SHREYAS PINGLE

Greenville, South Carolina, ***** 864-***-**** adh921@r.postjobfree.com LinkedIn SUMMARY

Automotive Engineering graduate student with experience in design and powertrain system modeling. My profile includes academic experience in component design and testing as a part of the BiW team of the Deep Orange 12 project and exposure in body design in CAD and steering system design and integration for all terrain vehicles as a SAE Baja team member. Seeking an intern/full-time opportunity in powertrain systems, design engineering and quality assurance roles.

SKILLS

● General Software: Microsoft office (PowerPoint, Excel, Word, Visio), Minitab.

● CAD: Siemens NX, AutoCAD, Solidworks, Creo Parametric, CATIA, GD & T, Drafting.

● Systems modeling and Analysis software: MATLAB, Simulink.

● Structural Analysis tools: ANSYS.

● Quality assurance: DFMEA, Rolled throughput yield analysis, Process capability analysis, Gage R&R, Statistical process control, 5S, Jidoka, ANOVA, Variable Gage R&R, Lean manufacturing. WORK EXPERIENCE

Deep Orange 12: Driverless Indy race car, Testing Engineer – BiW, January 2020-December 2020 Objective: To convert an existing open-wheel ‘Indy Lights’ racecar to a driverless configuration in collaboration with engineers and faculty at CU-ICAR that allows for head-to-head competition between multiple vehicles during the Indy Autonomous Challenge. This integrated hardware and software framework will be used by university teams around the world to develop fully- functional autonomous vehicle algorithms. These university teams will then race each other at high speeds at the Indianapolis Motor Speedway for a $1M grand prize on October 23, 2021. Link: DO12 Responsibilities:

Designing components in Siemens NX CAD.

Packaging components in the Indy Lights chassis.

Determining and implementing tethering options for the sensors and components.

Analysis and selection of adhesives based on the interaction between the adhesives and the materials under consideration.

FEA analysis of designed components.

EDUCATION

• MS Automotive engineering, May 2021, Clemson University International center for Automotive Research, Greenville, SC, Cumulative GPA: 3.66/4.0

• Diploma in product design, April 2019, LIVEWIRE, India.

• BE Mechanical engineering, July 2018, Bharati Vidyapeeth college of engineering (University of Mumbai), Navi Mumbai, Maharashtra, India, Cumulative GPA: 3.0/4.0

• Certification: Lean six sigma green belt.

PROJECTS

• SAE BAJA all-terrain vehicle, August 2017- June 2018 A team of 6 members including me, worked on manufacturing an ATV from scratch in accordance with the SAE BAJA Handbook for participation in Mega ATV national championship by Autosports, India. My duties included design of the ATV body in Solidworks and design and integration of the steering system.

• Automotive systems project: Maximize profit of a vehicle, November 2019 – December 2019 Maximized profit of a conceptual electric vehicle by optimizing vehicle design (dimensions of the vehicle, packaging of passenger and cargo, motor and battery selection) by considering performance measures such as aerodynamic drag force, 0-100 kmph acceleration time, top speed, range of vehicle on single charge, etc. In order to optimize vehicle design, MATLAB and Simulink were used and systems thinking, and powered by systems thinking and integration principles.

Results: Estimated demand = 48,579 units when price of the car is set to $49,000 Cost of production = $44,769 Gross Profit = (49,000-44,769) x demand = $205,537,749.00

• Cost optimization of an electric vehicle, March 2020– April2020 Optimized an existing electric vehicle’s cost and acceleration performance by replacing the conventional battery energy storage system. Utilized a hybrid battery supercapacitor energy storage system, optimal selection of motors and other components to replace existing ones. Analyzed by a multi-objective genetic algorithm in MATLAB. Slashed the price of the reference electric vehicle from $1,05,000 to $92,676 by replacing 2 existing motors on the car with 4 smaller motors, reducing the battery cell count from 7104 to 6510 and using 15 supercapacitors.

• Engine parameter determination

Based on dimension, crank angle, and cylinder pressure data for a gasoline engine, BSFC, IMEP, fuel conversion efficiency, BMEP, FMEP were estimated.

• Electric powertrain modeling with Simulink

Designed an electric vehicle powertrain model in Simulink to simulate vehicle performance characteristics like Maximum speed, 0-60 mph time, battery consumed over FHDS and FUDS drive cycles, Equivalent MPG, Drag force, etc.

• Analysis of effect of connecting rod length on piston motion The effects of connecting rod length on piston motion were investigated using piston speed data for varying connecting rod lengths.

• 2D vehicle frame (truss) analysis in MATLAB

Deflection analysis on a 2D -Truss frame model of a vehicle. The effect of changing stiffness of members and addition of members was investigated. Objective was to determine why adding some members has little impact on the deflection while adding others reduces the deflection significantly.

• Gage R&R: Schedl automotive, February 2020 – March 2020 The company’s tire inflation system was often fed wrong tolerance values by the operators due to misinterpretation of measurement units. Due to this, the tires produced were often of varying sizes and outside of acceptable dimensional tolerance limits. The project involved the use of a Gage R&R study and DFMEA to analyze the process of tire inflation to identify the points in the process at which errors occurred and led to frequent delays and incorrect dimensions of the tires produced. Unfortunately, the project was hindered due to the COVID-19 pandemic and we were no longer allowed to visit the factory in person and hence, the Gage R&R study was carried out based on data available at the time and the possible causes of the errors in tire sizes were determined along with possible solutions to fix these problems.

• Defect prioritization using Pareto chart analysis Using a data set count of 6 different defects, performed pareto analysis to determine which defect should be prioritized for rectification.

• Rolled throughput yield

Based on a data set of number of acceptable and defective parts undergoing 3 processes, the rolled throughput yield of the process was calculated and determined to be 71.69%.