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\ I Engineering Informatics: A Knowledge Based Engineering System in Product Design Mr.M.V.Sulakhe 3 , ProfS.G.Dhande b Mechanical Engineering Department Indian Institute of Technology-Kanpur. Ernail address:[email protected] "Professor in Mechanical Engineering and Computer Science Engineering Indian Institute of TechnologyKanpur, Email address:[email protected] "Research-scholar, r- Abstract :In today/s challenging global market enterprises must innovate to survive and compete with other organizations The application of product lifecycle management (PLM) concept based on knowledge based is a major strategic innovative approach . This will improve the enterprises competitiveness with shorter product development time .concurrent engineering approach, supplychain -management etc:This paper will describe the knowledge based system .Knowledge fusion is modeled in this paper to demonstrate the profile of modeling of knowledge based system. A case study will illustrate this. Key points: Knowledge, Knowledge based system. Knowledge Management, Product Lifecycle Management, Knowledge Fusion, and Engineering Informatics I. INTRODUCTION Definition of Knowledge: Definition of Knowledge range from the practical to the conceptual to the philosophical, and from narrow to broad in scope .The following definition are relevant to the topic of Knowledge Management: • Knowledge is organized information applicable to problem solving [17]. • Knowledge is information that has been organized and analyzed to make it understandable and applicable to problem solving or decision making [l5}. • • • Knowledge encompasses the implicit and explicit restrictions placed upon objects (entities ),operations and relationships along with general and specific heuristics and inference procedures involved in the situation being modeled[13]. Knowledge consists of truths and beliefs,perspectivea and conceptsjudgement and expectations. methodologies and know-howl 18]. Knowledge is the whole set of insights,experiences,and procedures that • are considered correct and true and that therefore guide the thoughts ,behaviors and communications of people] 16]. Knowledge is reasoning about information and data to actively enable performance, problem-solving, decision making, learning and teaching [3]. Knowledge Management (KM) Definition of knowledge management (KM): • KM is the systematic, explicit. and deliberate building, renewal and application of knowledge to maximize an enterprise's knowledge -related effectiveness and returns from its knowledge assets [ 19]. • KM is the process of capturing a company's collective expertise wherever it resides - in databases. on paper. or in people's heads-and distributing it to wherever it can help produce the biggest payofl1.5]. • KM is getting the right knowledge to the right people at the right time so they can make the best decision[ I I). • KM involves the identification and the analysis of available and required knowledge, and the subsequent planning and control of actions to develop knowledge assets so as to fulfill organization objectives[7]. • KM applies systematic approaches to find, understand ,and use knowledge to create value[ 10). • KHt is the explicit control and management of knowledge within an organization aimed at achieving the company's objectives [16]. KM is the formalization of and access to experience, knowledge. and expertise the create new capabilities. enable superior performance. encourage innovation and enhance customer value [3). r: Engineering System: Any system can be defined as: An organized or connected group of objectives; a set or assemblage of things connected ,associated ,or interdependent, so as to form complex unity; a whole composed of parts in orderly arrangement according to some scheme or plan; rarely applied to a simple or small assemblage of things ;.... ;as setoff principles ,ideas ,or statements belonging to some department of knowledge or belief; a department of knowledge or belief considered as an organized whole; a comprehensive body of doctrines, conclusions speculations ,or theses;.[ J 4JHence the system will have the following characteristics and play roles to describe the system as a whole: [4]: J. Assemblage: A system consists of a plural number of distinguishable units. II. Relationships: Several units assembled together are a "Group" or a "Set" Ill. Goal-seeking: An actual system as a whole performs a certain function or aims at single or multiple objectives. IV. Adaptability to environment: A specific, factual system behaves so as to adapt to the change in its surrounding, or external environment The engineering system will consist of the following distinguishable factors. a. Man: The man is seen as in the form of two types of roles .ln first case man acts as a controlling part in the entire system. In the second case man as information processing and taking appropriate decision making role. b. Mechanism or Machine: There will be some machine used for engineering activity. In manufacturing activity there will machine .which will carry out actual manufacturing activity. The machine itself ,will be powered by some prime mover. c. information: The feed back from the system will be in the form of some display or some other form of output. the man will be processing the information based on the goal or objectives of the system Knowledge-Based System Knowledge-based system as a computer system that is programmed to imitate human problem-solving by means of artificial intelligence and reference to a database of knowledge on a particular subject Knowledge- based systems are systems based on the methods and techniques of artificiaJ intelligence. Their core components are the knowledge base and the inference mechanisms. A knowledge base is a special kind of database for knowledge management It provides the means for the computerized collection, organization, and retrieval of knowledge. The most important aspect of a knowledgebase is the quality of information it contains. The best knowledge bases have carefully written articles that are kept up to date, an excellent information retrieval system (search engine), and a carefully designed content format and classification structure. Determining what type of information is captured, and where that information resides in a knowledge base is something that is determined by the processes that support the system. A robust process structure is the backbone of any successful knowledge base. Domain - Ex~ Koo:i,oo..J€ Er,jneer Fig.l Knowledge-Based System Architecture Software engineers: build the inference engine and user interface. Knowledge engineers: design, build, and debug the knowledge base in consultation with domain experts. Domain experts: know about the domain, but nothing about particular cases or how the system works. Users: have problems for the system, know about particular cases, but not about bow the system works or the domain Product Lifecycle Management: PLM is a concept that aims at integrating the various processes and phases involved during a typical product lifecycle with people participating in product development processes [I JConcept --+ Design --+ Prototype --+ Plan --+ Develop --+ manufacture -+ Market -+ Sell --+ Service --+ Re-cycle PLM is a concept that based on horizontal business processes as compared to vertical business units in organization .These business units are linked based on product life stages and processes. As such implementing a scalable and successful PLM strategy requires re-alignment or re-structuring of internal or external organizational structures. Teams and units formation needs to be studied with an aim to provide an effective structure that can utilize the benefits of PLM and technology .In order to create a scalable PLM or collaborative stratergy,studies on organization structure and its relevance with the new PLM strategy needs to be studied. 11.ENGINEERING INFORMAT1CS The explicit knowledge representation formalisms and advanced reasoning techniques are no longer the sole tenitory of artificial intelligence (AI).New knowledge management approaches have earned acceptance in many research communities and have started to emerge in commercial software. In general researchers and commercial developers now employ a large range of advanced computing techniques including AI research .Although some of these computational techniques are still applied for automating existing mundane task, many are capable of enhancing the working environment and empowering engineers in ways that have not previously been possible .ln all areas , which involve knowledge -intensive tasks ,a new philosophy that is specifically tailored to computer applications in engineering is revolutionizing the field an "'Engineering Informatics" is emerging] 12]. Engineering is knowledge -intensive activity guided by appropriately organized knowledge within computers, engineers can perform a variety of engineering tasks more efficiently and effectively then without computer support this has enhanced the value associated with the engineering tasks. Its uses knowledge intensive representations and flexible manipulation methods with the aid of computers .Thus ,through integration and management of various kinds of computational models knowledge - intensive design aims to synthesize more innovative artifacts .to analyze their properties in a better way .to evaluate their performance against requirements under certain circumstances .and to design flexible and effective manufacturing process. these knowledge - intensive models are not simply connected to each other but integrated , so that mutual data exchange and model sharing is possible. Informatics is the study of functions, designed structure ,behaviors and interactions of natural and artificial computational systems. Informatics integrates the use of computational systems. Informatics integrates the use of computational methods such as AI,database, visualization,and robotics. In addition .it also includes new formalizations of specialized engineering knowledge itself i:e .the computational model and analyses of the functions , designed structure and behaviors of engineering systems. Thus engineering informatics provides fundamental theories, tool, and methods to organize, mange, apply, share, and reuse engineering knowledge effectively. Thus, "Engineering Informatics": [6). • Explicit representations, describing physical and abstract components, their attributes and relationships in ways that enable shared reasoning support and incremental improvement. • Symbolic and numerical process models, allowing collaborative reasoning about the conception, design, construction, control and operational behavior of engineering artifacts. • Graphical user interfaces, presenting different visualization. including 3D views and animations of physical objects as well as other representations such as special symbols for engineering diagrams , formulas graphs, network schemas and tables. • Large-scale database, including perhaps millions of related shared, reusable and potentially changing elements. • interactive, enabling individual and distributed users to carry out activities such as interpreting intermediate solution stages; changing product and process model characteristics while examining the consequences; introducing new contextual information even at intermediate stages; modifying solution strategies on the fly; taking cues from active support and exploring solution spaces in order to belp evaluate criteria that are difficult to model explicitly . Engineering Jnformatics also includes areas such as information modes / ontologies for engineering applications, PDM systems, Enterprise Information Management, and data exchange standards] 12]. III. KNOWLEDGE MODELING Knowledge modelling is used In knowledge acquisition activities as a way of structuring projects, acquiring and validating knowledge and storing knowledge for future use. Knowledge models are structured representations of knowledge. They use symbols to represent pieces of knowledge and their relationships. Knowledge models are as follows: (I) symbolic character-based languages logic; (2) diagrammatic representations - networks and ladders; (3) tabular representations - matrices and frames and (4) structured text - hypertext. Most models are constructed from knowledge objects such as concepts, instances, processes (tasks, activities), attributes and values, rules and relations[9]. Knowledge representation IS one of the fundamental topics in the area of artificial intelligence (which investigates representation techniques, tools and languages). Knowledge about the domain and the implementation independent reasoning-process of the KBS however is usually addressed through the use of ontologies and problem-solving methods. There are five prominent representation techniques widely used in developing KBSs; they are attribute-value pairs, object-attribute-value triplets, semantic networks, frames and logic. Models are important for understanding the working mechanisms within a KBS; such mechanisms are: the tasks, methods, how knowledge is inferred, the domain knowledge and its schemas. Modelling contributes to the understanding of the source of knowledge. the inputs and outputs, the flow of knowledge and the identification of other variables such as the impact that management action has on the organizational knowledge, Using conceptual modelling, systems development can be faster and more efficient through the re-use of existing models for different areas of the same domain. Therefore. understanding and selecting the modelling technique that is appropriate for different domains of knowledge will ensure the success of the KBS being designed. IV. KNOWLEDGE-BASED SYSTEM ARCHITECTURE FOR KNOWLEDGE FUSION One of the preferred methods of automating geometry generation is to use parametric template parts. . Using Open-NX functions, a copy of template part can be brought from the TDM Cream Data Manager) library onto the workbench and values of different parameters can be modified programmatically to create a new part off the template part. This approach works well as long as part topology is fixed. If the topology varies among the different parts, which need to be created off the template part, it may require a unique template part for each variation, particularly if the variation is at the wire frame or sketch level. If the variation is at the feature level, sometime one can get away with using one or fewer template parts by suppressing/un-suppressing features programmatically to be able to create unique parts. Regardless, whether the variation is at wireframe level or at feature level, the solution is very cumbersome and awkward. In the case of wireframe variation, multiple template parts need to be maintained, which is a very time consuming and tedious tasks. And in the case of feature variation, the size of the history tree becomes very large as it contains superset of all possible features Knowledge-Fusion (KF) is a knowledge-driven programming environment within NX. KF has solution to both the problems described above. A given part can be broken into bunch of unique features. Udfs (user defined features) can then be created for each unique feature and stored in a library. Using KF, the logic can be defined (in a .dfa file) to use only required features (udfs) for a given situation, to create a unique part. Hence the part history does not contain extra features. Similarly, KF allows one to program logic within a single .dfa file to create unique wire frames (sketches).Knowledge Fusion permits the creation of powerful applications that take advantage of engineering knowledge. It supports the capture and re-use of design intent and user intelligence to increase design speed and productivity while intelligently controlling change propagation. Designers and application developers can work with Knowledge Fusion directly within the NX user environment to create rules that capture design intent. These rules can be used to drive product design, ensuring that engineering and design requirements are fully understood and fully met. Knowledge Fusion delivers new cost and time savings, and raises quality by standardizing design processes, enforcing sourcing practices and incorporating, up front, the manufacturing and performance constraints into the design environment. I KM",* F.,..,. , ! constructing and documenting the artifacts of knowledge-based systems. It is a knowledge modeling language that can be used with all major object technologies and applied to knowledge-based systems in various application domains and task types. The UML profile is based on the UML 2.0 specifications and is defined by using the metamodelling extension approach of UML. It is being designed with the following principles in mind: () UML integration: as a real UML based profile, the knowledge modeling profiles defined based on the metamodel provided in the UML superstructure and follows the principles of UML profiles as defined in the UML 2.0 and (2) Reuse and minimalist: wherever possible, the knowledge modeling profile makes direct use of the UM L concepts and extends them, adding new concepts only where needed. The initial knowledge modeling profile is composed using four main packages based on their role and relationship in modeling KBS. It consists of the Knowledge Model package. Task Knowledge package, Inference Knowledge package and Knowledge package. This package forms the knowledge modeling language core model and is shown in Figure 4 as the knowledge modeling profile. I ~MMt~PnIit I Kro~Mnl Rcle rypt ~I I ~!lS1 I I ~ '-----, KDl~B~ 1\ I IM~M~~ , Fig.3 Knowledge Fusion Methodologies T8SlKro~ v. I I I~' Com:epl$ I Kro~ I .s-> lIIfmm:e KmI>'Edte UML KNOWLEDGE MODELING PROFILE FOR KNOWLEDGE FUSION The aim of the UML Knowledge Modeling Profile is to define a language for designing, visualizing, specifying, analyzing, I FigA Knowledge Modeling Profile Core package 19] I I TlISkKro~ Fig.5 Domain Knowledge package Fig.8 Task Knowledge Package •••••• I Typo --~ I I I 1 --~ I~~~ I I C~.Rulo> Typo 1 OsItT~.fuooe.d Typo I I I I I 1 1 u,;...~ Do.-(ano.d Typo I I r I Fig.9 Rule Type Package Fig.6 Concept Package Kncw'lfflglt Ba..<-t' ~e<tclts.slt Fig.7 Relation Package Fig.l 0 Knowledge Base Package 1 c..... .. I qu_ 1 Uoo,o.-.. Typo I Qank Web 1 Crankshaft ~ \ ~ \ . I',. --I rveJ· - - ,I . :0 p,.;A~ormtion c, Crank . An TIITe in hrs II F\)st·Autormtion Crank Tlrre in hrs Fig.12 Radar Chart representing Pre-automation and Post-automation timings for Modeling Crankshaft Fig. I I Mathematical Model Package VI. CASE STUDY: CRANFSHAFT MODELING USING KF To plot result it was decided to measure the pre-automation time for each component of the Crankshaft. while completing the 3D model of the Crankshaft by different modelers having at least one-year experience in NX-4.1t was observed that there was difference in the timing of each modeler to complete the 3D model of the Crankshaft and Crankshaft component. As a result average time for completing the 3D model of Crankshaft and Crankshaft component was considered. Now post-automation nrmngs for completing the Crankshaft and Crankshaft component 3D model were also measured for validation of Automating process. Pre·Automation TIlDe in POlI·Automation TIlDe in ~s ~s ern Web 0.1 0.1 Crcd:Pin 01) 0.1 ern 03 0.1 MBJ 03 0.1 1 01 I Crroliaft Tables I: Timings for completing Crankshaft and Crankshaft component Radar chart shows that modeling time of Crankshaft has reduced to 90 percent by using Knowledge Fusion for automating the modeling procedure VII CONCLUSION: Engineering Informatics is k.nowledge based system and can be used in successful implementation of Knowledge Based Engineering application Knowledge Fusion. Knowledge Fusion delivers new cost and time savings, and raises quality by standardizing design processes. enforcing sourcing practices and -incorporating, up front, the manufacturing and performance constraints into the design environment. Managing knowledge through knowledge-based systems is an important part of an enterprise's knowledge management initiatives. The process of constructing KBSs is simiJar to other software systems with conceptual modelling playing an important role in the development process. Software engineering has adopted UML as a standard for modelling, but the field of knowledge engineering is still searching for the right technique. UML could be adopted for knowledge modelling but UML in its current state has its limitations, it is an extensible language and thus can be used to support the k.nowledge modelling activity through the profiles mechanism. VIII REFERENCE: [I) A. Sharma. "Collaborative product innovation: integrating elements of CPI via PLM framework" Computer-Aided Design, Science direct, Elsevier, 2 February 2005 [2] Broking, A. "Introduction to intellectual Capital." The Knowledge Broker Ltd. Cambridge, England.I 996 [3] Beckman, T. "A Methodology for Knowledge management." International Association of Science and Technology for Development (lASTED) Al and Soft Computing Conference. Banff, Canada. 1997. 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