Achieving a uniform thickness and defect-free production in the flat die extrusion of polymer sheets and films is a major challenge. Dies are designed for one extrusion scenario, for a polymer grade with specified rheological behavior, and for a given throughput rate. The extrusion of different polymer grades and at different flow rates requires trial-and-error procedures. This study investigated the application of machine learning (ML) to provide guidance for the extrusion of sheets and films with a reduced thickness, non-uniformities, and without defects. A dataset of 200 cases was generated using computer simulation software for flat die extrusion. The dataset encompassed variations in die geometry by varying the gap under a restrictor, polymer rheological and thermophysical properties, and processing conditions, including throughput rate and temperatures. The dataset was used to train and evaluate the following three powerful machine learning (ML) algorithms: Random Forest (RF), XGBoost, and Support Vector Regression (SVR). The ML models were trained to predict thickness variations, pressure drops, and the lowest wall shear rate (targets). Using the SHapley Additive exPlanations (SHAP) analysis provided valuable insights into the influence of input features, highlighting the critical roles of polymer rheology, throughput rate, and the gap beneath the restrictor in determining targets. This ML-based methodology has the potential to reduce or even eliminate the use of trial and error procedures.

Hyperthermia is a promising medical treatment that uses controlled heat to target and destroy cancer cells while minimizing damage to the surrounding healthy tissue. Unlike conventional methods, it offers reduced risks of infection and shorter recovery periods. This study focuses on the integration of carbon nanotubes (CNTs) within the blood to enable precise heat transfer to tumors. The central idea is that by adjusting the concentration, shape, and size of CNTs, as well as the strength of an external magnetic field, heat transfer can be controlled for targeted treatment. A theoretical model is developed to analyze laminar natural convection within a simplified rectangular porous enclosure resembling a tumor, considering the composition of blood, and the geometric characteristics of CNTs, including the interfacial nanolayer thickness. Using an asymptotic expansion method, ordinary differential equations for mass, momentum, and energy balances are derived and solved. Results show that increasing CNT concentration decelerates fluid flow and reduces heat transfer efficiency, while elongated CNTs and thicker nanolayers enhance conduction over convection, to the detriment of heat transfer. Finally, increased tissue permeability—characteristic of cancerous tumors—significantly impacts heat transfer. In conclusion, although the model simplifies real tumor geometries and treatment conditions, it provides valuable theoretical insights into hyperthermia and nanofluid applications for cancer therapy.

Fibrous biomaterials are essential in biomedical engineering, tissue engineering, and filtration due to their specific transport and mechanical properties. Fluid flow through these materials is critical for their function. However, many biological fluids exhibit non-Newtonian behavior, characterized by micro-rotational effects, which traditional models often overlook. The current study presents a machine learning (ML) framework for the prediction and understanding of hydraulic permeability in fibrous biomaterials with a micropolar fluid. A dataset of 1000 numerical simulations was generated by varying the micropolar fluid properties and the fiber volume fraction in a periodic porous structure with nine parallel cylindrical fibers in a square lattice. Six powerful ML algorithms were deployed: Decision Trees (DT), Random Forests (RF), XGBoost, LightGBM, Support Vector Regression (SVR), and k-Nearest Neighbors (kNN). The balance of predictive capacity to unseen data values (tracking $R^2$ values and error metrics) with computational efficiency for all algorithms was assessed. The best-performing ML algorithm was subsequently used to interpret the decisions made by the model using Shapley Additive exPlanations (SHAP) analysis and understand the role of feature importances. The SHAP findings highlight the potential of ML in capturing complex fluid interactions and guiding the design of advanced fibrous biomaterials with optimized hydraulic permeability.