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The tribology of dimple-shaped textures is investigated within a reciprocating polymeric sliding couple of Invibio poly-ether-ether-ketone (grade 450G). A novel theoretical model was developed for this aim, describing hydrodynamic pressure and frictional force due to surface modifications. Experiments show reduced friction across all textures compared to un-textured surfaces. The parameters of texture depth (5-20 µm) and diameter (50-200 µm) were isolated to analyse the effect of each independently. The diameter showed good agreement with the model; however, depth showed variation at the extremities of the parameters examined. The experimental friction force captured over a single stroke was compared to the instantaneous theoretical friction force, and good agreement was found in the form of the friction trace within the hydrodynamic region of a reciprocating stroke. The texture parameter combination of 50 µm diameter and 20 µm depth showed the most significant reduction in friction at 48.7%.

Graphical abstract:

In order to allow engineers to make decisions regarding laser settings in selective laser sintering and predict the mechanical properties of materials, conventional material models could provide accurate solutions and recommendations, however, they are potentially expensive and time-consuming. Thus, a number of computational data-driven methodologies have been introduced in this article, as alternatives, to formulate cross-correlations between the processing parameters and mechanical properties of selective laser sintered (SLS) nylon-12 components. Proposed in this article direct-from laser settings to material properties, and inverse-from desired material properties to laser settings, two estimation frameworks have provided accurate estimation results. The accuracy of three proposed data-driven methodologies: fuzzy inference system (FIS), artificial neural networks (ANN) and adaptive neural fuzzy inference system (ANFIS), have been compared and thoroughly analysed, with FIS being the most accurate solution.

Supplementary information: The online version contains supplementary material available at 10.1007/s44245-025-00094-7.

Many factors contribute to the inherent uncertainty of energy consumption modeling in buildings. It is essential to perform a calibration and sensitivity analysis in order to manage these uncertainties. Despite the availability of several calibration methods, they are often deterministic and lack quantified uncertainties. Moreover, the selection of parameters in building energy modeling for calibration depends on the user's experience. Therefore, a more rigorous selection process is required. This study developed a new automated Bayesian Inference calibration platform running as an R package. A sensitivity analysis module and a Bayesian inference module determine the calibration parameters and uncertainties, respectively. The Meta-model module is developed to replace the building energy model for the Markov Chain Monte Carlo process to save computing time. The proposed platform is successfully demonstrated on a synthetic high-rise office building and a real high-rise residential building in a hot and arid climate. The relationship between the number of calibration parameters, calibration performance, and the accuracy of the Meta-model is further discussed. The developed calibration platform in this study proved to have clear advantages over the existing platforms, with the ability to reasonably estimate building energy performance in a short computing time.