Deposition stability and process window optimization of polycaprolactone in robotic pellet-based additive manufacturing

Purpose Polymer pellet-based additive manufacturing (PBAM) enables large-scale, high deposition-rate; however, geometric deposition stability and comprehensive manufacturing process windows for low-viscosity, semi-crystalline polymers remain poorly understood. This study aims to investigate and model geometric deposition stability in polycaprolactone (PCL) under high deposition-rate conditions. Design/methodology/approach A structured design of experiments was conducted to evaluate the effects of screw speed, robot travel speed and nominal layer height. Multivariate regression models were developed to quantify parameter interactions and predict key deposition metrics, including layer height deviation (Δzdiff), mean layer height (hzmean) and mean width (wdtmean). Findings The regression models showed high explanatory capability (R2 = 0.959–0.961) and statistically significant overall effects (p < 0.05). Distinct deposition regimes − under-extrusion, geometrically stable deposition and over-extrusion − were identified from the geometric responses and interpreted in terms of material throughput and robotic kinematic. Research limitations/implications This study is limited to the investigated PCL material, robotic PBAM configuration and parameter ranges, without direct rheological characterization, independent dimensional validation, mechanical testing or clinical evaluation. The wrist orthosis case study is therefore presented as a manufacturing and size-customization proof of concept rather than a clinically validated device. Nevertheless, the findings provide an experimentally grounded basis for defining an operational geometric deposition process window and guiding parameter selection in robotic pellet-based additive manufacturing. Practical implications Validation through size-customized wrist orthoses demonstrates the feasibility of translating stability criteria into orthotic components (wrist orthoses) while maintaining the investigated deposition throughput. Originality/value This work proposes a regression-based framework for identifying geometric deposition geometric stability regimes and defining operational geometric deposition process windows in robotic PBAM. Although demonstrated using PCL, the methodology is transferable to other semi-crystalline thermoplastics.