Tu Ruixuan, Majewski Candice, Gitman Inna
Division of Surgery and Interventional Science, University College London, London, UK.
Department of Mechanical Engineering, University of Sheffield, Sheffield, UK.
Discov Mech Eng. 2025;4(1):10. doi: 10.1007/s44245-025-00094-7. Epub 2025 Mar 22.
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 -from laser settings to material properties, and -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.
The online version contains supplementary material available at 10.1007/s44245-025-00094-7.
为了让工程师能够在选择性激光烧结中就激光设置做出决策并预测材料的机械性能，传统材料模型可以提供准确的解决方案和建议，然而，它们可能成本高昂且耗时。因此，本文引入了一些计算数据驱动的方法作为替代方案，以建立选择性激光烧结（SLS）尼龙12部件的加工参数与机械性能之间的相互关联。本文提出了两个估计框架——从激光设置到材料性能，以及从期望的材料性能到激光设置，都提供了准确的估计结果。对所提出的三种数据驱动方法的准确性进行了比较和深入分析：模糊推理系统（FIS）、人工神经网络（ANN）和自适应神经模糊推理系统（ANFIS），其中FIS是最准确的解决方案。
在线版本包含可在10.1007/s44245-025-00094-7获取的补充材料。
