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Knowledge management is the process through which organizations generatevalue from their intellectual and knowledge-based assets. The world of industry hasbeen changed by the developing information infrastructure. Over the past few years,the Internet has been a market maker, a market destroyer, an industry change-agent,and even an inverter of traditional ways of conducting business. The Internet andWeb technologies have presented established firms with both opportunities as wellas threats. Several buzzwords such as E-hubs, Internet exchanges, E-markets, E-procurement, and E-exchanges have been coined by industry to refer to differentmodels of B2B E-commerce (Ranganathan, 2003).However, only few of the started projects have been sustainable. A survey oforganizations, in which enterprise systems management solutions were deployed,found that only 24% of the implementations were considered successful, 64% ofmanagement had mixed feelings about the success of the projects, and theremainder felt their projects were failures (Gallagher, 1998). That is becausemodern e-systems set high requirements to their reliability and safety. Reliabilitybenchmarking indicates that 70% of reliability issues are operations induced versusthe mechanical integrity concerns (Birchfield, 2000). The impact and knowledge ofa single person is a critical factor in success of the process unit operation(Schustereit, 2002). Especially it applies to rare conditions that require promptactions and are hard to predict or occur sporadically. Decision support systems thatcan predict process behaviour in critical situations will significantly reduce the riskof hazard conditions and allow optimising of process parameters. The conventionalmanufacturing planning is based on the priority of relations between the part andthe manufacturing plan. Available machine tools are defined from the set of existingin the companies’ machine tools. The set can be expanded when the concept ofvirtual enterprise or extended enterprise is used. Scheduling process should be ingood accord with process planners using different task variants (“down-up” processplanning) (Papstel, 2003). The specific machining database stores related data onthe available machines, the kinds of operation that can be performed by eachmachine, quality parameters including obtainable surface finish for individualmachines, and operating cost for each machine (Shehab, 2002). An enterpriseshould expand the business market to the customer and supplier, which causes theenterprises to be expanded and globalized. Advanced manufacturing models, inwhich the member enterprises located in different regions, or even distributedthroughout the world, have closer relationships and collaborative benefits bycooperating in the development of products, which need Internet/Extranet/Intranetbasedinformation systems to support mutually beneficial collaboration in qualitymanagement activities among its members (Tang, 2002).At a cluster level, knowing the real-time functionality expectations andevaluating the experience on speed performance and limits of data interactionamount of commercial solutions drive the cluster to build up a new system(Viharos, 2003).A survey of e-Work applications (Nof, 2005) has shown, that e-X is not the sameas X. e-Work and e-Production/e-Business are not the same as work, production,and business. A traditional operation cannot be copied as-is to an e-Activity, somechanges must be made. For example, comparing a search for information by threegenerations of specialists (a. Search through manual records; b. Search of a12
database by a query; c. Search of the Web by a browser) one can realise that they allare different.1.2. Problem settingThe recent trend in machining is to add intelligence to the machine tools and CNCsystem (Altintas, 2000). International technology research centres forecast that forthe year 2010 about 90–95% of information on the Web will be tied withintercommunication of intelligent machines for solving predetermined tasks, andonly the remaining relatively small part is connected to direct humancommunications. The term Intelligent Machining Module (IMM) has been broughtinto use to describe CNC systems that allow limited manipulation of machiningconditions by the end users, but where additional sensors, which can measure theforces, vibrations, temperature, and sound during machining, are installed onmachine tools. Various intelligent machining tasks, such as adaptive control, toolcondition monitoring and process control, can simultaneously run on the system(Håkansson, 1999). To reach this goal the system has to be equipped withmeasurement hardware and data transfer and -processing software and analysismethods. Integration of different sensors and measuring instruments into a uniformsystem is a problem of the modern technological machinery (Russo, 1999).Mathematical models, which correlate the relationship between measured sensorsignals and state of machining, are formed. The mathematical models are coded intoreal-time algorithms, which monitor the machining process and send commands toCNC for corrective actions. It appears that the existing knowledge based decisionsupport systems, e.g. Nexus Oz from Nexus Engineering or neural network systemshave certain disadvantages: a system could fail in previously unknown conditionsand require tens of years of experience to fulfil database of real needs. Therefore,some additional techniques should be developed for a more effective support ofcontrol of engineering systems.1.3. Main objectivesThe primary aim of this work is the development of a general concept andmathematical models for monitoring technological processes and productionsystems in engineering industry suitable for SME.To obtain the goal the following tasks have to be solved.• Firstly, data flows in machining units should be analysed and modelledregarding both technological process and characteristics of the machining units(MU).• Secondly, the models should be generated by using certain patterns which couldbe verified.13
Page 1 and 2: THESIS ON MECHANICAL AND INSTRUMENT
Page 3 and 4: MASINA- JA APARAADIEHITUS E283
Page 5 and 6: PREFACEThe mankind of the 21 st cen
Page 7 and 8: Paper I............................
Page 9 and 10: XII. Riives, J., Papstel, J., Otto,
Page 11: 1. INTRODUCTION1.1. BackgroundManuf
Page 15 and 16: Porter’s Diamond Model task an on
Page 17 and 18: Figure 2.1 Use of modelling in prod
Page 19 and 20: However, it should be noticed that
Page 21 and 22: Figure 2.6 Depth of field/magnifica
Page 23 and 24: 2.3. Prediction of machining accura
Page 25 and 26: uFy=ybsinptyOl 1ulFAϕFigure 2.10 C
Page 27 and 28: is2.5. Dynamical model with two deg
Page 29 and 30: According to de Moivre’s formulaw
Page 31 and 32: constant of the lathe. Figure 2.11
Page 33 and 34: Analogically, test results and resu
Page 35 and 36: Figure 3.1 Model checking in an ope
Page 37 and 38: Figure 3.3 A snapshot of Uppaal mod
Page 39 and 40: Figure 3.5 FMS at TUTM9M8M4M1M2M7M5
Page 41 and 42: Turning and milling operations can
Page 43 and 44: 4. MODELS FOR MONITORING THE RESOUR
Page 45 and 46: As assumptions to solve the queries
Page 47 and 48: Regarding co-operation networks the
Page 49 and 50: • The system offers dynamic and u
Page 51 and 52: Figure 4.5 Associations in the Inno
Page 53 and 54: An enterprise-centred mapping and a
Page 55 and 56: Better efficiency and transparency
Page 57 and 58: institutions: in enterprises of div
Page 59 and 60: 1. Tehnoloogiaseadme tasemel on loo
Page 61 and 62: REFERENCESAltintas, Y. (2000). Manu
Page 63 and 64:
Russo, M.F. (1999). Automating Scie
Page 65 and 66:
Paper IIAryassov, G., Otto, T., Gro
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Paper IVOtto, T., Papstel, J., Riiv
Page 69 and 70:
ELULOOKIRJELDUS (CV)1. IsikuandmedE
Page 71 and 72:
CURRICULUM VITAE (CV)1. Personal da
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DISSERTATIONS DEFENDED ATTALLINN UN
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