This document is a portfolio which explains the major engineering projects that I have done in the past.

1. ABHINIT A KOTHARI
212 W, 109th Street, Apt #1C, New York, NY, 10025| (814) 954-2929 | ak4429@columbia.edu
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Title: Undergraduate Research Assistant at Mechanics & Materials Lab (May,
2018 – May, 2019)
Project: Strengthening of Dental Crowns using epoxy – based multilayers that were made from
different compositions of Alumina. Testing of samples was done using 3-point bend tests and
calculating fracture toughness for different % of alumina in samples.
Results: A lot of experiments were conducted for alumina percentages ranging from 10% to 60%
and the highest fracture toughness was found at 20% Alumina by weight. The fracture paths
were also analyzed under a microscope to understand the way the samples would fracture in
order to improve the toughness.
Figure 1: Project poster which was presented at the Undergraduate Research Exhibition

2. ABHINIT A KOTHARI
212 W, 109th Street, Apt #1C, New York, NY, 10025| (814) 954-2929 | ak4429@columbia.edu
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Title: Evolutionary and Genetic Programming Projects at Columbia
University (Sep – Dec, 2019)
Projects: 3 main projects were performed which involved using genetic programming techniques
to solve problems.
1. Traveling Salesman Problem: This project involved finding the shortest path between a
set of 1000 randomly generated points on a graph using genetic programming techniques.
The shortest path had to be calculated using mutation and crossover techniques that are
common in genetic programming. This is a typical optimization problem that depends on
a person’s algorithm and computational power.
Figure 2: Random Traveling Salesman Path with a length of 522
meters.
Figure 3: Optimized Traveling Salesman Path generated using
the hill climber algorithm. Path length = 124 meters.
Figure 4: Optimizing path length using random search which is
very inefficient even over 10,000 evaluations.
Figure 5: Optimizing path length using hill climber
algorithm which is highly efficient in optimizing path
length over 10,000 evaluations.

3. ABHINIT A KOTHARI
212 W, 109th Street, Apt #1C, New York, NY, 10025| (814) 954-2929 | ak4429@columbia.edu
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2. Symbolic Regression: This project involved predicted a function that fits a curve made of
1000 sets of points. This problem was more complicated than the traveling salesman one
and involved using heap data structures to store information such as mathematical
operators and constants. These data structures were then put through mutation, crossover,
and diversity generation techniques to predict the function that fits the curve. This was
another optimization problem that required a lot of computational power.
3. Robot simulation: This project involved creating a custom – built physics simulator
using Python which can be used to simulate a robot walking in space. The physics
simulator would resemble real world conditions such as friction and gravity. The robot
was created based on multiple spherical masses and springs attached to each other in a
tetrahedron like structure. The masses and springs acted as the foundation of the robot
and these were genetically evolved using mutation and crossover techniques. The goal of
the project was to allow the robot to learn to walk over time as different mass and spring
structures were evolved. The springs were given spring constant values which would
represent different structures such as a human bone, muscle, and tissue.
Figure 6: Original curve provided as a part of the problem can be seen in red and the blue curve is
what was generating as a "curve fitter" using a crossover evolutionary algorithm over 25,000
evaluations. The RMS error between the predicted curve is 0.54 which is very effective for only 25,000
evaluations. The predicted curve had a function of:
sin(x + sin(2*x) + 6.2945) + cos(2*x) – 1

4. ABHINIT A KOTHARI
212 W, 109th Street, Apt #1C, New York, NY, 10025| (814) 954-2929 | ak4429@columbia.edu
Page 4
The following link shows a video of the best walking robot:
https://youtu.be/yBPrXmlqBFY
Results: These projects were performed to showcase the power of genetic programming which
can be used to solve a variety of problems without knowing a lot about the problem itself. In the
case of the robot project, the robot simply learned to walk by evolving itself in a good way
determined by the evolutionary algorithm. In the traveling salesman problem, a randomly
generated path was 522 meters, but a path generated via the hill climber algorithm was 124
meters which shows the effectiveness of evolutionary algorithms. In the symbolic regression
problem, the original curve was fit with an RMS error of 0.54 units over 25,000 evaluations
using a crossover evolutionary algorithm.
Figure 7: Foundation of the robot represented using spherical masses in black and springs of
different colors representing different spring constants. White: Bone, Pink & Blue: Muscle,
Yellow: Tissue. The visualization was done using vPython.
Figure 8: Best evolved robot from a crossover + mutation algorithm over 10,000 evaluations.
The vertical stance of the robot helps it walk better which is "human-like". The images show the
robot walking horizontally from its starting position (Red Dot).

5. ABHINIT A KOTHARI
212 W, 109th Street, Apt #1C, New York, NY, 10025| (814) 954-2929 | ak4429@columbia.edu
Page 5
Title: Ecocar 3 Design Project (Penn State Advanced Vehicle Team) at Penn
State University (Aug – Dec, 2018)
Project: This project involved re-designing the aluminum-based transmission mount of a 2016
Chevrolet Camaro to reduce its weight in order to improve car efficiency. The CAD model of the
transmission mount was modified using Siemens NX and the final modifications were done on
the mount in a garage using a mill. Finite element analysis was done on the transmission mount
model from all directions to make sure that the mount could act under stresses even after weight
reduction. The forces for the finite element analysis were calculated by creating a free-body
diagram of the Powertrain and determining the forces acting on the mount from other major
components such as the engine, and motor.
Results: The weight of the transmission mount was reduced by 3 pounds which was done
primarily by creating holes in parts of the mount that would not affect its functionality and would
not cause a steep increase in stress under loading. These holes were drilled into the transmission
mount using a mill in the car garage. The mount was taken out from the car for the process and
then replaced into the car after the changes were made.
Figure 9: Transmission mount on the car before modifications were made.
Figure 10: Transmission mount after modifications were made and
weight was reduced by 3 pounds.

6. ABHINIT A KOTHARI
212 W, 109th Street, Apt #1C, New York, NY, 10025| (814) 954-2929 | ak4429@columbia.edu
Page 6
Title: Wind Turbine Design Project (Jan – May, 2018)
Project: This project involved designing and manufacturing a small – scale wind turbine that
could generate electric power. The prototyping process was done by using AHP and weighed
decision matrices to narrow down effective designs. The coupler and the blades on the wind
turbine were designed using SolidWorks and then 3D printed. The base and the tower were
manufactured on a mill in a garage. A planetary set gearbox was used for the wind turbine. An
Excel model was created to mathematically test variations in blade size, gear ratio, and tower
height to determine the most effective small – scale wind turbine.
Figure 11: Picture A shows a side view of the wind turbine. Picture B shows a front view. Picture C shows the
gearbox attached to the rest of the turbine via the hub.
Results: The wind turbine had to go through a leaf blower test to see if it could withstand real
world conditions and the turbine successfully passed the test as it produced 0.89 Watts of Power.
The turbine was 3 feet in height and had a blade diameter of 1.6 feet.

7. ABHINIT A KOTHARI
212 W, 109th Street, Apt #1C, New York, NY, 10025| (814) 954-2929 | ak4429@columbia.edu
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Title: Lockheed Martin UAV Design Project (Mar – May, 2016)
Project: This project involved designing and 3D printing a shock absorber for a UAV used by
Lockheed Martin which would allow the UAV to land without impacting the tail and the elevator
of the UAV. A shock absorber design was created using SolidWorks and was tested in lab by
dropping different weights on it. The shock absorber was to be created for a small-scale UAV
which can resemble a drone. The model created involved placing a triangular cross section shock
absorber inside the elevator of the UAV to prevent any negative aerodynamic effects.
Figure 13: 3D printed shock absorber.
Results: Testing involved dropping a Bocce Ball on the design from various heights to determine
the cracking height. The breaking point was reached at 6 feet which is fine for a small – scale
UAV.
Figure 12: UAV elevator design used for the shock absorber.
Figure 13: Triangular cross section shock absorber which is to
be inserted in the hollow space in the elevator.