This repository contains a Design of Experiments (DOE) project conducted as part of SCM 517. The objective was to design and optimize a Lego race car using statistical experiments to maximize the distance traveled. The project follows a structured approach, leveraging Minitab for data analysis and factorial design techniques.
Legos.pdf – Project guidelines, constraints, and Bill of Materials (BOM). Lego DOE 313.pptx – Presentation with experimental setup, analysis, and results. Anova Minitab.mpx – Minitab analysis file for statistical modeling.
Apply Design of Experiments (DOE) principles to optimize a Lego race car. Identify the most significant design factors influencing performance. Use statistical modeling (ANOVA, regression) to analyze experimental results. Develop a cost-performance trade-off analysis based on BOM.
Response Variable (Y): Distance traveled by the Lego car. Factors Chosen: Tire Size (A) – Affects friction and rolling resistance. Wind Screen Size (B) – Influences air resistance. Axle Length (C) – Affects structural stability. Car Slant (D) – Alters center of gravity. Design Approach: Full Factorial DOE (k=4, n=2) Randomization of Trials Controlled Environment (No wind, fixed ramp angle)
Tire size (A) had the highest impact on distance traveled. Small windscreen and slanted design reduced drag and improved efficiency. Interaction effects were significant, especially between wheel size & slant. Final optimized model: Large tires, small windscreen, slanted design, small axle.
R-squared: 99.13% – Strong predictive capability. Residuals Analysis: Normally distributed, ensuring model validity. Cost Analysis: Best-performing model cost ₹13,200 while maximizing efficiency.
Use larger wheels & a smaller windscreen for optimal distance. Consider trade-offs between cost and performance in future designs. Further testing with additional factors (e.g., surface material) could refine results.