Over six demanding months, I optimized an XPlanar-based MEX process for implant-grade PCL scaffolds and turned a theoretical optimum into a practical, repeatable window. Although the model predicted 98.289% accuracy at (LH 0.261 mm, RW 0.563 mm, MH 3.631 mm), confirmation prints exposed bridge collapse. I therefore selected the robust setting (LH 0.20 mm, RW 0.45 mm, MH 2.25 mm), which consistently delivered ~97% dimensional accuracy with stable bridges. Across factors, layer height emerged as the dominant driver of geometric error. I documented clean-room SOPs for motion/thermal control, defined a clear measurement criterion (L, B, five-point P), and validated results with metrology and DIC.

I presented and defended this work successfully—feedback highlighted the clarity of the DoE rationale, the honesty around failure modes, and the strength of the practical window. The effort was worth it: I leveled up in DoE, precision mechatronics, clean-room process control, data analysis (MATLAB), DIC/metrology, and technical communication. Most importantly, I learned to prioritize reliability over headline numbers—choosing settings that print every time, not just once. This mindset now guides my next steps in robotics and HRI, where safe, repeatable performance matters as much as peak metrics.