The exponential growth of the Internet and the increasing communication and computational power have created many opportunities for advancing engineering, manufacturing and business activities. Active Catalog brings a conceptually new idea to design and electronic commerce by providing new forms of catalog information about engineering parts and physical objects. Active Catalog is designed for finding and using information in building design applications in the context of heterogeneous, Internet-based distributed computing environments. The information provided by Active Catalog is active, dynamic, composable, multi-dimensional, share-able, semantically accessible, interoperable and executable in distributed environments. Active Catalog allows engineers and designers to search for information about catalog parts syntactically and semantically, use that information to construct simulation programs rapidly, and test out their designs on the Internet before committing to actual fabrication of physical systems. It supports a new “Try Before You Buy” paradigm in the Internet-based design and electronic commerce. This paper presents a number of research issues and our approach in building an Internet-based catalog information resource. The paper describes an initial prototype toward realizing the “Try Before You Buy” paradigm, in the context of the electromechanical design of pump and motor systems. The prototype contains a pump ontology and a search engine for finding semantically equivalent parts. The paper also describes a distributed simulation, which runs on various machines, and exchanges dynamic information using commercial data exchange mechanisms.

This study focuses on two specific problems of knowledge intensive computer aided design: (a) how can design concepts be modeled and represented in a form that is understandable for human beings and can be processed by computers, (b) how can they be arranged in structures that enables a computer-based functional design of products. First, a methodology for definition of very high level modeling entities based on the ontology theory is presented. It formalizes design concepts in terms of all concerned entities, phenomena and situations and describes them by attributes, parameters and descriptors, respectively. Validity and interactions of design concepts are governed by constraints. The very high level modeling entities are arranged into domain oriented design ontologies based on their contents and semantic relationships. The formalism used for logical specification of design ontologies is based on a library of declarative expressions called ACN-Code. A design ontology lends itself to a specific knowledge base called associative concept network (ACN). The inference engine that works on an application oriented ACN selects the appropriate design concepts against a set of user specified functional requirements. Due to the pre-defined associations incomplete functional specifications can be completed to result in a fully functional design solution. The paper also presents an application example.

This paper describes an approach developed to support the early stages of designing multidisciplinary products. In the design of such products it is important for various specialists to share representations of the engineering models they use in making trade-off studies. Sharing information among diverse engineering tools at the data level is notoriously difficult and inflexible. When any of the modeling details, assumptions or constraints change, the translation must usually be rewritten. Our solution to this problem is to formalize the exchange of information among engineering specialists and their tools as a problem in communication among agents. The agents share a common ontology and a compositional modeling language (CML) in which models can be created such that conventions, constraints, and assumptions are defined formally and ultimately grounded in logic. We have applied CML in the context of a pick-up head design problem for DVD (digital versatile disc / digital video disc) players. We describe the pick-up head design problem, the CML models we have created to address early stages of pick-up head design, and the use of the CML models by a team of specialists, each participating as an agent in a concurrent engineering exercise.

In all types of communication, the ability to share information is often hindered because the meaning of information can be drastically affected by the context in which it is viewed and interpreted. This is especially true in manufacturing because of the growing complexity of manufacturing information and the increasing need to exchange this information among various software applications. Different manufacturing functions may use different terms to mean the exact same concept or use the exact same term to mean very different concepts. Often, the loosely defined natural language definitions associated with the terms contain so much ambiguity that they do not make the differences evident and/or do not provide enough information to resolve the differences. A solution to this problem is the development of a taxonomy, or ontology, of manufacturing concepts and terms along with their respective formal and unambiguous definitions. This paper focuses on an effort at the National Institute of Standards and Technology to identify, formally define, and structure the semantic concepts intrinsic to the capture and exchange of manufacturing information. Specifically, this paper documents the results of the first phase of this project — that of analyzing existing ontological systems to determine which is most appropriate for the manufacturing domain. In particular, this phase involved the exploration of efforts that are studying both the uses of ontologies in the general sense and those that are using ontologies for domain-specific purposes.

This paper reports on an investigation that aims to characterise the technical content of documentary information used by engineers within two aerospace companies. The work has been carried out in order to gain an understanding of the technical requirements for future information systems to support designers. A rigorous analysis of the textual content of exemplar information objects provided by engineers is initially described. From this analysis, Object Content Profiles for three distinct types of text-based objects are identified: (i) correspondence, (ii) technical (report) objects and (iii) manuals, catalogues and standards. This work is complemented by a survey of designers which identifies the attributes that engineers consider the most helpful as search criteria when trying to locate information objects. These results suggest that whilst drawings and CAD models can be represented by a small number of core attributes, for text-based objects a wider range of descriptive attributes is important. Finally, an outline framework to allow the development of hierarchical taxonomies is presented. This is based on ontology development guidelines but is modified to promote the reuse of existing hierarchically structured sources of indexing terms.

In analyzing design and manufacturing tasks and their mutual interactions, it appears that the underlying information of these tasks is of the utmost importance. If this information is managed on an integral level, in a formalized, structured way, it can serve as a basis for the control of design and manufacturing processes. The ontological description that is used for this purpose is elaborated upon. It is indicated how an ontological description of the information content can govern design and manufacturing processes, and support their execution with all required information and functionality of resources. Moreover, it is expound how the ontological description can serve as the basis for integrating information that, in industry, is often dispersedly managed in separate coexisting data management systems. Furthermore, a framework is proposed for the implementation of an Information Management system as well as for the additional applications that utilize its functionality. Finally, a pilot implementation at a large Dutch industrial company is discussed concerning the aspect of integrating dispersedly managed information. This implementation is also compared to some currently available commercial solutions in this area that were tested at the company as well.

Traceability of food products to their sources is critical for quick responses to a food emergency. US law now requires stakeholders in the agri-food supply chain to support traceability by tracking food materials they acquire and sell. However, having complete and consistent information needed to quickly investigate sources and identify affected material has proven difficult. There are multiple reasons that makes food traceability a challenging task including diversity of stakeholders and their lexicons, standards, tools and methods; unwillingness to expose information of internal operations; lack of a common understanding of steps in a supply chain; and incompleteness of data. Ontologies can address the traceability challenge by creating a shared understanding of the traceability model across stakeholders in a food supply chain. They can also support semantic mediation, data integration, and data exploration. This paper reports an on ongoing effort aimed at developing a formal ontology for supply chain traceability using use cases and data from partners in the bulk grain domain. The developed ontology was validated in VocBench environment through creating RDF triples from real datasets and executing SPARQL queries corresponding to predefined competency questions.

Several supply-chain ontologies have been introduced in the past decade with the promise of enabling supply chain interoperability. However, the existing supply-chain ontologies have several gaps with respect to completeness, logical consistency, domain accuracy, and the development approach. In this work, we propose a new, supply-chain, reference ontology that is 1) based on an existing top-level ontology and 2) developed using a collaborative, ontology-development, best practice. We chose this approach because empirical studies have shown the usefulness of adopting a top-level ontology both for improving the efficiency of the development process and enhancing the quality of the resulting ontology. The proposed proof-of-concept reference ontology is developed in the context of the Industrial Ontology Foundry (IOF). IOF is an international effort aimed at providing a coherent set of modular and publicly-available ontologies for the manufacturing sector. Although the proposed reference ontology is still at the draft stage, this paper shows that it has already benefited from the collaborative development process that involves inputs from the other working groups within IOF. Additionally, as a way to validate the proposed reference ontology, an application ontology related to a supplier discovery and evaluation use case is derived from the reference ontology and tested.

Design for additive manufacturing (DFAM) provides design freedom for creating complex geometries and guides designers to ensure manufacturability of parts fabricated using additive manufacturing (AM) processes. However, there is a lack of formalized DFAM knowledge that provides information on how to design parts and how to plan AM processes for achieving target goals, e.g., reducing build-time. Therefore, this study presents a DFAM ontology using the web ontology language (OWL) to formalize DFAM knowledge and support queries for retrieving that knowledge. The DFAM ontology has three high level classes to represent design rules specifically: feature, parameter, and AM capability. Furthermore, the manufacturing feature concept is defined to link part design to AM process parameters. Since manufacturing features contain information on feature constraints of AM processes, the DFAM ontology supports manufacturability analysis of design features by reasoning with Semantic Query-enhanced Web Rule Language (SQWRL). The SQWRL rules in this study also help retrieve design recommendations for improving manufacturability. A case study is performed to illustrate usefulness of the DFAM ontology and SQWRL rule application. This study contributes to developing a knowledge base that can be reusable and upgradable and to analyzing manufacturing analysis to provide feedback about part designs to designers.

Model Based Systems Engineering (MBSE) is a mainstream methodology for the design of complex systems. Verification is a necessary part of MBSE. Although there is significant past research on verification, some deficiencies still exist, such as behavior requirement verification in the early design stage is lacking. In this study, behavior verification at the early design stage is presented. First, a unified modeling method based on SysML is proposed and some transformation rules are defined to ensure the correctness and definiteness of the ontology generation. Second, behavior requirements are classified and formalized as rules. Finally, a hierarchical behavior verification approach based on ontology reasoning is proposed. This approach is convenient for designers to use and no additional expertise is needed. A case study is provided to demonstrate its effectiveness.

Pre-existing knowledge buried in high-end equipment manufacturing enterprises could be effectively reused to help decision-makers develop good judgements to make decisions about the problems in new product development, which in turn speeds up and improves the quality of product innovation. Nevertheless, a knowledge-based decision support system in high-end equipment domain is still not fully accomplished due to the complication of knowledge content, fragmentation of knowledge theme, heterogeneousness of knowledge format, and decentralization of knowledge storage. To address these issues, this paper develops a high-end equipment knowledge management system (HEKM) for supporting knowledge-driven decision-making in new product development. HEKM provides three steps for knowledge management and reuse. Firstly, knowledge resources are captured and structured through a standard knowledge description template. Then, OWL ontologies are employed to explicitly and unambiguously describe the concepts of the captured knowledge and also the relationships that hold between those concepts. Finally, the Personalized PageRank algorithm together with ontology reasoning approach is used to perform knowledge navigation, where decision-makers could acquire the most relevant knowledge for a given problem through knowledge query or customized active push. The feasibility and effectiveness of HEKM are demonstrated through three industrial application examples.

The development of complex product dynamic simulation models and the integration of design automation systems require knowledge from multiple heterogeneous data sources and tools. Because of the heterogeneity of model data, the integration of tools and data is a time-consuming and error-prone task. The main objective of this study is to provide a unified model of dynamic simulation for engineering design, which serves as a knowledge base to support the development of a dynamic simulation model. The integration of knowledge is realized through (i) definition of the structure and interface during the design phase of the dynamic simulation model, and (ii) definition of a model-driven integrated environment configuration process during the runtime phase. In order to achieve interoperability among the different simulation models in a collaborative design environment, we build a “Demand-Resources-Service-Knowledge-Process (DKRSP)” ontology that formally represents the semantics of dynamic simulation models. Based on the ontology, a knowledge base is created for the management of dynamic simulation knowledge. The efficacy of the ontology and the knowledge base are demonstrated using a transmission design example.

We hypothesize that by providing decision support for designers in industry we can speed up the design process and facilitate the creation of quality cost-effective designs. One of the challenges in providing design decision support is that the decision workflows embody various degrees of complexity due to the inherent complexity embodied in engineering systems. To tackle this, we propose a Knowledge-Based P latform for D ecision S upport i n the D esign of E ngineering S ystems (PDSIDES). PDSIDES is built on our earlier work that is anchored in modeling decision-related knowledge with templates using ontology to facilitate execution and reuse. In this paper, we extend the ontological decision templates to a computational platform that provides knowledge-based decision support for three types of users, namely, Template Creators, Template Editors, and Template Implementers, in original design, adaptive design, and variant design respectively. The efficacy of PDSIDES is demonstrated using a Hot Rod Rolling System (HRRS) design example.

Utilizing the enterprise capital related the knowledge of design processes has become a crucial to improve enterprise agility and respond to shifts or changes in markets. The complexity and uncertainty of design processes raise the challenge of capturing tacit knowledge and the ability to provide assistance in designing design processes. In this paper, an ontology is proposed for capturing, representing and documenting the knowledge related to hierarchical decision workflows in the meta-design of complex engineered systems. The ontology is developed in the context of Decision Support Problem Technique (DSPT), taking into account the requirements being able to guide assistance in designing design workflows, and integrating problem, product and process information in a design decision-making process. Then, the method of building procedure and design of process templates are presented to facilitate the reuse of the populated template instances in future design. Finally, the meta-design of the heat exchanger in a small thermal system is presented as an example to illustrate the effectiveness of this approach.

With the advent of the big-data era, massive textual information stored in electronic and digital documents have become valuable resources for knowledge discovery in the fields of design and engineering. Ontology technologies and semantic networks have been widely applied with text mining techniques including Natural Language Processing (NLP) to extract structured knowledge associations from the large-scale unstructured textual data. However, most existing works mainly focus on how to construct the semantic networks by developing various text mining methods such as statistical approaches and semantic approaches, while few studies are found to focus on how to subsequently analyze and fully utilize the already well-established semantic networks. In this paper, a specific network analysis method is proposed to discover the implicit knowledge associations from the existing semantic network for improving knowledge discovery and design innovation. Pythagorean means are applied with Dijkstra’s shortest path algorithm to discover the implicit knowledge associations either around a single knowledge concept or between two concepts. Six criteria are established to evaluate and rank the correlation degree of the implicit associations. Two engineering case studies were conducted to illustrate the proposed knowledge discovery process, and the results showed the effectiveness of the retrieved implicit knowledge associations on helping providing relevant knowledge from various aspects, and provoking creative ideas for engineering innovation.

Analogy-making has been deemed one of the core cognitive mechanisms which play a role in human creative thinking activities such as design and art. Designers can make use of analogies in various stages of design including ideation, planning and evaluation. However, human analogy-making is limited by experience and reliance of human memory on superficial attributes rather than relational or causal structure during analogy retrieval. In this regard, different design-by-analogy tools have been developed to assist designers in analogical reasoning. Analogical reasoning tools can be viewed as either based on hand-coded structured knowledge or natural-language-based design-by-analogy tools. The former are naturally limited in extent and scope to that which was hand coded [1]. Alternatively, natural language analogical reasoning can leverage the abundantly available textual resources. Current text-based analogy research for design have relied on analogies between individual word meanings. This leaves open consideration of the relational structure of the language where the relational similarity of texts can indicate a significant analogy. In this article, we develop four computational models of analogy that capture relational structure of the text. This includes spatial representation of semantics, multi-level deep neural reasoning, graph matching based model and transformation-based model. The models are then combined together into an ensemble model to achieve acceptable level of analogical accuracy for the end-user. The underlying design-related knowledge upon which analogies were drawn includes engineering ontologies, function hierarchy and raw patent texts. Instantiating this analogical reasoning model in design concept analogy retrieval system, we show this approach can help retrieve meaningful analogies from the World Intellectual Property Organization (WIPO) patent repository. We demonstrate this for a particular design problem.

The major goal of customer requirement formulation is to achieve a common understanding between the project stakeholders and the engineering requirements. Many times, this process can be ambiguous, incomplete, and time consuming especially when more than one engineering discipline is involved. Therefore, adequate requirement formulation tools can be a major contributor to solving these challenges. The use of ontologies provides a standardized way of describing concepts in a domain of interest and the relationships between these concepts to better understand the domain as a whole. This paper describes the methodology used to create an ontology derived from twenty customer requirements of a mid-size, twin-engine, commercial transport-class aircraft provided by NASA Ames Research Center. One key stipulation that NASA had was that this ontology effectively captures the relationships that exist between the hardware and software level of each customer requirement. The final ontology was created using Protégé OWL, an open source ontology editor, which will be used by NASA in order to improve the customer requirement creation phase of future NASA products. The ontology and requirements were further generalized into a set of common patterns for describing requirements in this domain. These pattern templates provide a tool to ensure that common styles of requirements have been considered, and that these common styles are uniform. This research paper fills a gap in the customer requirement research field by introducing the use of ontologies and common patterns to reduce ambiguity and repetition.

As additive manufacturing (AM) continues to mature as a production technology, the limiting factors that have hindered its adoption in the past still exist, for example, process repeatability and material availability issues. Overcoming many of these production hurdles requires a further understanding of geometry-process-structure-property relationships for additively manufactured parts. In smaller sample sizes, empirical approaches that seek to harness data have proven to be effective in identifying material process-structure-property relationships. This paper presents a collaborative AM data management system developed at the National Institute of Standards and Technology (NIST). This data management system is built with NoSQL (Not Only Structured Query Language) database technology and provides a Representational State Transfer (REST) interface for application integration. In addition, a web interface is provided for data curating, exploring, and downloading. An AM data schema is provided by NIST for an alpha release, as well as a set of data generated from an interlaboratory study of additively manufactured nickel alloy (IN625) parts. For data exploration, the data management system provides a mechanism for customized web graphic user interfaces configurable through a visualization ontology. As a collaboration platform, the data management system is set to evolve through sharing of both the AM schema and AM development data among the stakeholders in the AM community. As data sets continue to accumulate, it becomes possible to establish new correlations between processes, materials, and parts. The functionality of the data management system is demonstrated through the curation and querying of the curated AM datasets.

Customer needs are one of the first items gathered and examined in the design process. Currently there are few methods of examining the collected customer needs to help designers track how much of the customer need space has been explored. None of the current prominent design texts provide an universally accepted categorization scheme to help categorize and examine collected customer needs. This paper ventures into the process of building an ontology that can be used to categorize and examine customer needs. The finalized ontology presented here went through 11 iterations and multiple inter-rater reliability tests throughout the creation process. The paper then discusses the possible uses of this scheme and how it can be utilized early in the design process to ensure that a thorough exploration of the customer need space is represented in the designers’ list of customer needs.

Additive manufacturing (AM) is a promising technology that is expected to revolutionize industry by allowing the production of almost any shape directly from a 3D model. In metal-based AM, numerous process parameters are highly interconnected, and their interconnections are not yet understood. Understanding this interconnectivity is the first step in building process control models that help make the process more repeatable and reliable. Metamodels can be used to conceptualize models of complex AM processes and capture diverse parameters to provide a graphical view using common terminology and modeling protocols. In this paper we consider different process models (laser and thermal) for metal-based AM and develop an AM Process Ontology from first-principles. We discuss and demonstrate its implementation in Protégé.