The centre of a Flexible Manufacturing System is its computer control. This paper identifies the component parts for computer control and explains how they have been combined to produce a turnkey cell controller, which provides the intelligence requirements to control cells utilising up to six resources that may be a combination of numerically controlled machine tools, inspection machines, workstations, robots, etc. It provides for various types of part/tool transport and schedules the parts movement within the cell. A second computer handles part programme information within the cell and also gathers management information during the cells operation.

The introduction of Flexible Manufacturing Systems, Cell Technology, and Automated Machining Techniques with the related reduction in manning levels has resulted in the development of tooling systems, tool management systems, and, independent tool magazines to service TURNING MACHINES where a high number of tools are required to cover one shift or unmanned operation.

Actual cutting time (production time) represents a value between 5% and 20% of average machine utilisation time, and developments in cutting materials and geometries have largely exhausted rationalisation possibilities in this area.

Only hardware and software flexibility combined can yield the overall system flexibility required in newly designed FMSs in which the expected part mix is quite large and continually changing.

The paper shows how modular design and integration are fundamental steps in software design for the management and control of FMSs. The main subsystems of a control system, built up by putting together a number of standardized modules requiring little or no customization, are also described. An experimental control system designed according to these criteria is also presented.

In this paper the problem of grouping parts and fixtures in Flexible Manufacturing Systems (FMSs) is discussed. A network formulation of the grouping problem is presented. Based on this formulation an efficient heuristic algorithm is developed. The importance of grouping of parts and fixtures in FMSs as well as some of the computational results are discussed.

This paper describes the use of abstract or metamodels to optimize factory production. The methodology of such models is first outlined and goal functions are then defined. A case already illustrates the application of this simulation procedure.

In simulation studies of an FMS serious congestion problems were observed. Since they had not occurred in earlier FM systems built by the same manufacturer comprising similar machines and control software, it was concluded that they must be caused by the nature of the operation sequences and times of the parts to be produced. This paper reports the results of experiments to assess the dependence of the system's performance on the sequence in which jobs are launched, the relative times of successive operations, machine groupings and product mix. The principal performance measures were the production achieved within a certain time period and the ratio for each part of its processing time to its time in the system. The results are presented, and, contrary to expectation, in most cases there were no statistically significant effects. The possible reasons for and implications of the results are discussed.

The goal of manufacturing system development today is the integration of basic production elements to suit a variety of industrial and shop floor requirements. This may seem an awesome goal, but the authors suggest that careful assessment of requirement at each level and an appreciation of the true level of complexity needed at each level for control purposes, systematic integration will be possible as products become commercially available.

Six levels of integration can be defined for robot-based applications: Level 1 - the single robot application level Level 2 - the system with robots working in teams Level 3 - the combination of different robot systems Level 4 - the combination of feeding work automatically to islands of automation from fully automated warehouses. Level 5 - the integration of planning and control functions Level 6 - the integration of CAD based design functions.

The approach to integration and the level to which it extends will to a large extent be determined by a company's objective, present computer capacity, general production capability and availability of appropriate flexible automation products and systems for use in a given industrial environment. This paper examines the global needs of hierarchical integration, the level of control needed and the implication of VAL-II to this end.

A complete industrial case study is presented, including FMS machinery selection, preliminary production planning, layout and transportation design and performance evaluation, after pallets quantity and fixtures mix optimization.

The case refers to an actual preliminary design performed to submit an offer for a System to a potential customer.

The aim of the paper is to show the impressive help coming to FMS and FAS designers from the use of effective software tools, very easy to deal with, very quick and highly reliable in their quantitative evaluations.

The objectives of this paper are twofold: The first is to briefly review for manufacturing engineers some of the early work undertaken by Artificial Intelligence researchers and the issues addressed which have culminated in today's “expert systems’ or ‘intelligent knowledge based systems’ (IKBS), as they are becoming known.

The second is to indicate some early applications in manufacturing and to point out that any major success in this field requires long-term commitment, in depth familiarity with A.I. techniques and access to A.I. development tools, all of which are currently in short supply internationally.