2020-21-Even

ME6806 Introduction to Engineering Optimization

L-T-P-Cr: 3- 0- 0- 3

Pre-requisite: Basic Intermediate (10+2) level mathematics.

Objectives: To solve the various types of basic optimization problems in engineering.

Outcome: Ability to explore and experience the scopes of applications of optimization techniques in real engineering problems.


Module 1: Introduction to Engineering Optimization. Historical Development of Optimization Techniques. Various areas of applications.

Module 2: Optimal Problem Formulation: Design variables, Constraints, Objective Function, Variable bounds. Engineering Optimization Problems. Optimization Algorithms.

Module 3: Single-variable Optimization Algorithms: Optimality Criteria. Bracketing Methods. Region Elimination Methods. Point-Estimation Method. Gradient-based Methods, Methods Requiring Derivatives.

Module 4: Multi-variable Optimization Algorithms: Optimality Criteria. Direct Search Methods. Gradient-based Methods.

Module 5: Constrained Optimization Algorithms: Kuhn-Tucker Conditions. Transformation Methods. Sensitivity Analysis.

Module 6: Integer Programming. Introduction to Non-traditional Optimization Algorithms. Binary and Real-coded Genetic Algorithm..


Lecture No. 1: Introduction to Engineering Optimization. Various areas of applications, Optimal Problem Formulation, Design variables, Constraints. [PDF] [YouTube Video].

Lecture No. 2: Design variables, Design Constraints, Objective Function, Variable bounds. [PDF] [YouTube Video].

Lecture No. 3: Classification of Optimization Problems. [PDF] [YouTube Video].

Lecture No. 4: Classification of Optimization Problems. [PDF] [YouTube Video].

Lecture No. 5: Optimization Problems: Examples. [PDF] [YouTube Video].

Lecture No. 6: Optimization Problems Using MATLAB. [PDF] [YouTube Video].

Lecture No. 7: Single-variable Optimization Algorithms: Optimality Criteria [PDF] [YouTube Video].

Lecture No. 8: Algorithm to Find Global Optima, Region Elimination Methods – Bounding Phase [PDF] [YouTube Video].

Lecture No. 9: Region Elimination Methods – Interval halving [PDF] [YouTube Video].

Lecture No. 10: Quadratic Approximation Method [PDF] [YouTube Video].

Lecture No. 11: Successive Quadratic Approximation Method [PDF] [YouTube Video].

Lecture No. 12: Methods Requiring Derivatives: Newton–Raphson Method, Bisection Method. [PDF] [YouTube Video].

Lecture No. 13: Methods Requiring Derivatives: Secant Method, Cubic Search Method. [PDF] [YouTube Video].

Lecture No. 14: Multi-variable Optimization Algorithms: Optimality Criteria. Direct Search Methods – Nelder and Mead (Simplex Search) Method. [PDF] [YouTube Video].

Lecture No. 15: Multi-variable Optimization Algorithms: Hook and Jeeves (Pattern Search) Method [PDF] [YouTube Video].

Lecture No. 16: Continued….Hook and Jeeves (Pattern Search) Method [PDF] [YouTube Video].

Lecture No. 17: Multi-variable Optimization Algorithms: Powell’s Method [PDF] [YouTube Video].

Lecture No. 18: Multi-variable Optimization Algorithms: Gradient Based Methods – Steepest Descent (Cauchy’s) Method [PDF] [YouTube Video].

Lecture No. 19: Multi-variable Optimization Algorithms: Gradient-Based Methods – Newton’s Method [PDF] [YouTube Video].

Lecture No. 20: Multi-variable Optimization Algorithms: Gradient-Based Methods – Marquardt’s Method [PDF] [YouTube Video].

Lecture No. 21: Multi-variable Optimization Algorithms: Gradient-Based Methods – Conjugate Gradient (Fletcher–Reeves) Method [PDF] [YouTube Video].

Lecture No. 22: Multi-variable Optimization Algorithms: Gradient-Based Methods – Davidon–Fletcher–Powell Method [PDF] [YouTube Video].

Lecture No. 23: Multi-variable Optimization Algorithms: Gradient-Based Methods – Broyden–Fletcher–Goldfarb–Shanno (BFGS) Method [PDF] [YouTube Video].

Lecture No. 24: Constrained Optimization Algorithms: Kuhn-Tucker Conditions [PDF] [YouTube Video].

Lecture No. 25: Constrained Optimization Algorithms: Transformation Methods [PDF] [YouTube Video].

Lecture No. 26: Integer Programming [PDF] [YouTube Video].



ME340202: Reverse Engineering & Rapid Prototyping

L-T-P-Cr: 3- 0- 0- 3

Prerequisite: NIL

Objective: To learn the scope of reverse engineering & rapid prototyping, and implement the ideas in modern technology. 

Outcome: Students can learn the classification of manufacturing processes, Different Manufacturing Systems, Introduction to Rapid Prototyping (RP), Need of RP in context of batch production, FMS and CIM and its application; Basic Principles of Generative Manufacturing Processes.



Lecture No. 1: Introduction to Rapid Prototyping, Classification of Rapid Prototyping Technologies, Stereolithography [PDF] [YouTube Video].

Lecture No. 2: Introduction to Rapid Prototyping — Continued…. [PDF] [YouTube Video].

Lecture No. 3: Additive Manufacturing Process Chain [PDF] [YouTube Video].

Lecture No. 4: Design for Additive Manufacturing [PDF] [YouTube Video].

Lecture No. 5: Additive Manufacturing Technologies – VAT Polymerization [PDF] [YouTube Video].

Lecture No. 6: Materials for VAT Polymerization [PDF] [YouTube Video].

Lecture No. 7: Vector Scan SL [PDF] [].

Lecture No. 8: Extrusion-Based AM Processes [PDF] [].