Optimization of the engineering response of medical-graded polycaprolactone (PCL) over multiple generic control parameters in bioplotting

Bioplotting has high potential for the 3D printing of scaffolds and cellular structures. Medical-grade poly(ε-caprolactone) (PCL) is characterized by low 3D printing temperatures and strengths compared to those of common polymers. Thus far, research on PCL in bioplotting has mainly focused on improving its performance through the development of composites. In this study, the quantitative impact of six common 3D plotting settings on the engineering strength of PCL parts was evaluated. Three modeling approaches were implemented: linear regression modeling (LRM), reduced quadratic regression modeling, and quadratic regression modeling. The LRM results were not as accurate as those obtained using the other two approaches. The difference between the tensile strength results for parts fabricated applying certain control factor ranges reached a remarkable 600%, indicating the impact of appropriate 3D plotting settings on the workpiece response. Prediction models were compiled and validated experimentally. The results possess engineering and industrial value for establishing the best control parameters during manufacturing, thus achieving the intended functionality of the end products. The findings can be used as a roadmap for optimum parameter selection and prediction of mechanical property metrics, leading to more robust and efficient parts.