Details
Last Updated on Monday, 05 August 2024 15:27
 

Additive Manufacturing and Solidification

The main focus of the lab is on solid-liquid phase change (melting/solidification) involving theoretical work, multiscale computational heat transfer and fluid flow modelling (CFD), microstructure modelling, stress modelling, and experiments. The various research activities are in the area of metal additive manufacturing, welding, casting, coating and thermal energy storage.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

The comprehensive approach of our group, that includes development and implementation of benchmarks, validations with controlled laboratory and actual industrial-scale quantitative experiments, and process qualification, has helped to acquire advanced scientific understanding, and predictive and control capability for defects and microstructure in solidification processes. The advanced robust models have been successfully applied to various melting/solidification related manufacturing processes (additive manufacturing, welding, surface coating, casting). Further, the multi-scale physical models, engineering thermal predictors and process selection guidelines have been applied to the area of thermal energy storage (waste heat and renewable energy).

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Research Areas


Metal additive manufacturing — AM process, product and application development, DfAM; Heat transfer, CFD, DPM; Multiphysics, multiscale modelling of manufacturing processes (additive manufacturing, casting, welding, surface coating), Process, defects, microstructure and properties predictions; Machine learning tools for manufacturing; Droplet interaction with surfaces; Thermal storage, Waste heat recover.


Research Laboratories:

 

Solidification and Additive Manufacturing Laboratory

 

Associated Faculty

 

Dr. Arvind Kumar, PhD (IISc Bangalore)
Northern Laboratories, Manufacturing Science Lab
Department of Mechanical Engineering
IIT Kanpur
Kanpur 208016
Office : 0512-259-7484
Fax : 0512-259-7408
E-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

 
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Last Updated on Friday, 12 April 2024 12:05
 

ME644

MACHINE LEARNING FOR ENGINEERS

Credits:

  

 

3-0-0-9
 

Course contents:


Mathematical preliminaries, python programming, simple/multiple linear regression, nonlinear regression, logistic regression, k-nearest neighbours, perceptrons, random forest, naïve Bayes, support vector machines, artificial neural network, clustering, dimensionality reduction

Lecturewise Breakup (Based on 50 min per lecture)


Sl. No.TopicContentsLecture
1. Introduction Various learning paradigms, definitions, examples 1
2. Programming Programming in python, libraries: scientific computing, machine learning, plotting 2
3. Mathematics for machine learning Linear algebra and vector calculus: Vector space, vector-matrix operations, norm, eigenvalue and eigenvectors, matrix decompositions, differential calculus of vectors 3
Optimization: gradient-based techniques, metaheuristic techniques, numerical implementation 3
Statistics and probability: Probability distributions, hypotheses testing, Bayes’ theorem 3
4. Supervised learning Linear/nonlinear regression, overfitting, regularization, logistic regression, naive Bayes, k-NN, decision tree, random forest, maximum likelihood, support vector machine, applications in mechanical engineering 15
5. Unsupervised learning Singular value decomposition, principal component analysis, clustering, applications in mechanical engineering 8
6. Artificial neural network Single- and multi-layer networks, activation, backpropagation, stochastic gradient descent, physics-informed neural network, applications in mechanical engineering 5
Total 40

References:

  1. Machine Learning for Engineers, R. G. McClarren, Springer

  2. A First Course in Machine Learning, S. Rogera, M. Girolami, CRC Press

  3. Machine Learning, Z-H. Zhou, Springer

  4. An Introduction to Statistical Learning, G. James et al., Springer

  5. Data-Driven Science and Engineering, S. L. Brunton, J. L. Kutz, Cambridge Uni. press

  6. Probabilistic Machine Learning for Civil Engineers, J-A. Goulet, MIT Press

  7. Machine Learning Refined, 2nd ed., J. Watt et al., Cambridge University press

  8. Machine Learning, A. Lindholm et al., Cambridge University press
Details
Last Updated on Friday, 28 April 2023 16:47
Details
Last Updated on Thursday, 13 July 2023 18:00
 

Ph.D. admission (spot round)

Fresh applications are invited for Ph.D. admission for the following categories:


(i) direct Ph.D. admission (with B.E./B.Tech. as the qualifying degree)


For candidates having B.E./B.Tech. degree from a centrally funded technical institution (e.g., IITs, NITs, IIITDM, IIEST, etc.), the requirement of a GATE score is waived.


(ii) regular Ph.D. admission (with M.E./M.Tech. as the qualifying degree)


Eligibility criteria and other relevant details are available here.


Also, the relevant details on our Ph.D. curriculum are available here.


Interested candidates are required to fill the Google form: https://forms.gle/FGqaamGdbpXcWK2q7


The last date for application is 3rd July 2023.


Date of PhD admission test: 10th July, 2023.


Reporting time: 8:30 AM at Tutorial Block.


Result of PhD admission (second round) is available here.
Details
Last Updated on Wednesday, 29 March 2023 15:07
 

ME334

Experiments in Mechanical Engineering - II

Credits:

  

 

L-0T-4P-0A (4 Credits)
 

Objectives


This course exposes the students to experiments on (a) Energy conversion (b) Mechanisms (c) Heat Transfer (d) Experimental stress analysis.

Course content


Experiments on (a) Energy conversion (b) Mechanisms (c) Heat Transfer (d) Experimental stress analysis.

Tentative list of experiments

A.

1. Evaluation of performance characteristics for a Francis Turbine

2. Evaluation of performance characteristics for a Pelton Turbine

3. Performance Study of a single cylinder four-Stroke diesel engine with variable compression ratio

4. Study of a refrigeration system with series and parallel evaporators

B.


Lab 1

Make the following mechanisms using the kit provided

1. Mechanism 1A: Watt’s mechanism

2. Mechanism 1B: The multi-bar pantograph mechanism

3. Mechanism 1C: Four bar mechanism with a translational link

4. Mechanism 1D: Peaucellier-Lipkin linkage


Lab 2

1. Mechanisms 2A

2. Need to form

3. Various inversions of a 3R-1P mechanism satisfying Grashof Criterion)

4. One 3R-1P non-Grashof mechanism

5. Mechanisms 2B

6. Construct a Grashof and a non-Grashof 4R mechanism. consider all possible cases of a Grashof linkage

7. Mechanisms 2C

8. Make a constrained 8-bar mechanism which consists of only binary and ternary links

9. Mechanisms 2D (BONUS)

10. Various inversions of R-P-R-P and 2R-2P mechanism satisfying Grashof Criterion


Lab 3

1. Verify the torque relationship as Ti+ T0 + Th= 0of epicyclic gear trains


Lab 4

1. Cams

2. Aim: Come up with the cam profile from the observed displacement diagram


Lab 5

1. Static and Dynamic Balancing of Rotor


Lab 6: Two Plain Balancing


C.

1. Unsteady Heat Conduction

2. Heat Transfer by Force Convection (Pin-fin)

3. Heat Transfer through Extended Surface

4. Critical Heat Flux (Pool boiling)

5. Heat Transfer by Natural Convection

6. Measurement of Emissivity

7. Calibration of Thermocouples


D.

1. Cantilver Beam

2. Portal Frame

3. Combined Stresses

4. Stress Analysis Using Photelasticity

5. Determination of Shear Modulusn

6. DIC on Universal Testing Machine (UTM)

Proposing instructors: DUGC, ME

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