Towards a Universal Social Impact Metric for Engineered Products That Alleviate Poverty

More than ever before, engineers are creating products for developing countries. One of the purposes of these products is to improve the consumer’s quality of life. Currently, there is no established method of measuring the social impact of these types of products. As a result, engineers have used their own metrics to assess their product’s impact, if at all. Some of the common metrics used include products sold and revenue, which measure the financial success of a product without recognizing the social successes or failures it might have. In this paper we introduce a potential metric, the Product Impact Metric (PIM), which quantifies the impact a product has on impoverished individuals — especially those living in developing countries. It measures social impact broadly in five dimensions: health, education, standard of living, employment quality, and security. The PIM is inspired by the Multidimensional Poverty Index (MPI) created by the United Nations Development Programme. The MPI measures how the depth of poverty within a nation changes year after year, and the PIM measures how an individual’s quality of life changes after being affected by an engineered product. The Product Impact Metric can be used to predict social impacts (using personas that represent real individuals) or measure social impacts (using specific data from products introduced into the market).

Aerodynamic Automobile Shape Optimization by Incorporating Reverse Shape Design Method With CFD Analysis

In modern automotive industry market, there have been a lot of state-of-art methodologies to perform a conceptual design of a car; functional methods and 3D scanning technology are widely used. Naturally, the issues frequently boiled down to a trade-off decision making problem between quality and cost. Besides, to incorporate the design method with advanced optimization methodologies such as design-of-experiments (DOE), surrogate modeling, how efficiently a method can morph or recreate a vehicle’s shape is crucial. This paper accomplishes an aerodynamic design optimization of rear shape of a sedan by incorporating a reverse shape design method (RSDM) with the aforementioned methodologies based on CFD analysis for aerodynamic drag reduction. RSDM reversely recovers a 3D geometry of a car from several 2D schematics. The backbone boundary lines of 2D schematic are identified and regressed by appropriate interpolation function and a 3D shape is yielded by a series of simple arithmetic calculations without losing the detail geometric features. Besides, RSDM can parametrize every geometric entity to efficiently manipulate the shape for application to design optimization studies. As the baseline, an Audi A6 is modeled by RSDM and explored through CFD analysis for model validation. Choosing six design variables around the rear shape, 77 design points are created to build neural networks. Finally, a significant amount of CD reduction is obtained and corresponding configuration is validated via CFD.

A Goal-Oriented, Inverse Decision-Based Design Method to Achieve the Vertical and Horizontal Integration of Models in a Hot Rod Rolling Process Chain

Reducing the manufacturing and marketing time of products by means of integrated simulation-based design and development of the material, product, and the associated manufacturing processes is the need of the hour for industry. This requires the design of materials to targeted performance goals through bottom-up and top-down modeling and simulation practices that enables handshakes between modelers and designers along the entire product realization process. Manufacturing a product involves a host of unit operations and the final properties of the manufactured product depends on the processing steps carried out at each of these unit operations. In order to effectively couple the material processing-structure-property-performance spaces, there needs to be an interplay of the systems-based design of materials with enhancement of models of various unit operations through multiscale modeling methodologies and integration of these models at different length scales (vertical integration). This ensures the flow of information from one unit operation to another thereby establishing the integration of manufacturing processes (horizontal integration). Together these types of integration will support the decision-based design of the manufacturing process chain so as to realize the end product. In this paper, we present a goal-oriented, inverse decision-based design method to achieve the vertical and horizontal integration of models for the hot rolling and cooling stages of the steel manufacturing process chain for the production of a rod with defined properties. The primary mathematical construct used for the method presented is the compromise Decision Support Problem (cDSP) supported by the proposed Concept Exploration Framework (CEF) to generate satisficing solutions under uncertainty. The efficacy of the method is illustrated by exploring the design space for the microstructure after cooling that satisfies the requirements identified by the end mechanical properties of the product. The design decisions made are then communicated in an inverse manner to carry out the design exploration of the cooling stage to identify the design set points for cooling that satisfies the new target microstructure requirements identified. Specific requirements such as managing the banded microstructure to minimize distortion in forged gear blanks are considered in the problem. The proposed method is generic and we plan to extend the work by carrying out the integrated decision-based design exploration of rolling and reheating stages that precede to realize the end product.

A Simple Starting Point for Designing for and/or Assessing the Social Impact of Products

In this paper we present a starting point for designing for and/or assessing the social impact of engineered products. The starting point is a set of tables comprising products, their general functional characteristics, and the accompanying social impacts. We have constructed these tables by first extracting a set of social impact categories from the literature, then 65 products were qualitatively reviewed to find their social impact. The resulting product impact tables can be used at either the beginning of the product development process to decide what social impact to design for and discover product functions that lead to it, or later to qualitatively assess the social impact of a product being designed and/or to assess the impact of an existing product.

Design Exploration of Affordable Refreshable Braille Display Technology for Low-Income Visually Impaired Users

People with visual disability need assistance in reading and writing by converting text to braille. Braille allows tactile display of the information for the visually impaired. Refreshable braille displays are commonly available in developed countries for a high price with the number of cells the display contains being the most influential factor on that price. Low-income blind individuals from developing countries cannot afford an expensive refreshable braille display, which in turn limits their access to digital information. The purpose to this paper is to explore design options for reducing the cost of refreshable braille displays. The paper begins with a summary of currently available refreshable braille displays on the market and their features. Next, the design requirements are explored for developing a low-cost device for visually impaired users in the developing countries. The paper also explains the state-of-the-art technologies for actuating the braille dots that may reduce the cost of the device. Finally, the recommendations for reducing the cost of these displays are presented.

Village-Scale Electrodialysis Desalination: Field Trial Validation

This paper presents the design and initial testing of a village-scale photovoltaic (PV) powered electrodialysis reversal (EDR) desalination system for rural India. The system was built by the authors and tested at the Brackish Groundwater National Desalination Research Facility in New Mexico. EDR has the potential to be more cost effective than currently installed village-scale reverse osmosis (RO) systems in off-grid locations due to the lower specific energy consumption of EDR versus RO at high recovery ratios. Lower energetic demand leads to lower solar power system costs for off-grid areas. The system tested in this study is designed to validate that energetic, product water quality, and water recovery requirements can be met. An analytical model of the system that accounts for the composition of natural groundwater is presented and compared to initial experimental results. Additionally, results from the USAID Desal Prize are presented showing the system’s performance in regards to recovery ratio and product water quality. This paper presents the design methodology, resulting system parameters, and experimental results for an initial village-scale PV-EDR field trial.

Optimal Surrogate and Neural Network Modeling for Day-Ahead Forecasting of the Hourly Energy Consumption of University Buildings

This paper presents the development and evaluation of Artificial Neural Networks (ANN) based models and optimally selected surrogate models to provide the day-ahead forecast of the hourly-averaged energy load of buildings, by relating it to eight weather parameters as well as the hour of the day. Although ANN and other surrogate models have been used to predict building energy loads in the past, there is a limited understanding of what type of model prescriptions impact their performance as well as how un-recorded impact factors (e.g., human behavior and building repair work) should be accounted for. Here, the recorded energy data of three university buildings, from 9/2013–12/2015, is cleaned and synchronized with the local weather data. The data is then classified into eight classes; weekends and weekdays of Fall/Winter/Spring/Summer semesters. Both Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) NNs are explored. Differing number of hidden layers and transfer function choices are also explored, leading to the choice of the hyperbolic-tangent-sigmoid transfer function and 60 hidden layers. Similarly, an automated surrogate modeling framework is used to select the best models from among a pool of Kriging, RBF, and SVR models. A baseline concept, that uses energy information from the previous day as an added input to the ANN, helps to account for otherwise unrecorded recent changes in the building behavior, leading to improvement in fidelity of up to 30%.

Investigating Grey-Box Modeling for Predictive Analytics in Smart Manufacturing

This paper develops a two-stage grey-box modeling approach that combines manufacturing knowledge-based (white-box) models with statistical (black-box) metamodels to improve model reusability and predictability. A white-box model can use various types of existing knowledge such as physical theory, high fidelity simulation or empirical data to build the foundation of the general model. The residual between a white-box prediction and empirical data can be represented with a black-box model. The combination of the white-box and black-box models provides the parallel hybrid structure of a grey-box. For any new point prediction, the estimated residual from the black-box is combined with white-box knowledge to produce the final grey-box solution. This approach was developed for use with manufacturing processes, and applied to a powder bed fusion additive manufacturing process. It can be applied in other common modeling scenarios. Two illustrative case studies are brought into the work to test this grey-box modeling approach; first for pure mathematical rigor and second for manufacturing specifically. The results of the case studies suggest that the use of grey-box models can lower predictive errors. Moreover, the resulting black-box model that represents any residual is a usable, accurate metamodel.

A Systematic Approach to Identifying a Set of Feasible Designs

In engineering design problems, designers set boundaries of design variables and solve the system to find the design variables that satisfy a target performance. Once lower and upper bounds for each performance index are set, the design problem becomes Constraint Satisfaction Problem (CSP). In this paper, CSP problem is transformed into an optimization problem with a penalty function. Also, by applying optimization technique, set of feasible solutions are acquired. The set of solutions and all the function evaluation during the iteration process are stored in database. By utilizing a database query, the best solution among the data points are selected for the design problem. For the numerical experiment, a CSP with three variables and a bicycle model of vehicle design is tested with different scenarios.

Parametric Shape and Topology Optimization: A New Level Set Approach Based on Cardinal Kernel Functions

The parametric level set method is an extension of the conventional level set methods for topology optimization. By parameterizing the level set function, conventional levels let methods can be easily coupled with mathematical programming to achieve better numerical robustness and computational efficiency. Furthermore, the parametric level set scheme not only can inherit the original advantages of the conventional level set methods, such as clear boundary representation and high topological changes handling flexibility but also can alleviate some un-preferred features from the conventional level set methods, such as needing re-initialization. However, in the RBF-based parametric level set method, it was difficult to determine the range of the design variables. Moreover, with the mathematically driven optimization process, the level set function often results in significant fluctuations during the optimization process. This brings difficulties in both numerical stability control and material property interpolation. In this paper, an RBF partition of unity collocation method is implemented to create a new type of kernel function termed as the Cardinal Basis Function (CBF), which employed as the kernel function to parameterize the level set function. The advantage of using the CBF is that the range of the design variable, which was the weight factor in conventional RBF, can be explicitly specified. Additionally, a distance regularization energy functional is introduced to maintain a desired distance regularized level set function evolution. With this desired distance regularization feature, the level set evolution is stabilized against significant fluctuations. Besides, the material property interpolation from the level set function to the finite element model can be more accurate.