SYLLABI

and

SCHEME OF TEACHING

MASTER OF ENGINEERING

IN

MECHANICAL ENGINEERING

(ROBOTICS)

(2020-22)

MECHANICAL ENGINEERING DEPARTMENT

NATIONAL INSTITUTE OF TECHNICAL TEACHERS TRAINING & RESEARCH

CHANDIGARH

January, 2020

Instructions to Paper Setter

1. The examiner shall set eight questions, taking four questions each from Part-

A and Part-B of the syllabus. The candidate is required to attempt five

questions in all, taking at-least two questions from each Part-A and Part-B of

the question paper. The Question-paper should be fairly distributed over the

whole course of study and not concentrated on any one or a few portions only.

2. The Question-paper is to be set strictly according to the syllabus and not

according to the last years question paper, which is sent just as a sample only.

3. Special instructions if any, in regard to the paper should be followed.

4. For University examination the maximum marks are 50 and duration of

examination is 3 hours.

STUDY & EVALUATION SCHEME

M.E. MECHANICAL ENGINEERING (ROBOTICS) – REGULAR PROGRAMME

FIRST SEMESTER

CODE SUBJECT

SCHEDULE FOR

TEACHING CREDITS

MARKS

L P TOTAL Internal

Assessment

University

Examination TOTAL

MER-601 Industrial Robotics 4 - 4 4 50 50 100

MER-602 Mechanism Design &

Analysis 4 - 4 4 50 50 100

----- Elective Subject – I 4 - 4 4 50 50 100

CSEI-8106* Machine Learning 3 - 3 3 50 50 100

CSEI-8107* Fundamentals of IoT 3 - 3 3 50 50 100

MER-651 Robotics Lab – I - 4 4 2 50 - 50

SEMESTER TOTAL: 20 300 250 550

* Industry Core I & II, Common with M.E. Computer Science & Engineering (Internet of Things)

SECOND SEMESTER

CODE SUBJECT

SCHEDULE FOR

TEACHING CREDITS

MARKS

L P TOTAL Internal

Assessment

University

Examination TOTAL

MER-603 Sensors & End-Effectors 4 - 4 4 50 50 100

MER-604 Computer Programming for

Robotic Applications 4 - 4 4 50 50 100

----- Elective Subject – II 4 - 4 4 50 50 100

CSEI-8206** Industrial IoT 3 - 3 3 50 50 100

CSEI-8207** Big Data Analytics 3 - 3 3 50 50 100

MER-652 Robotics Lab – II - 4 4 2 50 - 50

SEMESTER TOTAL: 20 300 250 550

** Industry Core III & IV, Common with M.E. Computer Science & Engineering (Internet of Things)

THIRD SEMESTER

CODE SUBJECT

SCHEDULE FOR

TEACHING CREDITS

MARKS

L P TOTAL Internal

Assessment

University

Examination TOTAL

----- Elective Subject - III 2 - 2 2 50 50 100

----- Elective Subject - IV 2 - 2 2 50 50 100

MER-751 Preliminary Thesis - 20 20 10 - - -

SEMESTER TOTAL: 14 100 100 200

FOURTH SEMESTER

CODE SUBJECT

SCHEDULE FOR

TEACHING CREDITS

MARKS

L P TOTAL Internal

Assessment

University

Examination TOTAL

MER-752 Thesis - 32 32 16 100 100 200

Internal assessment is based on the following criterion:

Grade Condition

A+ Publication from Thesis in SCI indexed journal

A Publication from Thesis in Scopus indexed journal

B+ Publication from Thesis in UGC journal OR Scopus indexed conference proceedings

B Publication from Thesis in International Conference

C+ Publication from Thesis in National Conference

Final Grade will be average of the grades of internal assessment and university viva-voce

examination

NOTE: Requirement for the award of ME Mechanical Engineering (Robotics) degree is 70

credits with minimum CGPA of 6.0

LIST OF ELECTIVES - M.E. MECHANICAL ENGINEERING (ROBOTICS)

CODE SUBJECT Credits

ELECTIVE SUBJECT – I

MER-701 Computer Aided Design 4

MER-702 Mechatronics Systems 4

MER-703 Digital Logic & Circuits 4

MEI-6103# Digital Signal Processing 4

ELECTIVE SUBJECT – II

MER-704 Robot Motion Planning 4

MMT-608## Digital Manufacturing 4

MMT-655## Optimization Techniques 4

MMT-657## Research Methodology 4

ELECTIVE SUBJECT – III

MMT-671##

Introduction to Materials Science and Engineering 2

MMT-676##

Rapid Manufacturing 2

MMT-677##

Basics of Finite Element Analysis - I 2

MMT-678##

Machinery Fault Diagnosis And Signal Processing 2

MER-705 Advanced Materials and Processes 2

MER-706 Manufacturing Systems Technology 2

MER-707 Mathematical Modeling of Manufacturing Processes 2

MER-708 Industrial Safety Engineering 2

MER-709 System Design for Sustainability 2

MER-710 Applied Ergonomics 2

ELECTIVE SUBJECT – IV

MMT-682##

Blockchain Architecture Design and Use Cases 2

CSEI-8303###

Deep Learning 2

MER-712 The Joy of Computing using Python 2

MER-713 Natural Language Processing 2

MER-714 Fuzzy Systems and Applications 2

MER-715 Introduction to Computer Vision 2

MER-716 Computer Vision 2

MER-717 Digital Image Processing 2

MER-718 Pattern Recognition and Application 2

MER-719 E-Business 2

MER-720 Patent Law For Engineers And Scientists 2

MER-721 Soft skills 2

# Common with M.E. Electrical Engineering (Instrumentation & Control)

## Common with M.E. Mechanical Engineering (Manufacturing Technology)

### Common with M.E. Computer Science & Engineering (Internet of Things)

MER-601: INDUSTRIAL ROBOTICS

Maximum marks: 50 L P

Time Allowed: 3 hours 4 --

PRE REQUISITES

Basic Engineering Mathematics

OBJECTIVE

The objective of this course is to impart knowledge about industrial robots for their control and design.

LEARNING OUTCOMES

After the completion of this course, the students will be able to

Perform kinematic and dynamic analyses with simulation

Design control laws for a robot

Integrate mechanical and electrical hardware for a real prototype of robotic device.

Select a robotic system for given application.

DETAILED CONTENTS

PART − A

1. Introduction to Robotics

1.1. Robot Subsystems

1.2. Classification of Robots

1.3. Grippers

1.4. Sensors

1.5. Industrial Applications

2. Transformations

2.1. Robot Architecture

2.2. Pose of a Rigid body

2.3. Coordinate Transformation

2.4. Denavit and Hartenberg (DH) Parameters

3. Kinematics

3.1. Forward Position Analysis

3.2. Inverse Position Analysis

3.3. Velocity Analysis: The Jacobian Matrix

3.4. Link Velocities

3.5. Jacobian Computations

3.6. Forward and Inverse Velocity Analysis

3.7. Acceleration Analysis

PART − B

4. Statics and Manipulator Design

4.1. Forces and Moments Balance

4.2. Equivalent Joint Torques

4.3. Role of Jacobian in Statics

4.4. Manipulator Design

5. Dynamics

5.1. Inertia Properties

5.2. Euler-Lagrange Formulation

5.3. Newton-Euler Formulation

5.4. Recursive Newton-Euler Algorithm

5.5. Dynamic Algorithms

6. Robot Trajectory Planning and Control

6.1. Multivariable Robot Control

6.2. Stability of Multi-DOF Robot

6.3. Linearized Control

6.4. Proportional Derivative (PD) Position Control

6.5. Computed-torque (Inverse Dynamics) Control

6.6. Joint Space Planning

6.7. Cartesian Space Planning

6.8. Point-to-Point vs. Continuous Path Planning

BOOKS:

Introduction to Robotics, S. K. Saha, McGraw Hill Education (India) Pvt. Ltd.

Introduction to Robotics – Analysis, Control, Applications, Saeed B. Niku, Wiley India Pvt. Ltd.

Introduction to Robotics – Mechanics and Control, John J. Craig, Pearson Education Inc.

Robotics & Control – R.K. Mittal & I.J. Nagrath – TMH Publications

Robotics for Engineers - Yoram Korean- McGrew Hill Co. Industrial Robotics – Technology, Programming and Applications - M.P.Groover, M.Weiss, R.N.Nagel,

N.G.Odrey.

MER-602: MECHANISM DESIGN AND ANALYSIS

Maximum marks: 50 L P

Time Allowed: 3 hours 4 --

PRE REQUISITES

Engineering mathematics

Engineering mechanics

OBJECTIVE

The objective of this course is to develop competency in graphical and analytical method for solving

problems in static and dynamic force analysis.

LEARNING OUTCOMES

After the completion of this course, the students will be able to

Understand the fundamentals of the theory of kinematics and dynamics of machines.

Understand techniques for studying motion of machines and their components.

Use computer software packages in modern design of machines

DETAILED CONTENTS

PART − A

1. Basics of Mechanisms and Machines

1.1 Types of Motion, Links, Kinematic Pair, Types of Joints, Degree of Freedom

1.2 Classification of Kinematic Pairs, Kinematic Chain, Linkage, Mechanism and Structure

1.3 Planar, Spheric, and Spatial Mechanisms

1.4 Inversions of Four-bar and Slider Crank Mechanism

1.5 Grashof's Law and Mechanical Advantage

1.6 Mobility of Mechanisms, Transmission Angle

1.7 Pantograph, Straight Line Mechanisms

2. Kinematic Synthesis of Mechanisms

2.1 Velocity and Acceleration Diagrams for four bar and six bar mechanisms, Velocity by Instantaneous

Centre Method, Klein Construction, Aronhold-Kennedy Theorem of Three Centers

2.2 Radius of Curvature of a Point Trajectory Using Kinematic Coefficients

2.3 Stages of Kinematic Synthesis and Errors, Chebyshev Spacing of Precision points

3. Vibrations

3.1 Definition and Types of vibrations

3.2 Natural frequencies of simple systems

3.3 Types of damping- Analysis with viscous damping - Derivations for over, critical and under damped

systems, Logarithmic decrement and Problems.

3.4 Principle modes of vibrations, Normal mode and natural frequencies of systems

3.5 Dynamic testing of machines and structures

PART − B

4. Friction and Friction Drives

4.1 Types & Laws of friction, Coefficient of Friction, Uniform Pressure and Uniform Wear

4.2 Law of Belting, Ratio of Friction Tensions in Belts, Power Transmitted by Belts and Ropes,

Maximum Power Transmission by Belt

4.3 Classification of Gears, Gear Terminology

4.4 Law of Gearing, Velocity of sliding, Gear Teeth Profile, Path of Contact, Arc of Contact, Contact

Ratio

4.5 Interference of in Involute Gears, Minimum Number of Teeth, Undercutting, Gear Forces, Different

Types of Gear Trains, Analysis of Epicyclic Gear Train

5. Structural Analysis

5.1 Free body diagram and its importance

5.2 Classification of structures and components

5.3 Notion of stress – normal stress, shear stress and bearing stresses

5.4 Stresses on inclined plane in an axial member

5.5 Strain – normal strain, shear strain

5.6 Mechanical properties – elasticity, plasticity, creep, fatigue, buckling etc.

5.7 Deformation of axial members

6. Finite Element Analysis

6.1 Introduction to FEA

6.2 Steps of Finite Element Modeling & Analysis

6.3 Structural Analysis of mechanism

6.4 Modal Analysis of mechanism

6.5 Optimization using FEA Technique

Text books:

1. Dally and Riley, “Experimental stress analysis”, McGraw-Hill International Student Edition, McGraw-

Hill Book Company.

2. Theory of Machines and Mechanisms, A. Ghosh and A.K. Mallik, Affiliated East- West Press

3. Theory of Machines and Mechanisms, J. E. Shigley and J. J. Uicker, 2nd Ed., McGraw-Hill

4. Ferdinand P. Beer, E. Russell Johnston and Jr.John T. DeWolf “Mechanics of Materials”, Tata

McGraw-Hill, Third Edition, SI Units

References:

1. Kinematic Synthesis of Linkages, R. S. Hartenberg and J. Denavit, McGraw-Hil

2. Design of Machinery: An Introduction to the Synthesis and Analysis of Mechanisms and Machines,

Robert L.Norton, Tata McGraw-Hill, 3rd Edition

3. Ferdinand P. Beer, E. Russell Johnston and Jr.John T. DeWolf “Mechanics of Materials”, Tata

McGraw-Hill, Third Edition, SI Units

CSE 8106: MACHINE LEARNING (Common with M.E. Computer Science & Engineering - Internet of Things)

Maximum marks: 50 L P

Time Allowed: 3 hours 4 --

PRE REQUISITES

Basics of Probability

Linear Algebra

Calculus

OBJECTIVE

This course will serve as a comprehensive introduction to various topics in machine learning.The objective

is to familiarize the audience with some basic learning algorithms and techniques and their applications, as

well as general questions related to analyzing and handling large data sets. At the end of the course the

students should be able to design and implement machine learning solutions to classification, regression, and

clustering problems; and be able to evaluate and interpret the results of the algorithms.

LEARNING OUTCOMES

At the end of the course, the students will be able to:

Understand the fundamental issues and challenges of machine learning.

Understand the strengths and weaknesses of many popular machine learning approaches.

Interpret the underlying mathematical relationships within and across Machine Learning algorithms

and the paradigms of supervised and un-supervised learning.

Design and implement various machine learning algorithms in a range of real-world applications.

DETAILED CONTENTS

PART − A

1. Introduction

1.1. Supervised Learning

1.2. Decision Trees & CART

1.3. Linear regression

1.4. SGradient Descen

2. Probabilistic Models

2.1. Bayes classifier

2.2. Bayes classifier

2.3. Hidden Markov models (HMMs) for pattern classification

3. Design and Analysis of Experiments

3.1. Cross validatio

3.2. Performance measures

3.3. CI Estimation

3.4. Hypothesis Testing

PART − B

4. Unsupervised Learning

4.1. Criterion functions for clustering

4.2. Techniques for clustering -- K-means clustering

4.3. Gaussian Mixture Models

4.4. Hierarchical clustering

4.5. Density based clustering

5. Dimensionality Reduction Techniques

5.1. Principal component analysis

5.2. Fisher discriminant analysis

5.3. Multiple discriminant analys

BOOKS

Introduction Machine Learning by Tom Mitchell

Introduction to Machine Learning by Ethem Alpaydin

Introduction to Statistical Learning, Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani,

Springer, 2013.

Pattern Classification, 2nd Ed., Richard Duda, Peter Hart, David Stork, John Wiley & Sons, 2001.

CSEI 8107 FUNDAMENTALS OF IOT (Common with M.E. Computer Science & Engineering - Internet of Things)

Maximum marks: 50 L P

Time Allowed: 3 hours 4 --

PRE REQUISITES

Basics of Sensors

Machine Level Programming

OBJECTIVE

This course focuses on the latest microcontrollers with application development, product design and

prototyping. This also focuses on interoperability in IoT along with various IoT Platforms for application

development.

LEARNING OUTCOMES

At the end of the course, the students will be able to:

Understand the various network protocols used in IoT

Understand the role of Big Data, Cloud Computing and Data Analytics in a typical IoT system.

Design a simple IoT system made up of sensors, wireless network connection, data analytics and

display/actuators, and write the necessary control software.

Build and test a complete IoT system.

DETAILED CONTENTS

PART − A

1. Introduction

1.1. Introduction to IoT

1.2. Sensing

1.3. Actuation

1.4. Basics of Networking

1.5. Communication Protocols

1.6. Sensor Networks

1.7. Machine to Machine Communications

1.8. Understanding of the IoT ecosystem

1.9. various layers in building an IoT application and interdependencies

2. INTEROPERABILITY IN IoT

2.1. Introduction to Arduino Programming

2.2. Integration of Sensors and Actuators with Arduino

2.3. Introduction to Python programming

PART − B

3. SDN FOR IoT

3.1. Introduction to SDN

3.2. SDN for IoT

3.3. Data Aggregation

3.4. Handling and Analytics

3.5. Cloud Computing

3.6. Sensors

3.7. Fog Computing

3.8. Understanding of the various protocols being used in IoT like MQTT, AMQP, REST API

4. Iot Platforms and Applications

4.1. Understanding of the IoT platforms like PTC Thingworx and IoT frameworks like MS Azure

4.2. Understanding of the usage of these platforms to build applications like Smart Cities and Smart

Homes

4.3. Connected Vehicles

4.4. Smart Grid

4.5. Case Study: Agriculture, Healthcare, Activity Monitoring

BOOKS

David Etter, “IoT (Internet of Things) Programming: A Simple and Fast Way of Learning IoT,” Kindle

Edition

Jan Holler, VlasiosTsiatsis, Catherine Mulligan, Stefan Avesand, Stamatis Karnouskos, and David

Boyle, “From Machine to Machine to the Internet of Things: Introduction to a New Age of Intelligence,”

Elsevier Science Publishing Co. Inc, 2014.

Pethuru Raj and Anupama C. Raman, “The Internet of Things: Enabling Technologies, Platforms, and

Use Cases,” 1st Edition, Auerbach Publications, 2017

Yasuura, H., Kyung C.M., Liu Y., and Lin Y.L., “Smart Sensors at the IoT Frontier,” 1st Edition,

Springer International Publishing, 2018.

MER-651: ROBOTICS LAB – I

L P

-- 4

Note: The internal evaluation of the work done by the student will be based on a file documenting the practical work

carried out during the course followed by a viva-voce examination.

PRACTICE TASKS

1. Study of a robotic manipulator and its components.

2. Study of robot Teach-pendant functions and its use in moving robot wrist in joint space and

Cartesian space.

3. Use of teach-pendant for recording wrist positions and speed control.

4. Use of teach-pendant for making simple robot programs for pick and place operations.

5. Creating geometrical models of simple structural components using CAD software.

6. Structural analysis of a cantilever in FEA software.

7. Modal analysis of a cantilever in FEA software.

8. Study of basic components used in IoT.

9. Building a simple IoT application using smartphone and free cloud service.

10. Implementing a simple machine learning algorithms using ANN.

MER-603: SENSORS AND END-EFFECTORS

Maximum marks: 50 L P

Time Allowed: 3 hours 4 --

PRE REQUISITES

Basic electronics

OBJECTIVE

The objective of this course is to understand the basic concepts of sensor and instruments and to select

appropriate instruments for various applications.

LEARNING OUTCOMES

After the completion of this course, the students will be able to

Compare the different types of sensors and transducers.

Classify the need of sensors for various processes

Rectify errors in sensors.

DETAILED CONTENTS

PART − A

1. INTRODUCTION

1.1 Role of Sensors in Robotic system

1.2 Internal and External Sensors

1.3 Gripper for Mechanization and Automation

1.4 Sensors specifications

1.5 Selection of Sensors and Grippers

2. Proximity, Displacement, Velocity and Acceleration Sensors

2.1 Proximity Sensors of various types

2.2 Encoder

2.3 Potentiometer

2.4 LVDT

2.5 Synchros and Resolvers

2.6 Tachometer

2.7 Hall-Effect Sensor

2.8 Accelerometer

3. Force and Touch Sensors

3.1 Strain Gauge

3.2 Piezoelectric Sensor

3.3 Current Based Sensing

3.4 Pressure transducers

3.5 Piezo resistive sensors

3.6 Surface acoustic wave

PART − B

4. Range Sensors

4.1 Ultrasonic sensors

4.2 Microwave sensors

4.3 Laser sensors

4.4 Infrared range sensor

5. Vision System Sensors:

5.1 Elements in Vision Sensor

5.2 Camera Vision Sensing

5.3 Image processing in vision system

6. End-Effectors

6.1 End Effectors and Types-Mechanical, Pneumatic and Hydraulic, Magnetic, Vacuum

6.2 Two Fingered and Three Fingered Mechanical gripper

6.3 Selection and Design Considerations.

6.4 End Effector design case studies

Recommended Books:

1. Sensor & transducers, D. Patranabis, 2nd edition, PHI

2. Instrument transducers, H.K.P. Neubert, Oxford University press.

3. Measurement systems: application & design, E.A.Doebelin, Mc Graw Hill

4. Klafter R.D., Chmielewski T.A and Negin M., “Robotic Engineering – An Integrated Approach”,

Prentice Hall, 2003.

MER-604: COMPUTER PROGRAMMING FOR ROBOT APPLICATIONS

Maximum marks: 50 L P

Time Allowed: 3 hours --

PRE REQUISITES

Basic Engineering Mathematics

OBJECTIVE

The objective of this course is to provide a practical understanding and applications of various

programming tools for industrial automation.

LEARNING OUTCOMES

After the completion of this course, the students will be able to

Learn basics of MATLAB programming

Understand the main features of the MATLAB program development environment to enable their

usage in the higher learning.

Interpret and visualize simple mathematical functions and operations thereon using plots/display.

DETAILED CONTENTS

PART − A

1. Computer Programming

1.1 Introduction to Computer Programming

1.2 Algorithm and Pseudo-code

1.3 Compilers and Interpreters

1.4 Overview of High Level Programming Languages

2. Introduction to MATLAB

2.1 MATLAB Environment

2.2 Scalar and Vector Data types

2.3 Matrix manipulation

2.4 Saving and Retrieving Data using MAT-Files

2.5 Cell Arrays and Structures

2.6 Character Strings

2.7 Relational and Logical Operations

2.8 Plotting 2D and 3D graphs

2.9 Applications – Solving linear systems of equations, Curve fitting and Interpolation

3. Programming using MATLAB

3.1 Introduction – M-Files, User Input/output, Script-Files and Function-Files

3.2 Control Flow – For Loops, While Loops, If-Else-End Constructs, Switch-Case Constructs, Try-Catch

Blocks

3.3 Functions – Function Construction Rules, Input and Output Arguments, Scope of Variables,

Function Handles, Anonymous Functions, Nested Functions, Private Functions, Overloaded Functions

3.4 Exchanging Data with MAT-Files

3.5 Low level File I/O

PART − B

4. Modeling and Simulation using MATLAB/Simulink

4.1 Introduction to Graphical Programming

4.2 Simulink Basics

4.3 Creating Models using Blocks and Signals

4.4 Running Simulations and Analyzing Results

4.5 Modeling and Simulating Dynamic Systems

5. Robotics System Toolbox

5.1 Features of Robotic System Toolbox

5.2 Building Robot Models

5.3 Coordinate System Transformations

5.4 Inverse Kinematics and Dynamics

5.5 Trajectory Tracking

5.6 Using Robot Operating System (ROS)

BOOKS:

1. Rudra Pratap, “Getting Started with MATLAB 7”, Oxford University Press, 2009

2. Duane Hanselman and Bruce Littlefield, “Mastering MATLAB 7”, Pearson Education, 2009

3. S. J. Chapman, “Programming in MATLAB for Engineers”, Brooks/Cole Thomson Learning, 2004.

4. Agam Kumar Tyagi, “MATLAB and Simulink for Engineers”, Oxford University Press, 2012

CSEI 8206 INDUSTRIAL IOT (Common with M.E. Computer Science & Engineering - Internet of Things)

Maximum marks: 50 L P

Time Allowed: 3 hours 4 --

PRE REQUISITES

Fundamentals of IoT

OBJECTIVE

To Introduce the state of art of Industrial IoT with smart machines that performs pervasive sensing distinct

from M2M communication. The course is a blend of engineering and business of IoT. It deals with

connectivity in industrial networks, building systems to enable delivery of software services networked to

the cloud platforms. At the end of the course, the students will be in a position to start an Industrial IoT

business.

LEARNING OUTCOMES

At the end of the course, the students will be able to:

Understand Industry 4.0 Standards and IIOT Architecture.

Apply Intelligent algorithms for IIOT based Applications.

Analyse the security threats of IIOT.

Evaluate various components of Cyber Physical Systems in the context of Industry 4.0

DETAILED CONTENTS

PART − A

1. Introduction to Industry 4.0

1.1. Globalization and Emerging Issues

1.2. The Fourth Revolution

1.3. LEAN Production Systems

1.4. Smart and Connected Business Perspective

1.5. Smart Factories

1.6. Cyber Physical Systems and Next Generation Sensors

1.7. Collaborative Platform and Product Lifecycle Management

1.8. Augmented Reality and Virtual Reality

1.9. Artificial Intelligence

1.10. Big Data and Advanced Analysis.

1.11. Cybersecurity in Industry 4.0

2. Basics of Industrial IoT

2.1. Industrial Processes

2.2. Industrial Sensing & Actuation

2.3. Industrial Internet Systems

3. IIoT-Introduction, Industrial IoT

3.1. Business Model and Reference Architecture: IIoT-Business Models-Part I, Part II

3.2. IIoT Reference Architecture

4. Industrial IoT- Layers

4.1. IIoT Sensing

4.2. IIoT Processing

4.3. IIoT Communication

4.4. IIoT Networking

PART − B

5. Industrial IoT

5.1. Big Data Analytics and Software Defined Networks

5.2. Fisher discriminant analysis

5.3. Multiple discriminant analys

6. IIoT Analytics

6.1. Introduction

6.2. Machine Learning and Data Science, and Julia Programming,

6.3. Data Management with Hadoop

6.4. Data Center Networks

6.5. Security and Fog Computing: Cloud Computing in IIoT

7. Industrial IoT-

7.1. Security and Fog Computing

7.2. Application Domains: Factories and Assembly Line

7.3. Food Industry

7.4. Healthcare

7.5. Power Plants

7.6. Inventory Management & Quality Control

7.7. Plant Safety and Security (Including AR and VR safety applications)

7.8. Facility Management

8. Industrial IoT- Application Domains

8.1. Oil

8.2. chemical and pharmaceutical industry

8.3. Applications of UAVs in Industries

8.4. Real case studies

BOOKS:

Enterprise IoT Strategies and Best Practice for Connected Products and Services. – Dirk Slama, Frank

Puhlmann, Jim Mirrish, Rishi M Bhatnagar

The Internet of Things: Key Applications and Protocols - David Boswarthick

The Silent Intelligence, The Internet of Things. By – Daniel Kellmereit, Daniel Obodovski.

Industry 4.0: The Industrial Internet of Things”, by Alasdair Gilchrist (Apress)

Industrial Internet of Things: CybermanufacturingSystems”by Sabina Jeschke, Christian Brecher,

Houbing Song, Danda B. Rawat (Springer).

CSE 8207 BIG DATA ANALYTICS (Common with M.E. Computer Science & Engineering - Internet of Things)

Maximum marks: 50 L P

Time Allowed: 3 hours 4 --

PRE REQUISITES

Basics knowledge of Python or any Object Oriented Programming Language

OBJECTIVE

The objective of this course is to teach the emerging concepts and case studies of Big Data with the real

world case studies. In addition, the course focuses towards the coverage of data acquisition, storage,

processing, querying and visualization with hands-on-practice using various big data analytics tools.

LEARNING OUTCOMES

At the end of the course, the students will be able to:

Understand the concepts of Big Data Analytics with real world case studies.

Acquire, store and process Big Data from various sources.

Analyse and visualize Big Data.

Apply Big Data Analytics in various domains

DETAILED CONTENTS

PART − A

1. Introduction to Big Data

1.1. Definition

1.2. various tools for Big Data

1.3. Possibilities of Big Data storage using RDBMS

1.4. Data Warehousing and Data Marts concept

1.5. Types of analytics - Descriptive, Diagnostic, Predictive, Prescriptive

1.6. Big Data characteristics - Volume, Velocity, Variety, Veracity, Value, Data analysis flow

1.7. Big data examples, applications & case studies.

2. Big Data Architectures & Patterns

2.1. MapReduce

2.2. Sharding

2.3. Bloom Filters

2.4. Lambda Architecture

2.5. Consistency

2.6. Availability & Partition Tolerance (CAP)

2.7. Consensus in Distributed Systems

2.8. Leader Election and Other analytics patterns

3. Python Programming for Big Data Applications

3.1. Introduction to Python

3.2. Big Data stack setup and examples

3.3. Hortonworks Data Platform/Apache Ambari

3.4. Amazon EMR, Running Python

3.5. MapReduce examples on big data stack.

4. Data Acquisition

4.1. Apache Flume

4.2. Apache Sqoop

4.3. Publish - Subscribe Messaging Frameworks

4.4. Big Data Collection Systems

4.5. Messaging queues

4.6. Custom connectors

4.7. Implementation example

5. Big Data Storage

5.1. HDFS

5.2. HBase

5.3. Kudu

6. NoSQL Databases

6.1. Key-value databases

6.2. Document databases

6.3. Column Family databases

6.4. Graph database

PART − B

7. Standard ETL Tools

7.1. Standard Industry tools

8. Batch Data Analysis

8.1. Hadoop & YARN

8.2. MapReduce & Pig

8.3. Spark core

8.4. Batch data analysis examples & case studies

9. Real-time Analysis

9.1. Stream processing with Storm

9.2. In-memory processing with Spark Streaming

9.3. Real-time analysis examples & case studies

10. Interactive Querying

10.1. Hive,

10.2. Spark SQL,

10.3. Interactive querying examples & case studies

11. Cloud Computing Platforms

11.1. Amazon Web Services (AWS)

11.2. Deploying Big Data applications in the cloud

12. Web Frameworks & Serving Databases

12.1. Django - Python web framework

12.2. Using different serving databases with Django

13. Data Visualization

13.1. Building visualizations with Lightning,

13.2. pyGal&Seaborn

BOOKS:

Arshdeep Bahga, Vijay Madisetti, "Big Data Analytics: A Hands-On Approach", VPT Publishers,

2018

Big Data Black Book, D T editorial service, Dreamtech Press, Wiley India; 1st edition, 2016.

Baesens Bart, "Analytics in A Big Data World - The Essential Guide To Data Science and Its

Applications", Wiley, 2014

Radha Shankarmani, M. Vijayalakshmi, "Big Data Analytics", Wiley, 2016

Acharya Seema, Subhashini Chellappan, "Big Data and Analytics", Wiley, 2015

NPTEL Course on “The Joy of Computing using Python” by Dr.

SudarshanIyengarhttps://onlinecourses.nptel.ac.in/noc18_cs35/preview

NPTEL Course on “Programming, Data Structures and Algorithms in Python” by Dr.

MadhavanMukundhttps://onlinecourses.nptel.ac.in/noc16_cs11/preview

NPTEL Course on “Big Data Computing” by Dr. Rajiv

Misrahttps://onlinecourses.nptel.ac.in/noc19_cs33/preview

Dive into Python 3, Mark Pilgrim, http://www.diveintopython3.net).

MER-652: ROBOTICS LAB – II

L P

-- 4

Note: The internal evaluation of the work done by the student will be based on a file documenting the practical work

carried out during the course followed by a viva-voce examination.

PRACTICE TASKS

1. Study of functioning of proximity sensor and potentiometer using digital multimeter.

2. Study of sensors used for sensing/measuring various parameters in robotic systems.

3. Study of working of different types of robotic grippers.

4. Write a MATLAB program for inverse kinematics of 3-link planar robotic arm.

5. Write a MATLAB program for plotting joint angles and torque for a 2-link planar robotic arm

6. Use of Robotic Systems Toolbox for building robot models.

7. Use of Robotic Systems Toolbox for forward/inverse kinematics/dynamics in robots.

8. Extraction and uploading of machine data on a free cloud service.

9. Exercises on use of RDBMS for storage of big data.

10. Using Amazon Web Service for deployment and retrieval of Big Data.

MER-701: COMPUTER AIDED DESIGN

Maximum marks: 50 L P

Time Allowed: 3 hours 4 --

PRE REQUISITES

NIL

OBJECTIVE

The objective of this course is to provides an introduction into engineering design and communication

through the use of computer aided design (CAD) software

LEARNING OUTCOMES

After the completion of this course, the students will be able to

Integrate the role of graphic communication in the engineering design process

Generate and interpret engineering technical drawings of parts and assemblies according to

engineering design standards.

Use CAD software to generate a computer model and technical drawing for a simple, well-defined part

or assembly.

DETAILED CONTENTS

PART − A

1. Computer Aided Design

1.1 Design processes and computer aided design

1.2 Hardware and software in CAD

1.3 Parametric and variational design

2. CAD Features

2.1 Components of CAD software and their function

2.2 Introduction and essential features of graphics package

2.3 Configuration of graphics package

2.4 Geometric transformations-2D and 3D

3. Geometric Modelling

3.1 Curves-mathematical representation of analytical and synthetic curves

3.2 Curve manipulation

3.3 Surfaces and their representation

3.4 Solids-representation techniques and manipulation of solids

PART − B

4. CAD Database

4.1 Need and features of CAD data base

4.2 Data structures

4.3 Data exchange and data exchange formats

5. Features of some Software Packages

5.1 Pro-Engineer

5.2 CATIA or INVENTOR

5.3 Geometrical modeling of simple components

6. CAD in Robotics

6.1 Geometrical Modeling of Manipulator Links

6.2 Assembly of Links and Joints

6.3 Kinematic Analysis

BOOKS:

1. Chris McMahon and Jimmie Browne, “CAD/CAM Principles, Practices and Manufacturing

Management”, Prentice Hall, 1999

2. Groover M. P., “Automation, Production Systems and Computer Aided Manufacturing”, Pearson

Education, New Delhi, 2015

Ibrahim Zeid, R Sivasubramanian, “CAD/CAM – Theory and Practice”, Tata-McGraw Hill Ltd., New

Delhi, 2009

MER-702: MECHATRONICS SYSTEMS

Maximum marks: 50 L P

Time Allowed: 3 hours 4 --

PRE REQUISITES

Basic Electronics and Mathematics

OBJECTIVE

The objective of this course is to provide the student with basic skills useful in identifying the concepts

of automated machines and equipment and describe the terms and phrases associated with

mechatronics.

LEARNING OUTCOMES

After the completion of this course, the students will be able to

Select & identify suitable mechatronics hardware for the given application.

Describe & explain potential areas of mechatronics

Differentiate various control aspects of automation

Demonstrate the self-learning capability of Industrial Automation.

DETAILED CONTENTS

PART − A

1. Introduction

1.1. Mechatronics & its Elements

1.2. Mechatronics Design Process

1.3. Integrated Design Issues in Mechatronics

1.4. Applications of Mechatronics

2. Modeling & Simulation of Physical Systems

2.1. Mathematical modeling of physical systems

2.2. Dynamic response of first and second order systems

2.3. System transfer functions

2.4. Block Diagram Approach

2.5. State Space Approach

3. Actuators

3.1. Fluid power control elements and standard graphical symbols

3.2. Directional, Pressure and Flow Control Valves – Construction and Working

3.3. Basic fluid power circuits

3.4. Mechanical & Solid state switches

3.5. AC and DC motors

3.6. Stepper motors

PART − B

4. Control Theory

4.1. Introduction to Open Loop & Closed Loop Control

4.2. Transient & Steady state performance characteristics

4.3. Frequency response

4.4. PID Controllers & their Tuning

4.5. Adaptive Control

5. Data Acquisition

5.1. Sensors

5.2. Operational amplifier

5.3. Protection and filtering

5.4. Digital signals

5.5. Data acquisition systems

6. Mechatronics System Design

6.1. Traditional & Mechatronics Design

6.2. Possible Mechatronics Design Solutions

6.3. Digital logic

6.4. Programmable logic controllers

6.5. Microcontrollers

6.6. Simple Logic Circuits using PLC and microcontroller

BOOKS:

1. David G. Alciatore, ‎Michael B. Histand, “Introduction to Mechatronics and Measurement Systems”,

Tata McGraw Hill, 4th

Edition, 2014

2. W Bolton, “Mechatronics: A Multidisciplinary Approach”, Pearson Education, 4th

Edition, 2014

3. S R Majumdar, “Pneumatic Systems”, Tata McGraw Hill, New Delhi, 2008.

4. S R Majumdar, “Oil Hydraulic Systems”, Tata McGraw Hill, New Delhi, 2010

5. Groover M. P., “Automation, Production Systems and Computer Aided Manufacturing”, Pearson

Education, New Delhi, 2015

MER-703: Digital Logic and Circuits

Maximum marks: 50 L P

Time Allowed: 3 hours 4 --

PRE REQUISITES

Basic Electronics and Mathematics

OBJECTIVE

The knowledge of digital logic and circuits is required for designing a robotic control system. This

subject will also enable the students to formulate Boolean expressions for programming the robot for

performing given tasks.

LEARNING OUTCOMES

After the completion of this course, the students will be able to

Formulate truth tables for different logic problems

Derive Boolean expressions for implementing logic

Identify components required for developing hardware for implementing logic

DETAILED CONTENTS

PART − A

1. Number System and Codes

1.1. Number System

1.2. Floating Point Representation of Numbers

1.3. Arithmetic Operations

1.4. Binary Coded Decimal (BCD)

1.5. Weighted Binary Codes

1.6. Non-Weighted Codes

1.7. Error Detecting & Correcting Codes

1.8. Alphanumeric Codes

2. Boolean Algebra and Minimization Techniques

2.1. Boolean Logic Operations

2.2. Basic Laws Of Boolean Algebra

2.3. Demorgan’s Theorems

2.4. Minimization Techniques

2.4.1. Sum Of Products and Product Of Sums

2.4.2. Karnaugh map

3. Logic Gates

3.1. Different Logic gates

3.2. Mixed logic

3.3. Multilevel gating networks

3.4. Multiple output gate networks

4. Logic families

4.1. Digital integrated circuits

4.2. Current-sourcing and current-sinking logic

4.3. Resistor-transistor logic

4.4. Resistor-capacitor-transistor logic

4.5. Diode-transistor logic

4.6. Transistor-transistor logic

5. Combinational Circuits

5.1. Multiplexers and Demultiplexers

5.2. Decoders and Encoders

5.3. Parity generators/checkers

5.4. Code converters

5.5. Magnitude comparator

PART − B

6. Flip-Flops

6.1. Latches

6.2. S-R flip flop

6.3. D flip-flop

6.4. J-K flip-flop

6.5. T flip-flop

7. Counters and Registers

7.1. Asynchronous counter

7.2. Synchronous counters

7.3. Counter ICs

7.4. Shift registers

7.5. Shift register counters

7.6. Sequence generator

8. Sequential Circuits

8.1. General sequential circuit model

8.2. Classification of sequential circuits

8.3. Synchronous sequential circuits

8.4. Fundamental mode Asynchronous sequential circuits

8.5. Pulse Mode Asynchronous sequential circuits

9. D/A and A/D Converters

9.1. Analog and digital data conversions

9.2. Characteristics of D/A converter

9.3. Types of D/A converters

9.4. Characteristics of A/D converter

9.5. Types of A/D converters

10. Clock Generators

10.1. Astable multivibrator

10.2. Monostable multivibrator

10.3. Schmitt trigger

BOOKS:

1. Digital Logic Design Principles by Norman Balabanian, Bradley Carlson, Wiley Student Edition

2. Digital Circuits & Design by D.P Kothari, J.S Dhillon, Pearson Education India

3. Digital Circuits and Design by Salivahanan and Arivazhagan, Vikas Publishing House, Noida

MEI-6103: DIGITAL SIGNAL PROCESSING (Common with M.E. Electrical Engineering – Instrumentation & Control)

Maximum marks: 50 L P

Time Allowed: 3 hours 4 --

PRE REQUISITES

Signal and system

Engineering mathematics

OBJECTIVE

The objective of this course is to provide a thorough understanding and working knowledge of design,

implementation and analysis DSP systems.

LEARNING OUTCOMES

After the completion of this course, the students will be able to

Interpret, represent and process discrete/digital signals and sytems

Thorough understanding of frequency domain analysis of discrete time signals.

Ability to design & analyze DSP systems like FIR and IIR Filter etc.

Practical implementation issues such as computational complexity, hardware resource limitations as

well as cost of DSP systems or DSP Processors.

Understanding of spectral analysis of the signals

DETAILED CONTENTS

PART − A

1. Transformations:

1.1 Review of Z-Transform

1.2 Solution of Linear Difference Equations

1.3 Fourier series and Fourier Transform, Discrete Fourier Transform, Radix-2 FFT

1.4 Introduction to Radix-4 and Split Radix FFT

1.5 Discrete Cosine Transform, DCT as Orthogonal Transform, Walsh Transform, Hadamard Transform,

Wavelet Transform.

2. Digital Filters

2.1 Filter Design: Filter Specifications, Coefficient Calculation Methods- Window method, optimal

method, Frequency Sampling method.

2.2 Realization Structures, Finite Word Length Effects.

2.3 IIR Filter Design: Specifications, Coefficient Calculation methods- Pole-Zero Placement method,

Impulse Invariant method,

2.4 Matched Z-Transform method, Bilinear Z- Transformation method, Use of BZT and Classical Analog

Filters to design IIR Filters

2.5 Realization Structures, Finite Word Length Effects.

2.6 Qualitative and quantitative methods

3. Multirate Digital Signal Processing:

3.1 Sampling Rate Alteration Devices, Multirate Structures for sampling rate conversion

3.2 Multistage design of Decimator and Interpolator, The Polyphase Decomposition

3.3 Arbitrary Rate Sampling Rate Converter, Filter Banks, QMF banks

3.4 Multilevel Filter Banks, Sub-band Coding, Discrete Wavelet Transform.

4. Linear Prediction and Optimum Linear Filters:

4.1 Forward and Backward Linear Prediction

4.2 Properties of Linear Prediction-Error Filters

4.3 AR Lattice and ARMA Lattice-Ladder Filters

4.4 Wiener Filters for Filtering and Prediction.

PART − B

5. Adaptive Digital Filters:

5.1 Concepts of Adaptive Filtering

5.2 LMS Adaptive Algorithm, Recursive Least Squares Algorithm, Applications.

6. Power Spectrum Estimation:

7.8 Nonparametric methods for Power Spectrum Estimation

7.9 Bartlett method, Welch method, Blackman and Tukey method

7.10 Parametric methods for Power Spectrum Estimation, Yule- Walker method, Burg method

7.11 Unconstrained Least-Squares method, Sequential Estimation methods

7.12 Selection of AR Model Order, MA model for Power Spectrum Estimation,

7.13 ARMA model for Power Spectrum Estimation.

7. Monitoring DSP Chips:

7.1. Introduction to fixed point and floating point processors

7.2. ADSP21xx and TMS320Cxx- Architecture, Memory, Addressing Modes, Interrupts, Applications.

7.3. Comparison of ADSP21xx and TMS320Cxx series.

BOOKS:

1 “Digital Signal Processing: A Practical Approach”, by Ifeacher & Jervis, -Pearson Education.

2 “Digital Signal Processing: Principles, Algorithms and Applications”, by Proakis & Manolakis, 4e, -

Pearson Education

3 “Digital Signal Processing”, by S.K.Mitra, -Tata-Mcgraw Hill

4 “Discrete Time Signal Processing”, Oppenheim & Schafer. PHI

5 “Fundamentals of Digital Signal Processing using MATLAB”, by Robert J. Schilling & Sndra L.

Harris. -CENGAGE Learning

6 6. “Modern Digital Signal Processing”, Roberto Cristi.

7 “Modern Filter Theory”, by Johnson & Johnson

8 “Theory and application of Digital Signal Processing”, by Rabiner & Gold

9 “Digital Signal Processing”, Schuam’s Series.

“Digital Signal Processing”, by Salivahanan, Vallavaraj & Gnanapriya, - Tata-Mcgraw Hill.

MER-704: ROBOT MOTION PLANNING

Maximum marks: 50 L P

Time Allowed: 3 hours 4 --

PRE REQUISITES

Engineering Mathematics and Basics of Robotics

OBJECTIVE

The objective of this course is to provide the student with some knowledge and analysis skills

associated with trajectory planning and robot control.

LEARNING OUTCOMES

After the completion of this course, the students will be able to

demonstrate knowledge of the relationship between mechanical structures of industrial robots and their

operational workspace characteristics

demonstrate an ability to generate joint trajectory for motion planning

demonstrate knowledge of robot controllers

DETAILED CONTENTS

PART − A

1. 1. Configuration Space

1.1 Specifying a Robot's Configuration

1.2 Obstacles and the Configuration Space

1.3 The Dimension of the Configuration Space

1.4 The Topology of the Configuration Space

1.5 Parameterizations of S O(3)

1.6 Example Configuration Spaces

1.7 Transforming Configuration and Velocity Representations

2. Motion Planning

2.1 Joint Space Planning

2.2 Cartesian Space Planning

2.3 Position and Orientation Trajectories

2.4 Point-to-Point Planning

2.5 Continuous Path Generation

PART − B

3. Trajectory Planning

4.1 Preliminaries

4.2 Decoupled Trajectory Planning

4.3 Direct Trajectory Planning

4. Robot Motion Control

5.1 Control Systems

5.2 Controllability

5.3 Simple Mechanical Control Systems

5.4 Motion Planning

BOOKS:

Latombe, J. C. (2012). Robot motion planning (Vol. 124). Springer Science & Business Media.

Hegde, G.S. (2009). Industrial Robotics, University Science Press.

MMT-608: DIGITAL MANUFACTURING (Common with M.E. Mechanical Engineering - Manufacturing Technology)

Maximum marks: 50 L P

Time Allowed: 3 hours 4 --

PRE REQUISITES

Basics of manufacturing

OBJECTIVE

The objective of this course is to understand the transformation taking place, throughout the world, in

design and manufacturing of products through digital manufacturing – a shift from paper-based

processes to digital processes in the manufacturing industry.

LEARNING OUTCOMES

After the completion of this course, the students will be able to

Understand product design and development process, along with the manufacturing aspects.

Use parametric 3D CAD software tools in the correct manner for making geometric part models,

assemblies and automated drawings of mechanical components and assemblies.

Apply geometric transformations on the created wireframe, surface and solid models

DETAILED CONTENTS

PART − A

1. Introduction

1.1. Types of manufacturing systems and their characteristics

1.2. Computer aided Manufacturing (NC, CNC, DNC and adaptive control systems)

1.3. Computer Network architectures and protocols

1.4. Computer Integrated Manufacturing Systems

1.5. What makes a manufacturing process “digital”

2. CNC Machines

2.1. Constructional details

2.2. Design features

2.3. Safety devices

2.4. Part programming

3. Group Technology and Cellular Manufacturing

3.1. Parts classification and part coding – approaches and systems

3.2. Benefits of group technology

3.3. Cellular manufacturing-basics, layout considerations

3.4. Cell formation approaches and evaluation of cell designs

3.5. Planning and control in cellular manufacturing

3.6. Applications in Manufacturing

4. Computer Aided Process Planning

4.1. Role of Computer in Planning function

4.2. CAPP Approaches

4.3. Benefits of CAPP

4.4. Machinability Data Systems

4.5. Computer – Generated Time Standards

5. Computer Aided Quality Control

5.1. Computers in quality control

5.2. Contact and non-contact inspection methods

5.3. Computer aided testing

6. Flexible Manufacturing Systems

6.1. FMS and its Components

6.2. Layout considerations in FMS

6.3. Material Handling in FMS

PART − B

7. Reverse Engineering

7.1. Reverse Engineering – Principles and Technology

7.2. Contact type methods

7.3. Non-contact type methods

7.4. Applications in Product Manufacturing

8. Additive Manufacturing

8.1. Additive Manufacturing Processes

8.2. Steps in Additive Manufacturing

8.3. Materials used in Additive Manufacturing

8.4. Post processing

8.5. Challenges, Benefits and Applications

9. Cloud Based Manufacturing

9.1. Introduction to Cloud computing

9.2. Data Analytics in Manufacturing

9.3. Networked manufacturing

9.4. Industrial Internet of Things

9.5. Industry 4.0 Standard

9.6. Applications of Cloud based Manufacturing

BOOKS:

1. Groover M. P. and Zimmers E. W., “Computer Aided Design and Manufacturing”, Pearson Education,

New Delhi, 2003

2. Groover M. P., “Automation, Production Systems and Computer Aided Manufacturing”, Pearson

Education, New Delhi, 2015

3. P. Radhakrishnan, S. Subramanyan, V. Raju, “CAD/CAM/CIM”, New Age International, 2008

4. C.K. Chua,‎‎K.F. Leong,‎‎C.S. Lim, “Rapid Prototyping: Principles And Applications”, 3rd Edition,

World Scientific Publishing Co Pte Ltd, 2008

5. Alasdair Gilchrist, “Industry 4.0: The Industrial Internet of Things”, Apress, 2016

REFERENCE BOOKS

6. Alp Ustundag,‎ Emre Cevikcan, “Industry 4.0: Managing The Digital Transformation”, Springer Series

in Advanced Manufacturing, 2017

7. Zude Zhou, Shane Shengquan Xie, Dejun Chen, Fundamentals of Digital Manufacturing Science”,

Springer Series in Advanced Manufacturing, 2011

MMT-655: OPTIMIZATION TECHNIQUES (Common with M.E. Mechanical Engineering - Manufacturing Technology)

Maximum marks: 50 L P

Time Allowed: 3 hours 4 --

PRE REQUISITES

Back ground in differential calculus and basic mathematics

OBJECTIVE

The objective of this course is to to formulate mathematical models and to understand solution

methods for real life optimal decision problems.

LEARNING OUTCOMES

After the completion of this course, the students will be able to

Cast engineering minima/maxima problems into optimization framework.

Learn efficient computational procedures to solve optimization problems.

Use MATLAB to implement important optimization methods.

DETAILED CONTENTS

PART − A

1. Numerical Techniques

1.1 Introduction to numerical techniques

1.2 Numerical differentiation and numerical integration

1.3 Eigen value problems

1.4 Newton-Raphson’s method

1.5 Computer based numerical analysis.

2. Introduction to optimization

2.1 Introduction and Engineering applications of optimization

2.2 Optimal Problem Formulation; Design – variables, Constraints, Objective function, Variable bounds.

2.3 Integer Programming

2.4 Geometric Programming

3. Single-variable Optimization

3.1 Optimality Criteria

3.2 Bracketing Methods – Exhaustive search and Bounding phase methods

3.3 Region-Elimination Methods-Interval halving method; Fibonacci search method, golden section

search method

3.4 Point-Estimation Method : Successive quadratic estimation method

3.5 Gradient-based Methods : Newton-Raphson method, Bisection method, Secant method, Cubic search

method

PART − B

4. Multivariable Optimization

4.1 Optimality Criteria

4.2 Unidirectional Search

4.3 Direct Search Methods: Simplex, Hooke-Jeeves pattern search and Powell’s conjugate direction

method.

4.4 Gradient-based Methods: Cauchy’s (steepest descent) method, Newton’s method, conjugate gradient

method, variable – metric method.

5. Constrained Optimization

5.1 Kuhn-Tucker Conditions

5.2 Transformation Methods: Penalty function method.

5.3 Sensitivity Analysis

5.4 Direct Search for Constrained Minimization : Variable elimination, Complex search and Random

search methods.

5.5 Linearized Search Techniques: Frank-Wolfe method, Cutting plane method, Feasible Direction

Method, Generalized Reduced Gradient Method, Gradient Projection Method.

BOOKS:

1. Sastry S.S, “Introductory methods of Numerical Analysis”, Prentice Hall of India, 2009

2. Rao S.S., “Engineering Optimization: Theory and Practices”, John Wiley and Sons, 4th Edition, 2009

3. Kambo N.S., “Mathematical Programming Techniques”, East West Press, 2009

4. Deb Kalyanmoy, “Optimization for Engineering Design: Algorithms and Examples”, Prentice Hall of

India, New Delhi, 2005

5. Belegundu,Chandragupta, “ Optimization Concepts and application in Engineering”, Pearson, 2003

MMT-657: RESEARCH METHODOLOGY (Common with M.E. Mechanical Engineering - Manufacturing Technology)

Maximum marks: 50 L P

Time Allowed: 3 hours 4 --

PRE REQUISITES

NIL

OBJECTIVE

The objective of this course is to introduce students to quantitative and qualitative methods for

conducting meaningful research

LEARNING OUTCOMES

After the completion of this course, the students will be able to

Understand the important concepts of research design, data collection, statistical and interpretative

analysis, and final report presentation

Learn systematic approach to research studies

Learn statistical data analysis techniques

Apply estimation & hypothesis technique for engineering problems

DETAILED CONTENTS

PART − A

1. Introduction Research Methodology

1.1 Definition of Research

1.2 Need of Research

1.3 Concept and steps of Research Methodology

1.4 Uses of Research Methodology, Research Techniques

2. Reviewing Literature

2.1 Need

2.2 Sources – Primary and Secondary

2.3 Purposes of Review

2.4 Scope of Review

2.5 Steps in conducting review

3. Identifying and defining research problem

3.1 Locating. Analyzing stating and evaluating problem

3.2 Generating different types of hypotheses

3.3 Hypothesis evaluation

4. Method of Research

4.1 Descriptive research design-survey

4.2 Case study

4.3 Content analysis

4.4 Ex-post Facto Research

4.5 Co-relational and Experimental Research

PART − B

5. Sampling Techniques

5.1 Concept of population and sample

5.2 Sampling techniques

5.2.1 Simple random sampling

5.2.2 Stratified random sampling

5.2.3 Systematic sampling

5.2.4 Cluster sampling

5.3 Quota sampling techniques

5.4 Determining size of sample

6. Procedure of data collection

6.1 Aspects of data collection

6.2 Techniques of data Collection

7. Statistical Methods of Analysis

7.1 Descriptive statistics

7.1.1 Meaning

7.1.2 Graphical representations

7.1.3 Mean, range and standard deviation

7.2 Characteristics and uses of normal curve.

8. Inferential Statistics

8.1 T-test

8.2 Chi-square tests

8.3 Correlation (rank difference and product moment)

8.4 ANOVA (one way)

8.5 Softwares for statistical analysis: SPSS, MATLAB

9. Procedure for writing a research proposal and report

9.1 Purpose, types and components of research proposal

9.2 Audiences and types of research reports

9.3 Format of Research report and journal

10. Case Studies on software tools used for research work

BOOKS:

1. C.R. Kothari, “Research Methodology – Methods and Techniques”, Wiley Eastern Ltd, 2009

2. Richard I. Levin and David S. Rubin, “Statistics for Management”, 7th

Edition, Pearson Education

India, 2012

3. K. N. Krishnaswamy, Appa Iyer Sivakumar and M. Mathirajan, “Management Research

Methodology: Integration of Methods and Techniques”, Pearson, 2006

4. S. P. Gupta, “Statistical Methods”, Sultan Chand & Sons, 2006

5. Ronald E. Walpole, Raymond H. Myers, Sharon L. Myers and Keying Ye, “Probability and Statistics

for Engineers and Scientists”, Pearson Edition, 2002

6. P. K. Gupta and D. S. Hira, “Problem in Operations Research”, S. Chand and Co., 2007

7. Bernand Ostle and Richard N. Mensing, “Statistics in Research”, 3rd

edition, Oxford & IBH Pub Co.,

1975

8. Hines, Montgomery, Goldsman and Borror, “Probability and Statistics in Engineering”, 4th

edition,

John Wiley & Sons, 2003