bluuebunny/crossref_metadata_2025_split huggingface dataset:
Machine Learning Aided Design and Optimization of Conformal Porous Structures
Authors: Gao Zhenyang , Sossou Danièle , Zhao Yaoyao Fiona 
Published: November 2020

Abstract
The porous cooling system has been proved to have significant advantages over traditional 2D conformal cooling channels due to its rapid cooling performance during the injection molding process. Compared to conventional porous systems, the conformal porous structures (CPS) have been proven to have even more uniform cooling performance and a reduced temperature variance of the part. For the part with unevenly distributed thickness values however, the temperature variance problem remains unsolved. In addition, there is a lack of modeling and optimization efforts on developing an optimal CPS structure with varying cooling cell sizes to achieve better cooling performances. To solve this problem, a machine learning approach is applied to predict the part surface temperature based on identified CPS design parameters. With this surrogate temperature prediction model, the optimization is performed to generate a machine learning aided design of CPS. The simulation results of a swimming pedal case study indicate that the machine learning aided CPS is able to achieve a 76% reduction in temperature variance compared to conventional CPS.
Automated Heuristic Induction From Human Design Data
Authors: Puentes Lucas , Cagan Jonathan , McComb Christopher 
Published: November 2020

Abstract
Through experience, designers develop guiding principles, or heuristics, to aid decision-making in familiar design domains. Generalized versions of common design heuristics have been identified across multiple domains and applied by novices to design problems. Previous work leveraged a sample of these common heuristics to assist in an agent-based design process, which typically lacks heuristics. These predefined heuristics were translated into sequences of specifically applied design changes that followed the theme of the heuristic. To overcome the upfront burden, need for human interpretation, and lack of generality of this manual process, this paper presents a methodology that induces frequent heuristic sequences from an existing timeseries design change dataset. Individual induced sequences are then algorithmically grouped based on similarity to form groups that each represent a shared general heuristic. The heuristic induction methodology is applied to data from two human design studies in different design domains. The first dataset, collected from a truss design task, finds a highly similar set of general heuristics used by human designers to that which was hand selected for the previous computational agent study. The second dataset, collected from a cooling system design problem, demonstrates further applicability and generality of the heuristic induction process. Through this heuristic induction technique, designers working in a specified domain can learn from others’ prior problem-solving strategies and use these strategies in their own future design problems.
Design and Amplitude Dependence of Resonance Frequency of Origami-Inspired Vibration Isolators With Quasi-Zero-Stiffness Characteristic
Authors: Yamaguchi Kouya , Ishida Sachiko 
Published: November 2020

Abstract
This study aims to design two types of vibration isolators, with different spring mechanisms, using a foldable structure that is based on a cylinder torsional buckling pattern, and evaluate the vibration isolation performance of each design. Vibration isolation is achieved through nonlinear spring characteristics of the isolators, which have zero spring stiffness, achieved by attaching a linear spring to the foldable isolator structure. The two vibration isolators differ in the mechanical elements that constitute the foldable structure, which undergo tensile forces as the structure folds. For the first isolator, the mechanical elements are represented only by tension springs, which appropriately undergo tension. For the second isolator, the mechanical elements are designed so that embedded compression springs within the elements compress under tension, thus enabling the elements to work as tension springs. The excitation experiment results revealed that the different spring mechanisms produced equivalent resonance frequencies but different damping effects at the resonance and higher frequencies. When oscillations with multiple amplitudes were input, larger input amplitudes were found to correspond to lower resonance frequencies for both isolators. This trend contradicts that described in the nonlinear vibration theory modeled by the Duffing equation, and was determined to be caused by hysteresis of the spring phenomena in the vibration isolators.
Quality Assessment of Additively Manufactured Fiducial Markers to Support Augmented Reality-Based Part Inspection
Authors: Mathur Jayant , Basu Saurabh , Menold Jessica , Meisel Nicholas A. 
Published: November 2020

Abstract
This paper proposes an augmented reality (AR) framework and tool on smartphones as an alternative to conventional inspection for AM parts. The framework attempts to introduce the rapid inspection potential of smartphone based AR within manufacturing by leveraging the manufacturing capability of additive manufacturing (AM) to integrate markers onto AM parts. The key step from this framework that is explored in this paper is the design and quality assessment of AM markers for marker registration. As part of the marker design and quality assessment objectives, this research conducts an evaluation on the effects of different AM processes on the quality of augmentation achieved from AM fiducial markers. Furthermore, it evaluates the minimum fiducial pattern size that on integration onto AM parts will be viable for augmentation. The results suggest that the AM process and the size of the fiducial pattern play a significant role in determining the quality of the AM markers. The paper concludes by stating that dual material extrusion AM markers provide the highest number of detectable features and therefore the highest quality of AM markers, and the smallest viable fiducial pattern for Cybercode/QR code marker can be sized at 19 × 19mm2.
A New Extensible Continuum Manipulator Using Flexible Parallel Mechanism and Rigid Motion Transmission
Authors: Liu Yujiong , Ben-Tzvi Pinhas 
Published: November 2020

Abstract
An extensible continuum manipulator (ECM) has specific advantages over its non-extensible counterparts. For instance, in certain applications, such as minimally invasive surgery or tube inspection, the base motion might be limited or disallowed. The additional extensibility provides the robot with more dexterous manipulation and larger workspace. Existing continuum robot designs achieve extensibility mainly through artificial muscle/pneumatic, extensible backbone, concentric tube, and base extension etc. This paper proposes a new way to achieve this additional motion degree of freedom by taking advantage of the rigid coupling hybrid mechanism concept and a flexible parallel mechanism. More specifically, a rack and pinion set is used to transmit the motion of the i-th subsegment to drive the (i+1)-th subsegment. A six-chain flexible parallel mechanism is used to generate the desired spatial bending and one extension mobility for each subsegment. This way, the new manipulator is able to achieve tail-like spatial bending and worm-like extension at the same time. A proof-of-concept prototype was integrated to verify the mobility of the new mechanism. Corresponding kinematic analyses are conducted to estimate the workspace and the motion non-uniformity.
Alignment of a Collaborative Resistance Model With a Change Management Process in Industry: A Case Study on Production Automation
Authors: Zero Nicole , Summers Joshua D. 
Published: November 2020

Abstract
Current research and literature lack the discussion of how production automation is introduced to existing lines from the perspective of change management. This paper presents a case study conducted to understand the change management process for a large-scale automation implementation in a manufacturing environment producing highly complex products. Through a series of fifteen semi-structured interviews of eight engineers from three functional backgrounds, a process model was created to understand how the company of study introduced a new automation system into their existing production line, while also noting obstacles identified in the process. This process model illustrates the duration, sequencing, teaming, and complexity of the project. This model is compared to other change process models found in literature to understand critical elements found within change management. The process that was revealed in the case study appeared to contain some elements of a design process as compared to traditional change management processes found in literature. Finally, a collaborative resistance model is applied to the process model to identify and estimate the resistance for each task in the process. Based on the objective analysis of the collaborative situations, the areas of highest resistance are identified. By comparing the resistance model to the interview data, the results show that the resistance model does identify the challenges found in interviews. This means that the resistance model has the potential to identify obstacles within the process and open the opportunity to mitigate those challenges before they are encountered within the process.
srMO-BO-3GP: A Sequential Regularized Multi-Objective Constrained Bayesian Optimization for Design Applications
Authors: Tran Anh , Eldred Mike , McCann Scott , Wang Yan 
Published: November 2020

Abstract
Bayesian optimization (BO) is an efficient and flexible global optimization framework that is applicable to a very wide range of engineering applications. To leverage the capability of the classical BO, many extensions, including multi-objective, multi-fidelity, parallelization, and latent-variable modeling, have been proposed to address the limitations of the classical BO framework. In this work, we propose a novel multi-objective (MO) extension, called srMO-BO-3GP, to solve the MO optimization problems in a sequential setting. Three different Gaussian processes (GPs) are stacked together, where each of the GP is assigned with a different task: the first GP is used to approximate a single-objective computed from the MO definition, the second GP is used to learn the unknown constraints, and the third GP is used to learn the uncertain Pareto frontier. At each iteration, a MO augmented Tchebycheff function converting MO to single-objective is adopted and extended with a regularized ridge term, where the regularization is introduced to smooth the single-objective function. Finally, we couple the third GP along with the classical BO framework to explore the richness and diversity of the Pareto frontier by the exploitation and exploration acquisition function. The proposed framework is demonstrated using several numerical benchmark functions, as well as a thermomechanical finite element model for flip-chip package design optimization.
Development of a MEMS Chemical Sensor for Detection of Phthalates in Juice Using Electrochemical Impedance Spectroscopy
Authors: Ebrahimpour Tolouei Nadia , Bhuiyan Md Ebrahim Khalil , Hankins Michael , Shavezipur Mohammad 
Published: November 2020

Abstract
Presence of toxic chemicals in food products due to the use of different synthetic materials in food packages may cause long-term health hazard. Addition of chemical components such as phthalate family (for instance, Di(2-ethylhexyl) phthalate, DEHP) to plastics may result in diffusion of these materials in food specially in liquids such as bottled soft drink, water and juice. In this work, we present a chemical sensor that can detect DEHP in orange juice at extremely low concentrations. The sensor is made of two interdigitated electrodes, and electrochemical impedance spectroscopy (EIS) is used for the detection. Sensors with different overall dimensions and finger/gap sizes were fabricated using a polycrystalline silicon standard foundry. For simplification of the experiments, low concentration of citric acid in water (similar to orange juice) is used to represent the orange juice. The sensors are exposed to different concentrations of DEHP and their Nyquist and impedance-frequency plots are studied. The experimental data shows that the sensors can distinctly capture low concentrations of DEHP in the juice solution. An electrical model is developed that can simulate the frequency response of the system containing the sensor and the solution. The model includes dynamic physical parameters such as double-layer capacitance, solution resistance and Warburg impedance that can be used in detection. EIS curves fit to experimental data shows that the model well fits the experimental data.
A Topic Modeling Approach to Study the Impact of Manager Interventions on Design Team Cognition
Authors: Gyory Joshua T. , Kotovsky Kenneth , Cagan Jonathan 
Published: November 2020

Abstract
In order to computationally study design cognition under design process management, this work utilizes a topic modeling approach to analyze design team discourse during problem-solving. The particular experimental design, from previous work by the authors, places one of the design team conditions under the guidance of a human process manager. In that work, teams under this guidance outperformed the unmanaged teams in terms of their design solutions. This opens the opportunity to not only model design discourse during problem solving, but also explore the impact of process manager interventions and their impact on design cognition. Utilizing this approach, a topic model is trained on discourse of human designers, for both managed and unmanaged teams, collaboratively solving a design problem. Results show that the two team conditions significantly differ in a number of the extracted topics, and in particular, those topics that most pertain to the manager interventions. Furthermore, a before and after analysis of the topic-motivated interventions, reveals that the process manager interventions significantly shift the topic mixture of the team members’ discourse toward that of the interventions immediately after they are provided. Together, these results not only corroborate the effect of the process manager interventions on design team discourse and cognition, but provide promise in the computational detection and facilitation of design interventions based on real-time discourse data.
Broadband and Enhanced Energy Harvesting Using Inerter Pendulum Vibration Absorber
Authors: Gupta Aakash , Tai Wei-Che 
Published: November 2020

Abstract
Inerter-based vibration energy harvesters (VEHs) have been widely studied to harvest energy from large-scale structural vibrations. Recently, there have been efforts to increase the operation frequency bandwidth of VEHs by introducing a variety of stiffness and inertia nonlinearity. This paper proposes a new inerter-based VEH comprising an epicyclic-gearing inerter and a pendulum vibration absorber. The centrifugal force of the pendulum introduces a new type of inertia nonlinearity that broadens the frequency bandwidth. This inerter-pendulum VEH (IPVEH) is incorporated in a single-degree-of-freedom structure to demonstrate its performance and the equations of motion of the system are derived. The method of multiple scales is applied to derive the amplitude–frequency response relationship of the harvested power in the primary resonance. The harvested power is optimized through tuning the harvester’s electrical damping and the optimum power is benchmarked with that of conventional linear inerter-based VEHs. The results show that the IPVEH has larger bandwidth and harvested power and the improvement is correlated with the strength of its inertia nonlinearity.
Time-Dependent System Reliability Analysis With Second Order Reliability Method
Authors: Wu Hao , Du Xiaoping 
Published: November 2020

Abstract
System reliability is quantified by the probability that a system performs its intended function in a period of time without failure. System reliability can be predicted if all the limit-state functions of the components of the system are available, and such a prediction is usually time consuming. This work develops a time-dependent system reliability method that is extended from the component time-dependent reliability method that uses the envelop method and second order reliability method. The proposed method is efficient and is intended for series systems with limit-state functions whose input variables include random variables and time. The component reliability is estimated by the existing second order component reliability method, which produces component reliability indexes. The covariance between components responses are estimated with the first order approximations, which are available from the second order approximations of the component reliability analysis. Then the joint probability of all the component responses is approximated by a multivariate normal distribution with its mean vector being component reliability indexes and covariance being those between component responses. The proposed method is demonstrated and evaluated by three examples.
Computational Design of Compositionally Graded Alloys for Property Monotonicity
Authors: Kirk Tanner , Malak Richard , Arroyave Raymundo 
Published: November 2020

Abstract
Compositionally graded alloys can realize multiple conflicting properties in the same part, but the formation of secondary phases can often lead to cracks or deleterious properties. In prior work, a computational methodology was presented that can design compositional gradients to avoid these phases at any temperature in high dimensions [1]. The methodology also optimizes paths for a specified cost function, but prior work only considered minimizing path length or maximizing obstacle clearance. In this work, a new cost function is presented to produce compositional paths with optimal property gradients. Specifically, monotonicity is presented as the optimal quality of a pathwise property gradient because monotonic property gradients can be transformed to nearly any form on the part by controlling deposition rate. The proposed cost function uses a metric for non-monotonicity to find the shortest path with monotonic properties and is shown to be compatible with optimal path planners. A synthetic case study examines the effect of a cost function parameter on the trade-off between length and monotonicity. The cost function is also demonstrated in the Fe-Co-Cr system to find a compositional path with monotonic gradients in Coefficient of Thermal Expansion (CTE). The deposition of the path on a hypothetical part is then planned subject to a maximum deposition rate and CTE gradient. Future work is proposed to extend the framework to optimize multiple properties at once and to incorporate Multi-Material Topology Optimization (MMTO) techniques into a complete design methodology for functionally graded metal parts.
Consideration of Social Impacts During the Early Stages of Product Development for Sustainable Design
Authors: Jia Hong , Mattson Christopher A. , Johnson Gabrielle 
Published: November 2020

Abstract
Besides the explicit economic and environmental impacts, the product development process also produces an implicit social value — known as social impact. To help product designers better understand and plan for the social impact that their product may have, we present a social impact checklist table. This checklist table was constructed after a simple study was conducted on the design and reuse of corrugated cardboard. The checklist table provides the designer the opportunity to more deeply consider eleven social impact categories, map those categories to key indicators, and ultimately design parameters that influence social impact. We introduce this checklist table at the early stages of the product development process, aiming to make the otherwise implicit notion of social impact more explicit and recognizable. The checklist table has the potential to make the social dimension of sustainability more accessible to design engineers; they can then better conceive of sustainable solutions and create products that generate positive social impact.
A Non-Prismatic Beam Element for the Optimization of Flexure Mechanisms
Authors: Dwarshuis Koen , Aarts Ronald , Ellenbroek Marcel , Brouwer Dannis 
Published: November 2020

Abstract
Flexure joints are rapidly gaining ground in precision engineering because of their predictable behavior. However the range of motion of flexure joints is limited due to loss of support stiffness in deformed configurations. Most of the common flexure joints consist of prismatic leaf springs. This paper presents a simple non-prismatic beam formulation that can be used for the efficient modelling of non-prismatic leaf springs. The resulting stiffness and stress computed by the non-prismatic beam element are compared to the results of a finite element analysis. The paper shows that the support stiffness of two typical flexure joints can be increased up to a factor of 1.9 by using non-prismatic instead of prismatic leaf springs.
Reading the User’s Mind: Designers Show High Accuracy in Inferring Design-Related Thoughts and Feelings
Authors: Chang-Arana Álvaro M. , Surma-aho Antti , Li Jie , Yang Maria C. , Hölttä-Otto Katja 
Published: November 2020

Abstract
The success of design needfinding is largely dependent on how well designers understand their users. It is further claimed that user understanding and designers’ capacity to adopt users’ perspective, i.e. designers’ ability to understand others, are key skills that should lead to successful design outcomes. The general ability to understand someone else’s mental contents, such as what they else think, feel, wish, and believe, is called theory of mind. In this study, we connect concepts of human-centered design and theory of mind through empathic accuracy, a performance-based method for measuring empathy. We state two hypotheses. First, that designers are equally accurate at inferring thoughts as they are at inferring feelings. Second, that designers are more accurate in inferring design-related mental contents than those that are not related to design. We answer these hypotheses by analyzing results of altogether 24 designers watching recorded needfinding interviews of 6 users and inferring their mental contents. We observed that feelings were more accurately inferred than thoughts, although the data showed some inconsistencies. A stronger case can be made for designers’ accuracy of design-related entries, where designers were consistently more accurate at inferring design-related entries than non-design-related ones. These results provide concrete insight into how designers understand users and how empathy could be quantified in the design context.