Design and Manufacturing:

"Hydroyo", from ideation to mass production

Overview

For my MIT’s capstone product design and manufacturing course (2.007), my 4 person team designed and mass-produced the Hydroyo: a yo-yo inspired by the Hydro Flask aesthetic. In just one semester, we took the product from concept to 50 finished units, building a lightweight, durable yo-yo with a spinning “ice” window that creates a fluid, motion-forward effect.

We treated this like a real product launch: scoped a clear value prop, defined success criteria (spin feel, durability after drops, aesthetic uniformity), and worked within tight time and tooling constraints to deliver the product on time.

Design and Manufacturing

We converged on a five-piece architecture optimized for manufacturability and speed:

Top assembly: an injection-molded metal ring press-fit onto the body, with a thermoformed “ice” disc that spun independently for a fluid visual effect.
Bottom assembly: reused the same geometry on both halves to cut mold count and cycle time, then closed with a unique press-fit cap.

To keep production efficient, we:

Ran rapid prototyping cycles and A/B-style tests on geometry variants (different fit tolerances and ice-disc gaps) to measure stability vs. aesthetics.
Applied Design for Manufacturability (DFM) principles to simplify tooling and assembly.
Structured the build as a mini production line, running quick quality checks for press-fit retention, spin balance, and color consistency.

Data Analysis

One of the most valuable parts of the HydroYoYo project was analyzing the real manufacturing data we collected during production.

During production, I analyzed the stochastic variation in part tolerances and assembly time using X-bar charts, histograms, and cost models. The data revealed clear trade-offs: small shifts in injection molding parameters impacted yield, assembly bottlenecks slowed throughput, and cost curves showed injection molding only became economical beyond ~150 units. By coding scripts to simulate scenarios, I learned how to turn raw manufacturing data into actionable insights, optimizing processes, guiding design trade-offs, and connecting machine-level variability to system-level strategy.

Key findings:

Process variability: Some parts, like the “Bottom of the Top,” showed poor process capability (Cpk ~0.167), revealing how small shifts in machine warm-up cycles and press-fit tolerances could cascade into yield loss.
Deterministic disturbances: By intentionally adjusting injection pressure, we observed a measurable shift in part dimensions, helping us connect machine parameters to final quality outcomes.
Assembly time analysis: Batch assembly averaged ~1:45 per unit, but simulations showed that a reconfigured line could cut this under 1 minute, revealing clear bottlenecks and opportunities for throughput improvement.
Cost modeling: I coded MATLAB scripts to compare three scenarios: classroom prototyping, additive manufacturing, and high-volume injection molding. The analysis showed that additive methods work only for ultra-low volumes (cost >$1,000/unit), while injection molding becomes optimal beyond ~150 units, driving costs down to ~$1.63/unit in scale production.

What I learned: Manufacturing is not just about making parts, it’s about turning noisy, messy, real-world data into actionable insights. By combining statistical process control with cost and throughput modeling, I could guide our team’s design trade-offs (like part reuse and assembly strategy) with evidence rather than just random guesses. Data is indeed power!

Outcome

We delivered 50 Hydroyos on time! Drop tests confirmed durability, color consistency held across batches, and balance testing showed smooth spin performance. We scoped additional features (like the Hydro Flask cap/strap) as future iterations, a conscious trade-off to protect delivery speed and yield.

My Focus:

Scoped and validated the five-piece architecture, driving decisions on mold reuse, press-fit design, and part consolidation.
Coordinated across design and manufacturing sub-teams, aligning timelines and simplifying assembly into a repeatable sequence.
Gathered test data to validate assumptions (drop durability, spin performance), effectively running mini A/B experiments to back trade-off decisions with evidence.
Documented the process for knowledge transfer and scalability, ensuring reproducibility beyond the initial 50 units.

For the full development log (sketches, tooling decisions, assembly flow, and testing notes), check the complete write-up on my site: https://yoyoteamjaha.wixsite.com/home