Paul Witherell

Ontologies for supporting engineering design optimization

This paper presents an optimization ontology and its implementation into a prototype computational knowledge base tool dubbed ONTOP (ONT ology for OP timization). Salient features of ONTOP include a knowledge base which 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 Protege, 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 which 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.© 2006 ASME

A Web-Based Environment for Documentation and Sharing of Engineering Design Knowledge

This paper presents the foundation for a collaborative Web-based environment for improving communication by formally defining a platform for documentation and sharing of engineering design knowledge throughout the entire design process. In this work an ontological structure is utilized to concisely define a set of individual engineering concepts. This set of modular ontologies link together to create a flexible, yet consistent, product development knowledge-base. The resulting infrastructure uniquely enables the information stored within the knowledge-base to be readily inspectable and computable, thus allowing for design tools that reason on the information to assist designers and automate design processes. A case study of the structural optimization of a transfer plate for an aerospace circuit breaker is presented to demonstrate implementation and usefulness of the knowledge framework. The results indicate that the ontological knowledge-base can be used to prompt engineers to document important product development information, increase understanding of the design process, provide a means to intuitively retrieve information, and seamlessly access distributed information.© 2008 ASME

Semantic methods supporting engineering design innovation

In this paper, we present a metric based on semantic relatedness which operates on semantic knowledge representations of engineering design and show how it can support design innovation. Our semantic knowledge representation is composed of an ontology representing design concepts using the National Institute of Standards and Technology (NIST) functional basis formalism. We assert that the uniqueness of a design concept is directly proportional to the mean semantic distance between itself and the set of competing design concepts represented as instances within our functional basis ontology. This leads to our Semantic Relatedness Uniqueness Metric called SeRUM. SeRUM draws upon semantic functional model representations of design concepts and computer science semantic relatedness techniques. SeRUM provides design teams a measure of their effectiveness in terms of generating unique design concepts. To highlight SeRUM’s application in engineering design innovation, a design innovation case study is detailed and the results are discussed.

Improved knowledge management through first-order logic in engineering design ontologies

Abstract This paper presents the use of first-order logic to improve upon currently employed engineering design knowledge management techniques. Specifically, this work uses description logic in unison with Horn logic, to not only guide the knowledge acquisition process but also to offer much needed support in decision making during the engineering design process in a distributed environment. The knowledge management methods introduced are highlighted by the ability to identify modeling knowledge inconsistencies through the recognition of model characteristic limitations, such as those imposed by model idealizations. The adopted implementation languages include the Semantic Web Rule Language, which enables Horn-like rules to be applied to an ontological knowledge base and the Semantic Webs native Web Ontology Language. As part of this work, an ontological tool, OPTEAM, was developed to capture key aspects of the design process through a set of design-related ontologies and to serve as an application platform for facilitating the engineering design process. The design, analysis, and optimization of a classical I-beam problem are presented as a test-bed case study to illustrate the capabilities of these ontologies in OPTEAM. A second, more extensive test-bed example based on an industry-supplied medical device design problem is also introduced. Results indicate that well-defined, networked relationships within an ontological knowledge base can ultimately lead to a refined design process, with guidance provided by the identification of infeasible solutions and the introduction of “best-case” alternatives. These case studies also show how the application of first-order logic to engineering design improves the knowledge acquisition, knowledge management, and knowledge validation processes.

Investigating the Role of Geometric Dimensioning and Tolerancing in Additive Manufacturing

Additive manufacturing (AM) has increasingly gained attention in the last decade as a versatile manufacturing process for customized products. AM processes can create complex, freeform shapes while also introducing features, such as internal cavities and lattices. These complex geometries are either not feasible or very costly with traditional manufacturing processes. The geometric freedoms associated with AM create new challenges in maintaining and communicating dimensional and geometric accuracy of parts produced. This paper reviews the implications of AM processes on current geometric dimensioning and tolerancing (GD&T) practices, including specification standards, such as ASME Y14.5 and ISO 1101, and discusses challenges and possible solutions that lie ahead. Various issues highlighted in this paper are classified as (a) AM-driven specification issues and (b) specification issues highlighted by the capabilities of AM processes. AM-driven specification issues may include build direction, layer thickness, support structure related specification, and scan/track direction. Specification issues highlighted by the capabilities of AM processes may include region-based tolerances for complex freeform surfaces, tolerancing internal functional features, and tolerancing lattice and infills. We introduce methods to address these potential specification issues. Finally, we summarize potential impacts to upstream and downstream tolerancing steps, including tolerance analysis, tolerance transfer, and tolerance evaluation. [DOI: 10.1115/1.4031296]

FIDOE: A Framework for Intelligent Distributed Ontologies in Engineering

This paper presents FIDOE , a F ramework for I ntelligent D istributed O ntologies in E ngineering. FIDOE consists of a suite of logic rules and templates for interactively developing relationships between properties of linked ontologies. The logical rules embedded in FIDOE automatically operate on various discipline-specific ontologies to systematically identify influences, direct and indirect, of proposed design modifications on other aspects of the design through common domain concepts. Once potential influences are identified, FIDOE enables the user to precisely define the domain relationships, using predefined templates, between the identified domain concepts that enumerate influence types. This tool, thus, provides a pervasive, real time awareness of the implications of design changes during the design process in a distributed environment. The application of FIDOE to distributed and multidisciplinary design problems is detailed with the aid of an industry-provided printed circuit board (PCB) design. Here, commonalities among indirectly connected domain ontologies (electrical, mechanical and thermal domains) are identified using the developed query method and subsequent relationships are defined. These relationships are then applied to provide a collaborative understanding and awareness of the distributed process, all while demonstrating the effectiveness of this approach. This awareness was successfully able to address some previously identified industry concerns, returning promising results while laying a solid foundation for future work.Copyright

TOWARDS AN INTEGRATED DATA SCHEMA DESIGN FOR ADDITIVE MANUFACTURING: CONCEPTUAL MODELING

Large amounts of data are generated, exchanged, and used during an additive manufacturing (AM) build. While the AM data from a single build is essential for establishing part traceability, when methodically collected, the full processing history of thousands of components can be mined to advance our understanding of AM processes. Hence, this full body of data must be captured, stored, and properly managed for easy query and analysis. An innovative, AM-specific data model is necessary for establishing of a comprehensive AM information management system.This paper introduces our work towards designing a complete and integrated data model for AM processes. We begin by defining the scope and specifying the requirements of such a data schema. We investigate how information created and exchanged in the AM process chain is identified based on an AM process activity diagram. A comprehensive survey shows that existing AM standards are unable to provide both the breadth and the depth needed for an integrated AM information model. We propose a conceptual design for an additive manufacturing integrated data model, AMIDM, based on a well-defined product lifecycle management (PLM) data modeling method called PPR (product, process, and resource). The proposed AM model has a core scheme composed of product, process, and resource entities. The process entities play critical roles in transforming product input into product output using assigned resources such as equipment, material, personnel, and software tools. The proposed model has been applied to an information system design for Powder Bed Fusion based AM experimental data management. An XML (eXtensible Markup Language) schema is presented in the paper to demonstrate the effectiveness of the conceptual model.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.Copyright

AIERO: An algorithm for identifying engineering relationships in ontologies

Abstract Semantic technologies are playing an increasingly popular role as a means for advancing the capabilities of knowledge management systems. Among these advancements, researchers have successfully leveraged semantic technologies, and their accompanying techniques, to improve the representation and search capabilities of knowledge management systems. This paper introduces a further application of semantic techniques. We explore semantic relatedness as a means of facilitating the development of more “intelligent” engineering knowledge management systems. Using semantic relatedness quantifications to analyze and rank concept pairs, this novel approach exploits semantic relationships to help identify key engineering relationships, similar to those leveraged in change management systems, in product development processes. As part of this work, we review several different semantic relatedness techniques, including a meronomic technique recently introduced by the authors. We introduce an aggregate measure, termed “An Algorithm for Identifying Engineering Relationships in Ontologies,” or AIERO, as a means to purposely quantify semantic relationships within product development frameworks. To assess its consistency and accuracy, AIERO is tested using three separate, independently developed ontologies. The results indicate AIERO is capable of returning consistent rankings of concept pairs across varying knowledge frameworks. A PCB (printed circuit board) case study then highlights AIERO’s unique ability to leverage semantic relationships to systematically narrow where engineering interdependencies are likely to be found between various elements of product development processes.

Identifying uncertainty in laser powder bed fusion additive manufacturing models

As additive manufacturing (AM) matures, models are beginning to take a more prominent stage in design and process planning. A limitation frequently encountered in AM models is a lack of indication about their precision and accuracy. Often overlooked, model uncertainty is required for validation of AM models, qualification of AM-produced parts, and uncertainty management. This paper presents a discussion on the origin and propagation of uncertainty in laser powder bed fusion (L-PBF) models. Four sources of uncertainty are identified: modeling assumptions, unknown simulation parameters, numerical approximations, and measurement error in calibration data. Techniques to quantify uncertainty in each source are presented briefly, along with estimation algorithms to diminish prediction uncertainty with the incorporation of online measurements. The methods are illustrated with a case study based on a thermal model designed for melt pool width predictions. Model uncertainty is quantified for single track experiments, and the effect of online estimation in overhanging structures is studied via simulation. [DOI: 10.1115/1.4034103]