Set-based Design Method for Multi-objective Structural Design with Conflicting Performances Under Topological Change

In car manufacturing, sustainable structural design with multiple conflicting objectives like weight reduction for less CO2 emission, strength and rigidity is essential. This research focuses on topological optimization method with which greater weight reduction is expected to be achieved. In view of the application of preference set-based design (PSD) method, topologically satisfied design is considered. We applied PSD method to the design of topological model. This study indicates the efficacy of PSD method to topological design problems including weight reduction aspect

A weighted rough set based fuzzy axiomatic design approach for the selection of AM processes

Additive manufacturing (AM) or 3D printing, as an enabling technology for mass customization or personalization, has been developed rapidly in recent years. Various design tools, materials, machines and service bureaus can be found in the market. Clearly, the choices are abundant, but users can be easily confused as to which AM process they should use. This paper first reviews the existing multi-attribute decision-making methods for AM process selection and assesses their suitability with regard to two aspects, preference rating flexibility and performance evaluation objectivity. We propose that an approach that is capable of handling incomplete attribute information and objective assessment within inherent data has advantages over other approaches. Based on this proposition, this paper proposes a weighted preference graph method for personalized preference evaluation and a rough set based fuzzy axiomatic design approach for performance evaluation and the selection of appropriate AM processes. An example based on the previous research work of AM machine selection is given to validate its robustness for the priori articulation of AM process selection decision support

A web-based collaborative decision making system for construction project teams using fuzzy logic

In the construction industry, the adoption of concurrent engineering principles requires the development of effective enabling IT tools. Such tools need to address specific areas of need in the implementation of concurrent engineering in construction. Collaborative decision-making is an important area in this regard. A review of existing works has shown that none of the existing approaches to collaborative decision-making adequately addresses the needs of distributed construction project teams. The review also reveals that fuzzy logic offers great potential for application to collaborative decision-making. This thesis describes a Web-based collaborative decision-making system for construction project teams using fuzzy logic. Fuzzy logic is applied to tackle uncertainties and imprecision during the decision-making process. The prototype system is designed as Web-based to cope with the difficulty in the case where project team members are geographically distributed and physical meetings are inconvenient/or expensive. The prototype was developed into a Web-based software using Java and allows a virtual meeting to be held within a construction project team via a client-server system. The prototype system also supports objectivity in group decision-making and the approach encapsulated in the prototype system can be used for generic decision-making scenarios. The system implementation revealed that collaborative decision-making within a virtual construction project team can be significantly enhanced by the use of a fuzzybased approach. A generic scenario and a construction scenario were used to evaluate the system and the evaluation confirmed that the system does proffer many benefits in facilitating collaborative decision-making in construction. It is concluded that the prototype decision-making system represents a unique and innovative approach to collaborative decision-making in construction project teams. It not only contributes to the implementation of concurrent engineering in construction, but also it represents a substantial advance over existing approaches

Managing Epistemic Uncertainty in Design Models through Type-2 Fuzzy Logic Multidisciplinary Optimization

Humans have a natural ability to operate in dynamic environments and perform complex tasks with little perceived effort. An experienced ship designer can intuitively understand the general consequences of design choices and the general attributes of a good vessel. A person's knowledge is often ill-structured, subjective, and imprecise, but still incredibly effective at capturing general patterns of the real-world or of a design space. Computers on the other hand, can rapidly perform a large number of precise computations using well-structured, objective mathematical models, providing detailed analyses and formal evaluations of a specfic set of design candidates. In ship design, which involves generating knowledge for decision-making through time, engineers interactively use their own mental models and information gathered from computer-based optimization tools to make decisions which steer a vessel's design. In recent decades, the belief that large synthesis codes can help achieve cutting-edge ship performance has led to an increased popularity of optimization methods, potentially leading to rewarding results. And while optimization has proven fruitful to structural engineering and the aerospace industry, its applicability to early-stage design is more limited for three main reasons. First, mathematical models are by definition a reduction which cannot properly describe all aspects of the ship design problem. Second, in multidisciplinary optimization, a low-fidelity model may incorrectly drive a design, biasing the system level solution. Finally, early-stage design is plagued with limited information, limiting the designer's ability to develop models to inform decisions. This research extends previously done work by incorporating type-2 fuzzy logic into a human-centric multidisciplinary optimization framework. The original framework used type-1 fuzzy logic to incorporate human expertise into optimization models through linguistic variables. However, a type-1 system does not properly account for the uncertainty associated with linguistic terms, and thus does not properly represent the uncertainty associated with a human mental model. This limitation is corrected with the type-2 fuzzy logic multidisciplinary optimization presented in this work, which more accurately models a designer's ability to "communicate, reason and make rational decisions in an environment of imprecision, uncertainty, incompleteness of information and partiality of truth" (Mendel et al., 2010). It uses fuzzy definitions of linguistic variables and rule banks to incorporate "human intelligence" into design models, and better handles the linguistic uncertainty inherent to human knowledge and communication. A general mathematical optimization proof of concept and a planing craft case study are presented in this dissertation to show how mathematical models can be enhanced by incorporating expert opinion into them. Additionally, the planing craft case study shows how human mental models can be leveraged to quickly estimate plausible values of ship parameters when no model exists, increasing the designer's ability to run optimization methods when information is limited.PHDNaval Architecture & Marine EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145891/1/doriancb_1.pd

Quantitative Set-Based Design for Complex System Development

This dissertation comprises a body of research facilitating decision-making and complex system development with quantitative set-based design (SBD). SBD is concurrent product development methodology, which develops and analyzes many design alternatives for longer time periods enabling design maturation and uncertainty reduction. SBD improves design space exploration, facilitating the identification of resilient and affordable systems. The literature contains numerous qualitative descriptions and quantitative methodologies describing limited aspects of the SBD process. However, there exist no methodologies enabling the quantitative management of SBD programs throughout the entire product development cycle. This research addresses this knowledge gap by developing the process framework and supporting methodologies guiding product development from initial system concepts to a final design solution. This research provides several new research contributions. First, we provide a comprehensive SBD state-of-practice assessment identifying key knowledge and methodology gaps. Second, we demonstrate the physical implementation of the integrated analytics framework in a model-based engineering environment. Third, we develop a quantitative methodology enabling program management decision making in SBD. Fourth, we describe a supporting uncertainty reduction methodology using multiobjective value of information analysis to assess design set maturity and higher-resolution model usefulness. Finally, we describe a quantitative SBD process framework enabling sequential design maturation and uncertainty reduction decisions. Using an unmanned aerial vehicle case study, we demonstrate our methodology’s ability to resolve uncertainty and converge a complex design space onto a set of resilient and affordable design solutions

A framework for the characterization and analysis of software systems scalability

The term scalability appears frequently in computing literature, but it is a term that is poorly defined and poorly understood. It is an important attribute of computer systems that is frequently asserted but rarely validated in any meaningful, systematic way. The lack of a consistent, uniform and systematic treatment of scalability makes it difficult to identify and avoid scalability problems, clearly and objectively describe the scalability of software systems, evaluate claims of scalability, and compare claims from different sources. This thesis provides a definition of scalability and describes a systematic framework for the characterization and analysis of software systems scalability. The framework is comprised of a goal-oriented approach for describing, modeling and reasoning about scalability requirements, and an analysis technique that captures the dependency relationships that underlie typical notions of scalability. The framework is validated against a real-world data analysis system and is used to recast a number of examples taken from the computing literature and from industry in order to demonstrate its use across different application domains and system designs

A generic architecture style for self-adaptive cyber-physical systems

Die aktuellen Konzepte zur Gestaltung von Regelungssystemen basieren auf dynamischen Verhaltensmodellen, die mathematische Ansätze wie Differentialgleichungen zur Ableitung der entsprechenden Funktionen verwenden. Diese Konzepte stoßen jedoch aufgrund der zunehmenden Systemkomplexität allmählich an ihre Grenzen. Zusammen mit der Entwicklung dieser Konzepte entsteht eine Architekturevolution der Regelungssysteme. In dieser Dissertation wird eine Taxonomie definiert, um die genannte Architekturevolution anhand eines typischen Beispiels, der adaptiven Geschwindigkeitsregelung (ACC), zu veranschaulichen. Aktuelle ACC-Varianten, die auf der Regelungstheorie basieren, werden in Bezug auf ihre Architekturen analysiert. Die Analyseergebnisse zeigen, dass das zukünftige Regelungssystem im ACC eine umfangreichere Selbstadaptationsfähigkeit und Skalierbarkeit erfordert. Dafür sind kompliziertere Algorithmen mit unterschiedlichen Berechnungsmechanismen erforderlich. Somit wird die Systemkomplexität erhöht und führt dazu, dass das zukünftige Regelungssystem zu einem selbstadaptiven cyber-physischen System wird und signifikante Herausforderungen für die Architekturgestaltung des Systems darstellt. Inspiriert durch Ansätze des Software-Engineering zur Gestaltung von Architekturen von softwareintensiven Systemen wird in dieser Dissertation ein generischer Architekturstil entwickelt. Der entwickelte Architekturstil dient als Vorlage, um vernetzte Architekturen mit Verfolgung der entwickelten Designprinzipien nicht nur für die aktuellen Regelungssysteme, sondern auch für selbstadaptiven cyber-physischen Systeme in der Zukunft zu konstruieren. Unterschiedliche Auslösemechanismen und Kommunikationsparadigmen zur Gestaltung der dynamischen Verhalten von Komponenten sind in der vernetzten Architektur anwendbar. Zur Bewertung der Realisierbarkeit des Architekturstils werden aktuelle ACCs erneut aufgenommen, um entsprechende logische Architekturen abzuleiten und die Architekturkonsistenz im Vergleich zu den originalen Architekturen basierend auf der Regelungstheorie (z. B. in Form von Blockdiagrammen) zu untersuchen. Durch die Anwendung des entwickelten generischen Architekturstils wird in dieser Dissertation eine künstliche kognitive Geschwindigkeitsregelung (ACCC) als zukünftige ACC-Variante entworfen, implementiert und evaluiert. Die Evaluationsergebnisse zeigen signifikante Leistungsverbesserungen des ACCC im Vergleich zum menschlichen Fahrer und aktuellen ACC-Varianten.Current concepts of designing automatic control systems rely on dynamic behavioral modeling by using mathematical approaches like differential equations to derive corresponding functions, and slowly reach limitations due to increasing system complexity. Along with the development of these concepts, an architectural evolution of automatic control systems is raised. This dissertation defines a taxonomy to illustrate the aforementioned architectural evolution relying on a typical example of control application: adaptive cruise control (ACC). Current ACC variants, with their architectures considering control theory, are analyzed. The analysis results indicate that the future automatic control system in ACC requires more substantial self-adaptation capability and scalability. For this purpose, more complicated algorithms requiring different computation mechanisms must be integrated into the system and further increase system complexity. This makes the future automatic control system evolve into a self-adaptive cyber-physical system and consistitutes significant challenges for the system’s architecture design. Inspired by software engineering approaches for designing architectures of software-intensive systems, a generic architecture style is proposed. The proposed architecture style serves as a template by following the developed design principle to construct networked architectures not only for the current automatic control systems but also for self-adaptive cyber-physical systems in the future. Different triggering mechanisms and communication paradigms for designing dynamic behaviors are applicable in the networked architecture. To evaluate feasibility of the architecture style, current ACCs are retaken to derive corresponding logical architectures and examine architectural consistency compared to the previous architectures considering the control theory (e.g., in the form of block diagrams). By applying the proposed generic architecture style, an artificial cognitive cruise control (ACCC) is designed, implemented, and evaluated as a future ACC in this dissertation. The evaluation results show significant performance improvements in the ACCC compared to the human driver and current ACC variants

WikiSensing: A collaborative sensor management system with trust assessment for big data

Big Data for sensor networks and collaborative systems have become ever more important in the digital economy and is a focal point of technological interest while posing many noteworthy challenges. This research addresses some of the challenges in the areas of online collaboration and Big Data for sensor networks. This research demonstrates WikiSensing (www.wikisensing.org), a high performance, heterogeneous, collaborative data cloud for managing and analysis of real-time sensor data. The system is based on the Big Data architecture with comprehensive functionalities for smart city sensor data integration and analysis. The system is fully functional and served as the main data management platform for the 2013 UPLondon Hackathon. This system is unique as it introduced a novel methodology that incorporates online collaboration with sensor data. While there are other platforms available for sensor data management WikiSensing is one of the first platforms that enable online collaboration by providing services to store and query dynamic sensor information without any restriction of the type and format of sensor data. An emerging challenge of collaborative sensor systems is modelling and assessing the trustworthiness of sensors and their measurements. This is with direct relevance to WikiSensing as an open collaborative sensor data management system. Thus if the trustworthiness of the sensor data can be accurately assessed, WikiSensing will be more than just a collaborative data management system for sensor but also a platform that provides information to the users on the validity of its data. Hence this research presents a new generic framework for capturing and analysing sensor trustworthiness considering the different forms of evidence available to the user. It uses an extensible set of metrics that can represent such evidence and use Bayesian analysis to develop a trust classification model. Based on this work there are several publications and others are at the final stage of submission. Further improvement is also planned to make the platform serve as a cloud service accessible to any online user to build up a community of collaborators for smart city research.Open Acces

Aligning Perspectives and Methods for Value-Driven Design

Recent years have seen a push to use explicit consideration of “value” in order to drive design. This paper conveys the need to explicitly align perspectives on “value” with the method used to quantify “value.” Various concepts of value are introduced in the context of its evolution within economics in order to propose a holistic definition of value. Operationalization of value is discussed, including possible assumption violations in the aerospace domain. A series of prominent Value-Centric Design Methodologies for valuation are introduced, including Net Present Value, Multi-Attribute Utility Theory, and Cost-Benefit Analysis. These methods are compared in terms of the assumptions they make with regard to operationalizing value. It is shown that no method is fully complete in capturing the definition of value, but selecting the most appropriate one involves matching the particular system application being valued with acceptable assumptions for valuation. Two case studies, a telecommunications mission and a deep-space observation mission, are used to illustrate application of the three prior mentioned valuation methods. The results of the studies show that depending on method used for valuation, very different conclusions and insights will be derived, therefore an explicit consideration of the appropriate definition of value is necessary in order to align a chosen method with desired valuation insights.Massachusetts Institute of Technology. Systems Engineering Advancement Research Initiativ