Past, present and future of information and knowledge sharing in the construction industry: Towards semantic service-based e-construction

The paper reviews product data technology initiatives in the construction sector and provides a synthesis of related ICT industry needs. A comparison between (a) the data centric characteristics of Product Data Technology (PDT) and (b) ontology with a focus on semantics, is given, highlighting the pros and cons of each approach. The paper advocates the migration from data-centric application integration to ontology-based business process support, and proposes inter-enterprise collaboration architectures and frameworks based on semantic services, underpinned by ontology-based knowledge structures. The paper discusses the main reasons behind the low industry take up of product data technology, and proposes a preliminary roadmap for the wide industry diffusion of the proposed approach. In this respect, the paper stresses the value of adopting alliance-based modes of operation

User-adaptive sketch-based 3D CAD model retrieval

3D CAD models are an important digital resource in the manufacturing industry. 3D CAD model retrieval has become a key technology in product lifecycle management enabling the reuse of existing design data. In this paper, we propose a new method to retrieve 3D CAD models based on 2D pen-based sketch inputs. Sketching is a common and convenient method for communicating design intent during early stages of product design, e.g., conceptual design. However, converting sketched information into precise 3D engineering models is cumbersome, and much of this effort can be avoided by reuse of existing data. To achieve this purpose, we present a user-adaptive sketch-based retrieval method in this paper. The contributions of this work are twofold. Firstly, we propose a statistical measure for CAD model retrieval: the measure is based on sketch similarity and accounts for users’ drawing habits. Secondly, for 3D CAD models in the database, we propose a sketch generation pipeline that represents each 3D CAD model by a small yet sufficient set of sketches that are perceptually similar to human drawings. User studies and experiments that demonstrate the effectiveness of the proposed method in the design process are presented

Comparison of knowledge representation in PDM and by semantic networks

\u27Nowadays, computer-aided tools have enabled the creation of electronic design documents on an unprecedented scale, while determining and finding what can be reused for a new design is like searching for a \u27needle in a haystack\u27. (…) The availability of such extensive knowledge resources is creating new challenges as well as opportunities for research on how to retrieve and reuse the knowl-edge from existing designs.\u27 [1] If the requested knowledge is implicit (which means that it is only in the minds of the employees of a company) the retrieval and reuse of knowledge is even more com-plicated. By representing the (engineering) data backbone of a company, PDM systems are the software implementation which should support the designer to retrieve information about existing and successful design projects. This paper shows that the known data classification approaches of common PDM systems are not applicable to represent implicit (tacit) knowledge. Furthermore a new approach to knowledge representation is introduced by using Semantic Networks. The feasibility of the presented work is shown by a use-case scenario in which the conventional PDM system supported product development process is compared with the proposed way by using the soft-ware \u27The Semaril\u27 — a software tool developed at the Institute of Engineering Design/CAD based on Semantic Networks [2]

The 'what' and 'how' of learning in design, invited paper

Previous experiences hold a wealth of knowledge which we often take for granted and use unknowingly through our every day working lives. In design, those experiences can play a crucial role in the success or failure of a design project, having a great deal of influence on the quality, cost and development time of a product. But how can we empower computer based design systems to acquire this knowledge? How would we use such systems to support design? This paper outlines some of the work which has been carried out in applying and developing Machine Learning techniques to support the design activity; particularly in utilising previous designs and learning the design process

SIMILARITY METRICS APPLIED TO GRAPH BASED DESIGN MODEL AUTHORING

Model reuse is typically facilitated by search and retrieval tools, matching the sought model with models in a database. This research aims at providing similar assistance to users authoring design exemplars, a data structure to represent parametric and geometric design problems. The design exemplar represents design problems in the form of a bi-partite graph consisting of entities and relations. Authoring design exemplars for relatively complex design problems can be time consuming and error prone. This forms the motivation of developing a search and retrieval tool, capable of retrieving exemplars that are similar to the exemplar that a user is trying to author, from a database of previously authored exemplars. In order to develop such a tool, similarity measures have been developed to evaluate the similarity between the exemplar that a user is trying to author and target exemplars in the database. Two exemplars can be considered similar based on the number and types of entities and relations shared by them. However, exemplars meant for the same purpose can be authored using different entities and relations. Hence, the two main challenges in developing a search and retrieval tool are to evaluate the similarity between exemplars based on structure and semantics. In this research, four distinct similarity metrics are developed to evaluate the structural similarity between exemplars for exemplar retrieval: entity similarity, relation similarity, attribute similarity, and graph matching similarity. As well, a thorough understanding of semantics in engineering design has been developed. Different types of semantic information found in engineering design have been identified and classified. Design intent and rationale have been proposed as the two main types of semantic information necessary to evaluate the semantic similarity between exemplars. The semantic and structural similarity measures have been implemented as separate modules in an interactive modeling environment. Several experiments have been conducted in order to evaluate the accuracy and effectiveness of the proposed similarity measures. It is found that for most queries, the semantic retrieval module retrieves exemplars that are not retrieved by structural retrieval module and vice versa

CAD assembly descriptors for knowledge capitalization and model retrieval

Today, there exists a huge amount of digital data easily downloadable from Internet and/or simply accessible from large databases. Despite this rise, the methods to retrieve and search for specific data have not been sufficiently studied and developed, notably when considering 3D contents. Thus, it is sometime more efficient to define new 3D shapes starting from scratch rather than to try to make use of existing ones hardly identifiable within those databases. This is particularly true when considering CAD assembly models often resulting from a long and time-consuming modeling phase within the Product Development Process. Thus, having new methods, models and tools to capitalize, retrieve and reuse CAD assembly models would help saving a lot of time. This paper addresses such a difficult problem of finding a method to characterize and structure CAD assemblies so as to be able to search for similar ones. A framework has been designed for the retrieval of globally and/or partially similar assembly mod- els according to different user-specified search criteria. It is based on an assembly descriptor, called the Enriched Assembly Model (EAM), which encodes all the required data automatically extracted from the geometry and structure of the CAD models. The data are organized in several layers thus enabling multi-level structuring and queries. It also allows fuzzy queries, which can be further refined

A framework for developing engineering design ontologies within the aerospace industry

This paper presents a framework for developing engineering design ontologies within the aerospace industry. The aim of this approach is to strengthen the modularity and reuse of engineering design ontologies to support knowledge management initiatives within the aerospace industry. Successful development and effective utilisation of engineering ontologies strongly depends on the method/framework used to develop them. Ensuring modularity in ontology design is essential for engineering design activities due to the complexity of knowledge that is required to be brought together to support the product design decision-making process. The proposed approach adopts best practices from previous ontology development methods, but focuses on encouraging modular architectural ontology design. The framework is comprised of three phases namely: (1) Ontology design and development; (2) Ontology validation and (3) Implementation of ontology structure. A qualitative research methodology is employed which is composed of four phases. The first phase defines the capture of knowledge required for the framework development, followed by the ontology framework development, iterative refinement of engineering ontologies and ontology validation through case studies and experts’ opinion. The ontology-based framework is applied in the combustor and casing aerospace engineering domain. The modular ontologies developed as a result of applying the framework and are used in a case study to restructure and improve the accessibility of information on a product design information-sharing platform. Additionally, domain experts within the aerospace industry validated the strengths, benefits and limitations of the framework. Due to the modular nature of the developed ontologies, they were also employed to support other project initiatives within the case study company such as role-based computing (RBC), IT modernisation activity and knowledge management implementation across the sponsoring organisation. The major benefit of this approach is in the reduction of man-hours required for maintaining engineering design ontologies. Furthermore, this approach strengthens reuse of ontology knowledge and encourages modularity in the design and development of engineering ontologies