bluuebunny/crossref_metadata_2025 huggingface dataset:
Assessment of State of Charge Estimation Methods Based on Neural Networks and Support Vector Machine for Lithium-Ion Batteries Used in Vehicular Applications
Authors: Luciani Sara , Feraco Stefano , Silvagni Mario , Bonfitto Angelo , Amati Nicola , Tonoli Andrea
Published: November 2022

The State of Charge (SOC) estimation in Lithium-ion batteries is a challenging task that is currently assessed with different methods in a vast variety of applications. This paper presents the design and assessment of two SOC estimation methods, based on Artificial Neural Networks (ANNs) and Support Vector Machine (SVM) algorithms for Lithium-ion batteries used in vehicular applications. The paper validates the two proposed approaches with experimental data collected during a laboratory test campaign. The obtained results are compared in terms of estimation accuracy, proving the feasibility of the considered algorithms. Moreover, the paper describes the retained software architectures and the design procedure related to the two proposed techniques based on Artificial Intelligence (AI). In detail, the retained Lithium-ion battery is a 21.6V 3.3Ah battery pack that is used as an energy module for vehicular applications. The considered battery module is numerically modeled with a 2nd order RC equivalent Thevenin model to collect a sufficient amount of data for the algorithms design phase. The model parameters are identified with a grey-box approach based on a non-linear least squares algorithm designed to accurately estimate the battery SOC with both the ANN-based and SVM-based methods. Specifically, the resulting mean prediction error is always below 2.5% and 3.5% for the ANN-based and SVM-based algorithms, respectively.
Envelope Method for Time- and Space-Dependent Reliability-Based Design
Authors: Wu Hao , Du Xiaoping
Published: November 2022

Deterministic optimization may lead to unreliable design results if significant uncertainty exists. Including reliability constraints in reliability-based design optimization (RBDO) can solve such a problem. It is difficult to use current RBDO methods to deal with time- and space-independent reliability when responses vary randomly with respect to time and space. This study employs an envelope method for time- and space-dependent reliability for the optimal design. To achieve high accuracy, we propose an inverse envelope method that converts a time- and space-dependent limit-state function into a time- and space-independent counterpart, and then the second-order saddlepoint approximation is used to estimate the probability of failure. The strategy is to find an equivalent most probable point for a given permitted probability of failure for each of the reliability constraint. To achieve high efficiency, the new method uses a sequential optimization process to decouples the double-loop structure of RBDO. The overall optimization is performed with a sequence of cycles consisting of deterministic optimization and reliability analysis. The constraints of the deterministic optimization are formulated using the equivalent most probable points. The accuracy and efficiency are demonstrated with four examples, including one mathematical problem and three engineering problems.
On the Kinematic Analysis of an Orthogonal Bricard 6R Mechanism
Authors: Rea Pierluigi , Ruggiu Maurizio , Müller Andreas
Published: November 2022

This paper deals with the kinematics of the orthogonal Bricard 6R mechanism. The paper presents the loop equations formulated in terms of dual-quaternions. The analysis reveals a symmetric relationship between the odd and even pairs of revolute joint angles. The kinematic model was used to compute the instantaneous screw axis of the coupler. Finally, considering some practical applications, a mechanism constructed from a combination of three orthogonal Bricard 6R mechanisms is presented.
Design and Modeling of a Robot for De-Icing Overhead Power Lines
Authors: Nelson Carl A.
Published: November 2022

In cold climates, removing accumulated ice and snow from power lines is an important aspect of protecting critical infrastructure. Current methods for power line de-icing suffer from cost, efficiency, and/or scalability limitations. In this paper, a robotic solution is presented to quickly and scalably provide mechanical de-icing functionality. A design concept, dynamic model, and simulation results are presented.
Improved Method for Extracting Difficult-To-Machine Shapes Using Multiple Milling Simulation Results
Authors: Chen Qi , Umezu Nobuyuki , Inui Masatomo
Published: November 2022

We previously developed a software technology for assisting the design of machine parts with high machinability. Our system is milling-simulation-based and detects shapes that cannot be machined using the available cutters by repeatedly executing milling simulations with the cutters. This paper describes an enhanced software technology that improves the extraction and visualization of difficult-to-machine shapes. Contrary to our previously reported software, the current software is equipped with a recognition function for the shape features. By controlling the milling simulation process according to the recognized features, more accurate simulation results can be obtained. The current software can visualize the extracted difficult-to-machine shape by superposing and displaying the shape left unmachined after milling simulations on the CAD model of the part. By examining this shape, the reason for the difficulty in machining can be visually understood. We implemented the software and applied it to CAD models of machine parts to evaluate its practicality.
Where Next?: Exploring Opportunity Areas and Tool Functions for Sustainable Product Design
Authors: Damen Nicole B. , Wang Ye , Matejka Justin , Toh Christine
Published: November 2022

Shifts in policy and consumers’ awareness have raised the importance of sustainability in product design, inspiring the development of tools that support more sustainable design. However, such tools are not adopted as quickly as expected. To understand what tools designers consider useful, we explored how much control designers perceive over existing design strategies, and how much impact they think these strategies have. We used a survey (n = 42) and follow-up interviews (n = 12) to ask hardware product design professionals what areas they see opportunities in, and what functions they look for in tools. The findings reveal that designers perceive impact and control differently in different opportunity areas, so to increase the likelihood of adoption, tools should incorporate features that reflect those differences. Designers report the least control over aspects related to manufacturing, and also rate these as having low impact on sustainability. In contrast, designers attribute high control and impact to aspects related to their design practice and their organizations’ business model, which are tightly linked. To address these issues, designers pointed towards tools that improve information transparency, support decision-making, predict results, share knowledge, and discover user needs. Regardless of how much control designers have, they care about tools and strategies that are highly impactful.
Uncertainty Quantification for Dissimilar Material Joints Under Corrosion Environment
Authors: Bansal Parth , Zheng Zhuoyuan , Li Yumeng
Published: November 2022

Self-Piercing Riveting (SPR) is one of the most commonly used methods for joining dissimilar materials in the automotive industry. These joints are popular due to their adaptability, high performance and short cycle time. However, since these joints involve two dissimilar materials, they are susceptible to galvanic corrosion in the presence of an electrolyte which is driven by the difference in the equilibrium potential of the metals. This can affect the safety and resilience of these joints. In this paper, we focus on galvanic corrosion in Al-Fe SPR joints. A Machine learning (ML) based surrogate model, which is based off of FE simulations, for statistical corrosion analysis is developed. This model enables the resilience and reliability analysis of SPR joints under corrosion environment. In this study, first a physics-based finite element (FE) corrosion model has been developed to simulate the galvanic corrosion between a Fe cathode and an Al anode of a SPR joint. This model takes into account the effect of the crystal microstructure of the Al anode and the precipitation of the corrosion product. Several geometric and environmental factors including crevice gap, roughness of anode, conductivity, pH and the temperature of the electrolyte that effect corrosion are investigated. A thorough Uncertainty Quantification (UQ) analysis is conducted for the overall corrosion behavior of the Fe-Al SPR joints using a novelistic Probabilistic Confidence-Based Adaptive Sampling (PCAS) technique. PCAS is used to train the surrogate model by identifying the critical sampling points and thus reducing the overall computational costs. It is found that the electrolyte temperature has the largest effects on the material loss and needs to be managed closely for better corrosion control. By understanding the corrosion performance and resultant uncertainty impact on SPR joints, the reliability and resilience of these joints can be improved.
Machine Learning Models for Predicting Deflection and Shape of 2D Cantilever Beams
Authors: Jin Yi , Su Hai-Jun
Published: November 2022

Efficient predictive models for large deflection of beams are critical for the design and analysis of compliant mechanisms. Machine learning (ML) models for predicting large tip deflection and shape of 2D cantilever beams are proposed for the analysis of compliant mechanisms in this paper. First, the training data is collected by running massive finite element simulations of cantilever beams subject to various loading conditions. Second, these training data are fed into several nonlinear regression learners. The result is a generalized predictive model for any 2D beams subject to a wide range of tip loads. The evaluation results show the maximum error is about 3.8%. The machine learning models with high prediction speed and accuracy are obtained. Two extreme loading conditions are studied to evaluate our machine learning model of deflections. Our results show that the error of our ML model is less than that of the conventional PRB 3R model, and the speed is 98 times that of the FEM in these two studies. A numerical example of 4-bar linkages with a 2D beam is applied to demonstrate the use of the machine learning model of the beam shape. The result shows the very close shape and high efficiency of our model.
A Multi-Domain Knowledge Transfer Method for Conceptual Design Combine With FBS and Knowledge Graph
Authors: Lai Bing , Zhao Wu , Yu Zeyuan , Guo Xin , Zhang Kai
Published: November 2022

Conceptual design as an essential step in product innovative design, a definitive design process and effective solution methods enable the rapid solving of design problems. In the conceptual design process, conceptual design solutions are solved by mapping multi-domain knowledge to design problems. However, such a process exists with multiple mapping paths and weak connections between knowledge. Therefore, we propose a knowledge transfer method that combines Function Behavior Structure (FBS) and Knowledge Graph (KG) for the conceptual design solution. Based on the FBS model, we structure the design process, identify and evaluate the functions and behaviors that satisfy the design requirements, integrate multi-domain knowledge based on knowledge graph technology and transfer it to the design process to support the conceptual design. Finally, the paper verifies the feasibility of the method with the design of a hoisting structure for heavy-duty and high precision movement. The results show that the proposed method is helpful for solving the ideal design solution, reducing the iterative steps, and improving the design efficiency to a certain extent.
Hit, Miss, or Error? Predicting Errors in Design Decision Making for Radically Innovative Ideas Using Individual Attributes
Authors: Peng Aoran , Miller Scarlett R.
Published: November 2022

Researchers and practitioners alike agree that for companies to survive and thrive they must develop and support radical innovation. However, these ideas are complex and risky, and not all succeed. Because of this, decision makers are often left to make hard decisions in terms of which ideas move on and which are abandoned. The goal of this paper was to provide pilot evidence to identify the impact of individual attributes on the effectiveness of decision making for radical innovation. Specifically, we sought to identify how individual personality and preferences for creativity impacted the likelihood of students selecting radical ideas using principles from signal detection theory (SDT). To do this, we used data from a previous studies’ of 2252 idea evaluations by student designers and classified these decisions based on SDT to see if we could predict the likelihood of occurrence based on individual attributes: hit (correct identification), miss (Type 1 Error), false alarm (Type II Error), and correct rejection. The results showed that lower levels of risk tolerance resulted in an increased likelihood that a hit occurred. On the other hand, higher levels of neuroticism and motivation resulted in an increased likelihood of a Type I error, or the likelihood that an individual would neglect a good idea that had a high chance of future success. Finally, increased risk tolerance and decreased motivation resulted in an increased likelihood that Type II error occurred, or that an individual would expend resources on an idea with limited likelihood of success. The results of this study can serve as empirical evidence on decision making in radical tasks and provide a basis for future investigations. In addition, these results provide a methodology for studying decision making in innovative design.
Stapler Design With Stacked Tensegrity Mechanisms for Surgical Procedures
Authors: Khanzode Dhruva , Jha Ranjan , Chablat Damien , Duchalais Emilie
Published: November 2022

In this paper, a planar mechanism formed by stacking tensegrity mechanisms has been designed. This mechanism is formed by three segments but the approach can be applied to a larger number of segments. This mechanism is studied to serve as a stapler for laparoscopic rectal cancer surgery where conventional tools cannot be easily accessed. To do this, two identical mechanisms will be used and operated similarly: one to carry the clips and the cutting knife and the second in parallel to allow the closing of the clips by bringing the two mechanisms together. The parameterization of the segments allows a variation of all lengths. Thus, the size of the segments can decrease proportionally from the base to the top, resulting in a tapered shape from the base to the tip like an elephant trunk. The mechanism has linear springs and cables for its actuation. The singularities, as well as the stability of the parallel mechanism, were analyzed using the minimum energy principle. Optimization was also performed to obtain the largest angular deflection for a segment based on a ratio between the size of the base and the moving platform of the robotic system. The result of this work is a family of mechanisms that can generate the same workspace for different stability properties. After optimization, the radius of curvature of the mechanism will be studied to allow insertion into the patient’s belly.
Design Recommendations for Reducing the Environmental Impact of Battery Packs
Authors: Quijano-Ortiz Felipe , Seepersad Carolyn
Published: November 2022

Electrification of the transportation sector is expanding rapidly, and lithium-ion batteries are an essential technology making electromobility possible. Automobile manufacturers currently optimize their battery strategy for fast charge, long range, and safety for the duration of the vehicle’s life. Batteries must eventually be replaced as they degrade and lose performance. While end of life batteries are not useful in electric vehicles, they can be repurposed to work in alternative energy storage products with lower performance. Once they can no longer store energy, they can be recycled to recover valuable metals and reintroduce them into newer products. The environmental impact of batteries differs across the industry since there is a lack of battery design standards. The goal of this work is to study the design choices that facilitate reusability and recyclability of electric vehicle batteries and compile them into a list of design recommendations. The recommendations are derived from published literature, in which design tradeoffs between performance, economics and recycling and reuse are investigated. The purpose of this paper is to synthesize these recommendations and best practices from throughout the published literature and organize them into summary tables that serve as quick-reference design guidelines for the engineer.
Gait Prediction for Prosthesis Design Evaluation
Authors: Morrison Tyler , Su Hai-Jun
Published: November 2022

In this paper, we introduce the key elements of a computational tool and a comprehensive methodology for enabling simulation-informed design of robotic ankle prostheses. Our approach is based on trajectory optimization methods for predicting human walking gait applied to a model of a bilateral lower leg amputee with robotic ankle prostheses. The goal of this simulation tool and design methodology is to provide a means to evaluate the performance of a prosthetic design and its effect on adapting human walking gait before involving human subject studies. The gait prediction problem is formulated as a multi-objective trajectory optimization of a multibody dynamic system within the existing framework of the open-source biomechanics package, OpenSim. Our approach models the effects of a planar human model with legs and torso, Hill-type muscle actuators, a closed-loop linkage for the robotic prosthetic device, and a DC motor dynamics model. To demonstrate the gait prediction process in use for design evaluation, we include a pair of example design evaluation simulations. In the first example, the gait prediction method shows that the initially proposed prosthesis design fails to improve the human effort estimated by cubic muscle excitations. In the second example, after the selection of a different actuator, the gait prediction method shows that the modified design has the potential to relieve some of the effort of walking required from the amputee. These examples combine to demonstrate how our methodology may be useful for design.
Digital Twin Modeling Method for Container Terminal in Port
Authors: Wang Yan , Yang Wenqiang , Zheng Yu , Zhang Lei , Zhang Ziqing
Published: November 2022

With the acceleration of the construction of smart ports, the business process in ports is becoming more and more intelligent. Most container terminals in port are generally more automated than other terminals, so it is easier to promote the informatization construction of automated terminals. Through digital twin technology, a container terminal in port can be simulated into a complete system with different combinations of layout, equipment and technology, which can realize real-time monitoring, optimize business processes and bring huge economic benefits. In short, digital twin technology has become an important factor in promoting the development of container terminal. In this paper, digital twin technology is applied to the business of container terminal. Meanwhile, the business processes of container terminal are sorted out according to production factors, production information and production tasks. Then a digital twin model modeling method for container terminal from the three dimensions of element, behavior and rule is proposed to realize the virtual simulation of container terminal. The modeling process of element model, behavior model and rule model in port is elaborated in detail. Taking the container terminal in Qingdao Port as a case study, a digital twin system architecture of container terminal is studied. Part of the DT modeling process of container terminal in Qingdao Port is provided. The feasibility and effectiveness of the modeling method are verified. At last, some applications of digital twin container terminal are advised.
Design Optimization of Pelvic Prosthesis for Type-1 Resection
Authors: S. Rajaraman , Rakshit Sourav
Published: November 2022

Reconstruction of the pelvic region post the resection of pelvic bone sarcoma is vital for the rehabilitation and restoration of activities of daily living in patients. The prosthesis design should restore maximum functional outcomes for tumors that do not significantly decrease life expectancy. Currently, reconstruction methods use either bone grafts or prostheses. For tumors at regions such as the iliac wing and for young patients, there is minimal chance of bone grafts to remodel into the exact anatomy of the iliac wing. On the other hand, a customized pelvic prosthesis ensures restoration of the anatomical configuration and mechanical strength. A commercially available pelvic prosthesis is solid and is much stiffer than the bone leading to stress shielding effect and higher strains on the intact pelvic bone. This work proposes the design of a lightweight Ti6Al4V pelvic prosthesis for type-I resection due to a sarcoma at the iliac wing, which matches the exact anatomy of the resected pelvic bone. First, a compliance minimization problem subject to walking gait cycle loads with mass constraint equal to the mass of the resected iliac bone is solved using topology optimization. Then the density field from topology optimization is mapped into shell and lattice infills. Prosthesis designs with different lattice structures are subjected to a compression test simulation, and the heterogeneity of lattices on the structural performance of the prosthesis is studied. Thus, a lightweight prosthesis, similar in geometry, stiffness and mass to the natural iliac bone, is achieved with a decrease in stress shielding effect.
A Design Framework for Evolving Cyber-Physical-Social System (CPSS) Based on Force Field
Authors: Zhou Ziqing , Sun Yanwei , Ouyang Chun , Gan Zhongxue , Ming Zhenjun
Published: November 2022

A Cyber-Physical-Social System (CPSS), as an extension of cyber-physical system (CPS), has received considerable attention in recent years both in academia and industry. Compared to CPS, CPSS has great advantages on efficiency, adaption and robustness due to the existence of self-organizing social system. One of the difficulties in the design of CPSS is anchored in the lack of a control mechanism to achieve self-organizing capability while the system is evolving. To address this problem, we propose a design framework for evolving CPSS based on the force field, which is composed of inner force and task force field. In addition to the framework, we present several performance evaluation indicators to quantify the self-organizing capability of the system, including consumption, emergence, and scalability. We test the performance of the framework using a disaster-relief example, which involves the computational control algorithm (cyber system), robot (physical system), and self-organizing swarm (social system). The result shows that based on our framework, the system can automatically evolve from disorder to order, adapt to the changeable environment and successfully finish the task in a very efficient manner.
Data Driven Integrated Design Space Exploration Using iSOM
Authors: Sushil Rashmi Rama , Baby Mathew , Sharma Gehendra , Balu Nellippallil Anand , Ramu Palaniappan
Published: November 2022

Design preferences or targets are typically available at system level. A designer is usually interested in understanding patches of design space at component levels, across different stages and process that corresponds to such system targets or preferences. This demands a thorough design space exploration permitting both forward and inverse designs. Such exploration becomes cumbersome with large variables and complex systems with conflicting goals. Current work proposes using interpretable self organising map (iSOM) as a visual enabler of decision support system for exploring designs to understand the trade off in system targets/goals. The novelty lies in being able to use a visual form to compare greater than three conflicting goals simultaneously while accounting for design variables. The proposed approach is demonstrated using two test problems: i) hot rolling and cooling process chain design for the production of steel rods and ii) head and neck-injury risk evaluations for vehicular crashworthiness. Using the first problem, we demonstrate the capability of iSOM for the solution space exploration of a many-objective steel manufacturing process chain problem to realize the design of a steel product, in the context of a compromise Decision Support Problem formulation. In the second problem, we demonstrate the capability of iSOM for early-stage design space exploration to identify critical injury-risk regions of interest for different car crash scenarios. These two case studies illustrate the capability to carry out a forward and inverse design, by the proposed approach.
A Constraint-Handling Technique for Parametric Optimization and Control Co-Design
Authors: Tsai Ying-Kuan , Malak Richard J.
Published: November 2022

This paper proposes a novel technique for handling constraints in population-based algorithms (e.g., genetic algorithms) for parametric optimization and control co-design (CCD). Constraint boundaries are approximated during optimization using a machine-learning classification approach called a support vector machine (SVM). Population members classified as infeasible are shifted to the predicted boundary of the feasible set, promoting exploration in the most important regions of the search space. A numerical test problem and a case study of an elastic inverted pendulum on a cart are used for benchmarking. The inverted pendulum problem, which features inherently unstable dynamics and control system limitations (i.e., saturation), highlights the relevance of constraint handling in CCD problems. The case study includes constraints to ensure stability and account for controller saturation. Performance and computational cost are compared to the existing parametric optimization technique, Predicted Parameterized Pareto Genetic Algorithm (P3GA). Results show that the P3GA with the newly proposed constraint-handling technique outperforms the original P3GA both qualitatively and quantitatively.
Can We Get an Intervention, Please? The Utility of Teaming Interventions on Engineering Design Student Psychological Safety
Authors: Scarpinella Samantha , Cole Courtney , Ritter Sarah , Mohammed Susan , Jablokow Kathryn , Miller Scarlett
Published: November 2022

Research on psychological safety has been growing in recent years due to its role in promoting creativity and innovation, among other items. This is because teams with high levels of psychological safety feel safe to express ideas and opinions. While we are becoming more aware of the importance of psychological safety in teaming, there is limited evidence in how to facilitate or build it within teams, particularly in an educational context. This paper was developed to respond to this research void by identifying the impact of teaming interventions aimed at improving psychological safety in engineering design student teams. Specifically, we studied two cohorts of students in a cornerstone design class (N = 414 students), one who received a series of video interventions and introduced role playing (intervention) and one who did not (control). These role assignments — referred to as the Lenses of Psychologically Safety — were created to promote key leadership attributes that have been shown to be crucial in facilitating psychologically safe teams. To compare the utility of the intervention, Psychological Safety was gathered at 5 key time points of a multi-week design project. The results identified three key findings. First, the interventions were successful in increasing psychological safety in engineering teams. In addition, the results indicated the utility of the Lenses of Psychological Safety throughout the design process. Finally, the results identified that groups who used these lenses had higher perceptions of Psychological Safety in their teams. Overall, these results indicated that psychological safety can be improved in engineering education through the intervention methods described within.
Novel MEMS Capacitive Sensor Excited at Electrical Resonance for Detecting Helium Based on Changes in Air Electrical Properties
Authors: Mohaidat Sulaiman , Alsaleem Fadi
Published: November 2022

Dry cask storage (DCS) is increasingly used for extended long-term storage of spent nuclear fuel. The canister is vacuum dried and then pressurized with helium to ensure an inert atmosphere and efficient heat transfer. Thus, helium leakage detection plays an important part in ensuring the safety of nuclear waste storage facilities. However, as it is a noble light gas with no order or color, using conventional gas sensing techniques for detecting Helium is a challenge. To overcome this challenge, in this paper we present the working principle of a simple electrostatic MEMS structure to detect Helium in ambient air. The working principle of this novel sensor is based on the decrease in the air die-electric constant due to the presence of Helium. While this change is small, we show that activating the MEMS RLC circuit can significantly amplify the sensor response and hence increase its sensitivity. The sensor response is simulated at different Helium concentration levels using a finite element model. The simulations showed that an electrostatic MEMS sensor operating close to the electrical resonance frequency of an RLC circuit showed different deflection and as such a different capacitance at different levels of helium. A decrease up to 20% of the MEMS deflection was observed at 20% Helium concentration compared to near 0% concentration.
Compact Time Domain NMR Design for the Determination of Hydrogen Content in Gas Turbine Fuels
Authors: Martin Jacob , Downey Austin , Won Sang Hee
Published: November 2022

Low-resolution nuclear magnetic resonance is a tool that has been employed for many years as an effective method for analyzing complex materials in a non-destructive fashion. This work has been aimed at the development of a prototype compact NMR system with the potential to be utilized for simple and rapid in situ measurements of key properties of jet fuels. The compact system consists of a neodymium magnet configuration with a field strength of 0.645 T, custom printed circuit boards, and radio frequency electronics. It is driven by a National Instruments PXI chassis fitted with modules that control the excitation and detection of hydrogen proton resonance in various samples. The current system is suitable for transverse relaxation (T2) studies using the Carr-Purcell-Meiboom-Gill pulse sequence, as it can produce relaxation curves containing thousands of spin echoes. Determination of hydrogen content (mass %), which is strongly correlated to combustion properties of fuels, is the primary area of interest for this study. Utilizing an array of 16 reference samples, a direct correlation between initial signal amplitude and hydrogen density (kmol/m3) was established. This relationship, along with mass density measurements, was used to determine hydrogen content in six different jet fuels. The maximum error between measured and accepted values for the fuels was 0.7%.
Effect of Optimal Geometries and Performance Parameters on Airfoil Latent Space Dimension
Authors: Van Slooten Alec , Fuge Mark
Published: November 2022

Although learning low-dimensional airfoil manifolds can facilitate aerodynamic optimizations, the properties of these latent spaces are not well understood. This paper investigates airfoil manifolds to provide greater insight into the effects of optimized geometry and data set features on latent spaces. Specifically, we investigate if optimized geometries occupy lower dimensional manifolds than non-optimized geometries. We also examine the effect of including target optimization conditions as data set features for a range of latent space sizes. We explore these areas using the UIUC airfoil database and a subset of these airfoils optimized with CBGAN and CEBGAN models. Lower dimensional airfoil manifolds are learned using both autoencoders and principal component analysis (PCA) models. The performance of these models are also compared to each other in ranges of training sample sizes and latent dimension size using mean squared error (MSE) between the original testing samples and the reprojected data constructed from the models as a metric. The results of this study suggest that optimized geometry does not always lie in a lower dimensional latent space as the two data sets were observed to have similar intrinsic dimensionalities. This study further demonstrates that including input parameters used in airfoil coordinate generation as data set features does not necessarily decrease the latent space dimensionality.
Synthetic Fault Mode Generation for Resilience Analysis and Failure Mechanism Discovery
Authors: Hulse Daniel , Irshad Lukman
Published: November 2022

Traditional risk-based design processes seek to mitigate operational hazards by manually identifying possible faults and corresponding mitigation strategies — a tedious process which critically relies on the designer’s limited knowledge. Resilience-based design, on the other hand, seeks to embody generic hazard-mitigating properties in the system to mitigate unknown hazards, often by modelling the system’s response to potential randomly-generated hazardous events. This work creates a framework to adapt these scenario generation approaches to the traditional risk-based design process to synthetically generate fault modes, by representing them as a unique combination of internal component health-states which can then be injected and simulated in a model of the system failure dynamics. The design process may then reduce the risk of unknown internal hazards by iteratively mitigating the effects of these modes. The performance of this approach is evaluated in a model of an autonomous rover, where cluster analysis shows that elaborating the faulty state-space in the drive system using this approach uncovers a wider range of possible hazardous trajectories and failure consequences within each trajectory. However, this increase in hazard information gained from exhaustive mode sampling comes at a high computational expense, highlighting the need for advanced, efficient methods to search and sample the faulty state-space.
Dynamic 3D Mesh Reconstruction Based on Nonrigid Iterative Closest-Farthest Points Registration
Authors: Tajdari Farzam , Kwa Felix , Versteegh Christiaan , Huysmans Toon , Song Yu
Published: November 2022

Fitting apparel and apparel in performing different activities is essential for the functional yet comfortable experience of the user. 4D scans, i.e. 3D scans in continuous timestamps, of the body (part) in performing those activities are the basis for the design of garments/apparel in 4D. In this paper, we proposed a semi-automatic workflow for constructing 4D scans of the body parts with the emphasis on registering noisy scans at a given timestamp. Continuous 3D scans regarding the moving body parts are captured first from different depth cameras from different view angles. In a given timestamp, the collected 3D scans are roughly aligned to a template using the rigid Iterative Closest Points (ICP) algorithm. Then these scans are further registered using a newly proposed non-rigid Iterative Closest-Farthest Points (ICFP) algorithm, in which correspondences between the source and the target are established by either closest or farthest points based on the newly defined logical distance concept and the probability theory. Experimental results indicated that the ICFP method is robust against noise and the scanning accuracy can be as high as 3.4 %. It also reveals that, for the human foot, the differences of ball width and ball angles between the loaded and the unloaded situation can be as large as 8 mm and 2 degrees, respectively. This highlights the importance of using 4D scan in designing garments and apparel.
Automated Weld Path Generation Using Random Sample Consensus and Iterative Closest Point Workpiece Localization
Authors: Hill Tristan , Canfield Stephen , Shelton Robert
Published: November 2022

Jobs performed by small to medium enterprises (SMEs) are infrequently automated due to high setup costs and lack of technical expertise needed for robot training, however productivity and worker safety can be improved in SMEs with the use automated tooling. In a traditional automated manufacturing environment, tasks such a welding or painting are accomplished through execution of pre-programmed tool motions which rely on the location and orientation of the workpiece to be fixed and known. The lack of this spatial information is typically treated through positioning of the workpiece with respect to the robot arm using jigs or fixtures which are costly in initial setup and not easily modified. Further, the resulting toolpath associated with a desired task is typically defined through manual teaching resulting in a path appropriate for an individual job. For this reason, SMEs requiring variation in part geometry or arrangement are not commonly automated. This work presents a method for automated weld path generation for a 6DOF co-bot arm using random sample consensus (RANSAC) and iterative closest point (ICP) workpiece localization from LiDAR point clouds. Scans from a low cost 2D LiDAR mounted to the co-bot arm are used to generate 3D point clouds of the workspace scene with the Robot Operating System (ROS). The Point Cloud Library (PCL) is used to compare the generated point cloud with a CAD model to produce a rigid transformation to localize the workpiece. The estimated pose of the workpiece with respect to a fixed frame is used offline to generate a weld path as series of tool poses. An example welding processes in which two square tubes are joined through weldment are investigated and a physical implementation of the method is demonstrated using a 2D LiDAR mounted to a 6DOF co-bot arm.
A Multi-Fidelity Approach for Reliability Assessment Based on the Probability of Model Inconsistency
Authors: Pidaparthi Bharath , Missoum Samy
Published: November 2022

Most multi-fidelity schemes rely on regression surrogates, such as Gaussian Processes, to combine low- and high-fidelity data. Contrary to these approaches, we propose a classification-based multi-fidelity scheme for reliability assessment. This multi-fidelity technique leverages low- and high-fidelity model evaluations to locally construct the failure boundaries using support vector machine (SVM) classifiers. These SVMs can subsequently be used to estimate the probability of failure using Monte Carlo Simulations. At the core of this multi-fidelity scheme is an adaptive sampling routine driven by the probability of misclassification. This sampling routine explores sparsely sampled regions of inconsistency between low- and high-fidelity models to iteratively refine the SVM approximation of the failure boundaries. A lookahead check, which looks one step into the future without any model evaluations, is employed to selectively filter the adaptive samples. A novel model selection framework, which adaptively defines a neighborhood of no confidence around low fidelity model, is used in this study to determine if the adaptive samples should be evaluated with high- or low-fidelity model. The proposed multi-fidelity scheme is tested on a few analytical examples of dimensions ranging from 2 to 10, and finally applied to assess the reliability of a miniature shell and tube heat exchanger.
Multi-Axes Series Elastic Actuation: A Computer Model
Authors: Zhang Haohan
Published: November 2022

This paper proposes a novel approach to providing series elastic actuation for parallel robots. Current series elastic actuators (SEA) are limited to providing single degree-of-freedom (DoF) series elastic actuation. To provide series elastic actuation for each DoF of a parallel robot, an SEA unit must be added to each active joint. In this paper, we introduce a new approach: we recognize a parallel robot as a multi-axes actuator, and we connect the robot platform and the load through a parallel elastic element which has an approximately linear and decoupled wrench-displacement relationship in each dimension. We first introduce the structure of this elastic element and demonstrate its linearity and decoupling properties through a computer model. A constant diagonal stiffness matrix is obtained to relate the six-dimensional wrench and deflection of the elastic element. Then, an integration between this elastic element and a Stewart Platform is achieved in simulation. An experiment is then performed to control the robot platform to follow an input motion of the load while applying zero wrench on the load, which is inspired by a common task performed by rehabilitation robots. Using this approach, we show that the force control of the robot can be transformed into position control while providing necessary compliance between the robot and its load.
Conceptual Design of Cellular Auxetic Systems With Passive Adaptation to Loading
Authors: Prendergast Joshua , Oddiraju Manaswin , Nouh Mostafa , Chowdhury Souma
Published: November 2022

Auxetics refer to a class of engineered structures which exhibit an overall negative Poisson’s ratio. These structures open up various potential opportunities in impact resistance, high energy absorption, and flexible robotics, among others. Interestingly, auxetic structures could also be tailored to provide passive adaptation to changes in environmental stimuli — an adaptation of this concept is explored in this paper in the context of designing a novel load-adaptive gripper system. Defining the design in terms of repeating parametric unit cells from which the finite structure can be synthesized presents an attractive computationally-efficient approach to designing auxetic structures. This approach also decouples the optimization cost and the size of the overall structure, and avoids the pitfalls of system-scale design e.g., via topology optimization. In this paper, a surrogate-based design optimization framework is presented to implement the concept of passively load-adaptive structures (of given outer shape) synthesized from auxetic unit cells. Open-source meshing, FEA and Bayesian Optimization tools are integrated to develop this computational framework, enhancing it adopt-ability and extensibility. Demonstration of the concept and the underlying framework is performed by designing a simplified robotic gripper, with the objective to maximize the ratio of towards-load (gripping) horizontal displacement to the load-affected vertical displacement. Optimal auxetic cell-based design generated thereof is found to be four times better in terms of exhibited contact reaction force when compared to a design obtained with topology optimization that is subjected to the same specified maximum loading.
On Spiral Folding of Planar Membranes With Finite Thickness and Curved Creases
Authors: Parque Victor , Miyashita Tomoyuki
Published: November 2022

Spiral folding of flat and planar membranes with finite thickness is of relevant interest to develop the spin-type deployable membrane structures for space environments and for consumer applications. Examples involve the design and development of origami-based structures, airbags, antenna design, wrapping of food by thin membranes, wheel design, and membrane deployment for medical applications. In this paper, we propose the governing equations to fold planar membranes with finite thickness by using curved creases, whose governing equations render fold patterns whose radius of curvature tends to increase linearly by accommodating membrane thickness. The consideration of curvature along in the crease patterns is relevant and potential to balance the tension of outer layers with the compression of inner layers, and to distribute the out-of plane and localized bending near the creases and vertices. We present the mathematical formulations that consider the curved creases and describe folding examples of a planar membrane with a defined thickness. Our computational experiments have shown (1) the versatility to model a plural number of curvature profiles, and (2) the feasibility of global deployment by using the compliant and explicit numerical simulations. From viewpoints of configuration and deployment performance, the curved crease patterns are potential to extend the versatility and smoothness of spiral folding mechanisms.
On the Computation of the Average of Spatial Displacements
Authors: Ge Qiaode Jeffrey , Yu Zihan , Arbab Mona , Langer Mark
Published: November 2022

Many applications in biomechanics and medical imaging call for the analysis of the kinematic errors in a group of patients statistically using the average displacement and the standard deviations from the average. This paper studies the problem of computing the average displacement from a set of given spatial displacements using three types of parametric representations: Euler angles and translation vectors, unit quaternions and translation vectors, and dual quaternions. It has been shown that the use of Euclidean norm in the space of unit quaternions reduces the problem to that of computing the average for each quaternion component separately and independently. While the resulting algorithm is simple, the change of the sign of a unit quaternion could lead to an incorrect result. A novel kinematic measure based on dual quaternions is introduced to capture the separation between two spatial displacement. This kinematic measure is then used to formulate a constrained least squares minimization problem. It has been shown that the problem decomposes into that of finding the optimal translation vector and the optimal unit quaternion. The former is simply the centroid of the set of given translation vectors and the latter can be obtained as the eigenvector corresponding to the least eigenvalue of a 4 × 4 positive definite symmetric matrix. It is found that the weight factor used in combining rotations and translations in the formulation does not play a role in the final outcome. Examples are provided to show the comparisons of these methods.
ShapOrator: Enabling Design Iteration for Young Designers Through Shape Verbalization
Authors: Vyas Shantanu , Chen Ting-Ju , Woodward Jay , Krishnamurthy Vinayak R.
Published: November 2022

We investigate speech-based input as a means to enable reflective thinking for younger individuals (middle- and high-school students) during design iterations. Verbalization offers a unique way to externalize ideas in early design and could therefore lead to new pathways for exploration and iteration, especially for K-12 students who possess the creative potential but are not technically trained in the design process. Interactive design systems, however, by-and-large utilize sketching, multi-touch, and gestural inputs. As a result, (1) there is little know-how regarding how to operationalize verbal inputs as a meaningful way to facilitate idea exploration and (2) there is little fundamental understanding of the underlying cognitive mechanisms for iteration through verbal communication. We take the initial steps towards these gaps by first designing and implementing the ShapOrator interface that takes verbal descriptions of geometric parameters (shape, size, instances) in a semi-natural language form and determines the appropriate transformations to a given design artifact modeled as a shape assembly. Using ShapOrator as our experimental setup we conducted an in-depth observational study on 10 middle- and high-school students tasked with designing spaceships. Our study revealed that participants were able to create a variety of designs while associating functional and topical contexts to their spaceships throughout the design iteration process.