About me


A bit about me

  • 8 Years of total experience: Eaton-6.5 years, Siemens-1.5 years
  • M.Tech - Machine Design from IIT Guwahati
  • Leading and Reviewing Structural FEA projects of 15+ team members and Automation and Digital Design projects of 60+ team members.
  • Working on creating end-to-end Digital Design process through simulation automation, incorporating ML models for design optimization.
  • Performed FEA analyses on Eaton vehicle, e-mobility, and electrical products, Siemens Gas Turbine. Optimized designs to meet all requirements.
  • Expertise in Linear FEA, Non-linear FEA, Thermal FEA, Stress and Strain-life Fatigue, crack propagation life, and creep calculations.
  • Completing Eaton SkillLab's 18-month Data Science program.
  • Software/Tools: Ansys Workbench, APDL, Creo, Enovia, n-Code, Franc3D, SR1, Esteco, optiSlang, Office 365 apps, SharePoint sites, Power Automate, Power BI, Git, Docker, Linux terminal
  • Programming experience: Ansys ACT, APDL script, Python, Excel VBA, MATLAB, C, Full stack web apps (HTML, CSS, JS, Python Flask, SQL), Data science libraries(Pandas, NumPy, Scikit-learn)

my Personal info

SAI MOHAN REDDY NEELAM

  • Email: saimohan.35@gmail.com
  • Phone: +91-9085305400
  • DOB - 30 June 1992
  • Marital Status - Married
  • Present Address - N-403, Heliconia, Magarpatta, Pune, Maharashtra – 411028
  • Permanent Address - Lalithanagar, Chinnagopavaram, Kaluvoya, Nellore, Andhra Pradesh - 524341
  • Languages Known - English, Hindi, Telugu
  • Hobbies - Travel, Movies, Music, Workout, Learning

MY BACKGROUND


Education

  • 2013-2015

    CGPA : 9.13 @ M.tech - Machine Design

    IIT GUWAHATI

  • 2009-2013

    CGPA : 8.033 @ B.Tech - Mechanical Engg

    Sri Venkateswara University, Tirupati, Andhra Pradesh

  • 2007-2009

    96.40 % @ XII std

    Narayana Junior College, Nellore, Andhra Pradesh

  • 2006-2007

    92.67 % @ X std

    Viswam High School, Kaluvai, Andhra Pradesh

EXperiences

  • Aug 2022 - Present (11 months)

    EATON : Lead Engineer - Structural Analysis (FEA) and Digitalization

    • Technical Lead for structural analysis (FEA) activities in Vehicle and E-Mobility business programs.
    • Collaborating with multiple teams to simulate multi-physics aspects, ensuring all analyses are completed before testing and strengthening "Analysis first" strategy.
    • Mentoring and reviewing business critical projects and conduct RCAs for a team of 15+ engineers and senior engineers.
    • Developing Technology Road Maps that align with the present and future needs of the business, while securing funding for these initiatives.
    • Leading a team of 60+ professionals in digitalization, digital design, and automation activities.
    • Creating an end-to-end Digital Design process through simulation automation, incorporating ML models for design optimization. ~90% reduction in simulation process time.
    • Conducting global trainings on Ansys, Fatigue, Coding, Automation, and Digitalization.
    • Completing Eaton SkillLab's 18-month Data Science program.
  • Nov 2019 - Jul 2022 (2 years 9 months)

    EATON : Senior Engineer - Structural Analysis (FEA)

    • Hands-on and Led Structural Analysis (FEA) efforts for Differential and Valvetrain products.
    • Mentored and guided a team of 4+ Engineers on stress and fatigue analysis requirements.
    • Developed, validated, and enhanced new digital prototype methodologies for the Structural Analysis team.
    • Successfully managed end-to-end execution of key Eaton flagship projects, from pursuit to business win, detailed design to validation, and launch.
    • Created a custom fatigue tool that is now Eaton's standard tool. Faster yet equally accurate.
    • Developed duty cycle fatigue, and Accelerated life testing methods.
    • Created Engineering App Center at Eaton as a repository for engineering calculators, promoted globally by the Eaton CTO.
    • Developed a Rapid Qualification Tool for finding optimum print parameters in additive manufacturing. Patent filed for the same.
    • Led the Eaton Global Fatigue Forum, actively promoting fatigue awareness within the organization.
    • Automated software renewal process and Eaton Balance scorecard through Power Automate.
  • Dec 2016 - Oct 2019 (2 years 11 months)

    EATON : Engineer - Structural Analysis (FEA)

    • Responsible for Structural analysis (FEA) of various Eaton products (Vehicle, E-Mobility, Electrical) in ANSYS.
    • Created FEA models, optimized the design to meet specifications, and presented results to stakeholders.
    • Primarily focused on Differential product along with other Vehicle group products.
    • Performed Static linear and non linear structural FEA analysis, deterministic and probabilistic fatigue life calculations.
    • Partnered with global teams, understood the design intent, developed and validated structural analysis models to substantiate the engineering designs.
    • Deployed developed methodologies in Eaton products to strengthen “Analysis First” strategy.
    • Customized ANSYS Workbench environment using Ansys ACT and Python, creating internal tools to improve efficiency.
  • Jul 2015 - Nov 2016 (1 year 5 months)

    SIEMENS : Structural Analysis Engineer - FEA

    • Research and Continuous Development of Siemens Large Gas Turbine Rotating Components.
    • Mechanical/structural integrity analysis using finite element analysis (FEA) in ANSYS.
    • Static Steady state and Transient, Linear and Non Linear, Thermal - structural analysis.
    • Strength Assessment, Fatigue Life Calculation, Fracture mechanics Life (crack propagation) Calculation, Creep Calculations.
    • Components optimization/redesign based on above calculations to meet all design margins.
    • Writing ANSYS APDL macros, Excel VBA macros to accelerate all above calculations.
    • Member of submitted innovative idea on creep mitigation in SIEMENS gas turbines.
  • Jun 2014 - Jun 2015 (1 year 1 month)

    Internship @ Siemens Ltd

    • Excel tool creation to calculate rotating disk stresses analytically (which can be used during conceptual design stage of new gas turbine disks)
    • Setting up standard guidelines for realistic fracture mechanics life assessment of SIEMENS gas turbine rotor disks using 3D crack modeling technique
    • Comparison of Fracture life prediction by 2D analysis in IWM VERB and 3D crack modeling technique in ANSYS

Highlights


8
Years of experience
100+
Structural FEA analysis projects in Eaton
2
Outstanding Technical Awards (OTA) in Eaton
38
Recognition Awards (E-star) in Eaton
6
Employee of the Quarter awards in Eaton AHM
2
times appeared in CTO newsletters in Eaton
1%
Topper - Top 1% in NPTEL – Data Science 2022 exams
1
Patent filed

Skills


FEA

Ansys workbench, Ansys APDL, Abaqus

CAD

Creo, Enovia, Team Center

Fatigue

n-Code, Eaton tool, Siemens tool

Fracture Mechanics

FRANC3D, Ansys, IWM verb, NASGRO

Bolted Joints

SR1

Digital Design workflows

Esteco modeFrontier, optiSlang, Python

Dashboards

Power BI, HTML

Office 365 apps

SharePoint sites, Power Automate

Programming

Python, Ansys ACT, APDL script, Excel VBA, MATLAB, C

Full stack web apps

HTML, CSS, JS, Python Flask, SQL

Data science libraries

Pandas, NumPy, Matplotlib, Scikit-learn

Devops tools

Git, Docker, Linux terminal

Statistics

Minitab, @Risk, Python

Design for Six Sigma

DOE, RSM, DFMEA, DVPR

Reduced order models

Ansys Design Xplorer, optiSlang

Certifications


Eaton Skill Lab - Data Science - 18 months curriculum

Eaton

Skills: Python, Machine Learning, Data Analytics, Power BI

April 2023

Design for Six Sigma (DFSS) - Green Belt

Eaton

Skills: Statistics, Robust Design

August 2019

View Certificate

Introduction to Machine Learning, IIT Madras (Topper - Top 1% - 2nd rank among 27,877)

NPTEL

Skills: Python, Data Sciencce, Machine Learning

April 2022

View Certificate

Data Analytics with Python, IIT Roorkee (Topper - Top 1% - 10th rank among 40,485)

NPTEL

Skills: Python, Data Analytics, Statistics

April 2022

View Certificate

Power BI A-Z: Hands-On Power BI Training For Data Science

Udemy

Skills: Microsoft Power BI

September 2022

MY WORK

click on image to read more


M.tech Project - Crack Growth Analysis and Propagation Life Prediction of Gas Turbine Rotor Discs

Aim/Goal

Setting up standard guidelines for crack propagation life estimation of gas turbine rotor discs using 3D crack modeling technique in finite element analysis.

Problem Definition

  • Gas turbines are frequently employed in power generation due to their higher power to weight ratio and they usually rotate at high speeds and operate at high temperatures. So, these are heavily loaded due to mechanical and thermal loads.
  • This leads to the generation of surface cracks at stress concentration zones or at pre-existing defects. The presence of crack can significantly reduce the life of component and components fails in brittle fashion even if it is ductile material.
  • After reaching a critical state, these discs fracture into two or more number of parts due to the propagation of these cracks.
  • Hence, turbine discs are considered as critical components in the engine and their crack propagation life estimation under fatigue loading is prime importance.
  • Gas turbine rotor discs have complex geometries with cooling holes in different directions such as Axial, Radial or hybrid. This results in 3D stress field at many locations.
  • For such complex geometries, 3D crack modeling technique and crack growth analysis using stress intensity factor & J-Integral is required to predict the realistic crack propagation life.

Objectives

  • Initial FE and FM life (crack propagation life using fracture mechanics) assessment for standard components having standard crack geometries using 3D crack modeling technique and comparison with published or experimental results to setup the benchmark. 
  • Next step involves FM life prediction of various cracks in different locations of gas turbine disc using 3D crack modeling technique in ANSYS. 
  • Setting up standard guidelines for 3D crack modeling technique of GT rotor disks. 
  • Comparison of FM life prediction by 2D analysis in IWM VERB and 3D crack modeling technique in ANSYS.
surface and corner crack in plate

Methodology

  • This work is focused on the crack propagation life estimation of gas turbine rotor discs under different service conditions using fracture mechanics concepts. 3D crack modeling technique and crack growth analysis using stress intensity factor & J-Integral is required to predict the realistic crack propagation life. 
  • First, for benchmarking, stress intensity factors of surface and corner cracks in plates subjected to tensile loading are determined using 3D crack modelling technique in ANSYS. These results have been Compared with published results and good agreement was observed.Next, crack growth analysis and FM life estimation simulation is performed on plate having surface crcak and compared with results available and achieved excellent agreement.
  • 3D crack modeling, Stress intensity factors estimation and FM life prediction is performed for gas turbine disc which has been taken from the literature. Predicted life is compared with experimental results to validate the methodology and the results are very good.
  • Same methodology using 3D crack modelling is used for different gas turbine disc locations to predict the life of component.
  • Until now, this work is performed under LEFM (linear elastic fracture mechanics) conditions. From now onwards, it will be carried out under EPFM (elastic-plastic fracture mechanics) conditions.
  • This work is not completed yet. So, changes to the existing content are possible.

Result

This is the result of analysis performed on the literature disc.

crack in disc


crack propagation


Life estimation comparison with experimental results


Conclusions

  • 3D crack modeling technique is very effective for estimating accurate stress intensity factors, crack propagation life and critical crack length. 
  • Life from 2D analysis in IWM VERB is very much below the life estimated from Ansys.  
  • If  accurate life and the critical crack length is known, inspection periods and maintenance can be scheduled accordingly.  

Downloads 

will be updated soon

Acknowledgemnet

Project guide : Dr K S R Krishna Murthy, IIT Guwahati
Mentors in SIEMENS :
Mr. Siddharth Jain,
Mr. Pritam Kondalwar
Mr. Manish Kumar Purohit
Mr. Sunder Raman Mohan 
I thank above all for their guidance and support during this work.

Suggested links

Go to home page
Read about my B.tech project on fracture mechanics here

Feel free to comment below and after that contact me if you want to.

B.tech Project - Determination of Warpage in Injection Molded Plastic Components using Image Processing

Aim/Goal

Applying efficient and easy methodology for predicting warpage in plastic components.

Problem Definition

  • Plastic injection molding is one of the major net-shape-forming processes for thermoplastics materials. Mold temperature, injection pressure, filling time, injection speed are the four major parameters in manufacturing the injection molding components. 
  • There are several defects that occur in the injection molding process, including blush, blow holes, burn marks, warpage, bowing, bubbles, clear spots, cloudy appearance, contamination and cracking. 
  • Warpage is the possible defect which occurs with the variation in rate of solidification due to change in temperature differences in plastic components. 
  • Image processing technique is more convenient to predict warpage in injection molded plastic components. 
  • By minimising the warpage, considerable improvement in the performance characteristics of injection molded components can be observed. 

Objectives

  • Preparation of fixture for taking steady images of plastic components.
  • production of forward and reverse lever ( FR lever ).
  • Warpage determination in FR lever using image processing technique in MATLAB.
  • Applying the same methodology to other plastic components like bottle caps and computer speakers.

Methodology

          This work focused on determination of warpage for injection molded plastic components through image processing technique. In this context, a fixture has been prepared to capture digital images of plastic components in a fixed plane. Further this fixture helps to capture clear standby images of plastic components which help to get to more accurate warpage results using image processing. This work undergoes for determination of warpage in Forward and Reverse lever (FR lever), bottle caps and computer speakers. The two plastic components images are captured by using high resolution digital camera which are fixed in fixture. The high resolution captured plastic components of images, are processed using MATLAB Image Processing Tool. The distoreted injection molded plastic component is compared with the standard component by measuring at three different locations in longitudinal direction and also in lateral direction. Warpage has been calculated by taking the average of differences between those measurements in both directions. The warpage may be minimised by suitably selecting the process parameters of injection molding machine.

Result

warpage in FR lever by using image processing

Warpage in FR Lever

Conclusions

  • In MATLAB, image processing tool is very effective and fruitful in the determination of warpage in plastic components. The present method used for measuring warpage is easy, time saving.
  • If warpage is more than the acceptable limit, suitable process parameters of injection molding machine such as mold temperature, injection pressure, injection speed, filling time and cooling time can be adjusted to get the better quality of plastic component.

  • Project guide : Dr G Padmanabhan, Si Venkateswara University.
  • Mr. S Selvaraj helped in production of FR lever and provided guidance.
  • Mr. Sudhakar provided support in MATLAB code for image processing.
Project team members :
B R Silpa Sree
N Sai Mohan reddy
Y Purushotham 
I thank above all for their guidance and amazing time during this project work.

Suggested Links


Feel free to comment below and after that contact me if you want to. 

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Contact

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Email

saimohan.35@gmail.com

Phone number

+91 - 9085305400

Thank You

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