bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Study of Machine Learning Framework for Enabling Early Defect Detection in Wire Arc Additive Manufacturing Processes
Authors: Surovi Nowrin Akter , Hussain Shaista , Soh Gim Song 
Published: November 2022

Abstract
This paper presents the study on the performance of a variety of proposed time-domain acoustic features-based frameworks for the detection of geometrically defective print segments during the Wire Arc Additive Manufacturing (WAAM) process. Specifically, we investigate into a variety of acoustic features, namely the Root Mean Square of Pressure (RMSP), Energy, Mean Amplitude, Kurtosis, Zero Crossing Rate (ZCR), Skewness, Crest Factor and Peak-to-peak, and print process parameters, namely Torch Speed (TS) and Wire Feed Rate (WFR) combined with Machine Learning (ML) frameworks for detecting geometrically defective print segments. Experiments carried out on Inconel 718 show that among the studied frameworks, using acoustic features and process parameters with Random Forest (RF) performs best in terms of F1 score at 89%, while using acoustic features and process parameters with Support Vector Machine (SVM) performs best in picking out defective segments based on the Confusion Matrix. These findings serve as our first step in developing an intelligent sensing system for the early identification of defective beads in the WAAM printing process, so that appropriate intervention can be implemented to save printing resources and material costs. In addition, the proposed approach has the advantage of detecting defects within a more localized region for more targeted intervention.
The Foot in Walking - Towards Developing a Constrained Model of Stance Phase Dynamics
Authors: Renjewski Daniel , Lipfert Susanne , Günther Michael 
Published: November 2022

Abstract
Bipedal walking is the most prevalent form of human locomotion — versatile, robust, and efficient. However, it is a form of motion few other animals share, and plantigrade bipedalism is fairly unique to our species. Plantigrade feet play an important role in shaping the fundamental gait dynamics, and understanding their role unlocks a huge potential for biomechanical insights for locomotion research, gait rehabilitation, and humanoid robotics. Still, a comprehensive functional model of the foot, which ties the fundamental dynamics of walking to the interaction of foot and ground, does not yet exist due to the inherent mechanical complexity.
In this paper, we present a set of assumptions in detail to simplify the development of a mathematical description of the foot’s motion during stance phase in human walking. These assumptions are validated using experimental data. We can show that the complex motion of the foot can be ultimately reduced to a simple rotation, which allows the formulation of a single degree of freedom equation of motion that ties proximal dynamics and constraints into a planar foot model of dynamic interactions.
A Quantitative Approach and an Open-Source Tool for Social Impacts Assessment
Authors: Walters Justin , Mirkouei Amin , Makrakis Georgios Michail 
Published: November 2022

Abstract
Social impact analysis urgently needs attention in various sectors due to its critical roles in enhancing sustainability benefits. Properly assessing social impacts requires a consistent set of guidelines and requirements to reduce the practitioners’ bias. Life cycle assessment (LCA) is a widely recognized method that can be utilized to quantitatively assess three dimensions of sustainable development in an integrated manner. This study proposes an open-source tool for social impact analysis, that particularly evaluates work environment health impacts, following the LCA-defined principles, framework, requirements, and guidelines for sustainability assessment. The proposed tool in this study can quantify the social impacts and determine the categories that are being affected either negatively or positively. In addition, this study provides an overview of the existing social impact assessment approaches, identifies the critical challenges of the current techniques, and highlights opportunities for continued research to achieve more effective solutions. As of yet, a reliable and open-source tool has not been achieved to help decision-makers in both academia and industry. Thus, we developed a web-based tool for assessing social impacts in eight domains by allowing the use of multiple metrics and comparing two processes.
Mixed Plastic Waste Conversion to Value-Added Products: Sustainability Assessment and a Case Study in Idaho
Authors: Albor Galo , Mirkouei Amin , Struhs Ethan 
Published: November 2022

Abstract
Value-added products from petroleum-based wastes (e.g., mixed plastics) have shown great potential to address sustainability challenges, such as global waste management and environmental pollution from petroleum-based products. However, recycling mixed plastic wastes (MPW) has been debated for producing renewable materials and value-added products, such as C10-C50 liquid hydrocarbon compounds. This study investigates the economic feasibility and sustainability benefits of producing MPW pyrolysis oil (p-oil), using a transportable refinery unit. The presented approach was evaluated by conducting a case study in southeast Idaho, USA. A multi-criteria decision-making model consisting of techno-economic analysis and life cycle assessment is utilized to evaluate the proposed pathway. The techno-economic analysis estimates the total cost of MPW to value added products supply chains. The life cycle assessment (LCA) evaluates the negative environmental impacts of MPW-to-products life cycle. LCA assesses the global warming potential for a 100-year time horizon. The p-oil production cost per metric ton is $228, while the total emission is 2,262 kg CO2 per 100 metric tons of MPW. The results indicate that on-site operation can reduce MPW management and carbon footprint. Based on the results, MPW conversion to liquid hydrocarbon products using the mobile pyrolysis conversion process can address the supply chain sustainability challenges and lead to sustainable production.
Experimental Verification of Pulse Shaping in Elastic Metamaterials Under Impact Excitation
Authors: Dorgant Greg , DeLima Washington , Leamy Michael J. 
Published: November 2022

Abstract
In this paper, we develop a procedure to design, fabricate, and verify pulse shapers using elastic metamaterials under impact excitation as applied to the Split Hopkinson Pressure Bar (SHPB) test. The SHPB test, a fundamental dynamic test for over 70 years, has incorporated pulse shaping as a key feature which we re-examine through the use of elastic metamaterials. Elastic metamaterials, which exhibit properties atypical of their constitutive elements, hold promise for expanding on conventional pulse shaping abilities and improve the capabilities of the SHPB test. We first design the pulse shaper by numerically optimizing its geometry using finite element analysis. The pulse shaper consists of repeated unit cells which are based on a combination of a phononic crystal and a local resonator. Then, we fabricate and test pulse shaper candidates to validate the procedure efficacy. We incorporate an iterative element to this procedure such that we learn from inaccuracies in input force and material properties to converge on an appropriate pulse shaper. We carry-out this procedure by designing pulse shapers fabricated from 3D-printed PLA to achieve an extended dwell acceleration pulse shape. In experimental impact tests, we verify that the procedure results in ramp-up, acceleration dwell, and ramp-down comparable to that predicted, effectively confirming the efficacy of the presented procedure.
Does Empathising With Users Contribute to Better Need Finding?
Authors: Li Jie , Hölttä-Otto Katja 
Published: November 2022

Abstract
In user-centered product design, empathizing with users is an effective way to step into their shoes. Ideally, it helps designers understand user needs better and in more depth. However, the degree to which accurate empathic understanding contributes to needfinding and what exactly constitutes a ‘good’ need remain unclear. In this study, 13 designers watched a standard stimulus contextual user interview, performed an empathic accuracy task, and identified user needs based on the video. The needs identified from all participants were affinitized and summarized into a set of 23 user needs. They were scored for overall need quality, consisting of importance and satisfaction with current solutions as well as for need depth and latency. Designers’ empathic understanding was measured as the accuracy with which they could infer users’ mental contents and emotional tone. The designers’ emotional tone accuracy correlated with their performance in identifying important unfulfilled needs and needs where the users were most satisfied with existing solutions. However, no correlations were found for the empathic understanding of mental contents. In addition, need depth or latency was not found to correlate with any empathic understanding measure. These results are among the first to link empathy and designers’ performance in needfinding, but further studies are necessary to replicate and validate the results.
Sensitivity Study With Sobol’ Indices in Planar Multistable Mechanisms
Authors: Dold Edward J. , Voglewede Philip A. 
Published: November 2022

Abstract
This paper introduces the application of Sobol’ sensitivity indices to the design of toggle mechanisms. Sobol’ indices measure how the variance of the inputs (mechanism parameters) affect the variance of the output (potential energy). The kinematics of the mechanism are calculated to determine the mechanism parameters and potential energy. The indices reveal which parameter has the greatest effect on the variance of the potential energy, pointing designers to the area of the mechanism that should be the focus of improvement. Calculation and interpretation of Sobol’ indices are applied to three examples to show that the results are in line with engineering intuition. The applicability of the indices to any mechanism and their influence on design decisions are also shown. The benefits of the index being normalized are discussed along with methods of reducing computational cost.
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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.