Blue Origin Interview

Siemens Energy

Orlando, FL

2008

2011

Gas Turbine Analysis

Achievements & Responsibilities:

- Gathered data on service run GT components, looking for correlations between damage type and extent and time in service

Siemens Energy

Orlando, FL

2008

2011

Materials: Lifing Technologies

Achievements & Responsibilities:

- Utilized ANSYS to look at Material Lifing data, trying to determine a better way to calculate material life over several different materials and groove types

Siemens Energy

Orlando, FL

2008

2011

Generator Service Engineering

Achievements & Responsibilities:

- Helped design and run FEA analysis on components for service run GN frames

Siemens Energy

Charlotte, NC

2011

2015

Steam Stationary IE

Main Responsibilities:

- Coordinated with Design Engineering, Project Engineering, Manufacturing, CNC programmers, Quality Engineers, and Shop Floor personnel to create manufacturing processes in the form of lineups for Mods and Upgrades, New Apparatus, and Service jobs.

- Responsible for creating and updating routings and production orders, searching through material masters and projects, and finding manufacturing drawings and information.

Siemens Energy

Charlotte, NC

2011

2015

Steam Stationary IE

Process Developement & Improvement:

- Integral part of the team that brought New Apparatus Steam Turbine projects from MLH to CLT

- Lead meetings with Manufacturing Engineering and CNC Programmers before every project,verifying process flow and lineup accuracy

- Lead a project leading to upgrading the controls on the Coupling and Diaphragm Furnaces

Siemens Energy

Charlotte, NC

2015

2022

Lead Production Planner

Advance & Production Planning:

- Work with Marketing, Tendering, Project Management, Design Engineering, and Tech Services to create and execute project plans that meet customer expectations

- Communicate forecast hours monthly and report progress towards the FY budget to Steam Leadership

- Develop recovery plans when Projects fall behind schedule due to non-conformances, vendor/material issues, machine maintenance, or any other Production related issues

- Lead weekly meetings updating project status, and communicating needs and obstacles

Siemens Energy

Charlotte, NC

2015

2022

Lead Production Planner

Leadership:

- Make priority decisions for allocating mechanic and machine manpower to meet customer and project expectations leading to on time deliveries

- Lead a project leading to improving mechanic and machine operator clocking accuracy

- Determine overtime needs across all shifts and resources to keep projects on track

- Worked as the acting Stationary supervisor every 5th weekend covering the OT shift for over a year and continue to help when needed due to PTO.

Siemens Energy

Charlotte, NC

2015

2022

Lead Production Planner

Technical Team Lead:

- Set priorities for our Manufacturing Engineers and CNC Programmers daily, removing obstacles and ensuring they have what they need to support Shop execution and project needs

Skills

Group Communication

One-on-One Communication

Presenting

COMMUNICATION

Decision Making

Skills

Microsoft Suite [Including Project]

SAP

Tableau

COMPUTER

CAD Software (AutoCAD, ProE)

ANSYS

Scenario - Modified Shakedown Method

- GT Engineering was using two popular methods to predict the elastic-plastic strain range for low cycle fatigue life calculations

- Neuber's Method

- Strain Prediction is less accurate but more conservative

- Predicts less life than reality

- Glinka's Method

- Strain Prediction is more accurate but lacks conservatism

- Predicts more life than reality

Neuber's Method vs Glinka's Method vs FEA

Project Goal

- Investigate several different types of shakedown methods and find one that has greater accuracy than the existing methods, but still has the required conservatism of LCF life calculation

- Modify Neuber or Glinka, Seeger's Method

- New method must work regardless of notch geometry, material type, temperature, etc...

- New method will be verified using a compressor disk blade groove model in ANSYS

Analysis

- ANSYS simulations of 3 generic notch geometries

- 3 notches x 4 radii x 4 materials x 2 temps = 96 simulations

- 1/4 & 1/8 geometries were used to decrease simulation time, using mirroring in ANSYS to predict for a full model

- Mesh

- Quad Mesh vs Tet Mesh = More Accuracy

- Mesh size < 1/100th notch radius at point of max stress (center of notch)

- Otherwise, Mesh size was set to less than 100,000 elements to minimize the length of each simulation

- Selected Far Field Stress that wouldn't cause each material to yield

- Compared Neuber & Glinka Estimated Plastic Strain to FEA Plastic Strain for all simulations

- As expected, Neuber is more conservative but less accurate and Glinka is more accurate but non-conservative vs FEA Analysis

Analysis

Result

- Had to determine the best alternative shakedown method based on the data

- Seeger's Method - Only for specific geometries

- Modified Neuber or Glinka Method

- Average plastic strain ratio for both methods over all simulations

- Average ratios squared because the methods are energy based

- Squared ratios then multiplied by the plastic portion of each method

- Once the method was chosen, simulations were run on a model of a compressor disk blade groove

- Chosen method combined the accuracy of Glinka's Method while retaining the conservatism of Neuber's Method

Result

Learnings

- Prior to this project, I had no ANSYS experience

- Learned how much work goes into proving assumptions

- Running simulations on a server

- Data needed to convince groups to change

-Groups using Neuber's or Glinka's Method resistant to change

Scenario - Furnace Controls Upgrade

- Controls for the Diaphragm and Coupling Furnaces were antiquated

- Floppy disc for installing furnace PWHT programs

- Dot Matrix printer for recording thermocouple temps

- Program temperature based on air in oven, not part

- I learned about a recent upgrade to the Valve Area Nitriding Oven Controls

- I submitted a 3i (Improvement Idea) regarding performing a similar update to the Diaphragm and Coupling Furnaces

- Opportunity to lead a process improvement project

Project Goal

Problem Statements

- The Diaphragm and Coupling Furnace Heat Treatment process required manual monitoring and intervention of the ovens to keep the PWHT of Couplings and Diaphragms within the proper temperature range

- Current controls allowed only manual program input or loading via floppy disc

-Current controls monitored the air temperature of the ovens instead of the parts in the oven

- Needed separate TC monitoring of the part

The goal of this project was to put a business case together showing the cost of upgrading the oven controls would save the company money in the short and long term

Analysis

Project Steps

- Worked with our Maintenance Department to contact vendors with expertise in upgrading furnaces like ours, learning about the technology available and the associated cost

- Put together a business case for upgrading the furnace controls

- Spoke to our welders to understand the PWHT process

- PWHT program (ramp up, hold, and ramp down temp) had to be manually inputted into the current controls every single time by the ME

- A mechanic had to manually monitor the TCs attached to the part in the oven, and then adjust the oven temperature if the part temperature was close to or outside the PWHT temperature range

- Oven was not close to the normal work area

- Mechanic had to complete quality documents during monitoring

- Studied hours clocked to Diaphragm orders over the last 4 years to see how much time was purely for PWHT monitoring

Result

- Out of a 4 day PWHT process, manual monitoring required 50hrs from a mechanic

- Printed temperature monitoring (dot matrix printer) made the job of our quality department more difficult vs electronic data

- With the cost of the new controls and the savings with reduced manual monitoring needed, total savings over 3 years added up to $173,900

- Upgraded oven controls added more than cost savings

- Programming via software and USB input

- Monitoring electronically

- New controls measure part temperature and adjusted the oven accordingly

- Part temperature is captured on a SD card, not on paper, allowing QA to quickly load the PWHT temperatures to a spreadsheet

Learnings

- First opportunity to work with Maintenance and third party vendors quoting upgrades to machines in the shop

- More time talking to the mechanics on the shop floor performing the work vs just looking through data out of SAP

- More detailed dive into the actual time clocked to a project in the shop by the employees on the floor

- This would lead directly into the next project I was able to lead

Scenario - Shop Floor Clocking Accuracy

After Spring Outage Season 2015, Siemens Energy was receiving negative feedback from customers, both internal and external

- On Time Delivery trending negative

- Non-Conformance Costs too high, quality issues

- Efficiency unacceptably low

- Loss of expertise (retirement & moves) hurting the shop floor

Reinvention Workshop - Become the customer's first choice for service

- Management led with input from representatives from all groups on campus in CLT

- 5 topics chosen for Projects

1 - Structure, Process, Systems

2 - Culture, Ownership, Accountability

3 - Resource Management & Training

4 - Clocking Accuracy - Project I led

5 - Risk Management & Readiness Review Process

Project Goal

Problem Statements

- Manufacturing operators in all areas are not consistently clocking their hours

- This leads to poor data quality, which leads to an inability to form a strategy and or identify problem areas within manufacturing operations and equipment utilization

- Reliable data from the hours clocked in are needed, so areas of improvement can be identified

The goal of this project was to have accurate and consistent clocking data. This will establish a baseline within the CLT SU Steam Turbine business to make proactive business decisions, which will improve planning reliability, manufacturing execution, quoting in tendering, as well as provide other departments with good data quality to further enhance and drive continuous improvement.

Analysis

Project Steps

- Decided we needed input from all organizations on campus: Voice of the Customer

- Surveyed 38 employees - MF Managers, MF Supervisors, MF Planners, Operators, & BA

- 11 questions asked, covering what clocking accuracy means to them, why clocking is inaccurate, benefits of accurate clocking, etc...

- Examples of feedback

- Alignment of expectations of when it comes to clocking accuracy. What does clocking accuracy mean to you and what do you view as accurate clocking?

- Properly accounting for our time and providing accurate data.

- Clock in on the job your working on and clocking accordingly as the task you are performing.

- This is to provide transparency for all areas of the operation, i.e; MFG, ME, Planning, etc.)

- Do you believe there is an issue with clocking accuracy in your area?

- Yes, due to the metrics. People do not clock accurately, because the supervisor will massage the time afterwards.

- Yes, due to timing/system. Operations not released or available in OIL when the work must start

- What ideas do you have to improve clocking accuracy?

- Enforce the rules about clocking and set the expectation. Management has to hold people accountable.

- Supervisors check at beginning of every shift. Stay after your employees and they’ll do a better job staying on top of clocking (Supervisors stay visible/More visibility).

- Accountability from management and supervisors through blue collar. Simplify clocking in the OIL 2.1 system.

Result

- Culture change needed

- Leadership focus on accuracy vs metrics

- KPI focus on Efficiency vs Utilization

- Constant reminders on the need for accurate time on projects

- Posters on campus

- Accuracy impacts future quotes future work coming into the shop

- Other recommendations

- More computers on the shop floor

- Remove hours from shop floor operations

- Dashboard projected on monitors throughout the shop floor

- Regular audits were performed over several months to put a number to clocking accuracy/inaccuracy

- Improvement from 27% to 71% over 6 months

Learnings

- First opportunity to lead a major project impacting culture campus-wide

- I was the only project leader not in management

- Project was tough to measure success

- Surveys and audits vs downloading numbers out of a system

- Project was dealing with culture and behavioral change in employees

- Repetition and effort - behavior doesn't change immediately

- Trust but verify

- Constant communication on benefit to individual employees

- Perfect example on how KPIs directly impact behavior