Optimization of process parameters in 3D-nanomaterials printing for enhanced uniformity, quality, and dimensional precision using physics-guided artificial neural network.

ABSTRACT: Pneumatic 3D-nanomaterial printing, a prominent additive manufacturing technique, excels in processing advanced materials like MXene, crucial for applications in nano-energy, flexible electronics, and sensors. A key challenge in this domain is optimizing process parameters-applied pressure, ink concentration, nozzle diameter, and printing velocity-to achieve uniform, high-quality prints with the desired filament diameter. Traditional trial-and-error methods often result in significant material waste and time consumption. To address this, our study introduces a comprehensive pipeline that initially assesses whether the selected process parameters yield uniform, high-quality MXene prints. Subsequently, it employs a Physics-Guided Artificial Neural Network (PGANN) to predict the filament diam

SUBMITTER: Ghandehari A 

PROVIDER: S-EPMC11649613 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature