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Editorial

J. Mech. Des. 2011;133(10):100201-100201-1. doi:10.1115/1.4005078.
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I am delighted to present to our JMD community this special issue on design of complex engineered systems. You may recall that in the December 1010 issue of JMD we hosted an inspiring guest editorial by Christina Bloebaum and Anna-Maria McGowan (http://asmedl.aip.org/journals/doc/JMDEDB-ft/vol_132/iss_12/120301_1.html) setting the stage for submissions to the present special issue.

Topics: Design
Commentary by Dr. Valentin Fuster

Guest Editorial

J. Mech. Des. 2011;133(10):100301-100301-2. doi:10.1115/1.4005079.
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We face countless challenges in the 21st Century: national defense, building and transportation infrastructure, energy demand and clean water, frequent natural disasters, global market competition, rising healthcare costs, and an ageing population, to name a few.

Topics: Design
Commentary by Dr. Valentin Fuster

Research Papers

J. Mech. Des. 2011;133(10):101001-101001-12. doi:10.1115/1.4004463.

In distributed design processes, individual design subsystems have local control over design variables and seek to satisfy their own individual objectives, which may also be influenced by some system level objectives. The resulting network of coupled subsystems will either converge to a stable equilibrium or diverge in an unstable manner. In this paper, we study the dependence of system stability on the solution process architecture. The solution process architecture describes how the design subsystems are ordered and can be either sequential, parallel, or a hybrid that incorporates both parallel and sequential elements. In this paper, we demonstrate that the stability of a distributed design system does indeed depend on the solution process architecture chosen, and we create a general process architecture model based on linear systems theory. The model allows the stability of equilibrium solutions to be analyzed for distributed design systems by converting any process architecture into an equivalent parallel representation. Moreover, we show that this approach can accurately predict when the equilibrium is unstable and the system divergent when previous models suggest that the system is convergent.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2011;133(10):101002-101002-10. doi:10.1115/1.4004465.

Complex engineered systems are typically designed using a systems engineering framework that is showing its limitations. Multidisciplinary design optimization (MDO), which has evolved remarkably since its inception 25 years ago, offers alternatives to complement and enhance the systems engineering approach to help address the challenges inherent in the design of complex engineered systems. To gain insight into these challenges, a one-day workshop was organized that gathered 48 people from industry, academia, and government agencies. The goal was to examine MDO’s current and future role in designing complex engineered systems. This paper summarizes the views of five distinguished speakers on the “state of the research” and discussions from an industry panel comprised of representatives from Boeing, Caterpillar, Ford, NASA Glenn Research Center, and United Technologies Research Center on the “state of the practice.” Future research topics to advance MDO are also identified in five key areas: (1) modeling and the design space, (2) metrics, objectives, and requirements, (3) coupling in complex engineered systems, (4) dealing with uncertainty, and (5) people and workflow. Finally, five overarching themes are offered to advance MDO practice. First, MDO researchers need to engage disciplines outside of engineering and target opportunities outside of their traditional application areas. Second, MDO problem formulations must evolve to encompass a wider range of design criteria. Third, effective strategies are needed to put designers “back in the loop” during MDO. Fourth, the MDO community needs to do a better job of publicizing its successes to improve the “buy in” that is needed to advance MDO in academia, industry, and government agencies. Fifth, students and practitioners need to be better educated on systems design, optimization, and MDO methods and tools along with their benefits and drawbacks.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2011;133(10):101003-101003-8. doi:10.1115/1.4004118.

The quantity and age of the incoming stream of “feedstock” from product take-back systems are known as the major sources of the uncertainty that complicates the e-waste recovery. This paper presents the results of an analysis of data from an incoming stream for an e-waste collection center and analyzes the quantity and age of e-waste by product type and brand. The analysis results point out receiving of outdated products and processing of multiple generations, and brands of products at the same time are among main obstacles to the e-waste recovery. The potential role of product design in overcoming those obstacles is discussed with emphasis on design for upgrade, repurpose, and commonality.

Topics: Design
Commentary by Dr. Valentin Fuster
J. Mech. Des. 2011;133(10):101004-101004-15. doi:10.1115/1.4004969.

Development of a family of products that satisfies different market niches introduces significant challenges to today’s manufacturing industries—from development time to aftermarket services. A product family with a common platform paradigm offers a powerful solution to these daunting challenges. This paper presents a new approach, the Comprehensive Product Platform Planning (CP3 ) framework, to design optimal product platforms. The CP3 framework formulates a generalized mathematical model for the complex platform planning process. This model (i) is independent of the solution strategy, (ii) allows the formation of sub-families of products, (iii) allows the simultaneous identification of platform design variables and the determination of the corresponding variable values, and (iv) seeks to avoid traditional distinctions between modular and scalable product families from the optimization standpoint. The CP3 model yields a mixed integer nonlinear programming problem, which is carefully reformulated to allow for the application of continuous optimization using a novel Platform Segregating Mapping Function (PSMF). The PSMF can be employed using any standard global optimization methodology (hence not restrictive); particle swarm optimization has been used in this paper. A preliminary cost function is developed to represent the cost of a product family as a function of the number of products manufactured and the commonality among these products. The proposed CP3 framework is successfully implemented on a family of universal electric motors. Key observations are made regarding the sensitivity of the optimized product platform to the intended production volume.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2011;133(10):101005-101005-11. doi:10.1115/1.4004379.

Engineering changes are an inherent part of the design and development process and can play an important role in driving the overall success of the system. This work seeks to create a multidimensional understanding of change activity in large systems that can help in improving future design and development efforts. This is achieved by a posteriori analysis of design changes. It is proposed that by constructing a temporal, spatial, and financial view of change activity within and across these dimensions, it becomes possible to gain useful insights regarding the system of study. Engineering change data from the design and development of a multiyear, multibillion dollar development project of an offshore oil and gas production system is used as a case study in this work. It is shown that the results from such an analysis can be used for identifying better design and management strategies (in similar systems and projects) and for targeting design improvement in identified subsystems. The isolation and identification of change hotspots can be helpful in uncovering potential systemic design issues that may be prevalent. Similarly, strategic engineering and management decisions can be made if the major cost drivers are known.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2011;133(10):101006-101006-8. doi:10.1115/1.4004971.

The Contact and Channel Approach (C&C-A) has been developed to support the decomposition and design of technical systems. It is based on the principle that function and form emerge together during design, and therefore should be considered together in a design representation. This paper discusses the theory underlying the C&C-A, and describes its formalization and implementation in a software tool. The approach is applied to model the system architecture of a humanoid robot arm considering the impact of a proposed design change. This illustrates some of the main benefits of the Contact and Channel Approach: helping designers visualize, understand and communicate the complex dependencies between function and form in a system architecture.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2011;133(10):101007-101007-10. doi:10.1115/1.4005069.

All complex system development projects involve analysis of the system architecture. Thus far it has been assumed that there is some correct system decomposition that can be used in the architectural analysis without consideration of the sensitivity of the results to the chosen level of decomposition. We represent 88 idealized system architectures and a real complex system as a design structure matrix at two different levels of decomposition. We analyze these architectures for their degree of modularity. We find that the degree of modularity can vary for the same system when the system is represented at the two different levels of granularity. For example, the printing system used in the case study is considered slightly integral at a higher level of decomposition and quite modular at a lower level of decomposition. We further find that even though the overall results can be different depending on the level of decomposition, the direction of change toward more modular or more integral can be calculated the same regardless of the level of decomposition. We conclude that the level of decomposition can distort the results of architectural analysis and care must be taken in defining the system decomposition for any analysis.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2011;133(10):101008-101008-15. doi:10.1115/1.4004973.

Complex engineered systems tend to have architectures in which a small subset of components exhibits a disproportional number of linkages. Such components are known as hubs. This paper examines the degree distribution of systems to identify the presence of hubs and quantify the fraction of hub components. We examine how the presence and fraction of hubs relate to a system’s quality. We provide empirical evidence that the presence of hubs in a system’s architecture is associated with a low number of defects. Furthermore, we show that complex engineered systems may have an optimal fraction of hub components with respect to system quality. Our results suggest that architects and managers aiming to improve the quality of complex system designs must proactively identify and manage the use of hubs. Our paper provides a data-driven approach for identifying appropriate target levels of hub usage.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2011;133(10):101009-101009-9. doi:10.1115/1.4004975.

The development and acquisition of complex systems remain a challenge, especially in the aerospace/defense sector, due to complexities in both program management and engineering design that often are a result of interdependencies. The interdependencies between constituent systems form networks that, while enabling capabilities that are beyond those of individual systems, also increase risk since disruptions in the development of one system may propagate to other directly or indirectly dependent systems. This paper demonstrates an approach to aggregate the network interdependency characteristics and compare alternatives with respect to the time required to arrest the propagation of development delays in a network. Delay propagation is modeled as a Markov chain, where the states are defined as the constituent systems and the transition probabilities as the dependency strengths between systems. A proof-of-concept application shows the approach can distinguish between alternate networks and indicates its applicability for managing risk in design and development of systems with significant interdependencies.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2011;133(10):101010-101010-13. doi:10.1115/1.4004977.

Convergence products are multifunctional designs which are changing the way consumers use existing functionalities. Manufacturers’ ventures in developing convergence products abound in the marketplace. Smartphones, tablet computers, and internet TV are just a few examples. The complexity of designing a convergence product can differ significantly from that of single function products which most research in “design for market systems” aims at. In this paper, a new customer-driven approach for designing convergence products is proposed to address the following issues: (i) a design representation scheme that considers information from design solutions used in existing products. The representation facilitates the coupling of and combining multiple functionalities; (ii) a hierarchical Bayes model that evaluates consumers’ heterogeneous choices while revealing how usage of multiple functionalities impacts consumers’ preferences; and (iii) design metrics which help to evaluate profitability of design alternatives and account for future market penetration given evolving consumer preferences. An example problem for designing a tablet computer is used to demonstrate the proposed approach. The data for the example are collected by conducting a choice-based conjoint survey which yielded 92 responses. The proposed approach is demonstrated with three scenarios differentiated by the consideration of consumer heterogeneity and future market penetration, while comparing how the resulting optimal design solutions for the convergence product differ.

Topics: Design
Commentary by Dr. Valentin Fuster
J. Mech. Des. 2011;133(10):101011-101011-15. doi:10.1115/1.4004981.

Most engineered systems are designed with a passive and fixed design capacity and, therefore, may become unreliable in the presence of adverse events. Currently, most engineered systems are designed with system redundancies to ensure required system reliability under adverse events. However, a high level of system redundancy increases a system’s life-cycle cost (LCC). Recently, proactive maintenance decisions have been enabled through the development of prognostics and health management (PHM) methods that detect, diagnose, and predict the effects of adverse events. Capitalizing on PHM technology at an early design stage can transform passively reliable (or vulnerable) systems into adaptively reliable (or resilient) systems while considerably reducing their LCC. In this paper, we propose a resilience-driven system design (RDSD) framework with the goal of designing complex engineered systems with resilience characteristics. This design framework is composed of three hierarchical tasks: (i) the resilience allocation problem (RAP) as a top-level design problem to define a resilience measure as a function of reliability and PHM efficiency in an engineering context, (ii) the system reliability-based design optimization (RBDO) as the first bottom-level design problem for the detailed design of components, and (iii) the system PHM design as the second bottom-level design problem for the detailed design of PHM units. The proposed RDSD framework is demonstrated using a simplified aircraft control actuator design problem resulting in a highly resilient actuator with optimized reliability, PHM efficiency and redundancy for the given parameter settings.

Topics: Design , Reliability
Commentary by Dr. Valentin Fuster
J. Mech. Des. 2011;133(10):101012-101012-19. doi:10.1115/1.4004807.

Robust design optimization (RDO) seeks to find optimal designs which are less sensitive to the uncontrollable variations that are often inherent to the design process. Studies using Evolutionary Algorithms (EAs) for RDO are not too many. In this work, we propose enhancements to an EA based robust optimization procedure with explicit function evaluation saving strategies. The proposed algorithm, IDEAR, takes into account a specified expected uncertainty in the design variables and then imposes the desired robustness criteria during the optimization process to converge to robust optimal solution(s). We pick up a number of Bi-objective engineering design problems from the standard literature and study them in the proposed robust optimization framework to demonstrate the enhanced performance. A cross-validation study is performed to analyze whether the solutions obtained are truly robust and also make some observations on how robust optimal solutions differ from the performance maximizing solutions in the design space. We perform a rigorous analysis of the key features of IDEAR to illustrate its functioning. The proposed function evaluation saving strategies are generic and their applications are worth exploring in other areas of computational design optimization.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2011;133(10):101013-101013-11. doi:10.1115/1.4004974.

Assessing performance in developing new aerospace products is essential. However, choosing an accurate set of success indicators to measure the performance of complex products is a nontrivial task. Moreover, the most useful success indicators can change over the life of the product; therefore, different metrics need to be used at different phases of the product lifecycle (PLC). This paper describes the research undertaken to determine success measurement metrics for new product development (NPD) processes. The goal of this research was to ascertain an appropriate set of metrics used by aerospace companies for assessing success during different phases of the PLC. Furthermore, an evaluation of the differences and similarities of NPD success measurement was carried out between aerospace companies and the nonaerospace companies practicing in the business-to-business (B2B) market. Practical case studies were carried out for 16 Canadian and Danish companies. Seven companies belong to the aerospace sector, while nine are nonaerospace companies that are in the B2B market. The data were gathered from relevant product managers at participating companies. The outcomes of this research indicate that: (1) the measurement of success of aerospace NPD practices depends on the PLC phase being measured, (2) product and process management performance are the more important indicators of success in the early PLC phases with revenue and market share indicators being important during late phases, and (3) there are reasonable similarities in success measurement between aerospace and nonaerospace B2B companies. Sets of metrics for measuring success during each PLC phase of aerospace products are proposed, which can guide companies in determining their ideal practices.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2011;133(10):101014-101014-10. doi:10.1115/1.4004976.

In decomposition-based design optimization strategies such as analytical target cascading (ATC), it is sometimes necessary to use reduced representations of highly discretized functional data exchanged among subproblems to enable efficient design optimization. However, the variables used by such reduced representation methods are often abstract, making it difficult to constrain them directly beyond simple bounds. This problem is usually addressed by implementing a penalty value-based heuristic that indirectly constrains the reduced representation variables. Although this approach is effective, it leads to many ATC iterations, which in turn yields an ill-conditioned optimization problem and an extensive runtime. To address these issues, this paper introduces a direct constraint management technique that augments the penalty value-based heuristic with constraints generated by support vector domain description (SVDD). A comparative ATC study between the existing and proposed constraint management methods involving electric vehicle design indicates that the SVDD augmentation is the most appropriate within decomposition-based design optimization.

Commentary by Dr. Valentin Fuster

Technical Briefs

J. Mech. Des. 2011;133(10):104501-104501-7. doi:10.1115/1.4004483.

Model identification for machine system design, design optimization, and manufacturing planning is an important method that has high prediction accuracy and could become an essential stage in practical applications. In this paper, an effective fuzzy model identification algorithm for mechanical system design is developed. First, a fuzzy c-regression model clustering algorithm, in which hyperplane-shaped cluster representatives are utilized to provide a mathematical tool to partition the input–output space reasonably, is introduced. Then, an enhanced cluster validity criterion, in which the structural information hidden in the clusters can be reflected in the index, is proposed to choose the optimal number of clusters. In the proposed architecture, an improved Takagi–Sugeno fuzzy model is proposed to describe the system. Two illustrative examples under various conditions are provided, and their performances are indicated in comparison with other published works. In comparison to these fuzzy works, the proposed fuzzy model identification requires fewer fuzzy rules and a shorter tuning time.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2011;133(10):104502-104502-5. doi:10.1115/1.4004970.

This paper proposes a novel multi-objective collaborative optimization (MOCO) approach based on multi-objective evolutionary algorithms for complex systems with multiple disciplines and objectives, especially for those systems in which most of the disciplinary variables are shared. The shared variables will conflict when the disciplinary optimizers are implemented concurrently. In order to avoid the confliction, the shared variables are treated as fixed parameters at the discipline level in most of the MOCO approaches. But in this paper, a coordinator is introduced to handle the confliction, which allocates more design freedom and independence to the disciplinary optimizers. A numerical example is solved, and the results are discussed.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2011;133(10):104503-104503-15. doi:10.1115/1.4005083.

We provide an introduction and state of the art overview of integrated layout design of multicomponent systems. We review several packing optimization and overlap detection strategies, some tree-based methods, such as octrees and spheretrees, and a finite circle method (FCM) proposed to favor gradient-based optimization algorithms. Integrated layout design techniques for simultaneous packing and structure topology optimization of multicomponent systems are reviewed; two typical approaches for system stiffness maximization are reviewed and compared in detail. Design of multicomponent systems under inertia forces is presented using polynomial interpolation models; constraints to the centroid position, moment of inertia, and volume fraction are included. Applications to piezoelectric multi-actuated microtools and integrated layout design of bridge systems are presented. Finally, the effectiveness of the FCM, applications to 3D problems, and local optimum phenomena are discussed.

Commentary by Dr. Valentin Fuster

Design Innovation

J. Mech. Des. 2011;133(10):105001-105001-9. doi:10.1115/1.4004972.

This paper presents a multidisciplinary optimization framework developed by the authors and applied to small-size supersonic aircraft. The multidisciplinary analysis suite is based on the combination of low (empirical) and high-fidelity computational fluid dynamics (CFD) and computational structure mechanics (CSM) tools for predicting the overall aircraft performance and the sonic boom overpressure at supersonic flight, which represents the most challenging environmental constraint for supersonic aircraft. The analysis suite is coupled with a multi-objective optimization strategy for quantifying the trade-off between the maximum take-off weight, mission range, and the sonic boom overpressure. The optimization framework is applied to a small-size supersonic business-jet cruising at Mach number M = 1.8 and featuring a double delta wing. The trade-offs between disciplines are well captured and an optimized configuration achieving the target mission range with a lower maximum take-off weight, and a moderate sonic boom signature is obtained through changes in wing dihedral and sweep. A more drastic reduction of the sonic boom signature is also obtained but at the cost of a significant reduction of the aircraft performance.

Commentary by Dr. Valentin Fuster

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