Base pressure becomes a decisive factor in governing the base drag of aerodynamic vehicles. While several experimental and numerical methods have already been used for base pressure analysis in suddenly expanded flows, their implementation is quite time consuming. Therefore, we must develop a progressive approach to determine base pressure (β). Furthermore, a direct consideration of the influence of flow and geometric parameters cannot be studied by using these methods. This study develops a platform for data-driven analysis of base pressure (β) prediction in suddenly expanded flows, in which the influence of flow and geometric parameters including Mach number (M), nozzle pressure ratio (η), area ratio (α), and length to diameter ratio (φ) have been studied. Three different machine learning (ML) models, namely, artificial neural networks (ANN), support vector machine (SVM), and random forest (RF), have been trained using a large amount of data developed from response equations. The response equations for base pressure (β) were created using the response surface methodology (RSM) approach. The predicted results are compared with the experimental results to validate the proposed platform. The results obtained from this work can be applied in the right way to maximize base pressure in rockets and missiles to minimize base drag.

This study systematically investigates the effect of three Fused Deposition Modelling (FDM) printing parameters, i.e., wall thickness, layer height, and infill density, on the compressive behaviour of FDM-printed acrylonitrile butadiene styrene (ABS) and glass-fibre-reinforced ABS (ABS-GF). A Response Surface Methodology (RSM)-based Central Composite Design (CCD) comprising twenty experimental runs was employed to execute the experiments and develop regression models. The statistical significance of the process variables was determined through Analysis of Variance (ANOVA). Additionally, three nature-inspired optimization techniques, namely, the Firefly Algorithm (FA), Big Bang–Big Crunch (BB-BC), and Grey Wolf Optimization (GWO), were applied to identify the optimal printing conditions for achieving maximum compression strength. Experimental results demonstrated that glass-fibre-reinforced ABS exhibited superior compressive strength, greater load-carrying capacity, and more favourable deformation characteristics compared to neat ABS. Among the investigated parameters, infill density was found to be highly influential, as it controlled the internal porosity and structural continuity of the printed parts. The quadratic regression models developed exhibited excellent prediction accuracy, with coefficients of determination exceeding 95% for ABS and 97% for ABS-GF. Optimization results showed that the GWO algorithm achieved the highest fitness value of 97.15% and 98.48% for ABS and ABS-GF, respectively. The proposed optimization approach provides an effective framework for optimizing the compressive behaviour of FDM-printed polymer composites, thereby offering valuable guidance for the design and manufacture of high-performance engineering components through additive manufacturing techniques.