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Guest Editorial

J. Mech. Des. 2016;138(7):070301-070301-3. doi:10.1115/1.4033549.
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The dramatic and often unjust difference between life in the developed versus developing parts of the world is striking. For example, roughly 3 billion people still burn biomass to cook their meals; more people in the world have a mobile phone than have a toilet; Dallas Cowboy Stadium (AT&T stadium) requires three times the electrical power than the entire country of Liberia can produce; and a 73 s shower (using an EPA approved low-flow shower head) uses all of the daily clean water available per person in Rwanda. These conditions and others have given rise to an interesting area of design and research that spans multiple engineering disciplines and is called Engineering for Global Development (EGD). Work in this area is also often referred to as design for the developing world, design for development, and humanitarian engineering.

Commentary by Dr. Valentin Fuster

Research Papers: Design Theory and Methodology

J. Mech. Des. 2016;138(7):071101-071101-16. doi:10.1115/1.4033654.

Modular product platforms have been shown to provide substantial cost and time savings while still allowing companies to offer a variety of products. As a result, a multitude of product platform methods have been developed over the last decade within the design research community. However, comparison and integration of suitable methods is difficult since the methods have, for the most part, been developed in isolation from one another. In reviewing the literature in modularity and product platforms, we create a generic set of 13 platform design steps for developing a platform concept. We then examine a set of product platform concept development processes used at several different companies, and from this form a generic sequence of the steps. We then associate the various developed methods to the sequence, thereby enabling the chaining together of the various modular and platform design methods developed by the community.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2016;138(7):071102-071102-12. doi:10.1115/1.4032219.

Current design theory lacks a systematic method to identify what designers know that helps them to create innovative products. In the early stages of idea generation, designers may find novel ideas come readily to mind, or may become fixated on their own or existing products. This may limit the ability to consider more and more varied candidate concepts that may potentially lead to innovation. To aid in idea generation, we sought to identify “design heuristics,” or “rules of thumb,” evident in award-winning designs. In this paper, we demonstrate a content analysis method for discovering heuristics in the designs of innovative products. Our method depends on comparison to a baseline of existing products so that the innovative change can be readily identified. Through an analysis of key features and functional elements in the designs of over 400 award-winning products, 40 heuristic principles were extracted. These design heuristics are outlined according to their perceived role in changing an existing product concept into a novel design, and examples of other products using the heuristics are provided. To demonstrate the ease of use of these design heuristics, we examined outcomes from a classroom study and found that concepts created using design heuristics were rated as more creative and varied. The analysis of changes from existing to innovative products can provide evidence of useful heuristic principles to apply in creating new designs.

Topics: Design
Commentary by Dr. Valentin Fuster

Research Papers: Design Automation

J. Mech. Des. 2016;138(7):071401-071401-11. doi:10.1115/1.4033504.

A product family is a set of products that are derived from common sets of parts, interfaces, and processes, known as the product platform. To reduce development time and procurement and operating costs of product platform-based variants, the product platform can be designed after consideration of several characteristics, such as modularity, flexibility, sustainability, and complexity. In this paper, the product platform is viewed from the perspective of system architecting. The architectural complexities of both the platform and its variants, which together constitute a product family, can be quantitatively assessed using a specifically tailored metric. This will aid system architects in designing product platforms and resulting product variants with an emphasis on reducing complexity. Architectural complexity management is demonstrated through a case study of a train bogie platform.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2016;138(7):071402-071402-8. doi:10.1115/1.4033505.

This paper proposes an inverse structural modification method for eigenstructure assignment (EA), which allows to assign the desired mode shapes only at the parts of interest of the system. The presence of unimposed eigenvector entries leads to a nonconvex problem. Therefore, to boost the convergence to a global optimal solution, a homotopy optimization strategy is implemented based on the convex approximation of the cost function. Such a relaxation is performed through some auxiliary variables and through the McCormick's relaxation of the occurring bilinear terms. The approach handles general assignment tasks, with an arbitrary number of modification parameters and prescribed eigenpairs.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2016;138(7):071403-071403-14. doi:10.1115/1.4033548.

Reliability-based design optimization (RBDO) algorithms have been developed to solve design optimization problems with existence of uncertainties. Traditionally, the original random design space is transformed to the standard normal design space, where the reliability index can be measured in a standardized unit. In the standard normal design space, the modified reliability index approach (MRIA) measured the minimum distance from the design point to the failure region to represent the reliability index; on the other hand, the performance measure approach (PMA) performed inverse reliability analysis to evaluate the target function performance in a distance of reliability index away from the design point. MRIA was able to provide stable and accurate reliability analysis while PMA showed greater efficiency and was widely used in various engineering applications. However, the existing methods cannot properly perform reliability analysis in the standard normal design space if the transformation to the standard normal space does not exist or is difficult to determine. To this end, a new algorithm, ensemble of Gaussian reliability analyses (EoGRA), was developed to estimate the failure probability using Gaussian-based kernel density estimation (KDE) in the original design space. The probabilistic constraints were formulated based on each kernel reliability analysis for the optimization processes. This paper proposed an efficient way to estimate the constraint gradient and linearly approximate the probabilistic constraints with fewer function evaluations (FEs). Some numerical examples with various random distributions are studied to investigate the numerical performances of the proposed method. The results showed that EoGRA is capable of finding correct solutions in some problems that cannot be solved by traditional methods. Furthermore, experiments of image processing with arbitrarily distributed photo pixels are performed. The lighting of image pixels is maximized subject to the acceptable limit. Our implementation showed that the accuracy of the estimation of normal distribution is poor while the proposed method is capable of finding the optimal solution with acceptable accuracy.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2016;138(7):071404-071404-12. doi:10.1115/1.4033427.
OPEN ACCESS

Quantitative preference models are used to predict customer choices among design alternatives by collecting prior purchase data or survey answers. This paper examines how to improve the prediction accuracy of such models without collecting more data or changing the model. We propose to use features as an intermediary between the original customer-linked design variables and the preference model, transforming the original variables into a feature representation that captures the underlying design preference task more effectively. We apply this idea to automobile purchase decisions using three feature learning methods (principal component analysis (PCA), low rank and sparse matrix decomposition (LSD), and exponential sparse restricted Boltzmann machine (RBM)) and show that the use of features offers improvement in prediction accuracy using over 1 million real passenger vehicle purchase data. We then show that the interpretation and visualization of these feature representations may be used to help augment data-driven design decisions.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2016;138(7):071405-071405-9. doi:10.1115/1.4033655.

Effective electrification of automotive vehicles requires designing the powertrain's configuration along with sizing its components for a particular vehicle type. Employing planetary gear (PG) systems in hybrid electric vehicle (HEV) powertrain architectures allows various architecture alternatives to be explored, including single-mode architectures that are based on a fixed configuration and multimode architectures that allow switching power flow configuration during vehicle operation. Previous studies have addressed the configuration and sizing problems separately. However, the two problems are coupled and must be optimized together to achieve system optimality. An all-in-one (AIO) system solution approach to the combined problem is not viable due to the high complexity of the resulting optimization problem. This paper presents a partitioning and coordination strategy based on analytical target cascading (ATC) for simultaneous design of powertrain configuration and sizing for given vehicle applications. The capability of the proposed design framework is demonstrated by designing powertrains with one and two PGs for a midsize passenger vehicle.

Commentary by Dr. Valentin Fuster

Research Papers: Design of Direct Contact Systems

J. Mech. Des. 2016;138(7):073301-073301-10. doi:10.1115/1.4032264.

This paper addresses the fundamental issue on the conjugation for any gearing systems after tooth modification. It presents a rigorous theory on compensated conjugation for gear transmission error (TE) balance. The basic idea is that conjugation impaired by the loading condition can be compensated by modifying the transmission function. Thus, conjugation holds true after tooth modification. Because the modification is based on the universal concept of transmission rather than the tooth geometry, the proposed tooth modification method is universal rather than limited to involute or even planar gearing. A theorem about the continuity of motion and conjugate geometries is presented and proved for any desirable modification. The proposed theory is consistent with the standard manufacturing process for tooth modification. Tooth geometries and cutter geometries can be obtained after the theoretical TE function is designed. The proposed method is highlighted and demonstrated with an involute gear design, in which a convenient and practical method with a direct rack-cutter modification is presented and rigorously analyzed based on kinematics and differential geometry. Examples are presented to show the effectiveness of the methodology.

Topics: Gears , Geometry , Design
Commentary by Dr. Valentin Fuster

Research Papers: Design of Energy, Fluid, and Power Handing Systems

J. Mech. Des. 2016;138(7):073401-073401-10. doi:10.1115/1.4033694.

In this paper, an integral methodology for the modeling of a twin-screw compressor is presented. Starting from a known rotor profile, all the algorithms to calculate the second rotor profile, the size of the control volume, and the compressor's performance are presented. The proposed modeling approach can be applied in an optimization procedure to find the optimal rotor profiles for a given application, with corresponding working conditions. Furthermore, based on the modeling results and substantiated with measurements on different compressor types, a similarity law for positive displacement compressors seems to exist. The existence of a similarity law has large application potential as it could be used to predict the performance of a positive displacement compressor in other than the (lab) tested working conditions. Further investigation of the similarity law for positive displacement compressors is therefore proposed as a key topic for future research.

Commentary by Dr. Valentin Fuster

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