<scp>Optimization</scp> of Stretchable Fused Deposition Modeling Filament From Polypropylene‐Based Elastomer/Thermoplastic Elastomer Blends Using a Machine Learning Approach

ABSTRACT Soft filament‐based 3D printing is essential for future stretchable sensor development. Recently, commercially available soft filaments have a hardness range of 80A–95A. This hardness range hinders the flexibility of the application of soft materials, including soft stretchable sensors that require high elasticity. In this research, we fabricate soft material filaments using a dry blending and extrusion mechanism. We combine polypropylene‐based elastomer (PPE) (shore hardness [SH]: 66A) and thermoplastic elastomer (TPE) (SH: 83A). We expect that the blending can produce an SH lower than 83A. Our strategy is to mix PPE and TPE materials through direct granule mixing in the extrusion process. We vary temperature, speed, and extrusion repetition to get the optimized parameters for filament fabrication. Then, we maximize those variables using machine learning to get the optimal variable of the extrusion parameter. We found that the optimized filament with an SH of 60–62A appears at 200°C extrusion temperature with one‐time repetition and 10 rpm extrusion speed. Additionally, to help users design their material mixing, we establish a simplified software graphical user interface (GUI) for material parameter assistance. This GUI can automatically find the appropriate parameter for soft filament fabrication. We expect this finding can contribute to the rapid prototyping of stretchable sensors.