Douglas Eddy, Sundar Krishnamurty, Ian Grosse, Jack Wileden, Kemper Lewis, “A Robust Surrogate Modeling Approach for Material Selection in Sustainable Design of Products“, ASME 2014 IDETC/CIE, DETC2014-34280.
Material selection significantly affects environmental impacts and other objectives of a product design. Life Cycle Assessment (LCA) methods are not efficient enough for use at the early design stages to prune the entire design space. Material properties consist of discrete data sets, thus posing a significant challenge in the construction of surrogate models for numerical optimization. In this work, we address the unique challenges of material selection in sustainable product design in some important ways. Salient features of the robust surrogate modeling approach include achieving manageable dimensionality of LCA with a minimal loss of the important information by the consolidation of significant factors into categorized groups, as well as subsequent efficiency enhancement by a streamlined process that avoids the construction of full LCA. This novel approach combines efficiency of use with a mathematically rigorous representation of any pertinent objectives across an entire design space. To this end, we introduce an adapted two stage sampling approach in surrogate model construction based on a feasible approximation of a Latin Hypercube design at the first stage. The development and implementation of the method are illustrated with the aid of an automotive disc brake design, and the results are discussed in the context of robust optimal material selection in early sustainable product design.
Tom Hagedorn, Sundar Krishnamurty, Ian Grosse, Jack Wileden, “A Semantic Framework to Integrate Healthcare and Clinical Knowledge in Medical Device Innovation and Design”, ASME 2014 IDETC/CIE, DETC2014-35087.
There has been extensive research into how to effectively use semantic frameworks in engineering knowledge management and design in general, and specifically for the effective creation and documentation of functional basis models. In the specific realm of medical device design however, this process is complicated by a number of factors, including the complexity of the healthcare system and clinical knowledge, as well as a lack of domain specific expertise in the engineering field. Because of these challenges, effective transfer of information from medical domain experts to an engineering context and subsequent utilization of this information are essential to the success of a medical device innovation project. In this paper, we present a framework for directly integrating clinical knowledge relating to medical science and practice into the early phases of the engineering process to assist in medical device innovation and design. To accomplish this, existing medical and engineering ontologies were researched, obtained, and interlinked so as to explicitly tie functional models of medical device designs to the underlying medical clinical knowledge and procedures that define a product’s operational environment. The result is a framework that unifies the knowledge embodied in large medical ontologies with the functional basis ontology. This integration facilitates the effective preservation and use of medical knowledge in functional model creation and in the engineering design innovation process in general.