Design and Evaluation of Custom Intermixers for extrusion of blended PLA Using Machine Learning

The following data supports the manuscript titled "Design and Evaluation of Custom Intermixers for extrusion of blended PLA Using Machine Learning" submitted to "Virtual and Physical Prototyping", which details the following abstract:
"Uniform blending in multi-material extrusion is crucial for ensuring consistent material properties in additive manufacturing. This study evaluates the performance of five static mixer designs, Split Path, Helix Array, Full Turn Helix, Half Moon, and Cross Bars, integrated into a coaxial extrusion system for enhancing the blending of multi-colored PLA (polylactic acid) pellets. Each mixer was tested using a 50/50 mixture of red and blue PLA under controlled extrusion conditions at 210°C. Mixing performance was assessed through microscopic imaging and machine learning-based analysis, including histogram evaluation, clustering algorithms, and standard color uniformity indices. Results showed that the Split Path and Full Turn Helix mixers provided the most uniform color distribution, with minimal segregation. In contrast, the Helix Array, Half Moon, and Cross Bars designs produced moderate to inconsistent mixing, showing visible streaking and uneven blending. All mixer configurations, however, significantly outperformed the control (no mixer) setup. These findings offer quantitative insights into the effectiveness of various mixer geometries, providing a basis for optimizing mixing strategies in multi-material 3D printing. The study contributes to the development of more reliable extrusion systems for applications such as functionally graded materials, flexible electronics, and advanced polymer composites."