Developing Engineering Ontology for Information Retrieval

2008 ◽  
Vol 8 (1) ◽  
Author(s):  
Zhanjun Li ◽  
Victor Raskin ◽  
Karthik Ramani
Keyword(s):  
Information Retrieval ◽  
Life Cycle ◽  
Statistical Methods ◽  
Product Life Cycle ◽  
Effective Means ◽  
Preliminary Test ◽  
Computational Framework ◽  
Complex Queries ◽  
Engineering Documents ◽  
Standard Information

When engineering content is created and applied during the product life cycle, it is often stored and forgotten. Since search remains word based, engineers do not have the effective means to harness and reuse past designs and experiences. Current information retrieval approaches based on statistical methods and keyword matching do not satisfy users’ needs in the engineering domain. Therefore, we propose a new computational framework that includes an ontological basis and algorithms to retrieve unstructured engineering documents while handling complex queries. The results from the preliminary test demonstrate that our method outperforms the traditional keyword-based search with respect to the standard information retrieval measurement.

Developing Ontologies for Engineering Information Retrieval

2007 ◽  
Author(s):  
Zhanjun Li ◽  
Victor Raskin ◽  
Karthik Ramani
Keyword(s):  
Information Retrieval ◽  
Statistical Methods ◽  
Preliminary Test ◽  
The Other ◽  
Product Lifecycle ◽  
Computational Framework ◽  
Complex Queries ◽  
Engineering Documents ◽  
Engineering Information ◽  
Standard Information

When engineering content is created and applied during the product lifecycle, it is often stored and forgotten. Since search remains text-based, engineers do not have the means to harness and reuse past designs and experiences. On the other hand, current information retrieval approaches based on statistical methods and keyword matching are not directly applicable to the engineering domain. We propose a new computational framework that includes an ontological basis and algorithms to retrieve unstructured engineering documents while handling complex queries. The results from the preliminary test demonstrate that our method outperforms the traditional keyword-based search with respect to the standard information retrieval measurement.

A methodology for engineering ontology acquisition and validation

2008 ◽  
Vol 23 (1) ◽  
pp. 37-51 ◽  
Author(s):  
Zhanjun Li ◽  
Maria C. Yang ◽  
Karthik Ramani
Keyword(s):  
Information Retrieval ◽  
Product Life Cycle ◽  
Large Scale ◽  
Performance Test ◽  
Ontology Development ◽  
Knowledge Resources ◽  
Development Method ◽  
Concept Space ◽  
Engineering Documents ◽  
Engineering Information

AbstractWhen engineering content is created and applied during the product life cycle, it is often stored and forgotten. Current information retrieval approaches based on statistical methods and keyword matching are not effective in understanding the context of engineering content. They are not designed to be directly applicable to the engineering domain. Therefore, engineers have very limited means to harness and reuse past designs. The overall objective of our research is to develop an engineering ontology (EO)-based computational framework to structure unstructured engineering documents and achieve more effective information retrieval. This paper focuses on the method and process to acquire and validate the EO. The main contributions include a new, systematic, and more structured ontology development method assisted by a semiautomatic acquisition tool. This tool is integrated with Protégé ontology editing environment; an engineering lexicon (EL) that represents the associated lexical knowledge of the EO to bridge the gap between the concept space of the ontology and the word space of engineering documents and queries; the first large-scale EO and EL acquired from established knowledge resources for engineering information retrieval; and a comprehensive validation strategy and its implementations to justify the quality of the acquired EO. A search system based on the EO and EL has been developed and tested. The retrieval performance test further justifies the effectiveness of the EO and EL as well as the ontology development method.

scholarly journals CAD integrated automatic recognition of weld paths

2021 ◽  
Author(s):  
Tuan Anh Tran ◽  
Andrei Lobov ◽  
Tord Hansen Kaasa ◽  
Morten Bjelland ◽  
Ole Terje Midling
Keyword(s):  
Life Cycle ◽  
Spatial Analysis ◽  
Product Life Cycle ◽  
Real Life ◽  
Automatic Recognition ◽  
Integrated Method ◽  
Weld Joints ◽  
Related Information ◽  
Almost All ◽  
Sequential Assembly

AbstractIn this paper, a CAD integrated method is proposed for automatic recognition of potential weld locations in large assembly structures predominantly comprised of weld joints. The intention is to reduce the total man-hours spent on manually locating, assigning, and maintaining weld-related information throughout the product life cycle. The method utilizes spatial analysis of extracted stereolithographic data in combination with available CAD functions to determine whether the accessibility surrounding a given intersection edge is sufficient for welding. To demonstrate the method, a system is developed in Siemens NX using their NXOpen Python API. The paper presents the application of the method to real-life use cases in varying complexity in cooperation with industrial partners. The system is able to correctly recognize almost all weld lines for the parts considered within a few minutes. Some exceptions are known for particular intersection lines located deep within notched joints and geometries weldable through sequential assembly, which are left as a subject to further works.

Life-Cycle Engineering Design

1995 ◽  
Vol 117 (B) ◽  
pp. 42-47 ◽  
Author(s):  
K. Ishii
Keyword(s):  
Life Cycle ◽  
Product Life Cycle ◽  
Functional Performance ◽  
Semantic Network ◽  
Research Issues ◽  
Life Cycle Engineering ◽  
Engineering Research ◽  
Design Representation ◽  
Life Cycle Design ◽  
Representation Scheme

Life-cycle engineering seeks to incorporate various product life-cycle values into the early stages of design. These values include functional performance, manufacturability, serviceability, and environmental impact. We start with a survey of life-cycle engineering research focusing on methodologies and tools. Further, the paper addresses critical research issues in life-cycle design tools: design representation and measures for life-cycle evaluation. The paper describes our design representation scheme based on a semantic network that is effective for evaluating the structural layout. Evaluation measures for serviceability and recyclability illustrate the practical use of these representation schemes.

scholarly journals Sustainability Requirements of Digital Twin-Based Systems: A Meta Systematic Literature Review

2021 ◽  
Vol 11 (12) ◽  
pp. 5519
Author(s):  
Rui Carvalho ◽  
Alberto Rodrigues da Silva
Keyword(s):  
Life Cycle ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Environmental Sustainability ◽  
Product Life Cycle ◽  
Data Communication ◽  
Three Dimensions ◽  
Digital Twin ◽  
The Subject ◽  
Reducing Costs

Sustainable development was defined by the UN in 1987 as development that meets the needs of the present without compromising the ability of future generations to meet their own needs, and this is a core concept in this paper. This work acknowledges the three dimensions of sustainability, i.e., economic, social, and environmental, but its focus is on this last one. A digital twin (DT) is frequently described as a physical entity with a virtual counterpart, and the data, connections between the two, implying the existence of connectors and blocks for efficient and effective data communication. This paper provides a meta systematic literature review (SLR) (i.e., an SLR of SLRs) regarding the sustainability requirements of DT-based systems. Numerous papers on the subject of DT were also selected because they cited the analyzed SLRs and were considered relevant to the purposes of this research. From the selection and analysis of 29 papers, several limitations and challenges were identified: the perceived benefits of DTs are not clearly understood; DTs across the product life cycle or the DT life cycle are not sufficiently studied; it is not clear how DTs can contribute to reducing costs or supporting decision-making; technical implementation of DTs must be improved and better integrated in the context of the IoT; the level of fidelity of DTs is not entirely evaluated in terms of their parameters, accuracy, and level of abstraction; and the ownership of data stored within DTs should be better understood. Furthermore, from our research, it was not possible to find a paper discussing DTs only in regard to environmental sustainability.

scholarly journals Attributional & Consequential Life Cycle Assessment: Definitions, Conceptual Characteristics and Modelling Restrictions

2021 ◽  
Vol 13 (13) ◽  
pp. 7386
Author(s):  
Thomas Schaubroeck ◽  
Simon Schaubroeck ◽  
Reinout Heijungs ◽  
Alessandra Zamagni ◽  
Miguel Brandão ◽  
...  
Keyword(s):  
Life Cycle Assessment ◽  
Life Cycle ◽  
Environmental Impact ◽  
Product Life Cycle ◽  
Sustainability Assessment ◽  
Consequential Life Cycle Assessment ◽  
Modelling Framework ◽  
Potential Environmental Impact ◽  
Reference Environment ◽  
The Impact

To assess the potential environmental impact of human/industrial systems, life cycle assessment (LCA) is a very common method. There are two prominent types of LCA, namely attributional (ALCA) and consequential (CLCA). A lot of literature covers these approaches, but a general consensus on what they represent and an overview of all their differences seems lacking, nor has every prominent feature been fully explored. The two main objectives of this article are: (1) to argue for and select definitions for each concept and (2) specify all conceptual characteristics (including translation into modelling restrictions), re-evaluating and going beyond findings in the state of the art. For the first objective, mainly because the validity of interpretation of a term is also a matter of consensus, we argue the selection of definitions present in the 2011 UNEP-SETAC report. ALCA attributes a share of the potential environmental impact of the world to a product life cycle, while CLCA assesses the environmental consequences of a decision (e.g., increase of product demand). Regarding the second objective, the product system in ALCA constitutes all processes that are linked by physical, energy flows or services. Because of the requirement of additivity for ALCA, a double-counting check needs to be executed, modelling is restricted (e.g., guaranteed through linearity) and partitioning of multifunctional processes is systematically needed (for evaluation per single product). The latter matters also hold in a similar manner for the impact assessment, which is commonly overlooked. CLCA, is completely consequential and there is no limitation regarding what a modelling framework should entail, with the coverage of co-products through substitution being just one approach and not the only one (e.g., additional consumption is possible). Both ALCA and CLCA can be considered over any time span (past, present & future) and either using a reference environment or different scenarios. Furthermore, both ALCA and CLCA could be specific for average or marginal (small) products or decisions, and further datasets. These findings also hold for life cycle sustainability assessment.

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