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Engineering - Production & Process Engineering | Computer-Assisted Management and Control of Manufacturing Systems

Computer-Assisted Management and Control of Manufacturing Systems

Tzafestas, Spyros G. (Ed.)

Softcover reprint of the original 1st ed. 1997, XX, 483 pp. 178 figs.

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Modem manufacturing systems involve many processes and operations that can be monitored and controlled at several levels of intelligence. At the highest level there is a computer that supervises the various manufacturing functions, whereas at the lowest level there are stand alone computer controlled systems of manufacturing processes and robotic cells. Until recenty computer-aided manufacturing systems constituted isolated "islands" of automation, each oriented to a particular application, but present day systems offer integrated approaches to manufacturing and enterprise operations. These modem systems, known as computer-integrated manufacturing (CIM) systems, can easily meet the current performance and manufacturing competitiveness requirements under strong environmental changes. CIM systems are much of a challenge, and imply a systemic approach to the design and operation of a manufacturing enterprise. Actualy, a CIM system must take into account in a unified way the following three views : the user view, the technology view, and the enterprise view. This means that CIM includes both the engineering and enterprise planning and control activities, as well as the information flow activities across all the stages of the system.

Content Level » Research

Keywords » adaptive control - automation - business - control - development - management - manufacturing

Related subjects » Production & Process Engineering - Robotics

Table of contents 

1 Modern Manufacturing Systems: An Information Technology Perspective.- 1 Introduction.- 2 Basic engineering CIM functions.- 3 Information technology enhances the competitive advantage.- 4 An intra-organizational CIM model.- 5 An inter-organizational CIM model.- 6 Artificial intelligence in CIM systems.- 7 Economic evaluation of automated manufacturing.- 8 Conclusions.- References.- 2 Decision Support Systems in Manufacturing Systems Managemen.- 1 Introduction.- 2 DSS in general.- 3 Problem domains.- 4 Problem solving strategy.- 5 DSS for operation scheduling and dispatching.- 6 DSS for process planning.- 7 Conclusions.- References.- 3 AI in Manufacturing: Application to FM S Simulation, Scheduling and Control.- 1 Introduction.- 2 Important issues in application of AI manufacturing.- 3 Knowledge based simulation, evaluation, scheduling, quality control and real-time control of manufacturing systems.- 4 Conclusions.- References.- 4 Modelling and Analyzing Processes in Production and Administration.- 1 Motivation.- 2 ProMAX requirements.- 3 ProMAX architecture and environment.- 4 Impacts on software engineering.- 5 Applications in administration.- 6 Applications in production.- 7 Conclusions.- References.- 5 Quality Management in CIM.- 1 Introduction.- 2 Quality management in information system.- 3 Quality management in production control.- 4 Communication support for quality asssurance.- 5 Product based quality management and manufacturing control.- 6 Conclusions.- References.- 6 Best Practice in Shop Floor Schedulin.- 1 Introduction.- 2 The dominance of MRP and MRPII.- 3 Modern scheduling approaches.- 4 Research method.- 5 Assessment of planning and scheduling performance.- 6 Classification of planning and scheduling.- 7 Analysis of results.- 8 Conclusions.- References.- 7 A Stable, Distributed Routing Policy for Flexible Manufacturing Systems.- 1 Introduction.- 2 Lyapunov stability of DEDS.- 3 Part type routing policy.- 4 Simulation example.- 5 Conclusion.- References.- 8 Shop Controllers-Managers for Intelligent Manufacturing.- 1 Introduction.- 2 Efficiency through economy of scope.- 3 Govern-for-flexibility knowledge architecture.- 4 Applications and example development.- 5 Concluding comments.- References.- 9 A CIM Designed According to Ward and Mello.- 1 The Ward and Mellor methodology.- 2 The Ward and Mellor methodology applied to CIM.- 3 Evaluation of the Ward and Mellor methodology.- 4 Conclusions.- References.- 10 Monitoring and Automatic Supervision in Manufacturing Systems.- 1 Introduction.- 2 Classification of disturbances.- 3 Sensors and methods of monitoring.- 4 Methods of influencing manufacturing systems.- 5 Automatic supervisory systems.- 6 Conclusions.- References.- 11 Petri Nets for Designing Manufacturing System.- 1 Introduction.- 2 Petri nets.- 3 Modeling FMS using Petri nets.- 4 Validation of Petri net models.- 5 Coordination control of production systems by mean of Petri nets.- 6 Design, modelling and analysis of a FMS.- 7 Conclusions.- References.- 12 Petri Net-Based Approach to Synthesis of Intelligent Control Systems for DEDS.- 1 Introduction.- 2 Petri net-based modelling of DEDS.- 3 Analysis of the DEDS control possibilities.- 4 Petri nets in the rule-based knowledge representation.- 5 Utilizing the knowledge base in the control synthesis.- 6 Illustrative examples.- 7 Knowledge inference and automatic reasoning.- 8 An illustrative example.- 9 Conclusions.- References.- 13 New Methods and Tools for Commissioning of Manufacturing Lines with Robots.- 1 Introduction.- 2 State-of-the-art in robot planning and future needs.- 3 Methods and tools for commissioning time reduction.- 4 Conclusions.- References.- 14 Balanced Automation.- 1 Introduction.- 2 Difficulties and contributions.- 3 BAS and shop floor.- 4 BAS and scheduling.- 5 BAS and concurrent engineering.- 6 BAS and virtual enterprises.- 7 Conclusions.- References.- 15 Factory Principles Applied to Analytical Chemistry: An Integrated Laboratory Management System.- 1 Introduction.- 2 Business motives.- 3 Business integration.- 4 Laboratory integration.- 5 Anatomy of the ILMS.- 6 The integration process.- 7 The experience.- References.- 16 The Generalized Network Model: Algorithms and Application to Manufacturing Operations.- 1 Introduction.- 2 Network formulation and mathematical problem statement.- 3 Solution algorithms.- 4 Applications.- 5 Genet-optimizer and Genetexp: A new GN-modelling tool.- 6 Conclusions.- References.- Biographies of the Contributors.

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