Basic concepts and the function view of an open-systems architecture for modelling CIM systems, called CIM-OSA, have been presented in Part I of this paper. In this second part, the information view of CIM-OSA is discussed for modelling information systems of CIM systems at three distinct levels (requirements definition, design specification and implementation description). To serve this purpose, the CIM-OSA information modelling paradigm makes use of an object-entity relationship attribute (OREA) approach which is described.

CIM-OSA Part 2: information view

The paper reviews the reasons for the growing interest in product modelling and the application of data-modelling concepts to cad/cam development. The information needs for the design of products, as distinct from those for manufacturing, business and other applications, are articulated. Given these needs, five information models used in product modelling are reviewed: the entity-relationship model, niam, idef1x, express and EDM.

Information models for use in product design: a comparison

Product data models can be viewed as a class of semantic data models which take into account the needs of engineering data. In this paper the characteristics for semantic data models defined by Peckham and Maryanski [8] are taken as a starting point. The relevance of these characteristics to product data is shown. The capabilities of a prototype product description system are described in relation to the same characteristics. Additional data modeling characteristics such as parametrization and data sharing, which have been introduced to support engineering design, are then described. These can be viewed as extending beyond the accepted capabilities of semantic data models.

Product data models

An information modeling and design approach using a metadatabase framework and a two-stage entity-relationship (TSER) methodology to address the system integration problem is presented. The metadatabase framework is a simplification design entailing a federated system architecture, an integrated information model, and a knowledge-based control methodology. The TSER methodology, used for the information model, features both a semantic modeling construct encompassing the US Air Force's IDEF approach and an operational modeling construct for consolidating data structures across the manufacturing facility. Preliminary results of a pilot study, including both modeling and software system development, are included to illustrate the theoretical work. >

Information modeling for computerized manufacturing

Deductive databases result from the integration of relational database and logic programming techniques. However, significant problems remain inherent in this simple synthesis from the language point of view. In this paper, we discuss these problems from four different aspects: complex values, object orientation, higher-orderness, and updates. In each case, we examine four typical languages that address the corresponding issues.

/pdf/deductive-database-languages-problems-and-solutions-1cf428iw6m.pdf

Deductive database languages: problems and solutions

F ory a$itomation, a relatively ne ec ology, is being pursued by a ndustrial countries. It promises t ncrease factory productivity ; i ove product quality; relieve ers from repetitive and bonnt s and unpleasant, hazard-ou ,{vironments; and minimize Suction delays and resource rwaste. An economically competitive automated system integrates the various control processes and data used in design, manufacturing, sale, and service of products so that feedback from any of the processes can be used to effect better design, planning, control, and production. Computer-integrated manufacturing , or CIM, is a way to achieve such integration through computers and computational techniques in design, planning, and manufacturing. Central to CIM is a database management system that facilitates the sharing of data among the component systems. Recent efforts in manufacturing integration the IPAD, ICAM,I and AMRF2 projects among others34-have recognized the importance of a stabase management facility for m~ging diverse CAD/CAM data. Fuxe CIM systems will likely be networlsf heterogeneous computer systems, each of which will control and support the operations of numeric control machines, robots, transport vehicles, and machine tools. This network structure seems probable because (1) low-level manufacturing equipment is likely to be produced by different manufacturers who use different computer hardware and software for equipment control and support , and (2) factories that are moving towards automation are likely to add to existing computer facilities rather than to replace them entirely. Also, we can expect the component systems in a factory network to have widely differing data management facilities, from simple application programs, to file management systems, to full-scale DBMSs. Because the nature of a CIM system is heterogeneous, a number of database requirements must be considered. First, data to control and support design, manufacturing, sale, and service of products will be physically stored at and processed by the component systems. Data sharing among these component systems requires a common data model that explicitly defines the structures, constraints, and operations (e.g., expert rules) that COMPUTER 34

Modeling lntegrated Manufacturing Data with SAM

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Modeling lntegrated Manufacturing Data with SAM