Douglas Eddy

A normative decision analysis method for the sustainability-based design of products

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.

An Integrated Approach to Information Modeling for the Sustainable Design of Products

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, we propose 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.Copyright

A predictive modelling-based material selection method for sustainable product design

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 a design space. Material properties consist of discrete data sets, which are further complicated when LCA data are included, thus posing a significant challenge in the construction of surrogate models for prediction of all relevant behaviours and numerical optimisation. 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 modelling approach include achieving manageable dimensionality of LCA with a minimal loss of the important information by the consolidation of significant factors into categorised groups, as well as subsequent efficiency enhancement by a streamlined process that avoids the construction of full LCA. This approach combines efficiency of use with a mathematically rigorous representation of any pertinent objectives across a design space. To this end, we adapt a two-stage sampling approach in surrogate model construction for sustainability considerations 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.

Support of Product Innovation With a Modular Framework for Knowledge Management: A Case Study

This paper presents an e-Design framework for knowledge management through its application in an engineering design case study. The e-Design framework enables the implementation of integrated design information throughout the entire design process. It facilitates the ease of sharing real time information across multiple individual designers, departments, or organizations as would be required in large scale design efforts. Similarly, it allows for the ease of use of technical tools integral to the design process that small design departments depend upon. Thus, regardless of the scale, the efficiency of engineering design can be improved with the use of the e-Design framework. The many features of the e-Design framework are exemplified through its application in a practical industry design problem. The case study in this paper addresses the utility and ease of use of this framework and provides one potential implementation method. This study involves a representative application of an innovative new mast design to elevate a surveillance camera on a military vehicle. The design process utilizes the NIST functional basis [3] to improve effectiveness and efficiency during conceptual design. The decision tool module of the e-Design framework is then used to evaluate and select the best conceptual design based on product design criteria. We use this case study to illustrate information quality and the clarity of design intent throughout the entire design process. The results reveal a usable design process method that can improve the transparency of design knowledge from design conception to completion. Additional benefits include storing of the information generated at the early stages for sharing and reuse throughout the entire design process. Most of all, improved transparent communication throughout the design process will reduce duplication of efforts and trial and error occurrences.Copyright

A Robust Surrogate Modeling Approach for Material Selection in Sustainable Design of Products

A sustainable solution should holistically optimize all objectives related to the environment and a product’s cost and performance. As such, it should explicitly address material selection, which significantly affects environmental impacts and other objectives of a product design. While Life Cycle Assessment (LCA) provides credible methods to account for environmental impacts, current methods are not efficient enough for use at the early design stages to prune the entire design space without requiring execution of costly LCA analysis for each design scenario. Alternatively, surrogate modeling approaches can facilitate efficient concept selection during early design stages. However, 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.Copyright

AN INTEGRATED APPROACH TO INFORMATION MODELING FOR THE SUSTAINABLE DESIGN OF PRODUCTS

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, we propose 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.

Toward Integration of a Semantic Framework With a Commercial PLM System

Product lifecycle management (PLM) systems formally represent product information across the different functional departments of an organization throughout an entire product lifecycle. Integrating the PLM systems with the emerging semantic web will improve the knowledge management capabilities of these systems. In recent works, the authors have developed the e-Design framework, a collaborative web-based semantic environment for improving communication by formally defining an information model for documentation and sharing of engineering design knowledge throughout the entire design process.This paper addresses the integration of the e-Design semantic framework with a commercial PLM system. A key feature of this integration approach is a semantic extraction process that executes the interface from commercial PLM software to a framework compatible with the semantic web, while maintaining the PLM’s multilevel bill of materials (BOM) structure. This extraction process includes application of semantic queries to categorize information imported from a PLM system. The development and implementation of this semantic extraction process for integrating product information from PLM systems into the e-Design framework is demonstrated with the aid of an illustrative case study using PTC’s Windchill PLM system. Our findings show that the resulting design execution within the e-Design framework facilitates dynamic linking of product information throughout the design process. It also preserves and propagates the BOM related information from PLM in all design phases.Copyright

Approach Towards a Decision Support System for Additive Manufacturing

Advancements in the capabilities of additive manufacturing (AM) have increased its usage as an appropriate manufacturing process, particularly when the number of parts in an assembly can be significantly reduced, production volumes are low, or geometric complexity is difficult, if not impossible, to obtain through conventional subtractive processes. However, there are many reasons why it is best to not design a given part based on AM technology. The choice of conventional versus AM manufacturing must occur as early as possible in the design process as this choice can substantially affect how the product is designed. Making the wrong decision will lead to wasted design time, increased time to market the product, a functionally inferior design, and/or a costlier product. To address this critical manufacturing decision, we introduce a usable template and a decision making method for manufacturing process selection which is integrated early into the design process (DS-SAM). This work can serve as the logical foundation for a potential holistic and more mathematically rigorous formulation toward a decision making method that could infer design evaluations based on designer inputs. This approach improves early design efficiency and effectiveness by methodically focusing on the key design process elements to optimally compare alternatives earlier in a design process. The benefits and potential cost savings of using the DS-SAM approach are demonstrated by a pair of case studies, and the results are discussed.Copyright

Investigating Predictive Metamodeling for Additive Manufacturing

Additive manufacturing (AM) is a new and disruptive technology that comes with a set of unique challenges. One of them is the lack of understanding of the complex relationships between the numerous physical phenomena occurring in these processes. Metamodels can be used to provide a simplified mathematical framework for capturing the behavior of such complex systems. At the same time, they offer a reusable and composable paradigm to study, analyze, diagnose, forecast, and design AM parts and process plans. Training a metamodel requires a large number of experiments and even more so in AM due to the various process parameters involved. To address this challenge, this work analyzes and prescribes metamodeling techniques to select optimal sample points, construct and update metamodels, and test them for specific and isolated physical phenomena. A simplified case study of two different laser welding process experiments is presented to illustrate the potential use of these concepts. We conclude with a discussion on potential future directions, such as data and model integration while also accounting for sources of uncertainty.Copyright

Knowledge Management With an Intelligent Tool for Additive Manufacturing

Manufacturers face the challenge of deciding when additive manufacturing technology offers a suitable process to produce a given product. Information needed about process capabilities is constantly evolving and usually not organized well enough to support such decisions. This work introduces an ontological framework which identifies and semantically models the most applicable concepts of additive manufacturing relevant to process planning applications. Another salient feature includes the fit of this structural framework with both the new ASTM standard for additive manufacturing vocabulary and existing taxonomies for traditional manufacturing processes. Finally, within this framework we implemented description logic rules to identify the optimal set of processes for a product, the rationale for selecting this set of processes, and a logical link between a product’s features and its process plan. The reliability of the knowledge representation and its process planning capabilities are each tested and demonstrated by a case study example of the selection of the best processes to produce a steel spur gear.Copyright