper tackles this difficulty by assessing the geometric variations of the model by directly evaluating the ratio of the volume of each sliced layer’s geometric deviation to the volume of its corresponding region in the digital (triangulated surface) model. This assessment allows all the topological information of the corresponding region to be considered in measuring the geometric deviation (volume) between each sliced layer and its corresponding region, and therefore, it can accurately indicate the geometric variations of the model. Through this precise indication to modify each layer thickness, this paper has developed an adaptive slicing methodology that can better optimise the trade-off between build-time reduction and geometric accuracy improvement than other slicing methodologies. For example, it can reduce the build time by nearly half compared to other existing slicing methodologies by assuming a similar degree of the geometric accuracy of printed parts. This slicing was evaluated using six different test models and compared with three current slicing methodologies (voxelisation-based, cusp height-based, and uniform slicing).
A Material Removal State Prediction Method Based on Multi-Scale Attention Mechanism
Authors: Li Zhihang , Tang Qian , Liu Ying , Li Xiao
Published: November 2022

The exact removal of material in abrasive belt grinding determines the final machining quality of the workpiece. However, it is difficult to determine the removal state of materials in actual processing, which is affected by factors such as abrasive belt wear and processing errors. Therefore, a multi-scale attention convolutional neural network for material removal state prediction method is proposed based on the analysis of displacement data. First, the first-order difference and sliding-window expansion methods for displacement data are adopted, making it possible to use displacement data for deep learning, which is the premise of material removal state prediction. Then, the multi-scale convolutional neural network is Employed to extract important features of the displacement data. Due to the different importance of different features, Squeeze-and-Excitation Networks are used to independently assign the importance of features based on the loss function, so that the model pays more attention to those main features and ignores the secondary features, which can improve the convergence speed and prediction accuracy of the model. The K6 cross-validation of experiment results shows that this method can accurately predict the material removal state with an average prediction accuracy of 87.9%, which can be practically applied to the online prediction of the material removal state in industrial processing to further control the processing quality.
Designing Privacy Risk Frameworks for Evolving Cyber-Physical Social Systems: Knowledge Gaps Illuminated by the Case of Autonomous Vehicles and Bystander Privacy
Authors: Rao Vivek , Joshi Ankita , Kang Soo Min , Lin Susan , Song (Erin) Junghyun , Miller Drew , Goucher-Lambert Kosa , Agogino Alice
Published: November 2022

Designers and engineers increasingly engage with and must design for sociotechnical systems, also described as cyber-physical-social systems (CPSS). Leading frameworks like System-Theoretic Process Analysis and Value-Sensitive Design intend to help designers consider the consequences and impacts of their work with CPSS. However, such frameworks may not sufficiently account for human-centered scenarios. This complicates designers’ efforts to balance user needs with traditional forms of risk assessment. In this work, we explore foundations for the design of human-centered risk frameworks and examine a case study of autonomous vehicles and bystanders’ privacy as an example CPSS to address this gap. We develop an exploratory scenario-based risk framework and conduct expert interviews with experienced professionals (N = 7) working in the fields of autonomous vehicle design, development, policy and security to understand their perspectives on risk assessment and gather feedback on our framework. Reconciling interview findings with existing knowledge of evolving CPSS, we identify three broad knowledge gaps that could motivate future research in this space. First, we argue that there is a knowledge gap in developing human-centered frameworks and best practices to consider all stakeholders during the design of evolving CPSS. Second, we argue that a knowledge gap exists in acknowledging, reconciling, and proactively managing disciplinary discontinuities in vocabularies and mental models in evolving CPSS. Lastly, we argue that a critical knowledge gap exists around how to adapt scenario-based frameworks to accommodate the shifting challenges of designing evolving CPSS. We conclude with a discussion of preliminary implications for designing human-centered frameworks for autonomous vehicles and CPSS more generally.
Elasto-Plasticity, Damage and Multiphysics Effects on the Behavior of Adhesive Step Lap Joints
Authors: Michopoulos John G. , Apetre Nicole A. , Iliopoulos Athanasios P. , Steuben John C.
Published: November 2022

The presence of damage in the adhesive material as well as combined environmental excitation in multi-material adhesive step-lap joints (ASLJ) often encountered in aircraft industries are frequently neglected. Historically, their design is based only within the scope of elasto-plastic failure. The present work describes the implementation and application of a computational framework enabling the performance evaluation of such joints under quasi-static loading conditions under the simultaneous presence of plasticity, damage and environmental stimulus. In particular, a ASLJ involving Ti-6Al-4V alloy adherents with a FM-300K adhesive is modeled under the proposed framework for various material responses and mutliphysics excitations. It is shown that the assumption of assuming elasto-plastic failure as being the only behavior defining the failure of the adhesive, may not be an adequate assumption for designing and qualifying ASLJs. Specifically, considering presence of plasticity, damage and environmental effects indicates that there are reasons to re-examine the design practices of such joints.