Luis Daniel
Weiss Quiroz
Mechatronics engineering student focused on data-driven design, with a primary goal of a January 2027 full-time position.
Featured Engineering Challenges
These process-driven case studies highlight the intersection of simulation, validation, and hands-on iteration.
Drivetrain Ratio Optimization
Focus: System Modeling & Performance Prediction
Situation: As Driveline Lead for RIT Baja SAE, vehicle acceleration and top speed needed to be quantitatively optimized across varied competition terrains.
Task: Quantitatively set vehicle and system goals using a MATLAB model that predicts vehicle performance to determine ideal CVT and gearbox reduction ratios.
Actions: Developed multi-physics models in MATLAB for acceleration simulation. Validated simulation results against 100 ft sprint field data through Genchi Genbutsu testing.
Results: Reduced 100 ft sprint times by 12%. Achieved model correlation within 5% of physical testing parameters.
Lead Reduction Box Design
Focus: Gear Design, FEA & Fatigue Analysis
Situation: The RIT Baja vehicle required a secondary reduction gearbox capable of managing high torque shock loads without weight penalties during endurance cycles.
Task: Design gears and housings using KISSsoft, SolidWorks, and ANSYS, conducting fatigue analysis with Modified Goodman and Miner’s law.
Actions: Selected gear geometries in KISSsoft for surface durability. Performed structural FEA in ANSYS. Conducted field fatigue testing to validate life predictions.
Results: Achieved a weight reduction of 25% while maintaining reliability. Verified zero failures over 40+ hours of testing.
Gearbox Mounting Redesign
Focus: TBP, Serviceability & Part Integration
Situation: Previous mounting configurations led to excessive gearbox swap times and redundant brackets in the rear suspension architecture.
Task: Utilize Toyota Business Practice (TBP) to redesign mounting geometry, targeting a 1.1 FOS with improved field accessibility.
Actions: Conducted root cause analysis of maintenance delays. Integrated mounts into 7075-T6 housing geometry. Validated accessibility for rapid pit repair.
Results: Reduced swap time from 2 hours to 45 mins. Part count reduced by 30%, saving 1.2 lbs of mass.
Project Walkthrough: Drivetrain Ratio Optimization
A quick walkthrough of how I approached one of my most impactful engineering challenges, bridging simulation and real-world results.
Technical Process Breakdown: Luis Daniel Weiss Quiroz — Mechanics, Simulation & Validation Methodology
Skills & Tools
A technical toolkit bridging the gap between computational analysis and physical validation through industry-standard software and rigorous testing methodologies.
Software & Tools
ANSYS
KISSsoft
MATLAB
Python
SolidWorks
Microsoft Office
Teamcenter
PDM
Engineering & Manufacturing
GD&T
Laser cutting
3D printing
Design
Machining
Measurement tools & devices
Methods & Teamwork
Data acquisition
Testing and validation
Root cause analysis
Mechanical design
Drivetrain systems
Team collaboration
Design iteration
Additional Projects
Supporting technical projects showcasing specialized engineering skills, cross-functional collaboration, and practical design iteration.
Front Suspension Geometry Analysis
Optimization of unsprung mass and roll center migration for improved damping response in dynamic cornering.
Carbon Fiber Panel Fabrication
Hand-layup process and vacuum bagging for lightweight bodywork, reducing total vehicle mass by 4.5 lbs.
Brake System Thermal Validation
Validation of rotor heat dissipation during endurance cycles to prevent brake fade and system failure.
Focus: Kinematics & Simulation
Focus: Composites & Manufacturing
Focus: Thermodynamics & Testing
Data Acquisition System Setup
Integration of Hall Effect sensors and strain gauges to monitor live drivetrain torque and wheel speeds.
Steering System Fatigue FEA
Finite Element Analysis of steering brackets to ensure infinite fatigue life under high-frequency rough terrain loading.
Baja SAE Chassis Compliance
Ensuring 100% adherence to SAE safety rules through structural verification and simulated impact loading.
Focus: Sensors & Validation
Focus: FEA & Structural Design
Focus: Safety & Structural Integrity
Resume & Contact
luisdanielwq@gmail.com
LinkedIn Profile
Engineering Approach
Defining the real problem
Utilizing Genchi Genbutsu and Toyota Business Practice (TBP) to verify constraints and requirements at the source before modeling begins.
01
Modeling and analysis
Leveraging MATLAB, KISSsoft, and FEA to develop predictive models for drivetrain dynamics and component fatigue failure modes.
02
Testing and validation
Executing structured fatigue testing and hardware validation cycles to ensure that physical real-world performance correlates with theoretical analysis.
03
Iterating based on data
Refining mechanical designs based on empirical performance data acquired from both vehicle-integrated sensors and laboratory testing rig data to drive durablity refinements.
04
Closing the design loop
Closing the loop between design, analysis, and validation to balance performance results with long-term hardware reliability and mass reduction targets.
05
Subsystem cohesion and collaboration
Utilizing the Toyota Way and cross-functional collaboration to ensure integrated subsystem performance and total platform reliability under track conditions.
06
About Me
I am a Mechatronics Engineering Technology student at RIT (graduating December 2026) dedicated to closing the loop between design, analysis, and validation. My professional background includes experience at McLaren Performance Technologies, where I redesigned gear root fatigue testers and automated S-N curve generation using Python. During my Toyota Production Engineering co-op, I applied TBP and Genchi Genbutsu methodologies to equipment buy-offs for a new rear axle plant. As the RIT Baja SAE Co-Chief Engineer, Driveline Lead, and Lead Reduction Box Designer, I specialize in connecting computational modeling with empirical test data to build high-performance vehicle systems. I thrive on practical, data-driven engineering challenges from initial modeling to final validation.