JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD

Course Objectives:

• 1. To understand the computer integrated manufacturing systems
• 2. To provide an in-depth understanding of components of knowledge based systems
• 3. To provide an understanding of artificial intelligence
• 4. To design and develop automated process planning
• 5. To develop group technology for intelligent manufacturing systems. Course outcomes: After completing this course, the student will be able to
• 1. Select the necessary tools for computer integrated manufacturing systems
• 2. Use appropriate knowledge of components of knowledge based systems
• 3. Use machine learning techniques for intelligent manufacturing systems
• 4. Apply the concepts of automated process planning
• 5. Apply the group technology for intelligent manufacturing systems.

UNIT - I

Computer Integrated Manufacturing Systems Structure and functional areas of CIM system, - CAD, CAPP, CAM, CAQC, ASRS. Advantages of CIM. Manufacturing Communication Systems - MAP/TOP, OSI Model, Data Redundancy, Top- down and Bottom-up Approach, Volume of Information. Intelligent Manufacturing System Components, System Architecture and Data Flow, System Operation.

UNIT - II

Components of Knowledge Based Systems - Basic Components of Knowledge Based Systems, Knowledge Representation, Comparison of Knowledge Representation Schemes, Interference Engine, Knowledge Acquisition.

UNIT - III

Machine Learning - Concept of Artificial Intelligence, Conceptual Learning, Artificial Neural Networks - Biological Neuron, Artificial Neuron, Types of Neural Networks, Applications in Manufacturing.

UNIT - IV

Automated Process Planning - Variant Approach, Generative Approach, Expert Systems for Process Planning, Feature Recognition, Phases of Process planning. Knowledge Based System for Equipment Selection (KBSES) - Manufacturing system design. Equipment Selection Problem, Modeling the Manufacturing Equipment Selection Problem, Problem Solving approach in KBSES, Structure of the KRSES.

UNIT - V

Group Technology: Models and Algorithms Visual Method, Coding Method, Cluster Analysis Method, Matrix Formation - Similarity Coefficient Method, Sorting-based Algorithms, Bond Energy Algorithm, Cost Based method, Cluster Identification Method, Extended CI Method. Knowledge Based Group Technology - Group Technology in Automated Manufacturing System. Structure of Knowledge based system for group technology (KBSCIT) — Data Base, Knowledge Base, Clustering Algorithm.

REFERENCES:

1 Intelligent Manufacturing Systems/ Andrew Kusiak/Prentice Hall. 2 Artificial Neural Networks/ Yagna Narayana/PHI/2006 3 Automation, Production Systems and CIM / Groover M.P./PHI/2007

R22M.Tech. CAD/CAM JNTUH

4 Neural networks: A comprehensive foundation/ Simon Haykin/ PHI. 5 Artificial neural networks/ B. Vegnanarayana/PHI 6 Neural networks in Computer intelligence/ Li Min Fu/ TMH/2003 7 Neural networks/ James A Freeman David M S kapura/ Pearson education/2004 8 Introduction to Artificial Neural Systems/Jacek M. Zurada/JAICO Publishing House Ed. 2006.

R22M.Tech. CAD/CAM JNTUH

M. TECH. I Year II Sem. (CAD/CAM)