Data-driven approaches for predicting mechanical properties and determining processing parameters of selective laser sintered nylon-12 components.

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.