bluuebunny/crossref_metadata_2025 huggingface dataset:
Topology Optimization of the Pelvic Bone Prosthesis Under Single Leg Stance
Authors: Eswara Sai Kumar Kandula , Rakshit Sourav
Published: November 2020

Pelvic reconstruction is required to restore the functional activity after an internal hemipelvectomy. The available pelvic prosthesis has several complications and long terms issues (such as stability, dislocations etc.) due to non inclusion of the basic mechanics. In this study, a structural optimization based pelvic design is presented for an efficient pelvic prosthesis by including the effect of muscles under single leg stance. The single leg stance loading condition is divided into four categories, (a) all muscles, (b) lower muscles, (c) upper muscles and (d) average value of the muscles for entire stance phase. Thereafter, topology optimization is used to generate four prosthesis for pelvic reconstruction. Titanium alloy (Ti6Al4V) is used to design the pelvic prosthesis. The optimal designs are compared with the natural pelvic bone by measuring the shape similarity index. Results show that the stress and displacement produced by the optimal designs are less than the natural pelvic bone. The induced peak stresses in the optimal designs are low, within linear elastic zone and below the yield strength of the alloy. The moderate to good shape similarity values indicate less complications such as dislocations, stability and constraining the range of hip joint.
Multi-Objective Implementation of Additive Manufacturing in Make-to-Stock Production
Authors: Chua Ping Chong , Moon Seung Ki , Ng Yen Ting
Published: November 2020

As additive manufacturing (AM) develops and matures over the years, it has reached a stage where implementation into a conventional production system becomes possible. With additive manufacturing currently suitable for product personalization/high customization yet small volume production, there are various ways of implementation in a conventional production line. This aim of this paper is to explore the implementation of additive manufacturing in a complementary manner to process internal job orders of large quantities in make-to-stock (MTS) production. Splitting of production orders is allowed and production can be carried out by both injection moulding and additive manufacturing processes simultaneously, with the latter being able to produce various MTS parts in a single build. NSGA-III together with scheduling and rule-based heuristic for allocation of parts on build plate of additive manufacturing process is used to solve the multi-objective implementation problem, with performance measures being cost, scheduling and sustainability. The algorithm will be incorporated with scheduling and rule-based heuristic for allocation of parts on build plate of additive manufacturing process. An experiment using an industry case study is conducted to compare the performance measures with and without implementing additive manufacturing.
Kinematics of the Morph Origami Pattern and its Hybrid States
Authors: Pratapa Phanisri P. , Liu Ke , Paulino Glaucio H.
Published: November 2020

A new degree-four vertex origami, called the Morph pattern, has been recently proposed by the authors (Pratapa, Liu, Paulino, Phy. Rev. Lett. 2019), which exhibits interesting properties such as extreme tunability of Poisson’s ratio from negative infinity to positive infinity, and an ability to transform into hybrid states through rigid origami kinematics. We look at the geometry of the Morph unit cell that can exist in two characteristic modes differing in the mountain/valley assignment of the degree-four vertex and then assemble the unit cells to form complex tessellations that are inter-transformable and exhibit contrasting properties. We present alternative and detailed descriptions to (i) understand how the Morph pattern can smoothly transform across all its configuration states, (ii) characterize the configuration space of the Morph pattern with distinguishing paths for different sets of hybrid states, and (iii) derive the condition for Poisson’s ratio switching and explain the mode-locking phenomenon in the Morph pattern when subjected to in-plane deformation as a result of the inter-play between local and global kinematics.
An Approach to Bayesian Optimization for Design Feasibility Check on Discontinuous Black-Box Functions
Authors: Biswas Arpan , Hoyle Christopher
Published: November 2020

The paper presents a novel approach to apply Bayesian Optimization (BO) in predicting an unknown constraint boundary, also representing the discontinuity of an unknown function, for a feasibility check on the design space, thereby representing a classification tool to discern between a feasible and infeasible region. Bayesian optimization is an emerging field of study in the Sequential Design Methods where we learn and update our knowledge from prior evaluated designs, and proceed to the selection of new designs for future evaluation. It has been considered as a low-cost global optimization tool for design problems having expensive black-box objective functions. However, BO is mostly suited to problems with the assumption of a continuous objective function, and does not guarantee true convergence if the objective function has a discontinuity. This is because of the insufficient knowledge of the BO about the nature of the discontinuity of the unknown true function. Therefore, in this paper, we have proposed to predict the discontinuity of the objective function using a BO algorithm which can be considered as the pre-stage before optimizing the same unknown objective function. The proposed approach has been implemented in a thin tube design with the risk of creep-fatigue failure under constant loading of temperature and pressure. The stated risk depends on the location of the designs in terms of safe and unsafe regions, where the discontinuities lie at the transitions between those regions; therefore, the discontinuity has also been treated as an unknown constraint. The paper focuses on developing BO framework with maximizing the reformulated objective function on the same design space to predict the transition regions as a design methodology or classification tool between safe and unsafe designs, where we start with very limited data or no prior knowledge and then iteratively focus on sampling most designs near the transition region through better prior knowledge (training data) and thereby increase the accuracy of prediction to the true boundary while minimizing the number of expensive function evaluations. The converged model has been compared with the true solution for different design parameters and the results provided a classification error rate and function evaluations at an average of < 1% and ∼150, respectively. The results in this paper show some future research directions in extending the application of BO and considered as the proof of concept on large scale problem of complex diffusion bonded hybrid Compact Heat Exchangers.
Superharmonic Resonance of Third Order of Electrostatically Actuated MEMS Circular Plates: Effect of AC Frequency on Voltage Response
Authors: Beatriz Julio , Caruntu Dumitru I.
Published: November 2020

This paper uses the Reduced Order Model (ROM) as well as the Method of Multiple Scales (MMS) in order to investigate behavior of electrostatically actuated micro-electro-mechanical systems (MEMS) circular plates under superharmonic resonance of third order. ROM is solved using two methods, the first is a continuation and bifurcation approach by using software package called AUTO 07p in order to obtain the voltage response, and the second approach is a numerical integration using the Matlab built in function ode15s for obtaining time responses of the system. Overall MMS and ROM provide similar results, especially in the lower amplitudes. These methods seem to differ at higher amplitudes. The ROM shows a second unstable branch that MMS does not have. The time responses agree with the ROM voltage response. Furthermore, the influences of different parameters such as that of the detuning parameter, and damping are investigated.
Roughing It: Evaluating a Novel Experiential Design Course on Resiliency, Self-Leadership, and Engineering Design Self-Efficacy
Authors: Krishnakumar Sandeep , Sallai Gabriella , Berdanier Catherine , Handley Meg , Lang Dena , Menold Jessica
Published: November 2020

Despite continuous calls in literature for an increase in women in engineering, U.S. numbers hover around 20% women graduating with undergraduate degrees in engineering. Although diversity, inclusion, and engineering education researchers have made great strides in understanding the development of engineering identity and capturing the experiences of struggling women and underrepresented students, many women students still experience “chilly” climates in daily interactions in engineering education environments. Women need mentorship, peer support, and mastery experiences that build Self-leadership, resilience, and engineering self-efficacy beliefs to persevere and flourish in such environments. The current work explores the effectiveness of a novel experiential design intervention on these factors for women engineering students. Specifically, we assess the value of a unique experiential design course where women engineering students hiked a portion of the Appalachian Trail guided by women engineering faculty members, and then participated in a follow-on design thinking project. Results suggest such interventions may be effective methods to improve student outcomes.
Tooth Meshing Estimation Based on Monitoring Rotational Vibration and Infrared Thermography Image of Hypoid Gear
Authors: Yamashita Ren , Mukaiyama Koki , Sakuda Hiroki , Matsui Shota , Hirogaki Toshiki , Aoyama Eiichi
Published: November 2020

Nowadays, the improvement in Noise Vibration Harshness (NVH) is one of the most important issues for meeting the needs of the user in the automobile industry. It is well-known that the tooth surface accuracy significantly influences the vibration in gear meshing. The hypoid gear used in the automobile differential has a complex shape, and estimation of the contact conditions is difficult. Therefore, we attempt to develop a novel method to analyze the tooth contact conditions by using highly sensitive infrared thermography with a high response; this method acts as a noncontact analysis that is based on monitoring the temperature distribution during meshing between the pinion and the gear surface. In this report, we designed three types of hypoid gears with different offsets to observe changes in the meshing phenomenon, which occurred due to the differences in the offset. Moreover, by employing accelerometers during a driving test, the dynamic behavior was also examined. Simultaneously, a tooth contact analysis was carried out using thermography. Consequently, the relationship between the rise in temperature at the tooth contact area and the tendency of the rotational vibration was elucidated.
Numerical Modelling of the Biaxial Fatigue Test of Aluminium Wheels
Authors: Ballo Federico , Mastinu Giampiero , Previati Giorgio , Gobbi Massimiliano
Published: November 2020

The paper is devoted to the numerical simulation of fatigue life of lightweight aluminium wheels subject to biaxial fatigue test. A numerical model based on finite elements is developed for the scope. The model receives as input the test load sequences and outputs the fatigue life of wheel. Two different methods for modelling the load transfer mechanism of the tyre have been analysed, i.e. how the tyre-drum contact forces are transferred to the wheel rim. The first method consists of a simple cosine loading function acting on a fixed arch of the wheel rim. The second method relies on a physical model of the tyre that is fixed at the tyre-rim interface surface; the computed reaction forces are fed as input to the wheel model. The fatigue life of the wheel is estimated by using the Palmgrem-Miner approach. Both the Sines fatigue criterion and the Papadopoulos critical plane with gradient effect criterion are used and the results are compared. Experimental tests have been performed on an actual wheel mounted on the biaxial test bench for a preliminary validation of the method.
Sequential Sampling Based Reliability Analysis for High Dimensional Rare Events With Confidence Intervals
Authors: Xu Yanwen , Wang Pingfeng
Published: November 2020

Analysis of rare failure events accurately is often challenging with an affordable computational cost in many engineering applications, and this is especially true for problems with high dimensional system inputs. The extremely low probabilities of occurrences for those rare events often lead to large probability estimation errors and low computational efficiency. Thus, it is vital to develop advanced probability analysis methods that are capable of providing robust estimations of rare event probabilities with narrow confidence bounds. Generally, confidence intervals of an estimator can be established based on the central limit theorem, but one of the critical obstacles is the low computational efficiency, since the widely used Monte Carlo method often requires a large number of simulation samples to derive a reasonably narrow confidence interval. This paper develops a new probability analysis approach that can be used to derive the estimates of rare event probabilities efficiently with narrow estimation bounds simultaneously for high dimensional problems. The asymptotic behaviors of the developed estimator has also been proved theoretically without imposing strong assumptions. Further, an asymptotic confidence interval is established for the developed estimator. The presented study offers important insights into the robust estimations of the probability of occurrences for rare events. The accuracy and computational efficiency of the developed technique is assessed with numerical and engineering case studies. Case study results have demonstrated that narrow bounds can be built efficiently using the developed approach, and the true values have always been located within the estimation bounds, indicating that good estimation accuracy along with a significantly improved efficiency.
Machine Learning Aided Design and Optimization of Conformal Porous Structures
Authors: Gao Zhenyang , Sossou Danièle , Zhao Yaoyao Fiona
Published: November 2020

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.
Forecasting the Value of Excess in Personal Gaming Desktops
Authors: Long Daniel , Morkos Beshoy , Ferguson Scott
Published: November 2020

A widely held belief in engineering design is that the optimal design is the one that satisfies all requirements at the lowest cost. Recent research has begun challenging this notion by arguing that a system’s total lifetime value may be improved by designing for uncertain future requirements, even after considering additional incurred costs. Excess, the purposeful inclusion of margin beyond what is required for known uncertainties, is the method used in this work for addressing uncertain future requirements. This paper provides evidence that excess can make a system more robust (insensitive to changing requirements) and thereby increase its value. This evidence is drawn from the study from the video game industry data including: console and desktop hardware performance specifications with release dates, video game requirements, and contemporaneous expert suggested gaming desktops over a span of 20 years — from 2001 to mid-2019. Additionally, this paper advances the notion of strategic excess (excess added to a single component) by examining its potential impact on historical systems. The study of strategic excess provides guidance for how one might baseline the appropriate degree of excess inclusion based on technology and requirements trends. The analysis conducted in this paper shows that higher degrees of excess in desktops can increase overall system value, that strategic excess in RAM would have improved system performance by 14% (on average) for 7% of total system cost, and that trends in technology improvement can be used for baselining excess inclusion by suggesting specific performance tiers for CPUs and GPUs.
Characterisation, Design and Experimentation of a Fabric Based Wearable Joint Sensing Device
Authors: Lau Jun Liang , Soh Gim Song
Published: November 2020

The use of conductive fabrics (CF) in the design of wearables for joint sensing has recently received much interest in a wide range of applications such as robotics, rehabilitation, personal wellness, sports, and entertainment. In this paper, we evaluate a new wearable device concept that comprises of a CF strain-voltage sensor embedded as part of an inverted slider-crank mechanism for joint extension sensing. This has the benefit of not requiring anthropometric information from the user to related the joint parameters to the fabric strain readings, as opposed to an existing design. Firstly, we characterize the electromechanical property of a commercially available CF. Secondly, we formulate the geometric synthesis procedure of the joint sensing device as a constrained revolute joint system, where the CF is designed and introduced as an RPR chain to obtain an inverted slider-crank linkage. Lastly, we develop and validate our wearable joint sensing device against an experimental setup that represents an elbow joint. Our concept shows that our proposed joint sensing device can track the elbow extension motion of 140° with a maximum error of 7.66%.
Comparing Attribute- and Form-Based Machine Learning Techniques for Component Prediction
Authors: Williams Glen , Puentes Lucas , Nelson Jacob , Menold Jessica , Tucker Conrad , McComb Christopher
Published: November 2020

Online data repositories provide designers and engineers with a convenient source of data. Over time, the wealth and type of readily-available data within online repositories has greatly expanded. This data increase permits new uses for machine learning approaches which rely on large, high-dimensional datasets. However, a comparison of the efficacies of attribute-based data, which lends itself well to traditional machine learning algorithms, and form-based data, which lends itself to deep machine learning algorithms, has not fully been established. This paper presents one such comparison for an exemplar dataset. As the efficacy of different machine learning approaches may be dependent on the specific application, this method is intended to lay the groundwork for future studies that produce more extensive comparisons. Specifically, this work makes use of a manufactured gear dataset for sale price prediction. Two traditional machine learning algorithms, M5Rules and SMOreg, are selected due to their applicability to the gear attribute-based data. These algorithms are compared to a neural network model that is trained on a voxelized version of the gears’ 3D models, defined in this work as form-based data. Results show that both data types provide comparable predictive accuracy.
Smooth/Non-Smooth Multibody Co-Simulation of a Particle Damper
Authors: Zhang Runsen , Zhang Huimin , Zanoni Andrea , Tasora Alessandro , Masarati Pierangelo
Published: November 2020

To tackle coupled problems between smooth multibody systems and particle dampers, co-simulation between two general-purpose multibody codes is carried out, where the coupled system is decomposed by the force/displacement technique, and integrated by a loose coupling scheme or a tight one with iterative process. The stability properties of the coupling schemes are discussed using a two-mass oscillator, and it follows that the tight coupling scheme is more stable than the loose one, as expected. Besides, results also denote that the stability is influenced by the parameters of the integrator in each solver. Finally, the co-simulation schemes are applied to a simple system coupled with a particle damper, and the comparison among results obtained by co-simulation, monolithic simulation and experiments verifies the effectiveness of the proposed co-simulation scheme.
Brand Affiliation Through Curved and Angular Surfaces Using the Example of the Vehicle Front
Authors: Fischer Matthias Sebastian , Holder Daniel , Maier Thomas
Published: November 2020

With visual similarity products can be assigned to a brand. The brand assignment based on shapes has only examined the contours (orientation, proportion and position) of selected elements so far. This paper investigates whether brand affiliation is also related to different surface types (curved and angular). For the investigation the vehicle front was selected. Distinguishable contours and surfaces were selected according to their semantic effect (friendliness, aggressiveness and perceived gender). The contours of the headlights and the lower air intake were determined based on anthropometric investigations (smiling and frowning faces) and were selected to be neutral to existing brands. Comparable semantic effects were found for curved and angular surfaces. Self-made vehicle fronts were designed with these parameters. An online survey was conducted with 150 users. First of all, the effects of the stimulus patterns generated for the factors contour (face) and surface were confirmed in the survey. Then the stimulus patterns were evaluated in pairs regarding their brand affiliation. The results show that brand affiliation can be implemented with contours and surfaces. Furthermore, it was determined that there are two user groups which differ in the evaluation of the brand affiliation.
A Model Predictive Control Strategy for Lateral and Longitudinal Dynamics in Autonomous Driving
Authors: Khan Irfan , Feraco Stefano , Bonfitto Angelo , Amati Nicola
Published: November 2020

This paper presents a controller dedicated to the lateral and longitudinal vehicle dynamics control for autonomous driving. The proposed strategy exploits a Model Predictive Control strategy to perform lateral guidance and speed regulation. To this end, the algorithm controls the steering angle and the throttle and brake pedals for minimizing the vehicle’s lateral deviation and relative yaw angle with respect to the reference trajectory, while the vehicle speed is controlled to drive at the maximum acceptable longitudinal speed considering the adherence and legal speed limits. The technique exploits data computed by a simulated camera mounted on the top of the vehicle while moving in different driving scenarios. The longitudinal control strategy is based on a reference speed generator, which computes the maximum speed considering the road geometry and lateral motion of the vehicle at the same time. The proposed controller is tested in highway, interurban and urban driving scenarios to check the performance of the proposed method in different driving environments.
Advanced Vehicle Dynamics Through Active Aerodynamics and Active Body Control
Authors: de Carvalho Pinheiro Henrique , Russo Francesco , Sisca Lorenzo , Messana Alessandro , De Cupis Davide , Ferraris Alessandro , Airale Andrea Giancarlo , Carello Massimiliana
Published: November 2020

In this paper, the development procedure of an innovative control algorithm is shown, with the aim of improving handling performance of a high-end sport vehicle by actively controlling aerodynamic forces acting on the vehicle itself. The proposed control algorithm operates indirectly by modifying ride-heights of the vehicle through an active suspensions system.

The vehicle dynamics analysis is conducted in parallel to the aerodynamics analysis performed in a concurrent engineering operation.

The software used for control algorithms development is VI-CarRealTime, in co-simulation with Matlab-Simulink, with an extended use of the MaxPerformance package. Specific tracks have been implemented ad hoc to highlight the effects of the control systems operation in development phase.

To better explore the potential of the technique, a fuzzy logic system was developed.
Consistent and Inertia-Shape-Integral-Free Invariants of the Floating Frame of Reference Formulation
Authors: Zwölfer Andreas , Gerstmayr Johannes
Published: November 2020

The conventional continuum-mechanics-based floating frame of reference formulation involves unhandy so-called inertia-shape-integrals in the equations of motion, which is why, commercial multibody software codes resort to a lumped mass approximation to avoid the evaluation of these integrals in their computer implementations. This paper recaps the conventional continuum mechanics floating frame of reference formulation and addresses its drawbacks by summarizing recent developments of the so-called nodal-based floating frame of reference formulation, which avoids inertia shape integrals ab initio, does not rely on a lumped mass approximation, and exhibits a way to calculate the so-called invariants, which are constant “ingredients” required to set up the equations of motion, in a consistent way.