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Defenition of part features
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Classroom Contents
Manufacturing Systems Technology
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- 1 Manufacturing Systems Approach
- 2 Historical perspective of Design
- 3 Material Handling Systems
- 4 Introduction to computer Aided Design
- 5 Introduction to homogeneous transformation
- 6 Homogeneous Transformation (2)
- 7 3-D transformations
- 8 Introduction to Parametric and non-parametric equations
- 9 Non-parametric representation of equation (Implicitc case)
- 10 Introduction of Hermite cubic spline fit
- 11 Numerical examples of cubic spline curves
- 12 Bezier Curves
- 13 Bezier curves (2)
- 14 Representation of Surfaces
- 15 CAD/CAM data exchange
- 16 Introduction of Manufacturing Processes
- 17 Description of Various Manfaturing processes
- 18 Mathematical modelling of Manufacturing Processes
- 19 Steps involved in developing a process plan
- 20 Worked out examples on designing of various machining operations
- 21 Process plan developement
- 22 Determining machining conditions and machining time
- 23 Machining cost evaluation
- 24 Estimation of Tool life
- 25 Introduction of principles of process planning approach
- 26 The generative CAPP method
- 27 Knowledge based process planning
- 28 Feature recognisation in CAPP
- 29 Defenition of part features
- 30 Problem solving of feature recognisation system
- 31 Computer control of manufacturing systems
- 32 Introduction to Numerically controlled machines
- 33 Motion control of NC machines
- 34 Introduction to NC programming
- 35 Sequention format used in NC programming
- 36 Tab sequential format in NC programming
- 37 Word address format used in NC programming
- 38 Preparatory functions used in Numerical control programming
- 39 G codes , M-codes and Canned cycles
- 40 Motion commands in NC part programming
- 41 Problem solving of NC part programming
- 42 Problem solving of NC part programming -2
- 43 Just in time manufacturing systems
- 44 Toyta Production system
- 45 Pull systems
- 46 Kanban systems
- 47 Deterministic models for Kanban
- 48 Probabilistic cost model for detertmining optimal number of Kanban
