Practitioner requirements for integrated Knowledge-Based Engineering in Product Lifecycle Management.

The effective management of knowledge as capital is considered essential to the success of engineering product/service systems. As Knowledge Management (KM) and Product Lifecycle Management (PLM) practice gain industrial adoption, the question of functional overlaps between both the approaches becomes evident. This article explores the interoperability between PLM and Knowledge-Based Engineering (KBE) as a strategy for engineering KM. The opinion of key KBE/PLM practitioners are systematically captured and analysed. A set of ranked business functionalities to be fulfiled by the KBE/PLM systems integration is elicited. The article provides insights for the researchers and the practitioners playing both the user and development roles on the future needs for knowledge systems based on PLM

Reconfiguration model using knowledge based engineering systems

Globalization has forced enterprises to adapt their products and services to remain competitive in the free market. Manufacture plays an important role in the competitive aspect; it is where an innovation in the production system could lead to business advantage. These innovations usually involve the key elements in manufacturing systems: machines, tools and resources administration. A reconfigurable manufacturing system (RMS) is one designed for rapid change in its structure and components, to quickly adjust its production capacity and functionality in response to sudden market or intrinsic system changes. However, reconfiguration alone is not enough since it will provide information to produce a certain item but it won t provide the components that will automate the machine tool for mass production. The process of automation of machine tools is known as retrofit, process being developed and researched in emergent economies. The current retrofit kits are expensive and are not tailor made, thus, they are not attractive for small and medium enterprises. This article describes a solution for fast reconfiguration of machine tools using the Knowledge Based-Engineering System methodology (KBES) that allows to obtain, structure and manage the knowledge generated in a determined engineering process, in this case, the reconfiguration processHincapié Montoya, M.; Güemes-Castorena, D.; Contero, M.; Ramírez-Cadena, M.; Diaz, C. (2015). Reconfiguration model using knowledge based engineering systems. Journal of Manufacturing Technology Research. 6(1):63-81. http://hdl.handle.net/10251/77893S63816

An ontology framework for developing platform-independent knowledge-based engineering systems in the aerospace industry

This paper presents the development of a novel knowledge-based engineering (KBE) framework for implementing platform-independent knowledge-enabled product design systems within the aerospace industry. The aim of the KBE framework is to strengthen the structure, reuse and portability of knowledge consumed within KBE systems in view of supporting the cost-effective and long-term preservation of knowledge within such systems. The proposed KBE framework uses an ontology-based approach for semantic knowledge management and adopts a model-driven architecture style from the software engineering discipline. Its phases are mainly (1) Capture knowledge required for KBE system; (2) Ontology model construct of KBE system; (3) Platform-independent model (PIM) technology selection and implementation and (4) Integration of PIM KBE knowledge with computer-aided design system. A rigorous methodology is employed which is comprised of five qualitative phases namely, requirement analysis for the KBE framework, identifying software and ontological engineering elements, integration of both elements, proof of concept prototype demonstrator and finally experts validation. A case study investigating four primitive three-dimensional geometry shapes is used to quantify the applicability of the KBE framework in the aerospace industry. Additionally, experts within the aerospace and software engineering sector validated the strengths/benefits and limitations of the KBE framework. The major benefits of the developed approach are in the reduction of man-hours required for developing KBE systems within the aerospace industry and the maintainability and abstraction of the knowledge required for developing KBE systems. This approach strengthens knowledge reuse and eliminates platform-specific approaches to developing KBE systems ensuring the preservation of KBE knowledge for the long term

Processing Knowledge to Support Knowledge Based Engineering Systems Specification

International audienceDuring design phase, engineering activities typically involve large groups of people from different domains and disciplines. These differences often generate important information flows that are difficult to manage. To face these difficulties, a knowledge engineering process is necessary to structure the information and its use This article, presents a deployment of a knowledge capitalization process based on the enrichment of Methodology and tools Oriented to Knowledge based engineering Applications methodology to support the integration of Process Planning knowledge in a CAD System. Our goal is to help different actors to work collaboratively by proposing one referential view of the domain, the context and the objectives assuming that it will help them in better decision-making

A Methodological Approach to Knowledge-Based Engineering Systems for Manufacturing

A survey of implementations of the knowledge-based engineering approach in different technological sectors is presented. The main objectives and techniques of examined applications are pointed out to illustrate the trends and peculiarities for a number of manufacturing field. Existing methods for the development of these engineering systems are then examined in order to identify critical aspects when applied to manufacturing. A new methodological approach is proposed to overcome some specific limitations that emerged from the above-mentioned survey. The aim is to provide an innovative method for the implementation of knowledge-based engineering applications in the field of industrial production. As a starting point, the field of application of the system is defined using a spatial representation. The conceptual design phase is carried out with the aid of a matrix structure containing the most relevant elements of the system and their relations. In particular, objectives, descriptors, inputs and actions are defined and qualified using categorical attributes. The proposed method is then applied to three case studies with different locations in the applicability space. All the relevant elements of the detailed implementation of these systems are described. The relations with assumptions made during the design are highlighted to validate the effectiveness of the proposed method. The adoption of case studies with notably different applications also reveals the versatility in the application of the method

Knowledge-based engineering as a driver of economic development of the regions of Russia

The main purpose of this paper is the study of the characteristics and the evaluation of the impact of knowledge-based engineering enterprises on the regional economy, where they act as a main driver, a catalyst for the development of a number of sectors of the economy, by enhancing and strengthening the competitive advantages of other industrial enterprises. The fundamental provisions and papers of the reputable scientists in the field of management of regional innovation systems and knowledge-based enterprises, as well as the authoring constitute the theoretical and methodological basis of this study. The methods of empirical knowledge, the inductive and deductive methods are the basic methods used in this study. The reinterpretation of the importance of the knowledge-based engineering for the development of the regional economies is proposed, the principles of spatial distribution and the factors constraining the development of the enterprises in the industry are identified as a result of the work performed. The characteristics of the knowledge-based engineering in the context of increasing and intensifying crisis states of the economy are defined. The necessity of improvement of the state support instruments aimed at addressing the challenges of sustainable development is substantiated. The guidelines related to the requirement to strengthen the innovation and investment sector and development of the export potential of the knowledge-based engineering enterprises and regions are given. The instruments for the development of the knowledge-based engineering enterprises, allowing to bring the regions on the path of sustainable economic growth, are given.peer-reviewe

A new knowledge sourcing framework to support knowledge-based engineering development

New trends in Knowledge-Based Engineering (KBE) highlight the need for decoupling the automation aspect from the knowledge management side of KBE. In this direction, some authors argue that KBE is capable of effectively capturing, retaining and reusing engineering knowledge. However, there are some limitations associated with some aspects of KBE that present a barrier to deliver the knowledge sourcing process requested by the industry. To overcome some of these limitations this research proposes a new methodology for efficient knowledge capture and effective management of the complete knowledge life cycle. Current knowledge capture procedures represent one of the main constraints limiting the wide use of KBE in the industry. This is due to the extraction of knowledge from experts in high cost knowledge capture sessions. To reduce the amount of time required from experts to extract relevant knowledge, this research uses Artificial Intelligence (AI) techniques capable of generating new knowledge from company assets. Moreover the research reported here proposes the integration of AI methods and experts increasing as a result the accuracy of the predictions and the reliability of using advanced reasoning tools. The proposed knowledge sourcing framework integrates two features: (i) use of advanced data mining tools and expert knowledge to create new knowledge from raw data, (ii) adoption of a well-established and reliable methodology to systematically capture, transfer and reuse engineering knowledge. The methodology proposed in this research is validated through the development and implementation of two case studies aiming at the optimisation of wing design concepts. The results obtained in both use cases proved the extended KBE capability for fast and effective knowledge sourcing. This evidence was provided by the experts working in the development of each of the case studies through the implementation of structured quantitative and qualitative analyses

Industrial Design of Electric Machines Supported with Knowledge-Based Engineering Systems

The demand for electric machines has increased in the last decade, mainly due to applications that try to make a full transition from fuel to electricity. These applications encounter the need for tailor-made electric machines that must meet demanding requirements. Therefore, it is necessary for small-medium companies to adopt new technologies offering customized products fulfilling the customers’ requirements according to their investment capacity, simplify their development process, and reduce computational time to achieve a feasible design in shorter periods. Furthermore, they must find ways to retain know-how that is typically kept within each designer to retrieve it or transfer it to new designers. This paper presents a framework with an implementation example of a knowledge-based engineering (KBE) system to design industrial electric machines to support this issue. The devised KBE system groups the main functionalities that provide the best outcome for an electric machine designer as development-process traceability, knowledge accessibility, automation of tasks, and intelligent support. The results show that if the company effectively applies these functionalities, they can leverage the attributes of KBE systems to shorten time-to-market. They can also ensure not losing all knowledge, information, and data through the whole development process