Sundar Krishnamurty

The design of more sustainable products can be best accomplished in a tradeoff-based design process that methodically handles conflicting objectives. Such conflicts are often seen between, environmental impact, cost, and product performance. To support such a process, this paper proposes the development of an environment where sustainability considerations are explicitly introduced early into the design process. This explicitness is provided by integrating the requirements information of sustainability standards and regulations directly into the design process. The emergence of the semantic web provides an interoperable environment in which the context and meaning of knowledge about the relationships among various domains can be shared. This work presents an ontological framework designed to represent both the objectives that pertain to sustainable design and the applicable sustainability standards and regulations. This integrated approach not only can ease the adoption of the standards and regulations during a design process but can also influence a design toward sustainability considerations. The usefulness of this model integration is demonstrated by an illustrative brake disk rotor and pads case study. The results show that both the standards and criteria may be considered at early design stages by using this methodology. Furthermore, it can be used to capture, reveal, and propagate the design intent transparently to all design participants.

A new cognitive theory of innovation, the Obscure Features Hypothesis (OFH), is applied to the area of engineering design innovation. The OFH states that all innovative solutions are built upon at least one overlooked (i.e., obscure) feature of the problem at hand. In this paper, we first highlight the types of features that exist and the cognitive obstacles that hinder people's ability to notice the obscure members of various feature types. We then detail five innovation techniques we have developed to help designers search the obscure realms of the space of features. Each of these techniques counteracts a specific cognitive obstacle to innovation: design fixation, functional fixedness, narrow verb associations, assumption blindness, and analogy blindness. We compare our approach with other approaches to innovation in psychology (the representation change view and the distant association view) and engineering (theory of inventive problem solving and C–K theory). Finally, we show how the innovation techniques can be implemented in software to assist users in the design process.

We introduce a new software platform called Cahoots for enhancing innovation in both groups and individuals. The word cahoots means working together. Cahoots possesses two main features. First, Cahoots embodies a visualization method that is intuitive for users and matches the pattern of successful problem solving. Second, a standardized problem solving grammar is natural for users to express themselves and guides them to express goals in a function-focused manner—a manner that is suited for the automated finding of analogous solutions (i.e. situation transfers). Both visual and linguistic representations meet the requirements of being intuitive to use and robust at modeling the task—and thus are what we call ergosemantic (i.e. from the ergonomic study of semantic representations). Cahoots is a software platform that enhances innovation by facilitating both human-human collaboration and human-machine collaboration.

This paper introduces a new normative decision analysis method for the sustainability-based design of products (NASDOP). It is based on the fundamental principles of normative decision-making methods for design optimisation when multiple and often conflicting criteria influence the design of products. A unique feature of NASDOP is that it enables direct consideration of both environmental and economic impacts during a design process. Furthermore, NASDOP takes full advantage of the enhanced life cycle assessment (LCA) capabilities, reflecting the increase in knowledge about the material and energy flows of various processes in recent years, and offers a methodical approach to account for the inherent uncertainty associated with such knowledge. This paper details the development of sustainability models, including the effective integration of LCA mathematical models combined with compatible life cycle costing models from the early stages of conceptual design for use throughout the entire design process. This paper also highlights the design and deployment of hypothetical equivalents and inequivalents method, a proven normative decision method, to consistently model the preferences of a designer during sustainable product design. The implementation and usefulness of NASDOP are demonstrated with the aid of an illustrative case study and the results are discussed.

A semantic information model to improve reuse and communication of engineering design knowledge is presented in this paper. We consider design to be a process involving a sequence of decisions informed by the current state of information. As such, the information model developed is structured to reflect the conceptualizations of engineering design decisions with a particular emphasis on semantically capturing design rationale. Through the approach presented, knowledge reuse is achieved by communicating design rationale. A case study is presented to illustrate two key features of the approach: (1) seamless integration of separate modular domain ontologies and instance knowledge related to engineering design that are needed to support decision making and (2) the explicit documentation of design rationale through design decisions.

This paper addresses the critical issue of effectiveness and efficiency in simulation-based optimization using surrogate models as predictive models in engineering design. Specifically, it presents a novel clustering-based multilocation search (CMLS) procedure to iteratively improve the fidelity and efficacy of Kriging models in the context of design decisions. The application of this approach will overcome the potential drawback in surrogate-model-based design optimization, namely, the use of surrogate models may result in suboptimal solutions due to the possible smoothing out of the global optimal point if the sampling scheme fails to capture the critical points of interest with enough fidelity or clarity. The paper details how the problem of smoothing out the best (SOB) can remain unsolved in multimodal systems, even if a sequential model updating strategy has been employed, and lead to erroneous outcomes. Alternatively, to overcome the problem of SOB defect, this paper presents the CMLS method that uses a novel clustering-based methodical procedure to screen out distinct potential optimal points for subsequent model validation and updating from a design decision perspective. It is embedded within a genetic algorithm setup to capture the buried, transient, yet inherent data pattern in the design evolution based on the principles of data mining, which are then used to improve the overall performance and effectiveness of surrogate-model-based design optimization. Four illustrative case studies, including a 21bar truss problem, are detailed to demonstrate the application of the CMLS methodology and the results are discussed.

This paper presents an optimization ontology and its implementation into a prototype computational knowledge-based tool dubbed ONTOP (ontology for optimization). Salient feature of ONTOP include a knowledge base that incorporates both standardized optimization terminology, formal method definitions, and often unrecorded optimization details, such as any idealizations and assumptions that may be made when creating an optimization model, as well as the model developer’s rationale and justification behind these idealizations and assumptions. ONTOP was developed using Protégé, a Java-based, free open-source ontology development environment created by Stanford University. Two engineering design optimization case studies are presented. The first case study consists of the optimization of a structural beam element and demonstrates ONTOP’s ability to address the variations in an optimal solution that may arise when different techniques and approaches are used. A second case study, a more complex design problem that deals with the optimization of an impeller of a pediatric left ventricular heart assist device, demonstrates the wealth of knowledge ONTOP is able to capture. Together, these test beds help illustrate the potential value of an ontology in representing application-specific knowledge while facilitating both the sharing and exchanging of this knowledge in engineering design optimization.

Focusing on the efforts towards a consistent preference representation in decision based engineering design, this paper presents a learning-based comparison and preference modeling process. Through effective integration of a deductive reasoning-based on designer’s outcome ranking in a lottery questions-based elicitation process, this work offers a reliable framework for formulating utility functions that reflect designer’s priorities accurately and consistently. It is expected that this integrated approach will reduce designer’s cognitive burden, and lead to accurate and consistent preference representation. Salient features of this approach include a linear programming based dynamic preference learning method and a logical analysis of preference inconsistencies. The development of this method and its utilization in engineering design are presented in the context of a mechanism design problem and the results are discussed.