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

Discover Nano Research Optimization of process parameters in 3D‑nanomaterials printing for enhanced uniformity, quality, and dimensional precision using physics‑guided artificial neural network Anita Ghandehari1,2,3 · Jorge A. Tavares‑Negrete2,3,4 · Jerome Rajendran1,2,3 · Qian Yi1,3 · Rahim Esfandyarpour1,2,3,4,5 Received: 30 August 2024 / Accepted: 27 November 2024 © The Author(s) 2024  OPEN 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 diameter based on these parameters, integrating fundamental physical principles of the printing process with experimental data. Our findings demonstrate that using an XGBoost classifier, we can classify printed filament quality with an accuracy of 90.44%. Furthermore, the PGANN model shows exceptional performance in predicting the filament diameter, achieving a Pearson Correlation Coefficient (PCC) of 0.9488, a Mean Squared Error (MSE) of 0.000092 mm2, and a Mean Absolute Error (MAE) of 0.00711 mm. This pipeline significantly streamlines the process for researchers, facilitating the selection of optimal printing parameters to consistently achieve high-quality prints and accurately produce the desired filament diameter tailored to specific applications. Keywords Additive manufacturing · 3D-nanomaterial printing · MXene · Process parameters · Physics-guided artificial neural network · Machine learning · Data-driven 1 Introduction Pneumatic extrusion 3D-nanomaterial printing, a form of direct ink writing (DIW) in additive manufacturing, has emerged as a multipurpose technology with several advantages, including cost-effectiveness, rapid prototyping, and potential for high throughput [1–6]. This technology finds applications across various fields, such as biomedical Supplementary Information The online version contains supplementary material available at https://doi.org/10.1186/s11671-024-04155w. * Rahim Esfandyarpour, | 1Department of Electrical Engineering and Computer Science, University of California, Irvine, CA 92697, USA. 2Henry Samueli School of Engineering, University of California, Irvine, CA 92697, USA. 3Laboratory for Integrated Nano Bio Electronics Innovation, The Henry Samueli School of Engineering, University of California, Irvine, CA 92697, USA. 4Department of Biomedical Engineering, University of California, Irvine, CA 92697, USA. 5Department of Mechanical and Aerospace Engineering, University of California, Irvine, CA 92697, USA. Discover Nano (2024) 19:204 | https://doi.org/10.1186/s11671-024-04155-w Vol.:(0123456789) Research Discover Nano (2024) 19:204 | https://doi.org/10.1186/s11671-024-04155-w engineering, nanoengineering, and nanotechnology [7–12]. It inherits the general benefits of additive manufacturing, notably enhanced control over the volume and placement of extruded materials, establishing itself as the preferred technology for processing advanced nanomaterials [13, 14]. Among these advanced materials, MXene a two-dimensional (2D) transition nanomaterial with outstanding triboelectric, conductivity, and flexible properties has been processed with extrusion-based 3D nanomaterial printers [15]. Nonetheless, MXene’s highly reactive surface akin to transition metal oxides combined with the functional groups (-F, =O, -OH) that provide linking sites, renders MXene promising material with applications in nano-energy production, flexible electronics, and wearable sensors. The 3D-nanomaterial printing of MXene requires uniform high-quality printing and precise control of the filament diameter to print uniform, conductive, precise, flexible, and microscale sensors and circuits [15]. Uniform prints are defined as achieving consistent quality without defects. To control the diameter of the nanomaterial printed filament and maintain its uniformity, fine-tuning process parameters of pneumatic printing is essential. The most influential process parameters during pneumatic printing include adjusting applied pressure, material viscosity, nozzle diameter, and the velocity at which the extruder moves [13, 16–19]. Currently, researchers are facing a significant challenge in identifying the optimal process parameters for a specific filament diameter with uniform quality. They often resort to a trial-and-error approach to ensure the filament produced is uniform and matches the desired diameter [20]. Such a method, however, leads to considerable consumption of time and materials as researchers iteratively adjust and test process parameters to achieve defect-free printing with the desired filament diameter. This iterative optimization process underscores the need for a more efficient strategy in the calibration of 3D-nanomaterial printing process parameters to enhance printing quality. In this context, machine learning (ML) emerges as a powerful tool for enhancing the calibration of 3D printing process parameters. Fu et al., developed a Support Vector Machine (SVM) model, producing a process map aimed at aiding the selection of the best printing parameters to consistently achieve high-quality prints with a probability greater than 75% [21]. Tamir et al., combined open-loop and closed-loop ML models with a fuzzy system to optimize 3D printing parameters, improving print quality in additive manufacturing [22]. Deswal et al. optimized a process for enhancing the dimensional preciseness of fused deposition modeling (FDM) 3D printing. They applied hybrid statistical techniques, including response surface methodology combined with a genetic algorithm (RSM-GA), artificial neural networks (ANN), and a fusion of artificial neural networks and genetic algorithm (ANN-GA) [23]. Jeong et al., investigated the Aerosol Jet Printing (AJP) process, focusing on how its process parameters affect line width and resistivity. They established an operability window that outlines optimized process parameters for producing high-quality lines, accompanied by a regression equation for statistically estimating line width [24]. These studies show the advantages of ML, or in better words, data-driven approaches, in determining the quality and dimensions of 3D printed parts based on the process parameters of printing. However, a notable limitation of these methods is that they overlook the underlying phy (...truncated)