There is an increasing need for the use of additive manufacturing (AM) to produce improved critical application engineering components. However, the materials manufactured using AM perform well below their traditionally manufactured counterparts, particularly for creep and fatigue. Research has shown that this difference in performance is due to the complex relationships between AM process parameters which affect the material microstructure and consequently the mechanical performance as well. Therefore, it is necessary to understand the impact of different AM build parameters on the mechanical performance of parts. Machine learning (ML) models are able to find hidden relationships in data using iterative statistical analyses and have the potential to develop process–structure–property–performance relationships for manufacturing processes, including AM. The aim of this work is to apply ML techniques to materials testing data in order to understand the effect of AM process parameters on the creep rate of additively built nickel-based superalloy and to predict the creep rate of the material from these process parameters. In this work, the predictive capabilities of ML and its ability to develop process–structure–property relationships are applied to the creep properties of laser powder bed fused alloy 718. The input data for the ML model included the Laser Powder Bed Fusion (LPBF) build parameters used—build orientation, scan strategy and number of lasers—and geometrical material descriptors which were extracted from optical microscope porosity images using image analysis techniques. The ML model was used to predict the minimum creep rate of the Laser Powder Bed Fused alloy 718 samples, which had been creep tested at \(650\,^\circ \)C and 600 MPa. The ML model was also used to identify the most relevant material descriptors affecting the minimum creep rate of the material (determined by using an ensemble feature importance framework). The creep rate was accurately predicted with a percentage error of \(1.40\%\) in the best case. The most important material descriptors were found to be part density, number of pores, build orientation and scan strategy. These findings show the applicability and potential of using ML to determine and predict the mechanical properties of materials fabricated via different manufacturing processes, and to find process–structure–property relationships in AM. This increases the readiness of AM for use in critical applications.

越来越需要使用增材制造 (AM) 来生产改进的关键应用工程组件。然而，使用增材制造制造的材料的性能远低于传统制造的材料，尤其是在蠕变和疲劳方面。研究表明，这种性能差异是由于增材制造工艺参数之间的复杂关系造成的，这些参数会影响材料的微观结构，进而影响机械性能。因此，有必要了解不同的增材制造参数对零件机械性能的影响。机器学习 (ML) 模型能够使用迭代统计分析发现数据中的隐藏关系，并有可能为制造过程开发过程-结构-属性-性能关系，包括上午。这项工作的目的是将 ML 技术应用于材料测试数据，以了解增材制造工艺参数对增材制造镍基高温合金蠕变速率的影响，并根据这些工艺参数预测材料的蠕变速率。在这项工作中，ML 的预测能力及其开发过程-结构-性能关系的能力应用于激光粉末床熔融合金 718 的蠕变性能。ML 模型的输入数据包括激光粉末床熔融 (LPBF)使用的构建参数——构建方向、扫描策略和激光数量——以及使用图像分析技术从光学显微镜孔隙率图像中提取的几何材料描述符。\(650\,^\circ \) C 和 600 兆帕。ML 模型还用于识别影响材料最小蠕变速率的最相关材料描述符（通过使用整体特征重要性框架确定）。在最佳情况下，蠕变速率被准确预测，误差百分比为\(1.40\%\)。发现最重要的材料描述符是零件密度、孔数、构建方向和扫描策略。这些发现显示了使用 ML 来确定和预测通过不同制造工艺制造的材料的机械性能以及在 AM 中找到工艺-结构-性能关系的适用性和潜力。这增加了增材制造在关键应用中使用的准备程度。