Search

One of the current challenges for the additive manufacturing (AM) industry lies in providing component designs compatible with the AM manufacturability and constraints without compromising the component structural functionalities. To address this challenge, we present an automated correction system that provides geometrically feasible designs for additive processes by applying locally effective modifications while avoiding substantial changes in the current designs. Considering a minimum printable feature size from the process parameters, this system identifies the problematic features in an infeasible part’s design using a holistic geometric assessment algorithm. Based on the obtained manufacturability feedback, the system then corrects the detected problematic regions using a set of appropriate redesign solutions through an automated procedure. In addition, to reduce the difference between the current and modified part geometries, a novel optimization model for build orientation is presented. By using this model, one can identify appropriate orientations for obtaining a feasible design with a minimal amount of corrections while also reducing the postprocessing effort by minimizing the area of contact with the support structure. The functionalities of the presented correction system and the optimization model are illustrated using a number of case studies with varying geometries. The computational performance of the system and an experimental validation are also presented to demonstrate the effectiveness of the implemented detection and modification approaches.

Product development is a key component of engineering education taught at a number of universities through their capstone design course. This course provides students with an opportunity to apply their newly obtained knowledge in engineering to design, build, and test working prototypes. This educational approach also encourages students to place additional attention on time and group management. As students walk through the design process, their focus fluctuates between group organization, product development, and course deliverables. This paper observes this variation in focus to extract key insights related to who is focusing on what and when. Data was collected in the form of individual project journals for each student and these provide a detailed look into the design activities throughout the semester allowing for a focus mapping from week to week. The focus of each student is quantified by a topic distribution of each student’s weekly journal entries, automatically extracted using Latent Dirichlet Allocation. Our results place emphasis on the topic identification accuracy and interpretation, before identifying trends found that separate high performing students and groups from those with poor performances. It was found that efficient time management focusing on the required course deliverables, and group cohesion led to the most impactful performance variations. Using this knowledge, we identify future directions supporting the pedagogy for capstone projects.

Despite numerous recent advances in the classroom and in-vehicle driver training and education over the last quarter-century, traffic accidents remain a leading cause of mortality for young adults—particularly, those between the ages of 16 and 19. Obviously, despite recent advances in conventional driver training (e.g., classroom, in-vehicle, Graduated Driver Licensing programs), this remains a critical public safety and public health concern. As advanced vehicle technologies continue to evolve, so too does the unintended potential for mechanical, visual, and/or cognitive driver distraction and adverse safety events on national highways. For these reasons, a physics-based modeling and high-fidelity simulation have great potential to serve as a critical supplementary component of a near-future teen-driver training framework. Here, a case study is presented that examines the specification, development, and deployment of a “blueprint” for a simulation framework intended to increase driver training safety in North America. A multi-measure assessment of simulated driver performance was developed and instituted, including quantitative (e.g., simulator-measured), qualitative (e.g., evaluator-observed), and self-report metrics. Preliminary findings are presented, along with a summary of novel contributions through the deployment of the training framework, as well as planned improvements and suggestions for future directions.

Increasingly complex engineering design challenges requires the diversification of knowledge required on design teams. In the context of open innovation, positioning key members within these teams or groups based on their estimated abilities leads to more impactful results since mass collaboration is fundamentally a sociotechnical system. Determining how each individual influences the overall design process requires an understanding of the predicted mapping between their technical competency and performance. This work explores this relationship through the use of predictive models composed of various algorithms. With support of a dataset composed of documents related to the design performance of students working on their capstone design project in combination with textual descriptors representing individual technical aptitudes, correlations are explored as a method to predict overall project development performance. Each technical competency and project is represented as a distribution of topic knowledge to produce the performance metrics, which are referred to as topic competencies, since topic representations increase the ability to decompose and identify human-centric performance measures. Three methods of topic identification and five prediction models are compared based on their prediction accuracy. From this analysis, it is found that representing input variables as topics distributions and the resulting performance as a single indicator while using support vector regression provided the most accurate mapping between ability and performance. With these findings, complex open innovation projects will benefit from increased knowledge of individual ability and how that correlates to their predicted performances.

Additive Manufacturing (AM) is a revolutionary development that is being viewed as a core technology for fabricating current and future engineered products. While AM has many advantages over subtractive manufacturing processes, one of the primary limitations of AM is to swiftly evaluate precise part build orientations. Current algorithms are either computationally expensive or provide multiple alternative orientations, requiring additional decision tradeoffs. To hasten the process of finding accurate part build orientation, a data-driven predictive model is introduced by mapping standard machining features to build orientation angles. A combinatory learning algorithm of classification and regression is utilized for the prediction of build orientation. The framework uses 54,000 voxelized standard tessellated language (STL) files as input to train the classification algorithm for eighteen standard machining features using a nine-layer 3D Convolutional Neural Network (CNN). Additionally, a multi-machining feature dataset of 1000 voxelized STL files are evaluated in parallel by performing quaternion rotations to obtain build orientation angles based on minimization of support structure volume. A regression model is then developed to establish a relationship between the machining features and orientation angles to predict optimal build orientation for new parts.

To appropriately leverage the benefits of Additive Manufacturing (AM), it would be advantageous if a printing could be guaranteed before allocating the necessary resources. Further, when considering AM for an inventory of existing components traditionally fabricated through traditional means, such a guarantee could result in significant technical and economic advantages. To realize such advantages, this paper presents a platform that allows for a successful and efficient transition of part-inventories to AM. This is accomplished using a novel design recommender system supported by machine learning, capable of making suggestions towards effective design modifications. This system uses an automatic AM-feasibility analysis of existing parts and a clustering of the parts based on similarities in their AM-feasibilities to develop a set of recommendations for those part clusters whose current designs are deemed as infeasible and/or inefficient for AM. The design modifications leverage a re-design algorithm to address not only problematic geometric issues but also potential infeasibilities associated with resource consumption. The utility of the presented modification algorithm is demonstrated using a number of case studies.

The potential of smart cities in remediating environmental problems in general and waste management, in particular, is an important question that needs to be investigated in academic research. Built on an integrative review of the literature, this study offers insights into the potential of smart cities and connected communities in facilitating waste management efforts. Shortcomings of existing waste management practices are highlighted and a conceptual framework for a centralized waste management system is proposed, where three interconnected elements are discussed: (1) an infrastructure for proper collection of product lifecycle data to facilitate full visibility throughout the entire lifespan of a product, (2) a set of new business models relied on product lifecycle data to prevent waste generation, and (3) an intelligent sensor-based infrastructure for proper upstream waste separation and on-time collection. The proposed framework highlights the value of product lifecycle data in reducing waste and enhancing waste recovery and the need for connecting waste management practices to the whole product life-cycle. An example of the use of tracking and data sharing technologies for investigating the waste management issues has been discussed. Finally, the success factors for implementing the proposed framework and some thoughts on future research directions have been discussed.

As college students enter engineering, they face challenges when they navigate across various transitions. These challenges impact whether a student can successfully adapt to the rigorous curricular requirements of an engineering degree and to the norms and expectations that are particular to engineering. This article focuses on the transitions between disciplinary literacies, primarily addressing how ideas are communicated and which types of semiotic representations are valued, required, and ignored by the different cultures that come into play both prior and during a student’s engineering education. Specifically, it addresses navigation:Across secondary to postsecondary institutional expectations, contrasting common curricular experiences of secondary students and postsecondary curricular expectations.Between and within disciplines (including mathematics and science), which often have different ways of communicating and representing a single concept, especially in theoretical applications and practical ...

Due to the benefits associated with additive manufacturing (AM), there are increasingly more opportunities to leverage AM to enable the fabrication of components that were previously made using conventional techniques such as subtractive manufacturing or casting. To support this transition, it is critical to be able to rigorously evaluate the technical and economic feasibility of additively manufacturing an existing component design. In order to support this evaluation, this paper presents a novel feasibility analysis that performs a multi-criteria assessment of AM readiness. Along with the development of these assessments, we also present a novel scoring approach for qualitatively and quantitatively evaluating the feasibility of each component assessment. This scoring approach, which leverages preference models from physical programing, introduces a flexible set of feasibility levels to assess the manufacturability capabilities of AM technologies. It also allows for the integration of a designer’s preferences toward the AM assessments, supporting the decision whether to utilize AM technologies or not. The presented feasibility analysis allows for both technical and economic benefits since it suggests only using AM for those products whose feasibility results are within suitable ranges. The details of the approach are illustrated using four sample parts with varying geometries. Experimental validation is also performed to demonstrate the robustness of the evaluation. Results obtained show the capability and generalizability of these approaches to analyze intricate geometries and provide useful decision support in AM feasibility analysis.

The flexibility afforded by distributed energy resources in terms of energy generation and storage has the potential to disrupt the way we currently access and manage electricity. But as the energy grid moves to fully embrace this technology, grid designers and operators are having to come to terms with managing its adverse effects, exhibited through electricity price volatility, caused in part by the intermittency of renewable energy. With this concern however comes interest in exploiting this price volatility using arbitrage—the buying and selling of electricity to profit from a price imbalance—for energy cost savings for consumers. To this end, this paper aims to maximize arbitrage value through the data-driven design of optimal operational strategies for distributed energy resources (DERs). Formulated as an arbitrage maximization problem using design optimization principles and solved using reinforcement learning, the proposed approach is applied toward shared DERs within multi-building residential clusters. We demonstrate its feasibility across three unique building clusters, observing notable energy cost reductions over baseline values. This highlights a capability for generalized learning across multiple building clusters and the ability to design efficient arbitrage policies for energy cost minimization. The scalability of this approach is studied using two test cases, with results demonstrating an ability to scale with relatively minimal additional computational cost, and an ability to leverage system flexibility toward cost savings.

In the wake of increasing proliferation of renewable energy and distributed energy resources (DERs), grid designers and operators alike are faced with several emerging challenges in curbing allocative grid inefficiencies and maintaining operational stability. One such challenge relates to the increased price volatility within real-time electricity markets, a result of the inherent intermittency of renewable energy. With this challenge, however, comes heightened economic interest in exploiting the arbitrage potential of price volatility towards demand-side energy cost savings. To this end, this paper aims to maximize the arbitrage value of electricity through the optimal design of control strategies for DERs. Formulated as an arbitrage maximization problem using design optimization, and solved using reinforcement learning, the proposed approach is applied towards shared DERs within multi-building residential clusters. We demonstrate its feasibility across three unique building cluster demand profiles, observing notable energy cost reductions over baseline values. This highlights a capability for generalized learning across multiple building clusters and the ability to design efficient arbitrage policies towards energy cost minimization. Finally, the approach is shown to be computationally tractable, designing efficient strategies in approximately 5 hours of training over a simulation time horizon of 1 month.

The substantial role of additive manufacturing (AM) in fabricating unique geometries is undeniable in the domain of design and manufacturing. However, the successful implementation of AM technologies requires a consistency between the geometric specifications of a component and AM manufacturability capabilities and constraints. Otherwise, AM could result in failed prints and a wasteful use of resources. The goal of this research is to provide geometrically feasible designs for AM processes by rectifying the potentially infeasible geometries. To this end, a novel design modification system is presented that addresses the problematic areas of an AM-infeasible component using appropriate redesign solutions. This system also includes a geometric assessment algorithm which identifies the potential problematic part features using a comprehensive evaluation. Based on the obtained manufacturability feedback, the detected problematic features are then modified through a holistic design modification system. The functionality of the presented system is illustrated using a case study, and the effectiveness of the implemented modification approaches is also demonstrated through an experiment.

Increasing the complexity of engineering design projects expands of the diversity of required topic knowledge. Multi-disciplinary design processes have the need for expertise from multiple fields of study. In the context of mass collaboration within engineering design, positioning key members within multi-disciplinary teams is of great importance. Determining how each discipline impacts the overall design process requires an understanding of the mapping between competency and performance. This work explores this mapping through the use of predictive models composed of various regression algorithms. Design performance of students working on their capstone design project is analyzed and the relationship between individual competencies is compared against their overall project performance. Each competency and project is represented as a distribution of topic knowledge to produce the performance metrics. Following the automated topic extraction of the textual data, the regression algorithms are applied. Three topic models and five prediction models are compared for their prediction accuracy. From this analysis it was found that representing both input and output variables as a distribution of topics while performing a support vector regression provided the most accurate mapping between ability and performance.

Teenage drivers diagnosed with attention-deficit/hyperactivity disorder (ADHD) are at significant risk for negative driving outcomes related to morbidity and mortality. However, there are few viable psychosocial treatments for teens with ADHD and none focus on the key functional area of driving. The Supporting the Effective Entry to the Roadway (STEER) program was evaluated in a clinical trial to investigate whether it improved family functioning as a proximal outcome and driving behavior as a distal outcome. One hundred seventy-two teenagers with ADHD, combined type, were randomly assigned to STEER or a driver education driver practice program (DEDP). Relative to parents in the DEDP condition, parents in STEER were observed to be less negative at posttreatment and 6-month follow-up but not at 12-month follow-up, and there were no significant differences for observed positive parenting. Relative to teens in the DEDP condition, teens in STEER reported lower levels of risky driving behaviors at posttreatment and 6-month follow-up, but not at 12-month follow-up. Groups did not differ on objective observations of risky driving or citations/accidents. The STEER program for novice drivers with ADHD was effective in reducing observations of negative parenting behavior and teen self-reports of risky driving relative to DEDP; groups did not significantly differ on observations of positive parenting or driving behaviors. (PsycINFO Database Record(c) 2016 APA, all rights reserved). Language: en

The objective of this study is to provide an overview of Blockchain technology and Industry 4.0 for advancing supply chains towards sustainability. First, extracted from the existing literature, we evaluate the capabilities of Industry 4.0 for sustainability under three main topics of (1) Internet of things (IoT)-enabled energy management in smart factories; (2) smart logistics and transportation; and (3) smart business models. We expand beyond Industry 4.0 with unfolding the capabilities that Blockchain offers for increasing sustainability, under four main areas: (1) design of incentive mechanisms and tokenization to promote consumer green behavior; (2) enhance visibility across the entire product lifecycle; (3) increase systems efficiency while decreasing development and operational costs; and (4) foster sustainability monitoring and reporting performance across supply chain networks. Furthermore, Blockchain technology capabilities for contributing to social and environmental sustainability, research gaps, adversary effects of Blockchain, and future research directions are discussed.

Mass collaboration within the design engineering process supports the inclusion of unique perspectives when working on complex problems. Increasing the number of individuals providing input and support into these perplexing challenges can increase innovation, decrease product development times, and provide solutions that truly encompass the needs of the market. One of the greatest challenges within mass collaboration engineering projects is the organization of individuals within these large design efforts. Understanding which projects would most effectively benefit from additional designers or contributors is paramount to supporting mass collaboration design networks. Within such networks, there exists a large number of contributors as well as a large pool of potential projects. Matching individuals with the projects that they can provide the greatest benefit to or building a team of individuals for newly developed projects requires the consideration of previous performance and an understanding of individual competencies and design abilities. This work presents a framework which recommends individual project placement based on individual abilities and the project requirements. With this work, a pool of individuals and potential projects are simulated, and the application of a hybrid recommender system is explored. To complement the simulation, an additional case study with empirical data is performed to study the potential applicability of the proposed framework. Overall, it was found that recommended team compositions greatly outperform the baseline team development, most notably as greater consideration is placed on collaborative recommendations.

The control of shared energy assets within building clusters has traditionally been confined to a discrete action space, owing in part to a computationally intractable decision space. In this work, we leverage the current state of the art in reinforcement learning (RL) for continuous control tasks, the deep deterministic policy gradient (DDPG) algorithm, toward addressing this limitation. The goals of this paper are twofold: (i) to design an efficient charged/discharged dispatch policy for a shared battery system within a building cluster and (ii) to address the continuous domain task of determining how much energy should be charged/discharged at each decision cycle. Experimentally, our results demonstrate an ability to exploit factors such as energy arbitrage, along with the continuous action space toward demand peak minimization. This approach is shown to be computationally tractable, achieving efficient results after only 5 h of simulation. Additionally, the agent showed an ability to adapt to different building clusters, designing unique control strategies to address the energy demands of the clusters studied.

The use of additive manufacturing (AM) for fabricating industrial grade components has increased significantly in recent years. Numerous industrial entities are looking to leverage new AM techniques to enable fabrication of components that were typically manufactured previously using conventional manufacturing techniques such as subtractive manufacturing or casting. Therefore, it is becoming increasingly important to be able to rigorously evaluate the technical and economic feasibility of additively manufacturing a component relative to conventional alternatives. In order to support this evaluation, this paper presents a framework that investigates fabrication feasibility for AM from three perspectives: geometric evaluation, build orientation/support generation, and resources necessary (i.e., cost and time). The core functionality of the framework is enabled on voxelized model representation, discrete and binary formats of 3D continuous objects. AM fabrication feasibility analysis is applied to 34 various parts representing a wide range of manifolds and valves manufactured using conventional manufacturing techniques, components commonly found in the aerospace industry. Results obtained illustrate the capability and generalizability of the framework to analyze intricate geometries and provide a primary assessment for the feasibility of the AM process.

This work leverages the current state of the art in reinforcement learning for continuous control, the Deep Deterministic Policy Gradient (DDPG) algorithm, towards the optimal 24-hour dispatch of shared energy assets within building clusters. The modeled DDPG agent interacts with a battery environment, designed to emulate a shared battery system. The aim here is to not only learn an efficient charged/discharged policy, but to also address the continuous domain question of how much energy should be charged or discharged. Experimentally, we examine the impact of the learned dispatch strategy towards minimizing demand peaks within the building cluster. Our results show that across the variety of building cluster combinations studied, the algorithm is able to learn and exploit energy arbitrage, tailoring it into battery dispatch strategies for peak demand shifting.

Mass collaboration within the design engineering process supports the inclusion of unique perspectives when working on complex problems. Increasing the number of individuals providing input and support into these perplexing challenges can increase innovation, decrease product development times and provide solutions that truly encompass the needs of the market. One of the greatest challenges within mass collaboration engineering projects is the organization of individuals within these large design efforts. Understanding which projects would most effectively benefit from additional designers or contributors is paramount to supporting mass collaboration design networks. Within such networks, there exists a large number of contributors, as well as, a large pool of potential challenges. Matching individuals with the challenges that they can provide the greatest benefit to, or building a team of individuals for newly developed challenges requires the consideration of previous performance and an understanding of individual competencies and design abilities. This work presents a framework which recommends individual project placement based on individual abilities and the project requirements. With this work a pool of individuals and potential projects are simulated and the application of a hybrid recommender system is explored. Overall it was found that recommended team compositions greatly outperform the baseline team development, most notably as greater consideration is placed on collaborative recommendations.

Due to their volatile behavior, natural disasters are challenging problems as they often cannot be accurately predicted. An efficient method to gather updated information of the status of a disaster, such as the location of any trapped survivors, is extremely important to properly conduct rescue operations. To accomplish this, an algorithm is presented to control a swarm of UAVs (Unmanned Aerial Vehicles) and optimize the value of the information gathered. For this application, the UAVs are autonomously navigated with a decentralized control method. With sensor technology embedded, this swarm collects information from the environment as it operates. By using the swarm’s location history, areas of the environment that have gone the longest without exploration can be prioritized, ensuring a thorough search. Measures are also developed to prevent redundant or inefficient exploration, which would reduce the value of the gathered information. A case study of a flood scenario is examined and simulated. Through this approach, the value of the proposed swarm algorithm can be tested by tracking the number of survivors found as well as the rate at which they are discovered.

Objective: Youth with ADHD exhibit positive bias, an overestimation of ability, relative to external indicators. The positive bias construct is understudied in adolescents, particularly in the domain of driving. Study is needed as youth with ADHD experience greater negative outcomes in driving relative to typically developing teens. Method: Positive bias on a driving simulator task was investigated with 172 teenagers with ADHD, combined type. Youth participated in a driving simulation task and rated driving performance afterward. Results: Compared with external ratings of driving performance, youth overestimated driving competence for specific driving behaviors as well as globally. The global rating demonstrated a greater degree of positive bias. Greater positive bias on global ratings of driving ability also predicted greater rates of risky driving behaviors during the simulator exercise independent from disruptive behavior disorder symptoms. Conclusion: Results inform prevention and intervention efforts for teenage drivers with ADHD.

Material selection significantly affects environmental impacts and other objectives of a product design. Life cycle assessment (LCA) methods are not efficient enough for use at the early design stages to prune a design space. Material properties consist of discrete data sets, which are further complicated when LCA data are included, thus posing a significant challenge in the construction of surrogate models for prediction of all relevant behaviours and numerical optimisation. In this work, we address the unique challenges of material selection in sustainable product design in some important ways. Salient features of the robust surrogate modelling approach include achieving manageable dimensionality of LCA with a minimal loss of the important information by the consolidation of significant factors into categorised groups, as well as subsequent efficiency enhancement by a streamlined process that avoids the construction of full LCA. This approach combines efficiency of use with a mathematically rigorous rep...

Research on complex system optimization has focused on areas including algorithms, coordination strategies, and communication tools. This paper considers optimization from the perspective of information coordination during the solution process. This work aims to determine the potential impact of factors including the role of a system facilitator in managing system-level tradeoffs, the influence of an ideal information scenario, the choice of system variables, and the amount and form of passed information. The goal is not to develop the “best” communication structure or optimization algorithm, but to understand the significance of these factors. A traditional multidisciplinary design optimization coordination framework, a game theoretic approach, and a modified game theory approach are used to mimic design team behaviors. Results suggest these factors can influence the coordination process outcomes: The choice of system variable plays a significant role in the optimality of the final design; perfect inform...

The potential for mass collaboration and concurrent engineering among individuals working on a design project allows for increased innovation in idea generation while also supporting the parallelization of project development. The former of these two benefits is highlighted by recent corporate initiatives to incorporate crowdsourcing in their design process. This is done in an effort to garner new and unique perspectives while also driving designs that carry greater acceptance from the community and/or intended market. The latter benefit is emphasized by the pronounced success of concurrent engineering projects which tend to reduce production costs, all while enhancing overall product performance. This work aims to exploit these two concepts in a structured and methodical crowd centered design framework. A simulated pool of individuals, representing the crowd, are used to form design groups using game-theoretic principles. The outcome of each sub-game aims to increase the overall improvement to the designed system by maximizing what each individual can potentially add to the project. The optimization of resource allocation is then performed where time spent on individual or collaborative efforts is determined. The proposed framework is applied to simplified design projects at their initial conception, before detailed mathematical design models are determined.